SURI Apply - Fulton Schools of Engineering

Application Deadline

February 02, 2026 at 11:59pm

Program Information

The Ira A. Fulton Schools of Engineering at Arizona State University offers summer opportunities for qualified students to gain engineering research experience at a top research university. Participants acquire an in-depth understanding of what it would be like to pursue a PhD degree through an 8-week summer research program. Research projects across a wide range of engineering fields are available. Students between the junior and senior years of their undergraduate degree and those pursuing Master's degrees who are considering pursuing a PhD will be given highest consideration. Participants work under the mentorship of research faculty and have opportunities to network with industry partners, other faculty and their peers through a variety of events and activities.

SURI Program Information...

Contact

For questions about the SURI program, please email: suri.fulton@asu.edu

Application Information

Please submit only one application. If you submit more than one application, only your first application will be reviewed.

You may edit your response until the application deadline shown at left.
Important: after you submit your application you must click the link in your confirmation email to view your application and edit your responses.

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Please provide the name of your current or most recently attended university/college.

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Which graduate engineering degree are you interested in pursuing? (The purpose of SURI is to help students understand what it is like and help prepare for advance research leading to the PhD)

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As of Fall 2025, what is your class standing?

What is your undergraduate major?

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What is your major GPA?

For the next four (4) questions please consult the list of SURI 2026 Opportunities which can be found here:

SURI Program Information > (scroll down)

Please select the name of the faculty whose project you are interested in for your FIRST CHOICE

Please indicate the "Program/Expertise Area" selected from the list of available projects on the website for which you are applying for your FIRST CHOICE:

List of Projects for the selected Program/Expertise Area:

Project description: Understand the physical scale-dependent mechanical & dynamic properties of meteoritic materials and gain insight into their deformation mechanisms and fracture & impact dynamics.Students will: Assist in conducting ballistic impact and quasi-static compressive tests on meteorite samples coupled with digital image correlation techniques, and performing microscopy on the fragments from impact.Prerequisite skills/knowledge: Students from mechanical or aerospace engineering; some experience in hands-on experiments is a plus. Junior status would be preferable, but motivated sophomores who meet the requirements will also be considered.
Project description: This project involves applying Large Language Models (LLMs) to hardware verificationStudents will: The student will employ agentic techniques such as RAG, LangGraph, etc. to improve a framework we're developing for hardware verification. The student will benchmark various LLMs and develop problems to benchmark various LLMs. The student will fine-tune LLMs for verification tasks.Prerequisite skills/knowledge: Digital hardware design and verification (equivalent of CSE 320 and EEE 333)
Project description: This project involves developing datasets and ML models for designing FPGA chipletsStudents will: The student will work with open-source FPGA tools. The student will write Verilog and HLS designs. The student will develop ML techniques to model performance of these designs. The student will explore the architecture space of FPGA chiplets.Prerequisite skills/knowledge: Digital hardware design, FPGA concepts
Project description: This project aims to develop and validate a robust numerical model for metal-coated carbon nanotube (CNT) fibers, specifically to characterize their mechanical and electrical properties. By addressing the complexities of their random nano-micro structures, this work will establish a predictive numerical framework for these novel CNT-based electric transmission fibers.Students will: A student researcher will be assigned the task of developing intricate geometry models for the metal-coated CNT fibers, utilizing Representative Volume Element (RVE) principles and Monte Carlo methods to represent their random microstructures. Subsequently, these models will be used to conduct high-fidelity numerical simulations on ASU's supercomputing resources to compute and analyze the key mechanical and electrical properties. This work will directly contribute to identifying the predictive numerical model.Prerequisite skills/knowledge: Knowledge of electric conduction, mechanical tensile test, basic solid mechanics, finite element method, Python
Project description: This project will focus on biosensor design and validation in biological fluids to detect pathogens related to human or animal diseases (to be decided at the project initiation), including infectious diseases, cancer, or neurodegenerative diseases.Students will: Here the students will be mentored to create nanoparticle-supported rapid electronic detection (NasRED) sensors, perform experiments using established sensing protocols, and analyze the data. The results will be summarized.Prerequisite skills/knowledge: Prior experience to work in a BSL-2 setting; basic understanding of biosensors or assay development
Project description: Control systems engineering is being increasingly used as a means to develop "just-in-time" adaptive interventions to improve on healthy behaviors (such as increased physical activity, weight management, and smoking cessation). These control systems, based on dynamic representations of health behavior theories and Model Predictive Control, can be improved through the use of machine learning techniquies such as reinforcement learning (RL). However, developing solutions that are fundamental yet satisfy practical requirements (and can be solved in reasonable time) remains an open area of investigation. The SURI project will contribute to continuing efforts in this topic in ASU's Control Systems Engineering Laboratory.Students will: The student will become familiarized with some of our current computational platforms (based on MATLAB w/Simulink and Python) that implement existing solutions. The nature of the problem, however, implies that many diverse computational approaches can be examined. The student will, drawing from their expertise in numerical methods, data science, dynamical systems, and control engineering, stage and implement approaches that can then be compared to a series of benchmarks. The student is expected to submit a report a the end of the 8 week program.Prerequisite skills/knowledge: Knowledge of differential equations, numerical methods, and dynamical systems. An introductory controls course is strongly recommended. Working knowledge of MATLAB w/Simulink and Python.
Project description: This project focuses on understanding basic principles of quantum mechanics as it applies to operation of nanoscale devices. A simulation software will be developed for modeling single gate and dual gate MOS capacitors that are integral part of the state-of-the-art fully-depleted SOI device technology. Python programming language will be used for tool implementation and open-source web application Jupyter notebook will be used for the development of the graphical user interface (GUI). The simulation tool will be installed on https://nanohub.org, a well established platform for dissemination of tools and educational modules to the scientific community.Students will: The student will work on device simulator development that involves solution of the 1D Schroedinger and the 1D Poisson equation self-consistently for single-gate and dual gate MOS capacitors with conventional or a stack of high-k dielectrics. Graphical User Interface will be developed for easy selection on structures, models and gate-dielectric stacks.Prerequisite skills/knowledge: Knowledge on numerical analysis, partial differential equations, python language programming and basic Jupyter notebook understanding is necessary.
Project description: My research group leverages nanotechnology to design stimuli-responsive, functional polymeric and hybrid nanocomposite materials at the interface of polymer chemistry, self-assembly, and additive manufacturing. Our overarching goal is to establish fundamental structureâ€“propertyâ€“processing relationships that enable the development of sustainable polymer materials.Students will: Work with a graduate student mentor to learn how to synthesize polymers via controlled polymerization mechanisms and nanoparticles via sol gel methods. Surface-engineering nanoparticles will be assembled with polymers to alter the final behavior of polymers. The student will learn how to characterize polymers, nanoparticles, and polymer nanocomposites using NMR, size exclusion chromatography, UV-Vis spectroscopy, dynamic light scattering, dynamic mechanical analysis, and optical microscopy. The student will also learn 3D printing.Prerequisite skills/knowledge: Knowledge about polymer chemistry, nanoparticle synthesis and functionalization, and materials characterization (mechanical properties, particle size etc)
Project description: This research project focuses on thin-film solar cells and photodetectors.Students will: Thin film deposition and device fabrication, characterization.Prerequisite skills/knowledge: knowledge of materials science and engineering, semiconductor device physics electrical engineering.
Project description: Large language model systems increasingly rely on routing: deciding which model, module, or computation path to use for each input. This project develops statistically grounded routing strategies aimed at reducing inference time while maintaining accuracy. We will explore multi-fidelity bandit approaches, where inexpensive, fast LLMs provide early signals that help determine when a larger model is needed. In parallel, we will study routing inside modular transformer architectures by examining how small models learn to activate specialized components. The project emphasizes principled decision-making, empirical evaluation, and careful experimentation, offering students a structured introduction to scalable and efficient LLM system design.Students will: Students will learn and implement routing algorithms, multi-armed bandit algorithms, and run controlled experiments with open LLMs. There will also be an opportunity to dive into the rigorous mathematical foundations of these methods.Prerequisite skills/knowledge: Python, basic machine learning, probability, linear algebra; familiarity with PyTorch or JAX is helpful but not required.
Project description: This research project focuses on developing agentic AI for education, including material generation, human alignment of LLMs, and progressive learning.Students will: The student will explore LLM Agentic tools, compare the state-of-the-art industry products and conduct experiments for the comparison. The student will also work with faculty and PhD students to enhance new workflows and systems for better education results.Prerequisite skills/knowledge: Knowledge of LLM and agentic AI, python and Linux programming, quick implementation and learning skills, familiarity with AutoGen, LangGraph,CrewAI, etc with be a bonus, experiences with International Olympiad in Informatics is a plusl
Project description: Secure and Trustworthy Human-AI Interaction: Currently, there is a lack of principled methods to construct safe and secure human-ML interaction paradigms. Thus, to prevent harm in ML-based systems, it is paramount that we understand vulnerabilities and apply safeguards now. This set of projects will investigate how human interaction impacts ML security in two ways: How human factors can be 1) exploited to reduce security or 2) harnessed to improve security. Projects topics may include detecting and mitigating the effect of deepfakes (topic 1), or developing ways to prevent vulnerabilities and prevent exploitation in developed machine learning systems (topic 2). The specific topic can be tailored to the interests and proposals of the applicant. Students will: These projects can include developing UI to convey provenance information for media and indicators to reduce the effect of deepfakes, understand how to develop machine learning coursework to include modules on adversarial machine learning topics, or discovering ways that adversarial machine learning exploits occur in practice, and developing methods to mitigate them.Prerequisite skills/knowledge: This topic is extremely interdisciplinary; skill in any of these topics are relevant: Computer security & privacy; Machine learning; Human Factors methodologies (e.g., experimental design, interviews, qualitative coding)
Project description: This project focuses on designing high-performance and reconfigurable computer architectures tailored for AI and machine learning (AI/ML) workloads, addressing the growing demand for efficient and flexible computation. The goal is to co-optimize hardware and compiler toolchains to achieve superior speed, energy efficiency, and adaptability across diverse neural network models and data types. By leveraging reconfigurable fabrics, such as FPGAs or coarse-grained reconfigurable arrays (CGRAs), the system will dynamically adapt to varying algorithmic requirements, minimizing performance bottlenecks. The compiler framework will automate hardware-software co-design, translating high-level AI models into optimized hardware configurations and executable code. This holistic approach enables seamless integration of emerging AI techniques while maintaining scalability and programmability. Ultimately, the project seeks to advance the frontier of domain-specific architectures and intelligent compilation, enabling next-generation AI systems that deliver higher performance, lower power consumption, and improved flexibility for rapidly evolving machine learning applications.Students will: Architectural simulation, hardware design, ML development, and compiler designPrerequisite skills/knowledge: C/C++, Verilog, Pytorch
Project description: We target two problems arising from decoupling databases and deep learning systems in the existing architectures: (1) The lack of a consistent design for data and model privacy mechanisms poses obstacles to achieving optimal privacy and security for integrated use cases. (2) The lack of verification mechanisms for the compliance of vendor's privacy policies defined over its data/model with privacy regulations such as Health Insurance Portability and Accountability Act (HIPAA), Payment Card Industry Data Security Standard (PCI-DSS), and General Data Protection Regulation (GDPR). These regulations place strict limitations on how this information can be collected and shared. Thus, it is critical for organizations to take steps to protect sensitive information and prevent inadvertent disclosure following the related regulations. We will develop a novel database system that closes the above gaps.Students will: Task 1 proposes a unified privacy model for data and model management; Task 2 leverages LLM to extract knowledge from privacy regulation documentation and vendor's privacy policy documentation, and add such knowledge to the knowledge graph constructed in our preliminary work; and Task 3 leverages LLM to retrieve information from the knowledge graph and verify the compliance with target regulations. The student is expected to contribute to one or multiple of the tasks.Prerequisite skills/knowledge: Strong coding skills.
Project description: fabricate and evaluate organic electronic and optical devicesStudents will: fabricate and evaluate organic electronic and optical devicesPrerequisite skills/knowledge: basic understanding of organic semiconductors and skills for device fabrications
Project description: This project aims to develop reinforcement learning (RL) controllers for a custom-built, high-degree-of-freedom (DOF) quadruped robot capable of agile and dynamic behaviors such as leaping and turning. The focus is on leveraging the robotâ€™s extra body DOFs to achieve enhanced maneuverability and stability during rapid motion. A key challenge is bridging the simulation-to-reality (sim-to-real) gap to ensure that policies trained in simulation transfer effectively to the physical robot. The student will design, train, and test RL policies using high-fidelity simulation environments and deploy them on the real robot for validation.Students will: The student will work on reinforcement learningâ€“based control, including environment setup, policy training and optimization, sim-to-real transfer strategies, and hardware implementation.Prerequisite skills/knowledge: Knowledge of reinforcement learning and robotics (kinematics and dynamics); proficiency in Python/C++; experience with Isaac Lab and/or MuJoCo.Prior experience implementing learning-based controllers on hardware is prefered.
Project description: AI-based methods for chip design, especially LLMs, for hardware design and hardware security. Design of AI accelerator architectures.Students will: The student(s) will develop AI models (CNNs, DNNs, LLMs) for hardware synthesis and design-for-test solutions. There are also oppprtunities available to study AI-based attacks on hardware and corresponding defenses.Prerequisite skills/knowledge: Basic understanding of CNNs, DNNs, LLMs, knowledge of Python and Pytorch (or similar tools), familiarity with EDA tools for chip design.
Project description: Built-in self-test and built-in self-repair for advanced packaging and chip/package co-designStudents will: The student will design test solutions for fanout wafer-level packaging and hybrid bonding. The test methods will include fault detection, diagnosis (localization), and repair.Prerequisite skills/knowledge: Basic knowledge of VLSI design and integrated circuits, circuit simulation and timing analysis; familarity with EDA tools. Design and analysis of algorithms.
Project description: AI-based healthcare solutions and privavcy preserving AI solutions for healthcareStudents will: The student will use various AI methods (transformers LLMs,...) to analyze anonymized patient data (EEG, ECG, etc.) for security vulnerabilities and identify encoding techniques to preserve privacy without impacting diagnostic accuracy.Prerequisite skills/knowledge:
Project description: Evaluate the corrosion behavior of structural materials in liquid metals and molten saltsStudents will: Assist in analyzing the experimental data from corrosion tests conducted in liquid metals and molten salts, perform image analyses to identify depths of attack and phase fractions, compile the results for a journal publicationPrerequisite skills/knowledge: Students from mechanical or materials science engineering; some experience in hands-on experiments is a plus.
Project description: Evaluate the corrosion behavior of structural materialsStudents will: Analyze the role of different water vapor contents on the oxidation behavior of Fe- and Ni-based alloys, evaluate microstructural characterization data, perform image analyses to measure oxide thicknesses, compile the results for a journal publicationPrerequisite skills/knowledge: Students from mechanical or materials science engineering; some experience in hands-on experiments is a plus.
Project description: Prediction of failures in real-world robotic systems either requires accurate model information or extensive testing. Moreover, obtaining such demonstrations is expensive, and it could be risky for the robotic system to fail during data collection repeatedly. In this work, we will investigate the sim-to-real gap for various robotic platforms such as manipulator arm, ground vehicles as well as aerial vehicles, from the perspective of falsification, using existing testing pipelines for simulation while working with limited data from the true system to enable better prediction of failures.Students will: The student will be working with PhD student to design experiments for a given robotic platform for data collection, perform experimetns for data collection and retrain or fine-tune policies to fix the predicted failure modes.Prerequisite skills/knowledge: Strong coding background, a good math background (control theory, optimization, probability, statistics), working knowledge of ML and NN training, hands-on experience with ROS/ROS2
Project description: Shape-morphing structuresâ€”unlike conventional deployable systems based on discrete hinges and springsâ€”can continuously deform into complex 3D shapes, enabling transformative applications in soft robotics, biomedical devices, and advanced manufacturing. Their virtually infinite degrees of freedom allow for precise control of the final shape; however, this same high dimensionality makes the inverse design process extremely challenging, requiring extensive trial-and-error exploration of a large design space. This research project seeks to develop a data-driven, physics-informed framework to reduce the dimensionality of the inverse design problem and accelerate the discovery of optimal designs. Students will have the opportunity to compare traditional finite elementâ€“based design methods with a newly developed physics-informed neural network approach. By the end of the project, students will fabricate and experimentally validate their optimized shape-morphing structures using 3D printing technology.Students will: Students will develop and apply a physics-informed hypergraph neural network to model the dynamics of shape-morphing structures for applications in soft robotics and biomedical devices. The neural network will incorporate both geometric and nonlinear material properties, and will then be integrated into a topology optimization framework. By implementing gradient-based optimization, students will design shape-morphing structures tailored for specific mechanical performance targets. They will compare the neural-network-generated designs with results from traditional finite element simulations and, finally, validate their optimized designs through 3D printing and experimental testing.Prerequisite skills/knowledge: Matlab and Python programming. Knowledge in machine learning and finite element simulations.
Project description: This summer project examines how small interstitial atoms affect the structure and mechanical behavior of high-entropy alloys by modifying local chemical ordering, dislocation interactions, and grain-boundary cohesion. The project offers hands-on experience in computational materials mechanics and supports a broader effort to design next-generation alloys with improved strength, ductility, and fracture resistance.Students will: The student will use computational tools, such as density functional theory, molecular dynamics, and machine-learning potentials, to study microstructural evolution and mechanical stability.Prerequisite skills/knowledge: Programming experience in MATLAB and/or Python, familiarity with HPC workflows, and a basic understanding of materials mechanics.
Project description: Mu group is dedicating in designing sustainable materails for rechargeable batteries, and developping electrochemical method to directely extract critical minerals from various water resources. Leveraging by comprehensive characterizations methods, Mu group aims to design high-performance and low-cost materials for Li/Na batteries.Students will: Students will learn how to use electrochemical method to direct extract critical minerals from different water resources. Students will be trained on how to make a battery and evaluate the battery performance. Students will be evolved in different type of X-ray characterization methods to undersand working principles and degradation behaviors.Prerequisite skills/knowledge: Materials science engineering, chemsitry, characterization methods
Project description: We are constructing the most efficient solar electrolyzer in the world. It delivers 99% of the solar energy it receives to the electrolyzer, while reduing the system cost by 30%. this leads to a 50% lower cost of solar electricity for green hydrogen production. The project involves control electronics and algorithm, system integration and debugging, system modification and optimization, and performance verification.Students will: This opportunity is open American students only (citizen or green card). You will work with PhD students on system development. You will also work on a simpler version of the system. The simpler system is much easy to construct, operate, and trouble shoot. It is intended for remoate communities with little access to technical support.Prerequisite skills/knowledge:
Project description: The development of miniaturized and high-performance power electronic converters relies on a host of critical infrastucture, including performing finite element analysis (FEA) simulations to design power magnetics and high speed power converters, programming FPGAs and microcontrollers to synthesize gating signals, and assisting in the development of hardware prototypes. In this project, the student will assist a graduate student in undertaking one or more of these tasks for the development of a miniaturized, high performance power electronic converter.Students will: (1) Finite element analyis simulations; (2) programming of microcontrollers and/or FPGAs; (3) PCB design and hardware prototyping.Prerequisite skills/knowledge: Experience with power electronics is ideal.
Project description: This project aims to fabricate 316L+NbC metal matrix composites (MMCs) for high-temperature applications using the laser powder bed fusion (LPBF) additive manufacturing process. 316L stainless steel is widely used in energy, biomedical, chemical, and marine industries due to its excellent corrosion resistance and ductility. However, its relatively low strength, particularly at elevated temperatures, limits its suitability for extreme environments involving high stress and high temperatures. To address this limitation, 316L+NbC MMCs will be fabricated under various LPBF processing conditions. Their high-temperature mechanical performance will be evaluated through elevated-temperature tensile testing. Ultimately, the project seeks to establish process-structure-property relationships for these additively manufactured MMCs.Students will: (1) Fabrication of 316L+NbC with LPBF machine. (2) Microstructure characterization with scanning electron microscopy. (3) Tensile testing.Prerequisite skills/knowledge: Knowledge of metal additive manufacturing. Hand-on skills on microstructure characterization and tensile testing.
Project description: Creating new pathways for scalable, fast production of precise crystals, leveraging raw materials from non-conventional mixtureStudents will: Students get to learn and contribute to advanced flow chemistry platformPrerequisite skills/knowledge: Basic chemistry lab, or basic process modeling
Project description: This research project develops advanced technologies for subsurface exploration using robotics, acoustic waves, and artificial intelligence. The goal is to create high-resolution, non-invasive methods for detecting underground features such as voids, utilities, buried infrastructure, contaminated zones, and geotechnical hazards, challenges important to defense, space, and energy applications. The student will work with a multidisciplinary team to design robotic sensing platforms, integrate acoustic and ultrasonic tools, and develop AI models that improve subsurface imaging and anomaly detection. Activities will include laboratory testing, field data collection, signal processing, and participation in team discussions to interpret findings and plan next steps. Through this work, the student will gain hands-on experience in robotics, sensing systems, and data-driven geophysical analysis. The project supports DoD infrastructure resilience, NASA planetary exploration needs, and DOE priorities in environmental monitoring and underground storage safety.Students will: The student will help design, assemble, and operate robotic sensing platforms; collect acoustic and ultrasonic data in both laboratory and field settings; and assist in developing machine-learning models that improve subsurface imaging and anomaly detection. The studentâ€™s assigned tasks will include conducting experiments, performing signal processing, coding for AI workflows, documenting results, and participating in group discussions to interpret findings and plan next steps. Through this experience, the student will gain hands-on skills in robotics, sensing technologies, data analytics, and applied geophysics.Prerequisite skills/knowledge: Basic experience with Matlab or Python programming, General understanding of physics or signals, Interest in robotics, sensing, or AI tools
Project description: A compelling opportunity for higher energy density batteries is solid-state electrolytes (SSEs), which offer a host of advantages over the liquid electrolytes that dominate the market today: they are leak-proof, energy-dense, flame-resistant, contain no toxic organic solvents, and can charge faster. A challenge to the commercialization of solid-state batteries is the development of a stable SSE that can support the film stresses that develop from significant expansion during cycling and can be processed with low-cost manufacturing processes. The objective of this work is to two-fold: to improve the thermomechanical reliability of SSEs and to subsequently produce safe, durable, and high-specific energy solid state batteries with a robust thin film SSE. The overarching questions that will be investigated are the material (ionic and electronic conductivity) from thin-film processing of ceramic-based SSEs and mechanical properties that develop in SSEs for understanding of chemomechanical degradation modes.Students will: Students will: Learn how to make battery materials and devices from solution using printing processes and characterize them with electronic and ionic conductivity measurements. There is also funding for additional proejcts to work on perovskite-based photovoltaics materials and devices.Prerequisite skills/knowledge: Knowledge of electrochemistry, materials science, and mechanical properties is a plus (although not required)
Project description: This project will focus on design and fabrication of gallium oxide power semiconductor devicesStudents will: The student will design gallium oxide lateral power devices in Silvaco and fabricate transistors in the ASU Nanofab. These devices will then be characterized to evaluate device characteristicsPrerequisite skills/knowledge: Prior cleanroom experience with handling of lithography tools is required.
Project description: This research project develops and evaluates advanced materials and processes to remove emerging contaminants such as nutrients, PFAS and toxic metals from water. The student will work alongside a multidisciplinary team to synthesize novel adsorbents, develop sensors for atmospheric water harvesting, and test photochemical methods that enhance treatment efficiency and sustainability. Activities will include laboratory experiments, data analysis, and participation in group discussions to interpret results and guide next steps. Through this work, the student will gain hands-on experience in materials synthesis, water quality analysis, and environmental process design. The project bridges environmental chemistry, materials science, and process engineering, offering valuable preparation for graduate study or careers focused on clean water technologies and sustainable infrastructure.Students will: Students will assist in laboratory experiments focused on developing and testing advanced materials for water treatment. They will prepare and characterize samples, conduct water quality analyses, collect and interpret data, and contribute to experimental design discussions. Students will also participate in weekly research meetings, maintain lab notebooks, and help summarize findings for presentations or reports.Prerequisite skills/knowledge: Students should have a basic understanding of chemistry, environmental engineering, or materials science principles - Chemical, environmental, civil or mechanical engineers work in my lab; some biology or bio-engineering students are involved too. Prior laboratory experienceâ€”such as preparing solutions, measuring water quality parameters, or handling analytical instrumentsâ€”is helpful but not required. Familiarity with data analysis tools (e.g., Excel, MATLAB, or Python) and good documentation practices are desirable. Most importantly, students should demonstrate curiosity, attention to detail, and a willingness to learn new experimental and analytical techniques in a collaborative research environment.
Project description: The goal of this project is to develop new biobased materials to mitigate shrinkage in 3D printed concreteStudents will: The student will be responsible for synthesizing new biobased materials that absorb and release water in to the concrete matrix in a regulated manner. The students will perform shrinkage studies and prepare a report at teh ened of the 8 weeks program.Prerequisite skills/knowledge: Civil Engineering student with previous lab experience and a strong interest in MS/ PhD programs
Project description: The goal of this project is to predict fracture in Metal additive manufactured samplesStudents will: The student will be responsible for generating fracture data and training ML models to predict fracture initiation in additively manufactured steel components. The student is expected to submit a report a the end of the 8 weeks program.Prerequisite skills/knowledge: MS student in structural engineering or related area with an intention to join our PhD program
Project description: Use topological data analysis and AI to enhance time series forecasting; use multimodal data to build knowledge graphs for ADStudents will: Methodology developemtn, Python coding, and data analysisPrerequisite skills/knowledge: Knowledge in statistical modeling, machine learning, and the use of AI.
Project description: The project explores will develop techniques for software understanding of binary code. Binary code is extremely resistant to understanding by humans and AI techniques, and a number of promising approaches, which we will explore, hint at the potential to alleviate this problem.Students will: Understanding system security, developing new techniques in Software Understanding for both humans and AI, improving binary decompilation approaches.Prerequisite skills/knowledge: Binary reverse engineering, strong programming background, expert-level Python programming.
Project description: Electrification of some industrial processes are already economical in the state of Arizona (e.g., electrifying forklifts) while others need further analysis and development. This SURI project will use techno-economic models to assess the feasibility of current electrification strategies for industrial clients.Students will: Evaluation of multiple electrification strategies that are applicable in industry and assessment of the current capital costs and energy savings to evaluate the return on investment.Prerequisite skills/knowledge: Basic concepts from thermodynamics and energy conversion are beneficial
Project description: This project will consist of Aerosol Jet Printing experiments, in-situ and ex-situ data collection from these experiments, and advanced data processing and AI algorithm design and training for AJP prognostic analysis. Students will have the chance of getting trained for operating an OPTOMEC Aerosol Jet machine. The lab environment and experiments will be safe and nonhazadous.Students will: (1) AJP operations and experiments; (2) data collection with inline sensor and microscope; (3) data processing and analytics with Python/Matlab/R codingPrerequisite skills/knowledge: Interest in 3D printing; proficiency in coding
Project description: The project focuses on combining self-assembly and advanced 3D nanoprinting to create next-generation optical materials and on-chip photonic structures. Students will gain hands-on experience in nanofabrication, optical characterization, and computational design of photonic devices. Ideal candidates are curious, detail-oriented, and eager to learn interdisciplinary techniques at the intersection of materials science, optics, and nanotechnology. This position offers an excellent opportunity to contribute to emerging research in lightâ€“matter interaction and metamaterial engineering.Students will: Solution based self-assembly/3D printing, optical simulation and charaterizationPrerequisite skills/knowledge: ideally some experiences in "solution phase based self-assembly aand printing", or "basic optics knowledge". But not required
Project description: TBDStudents will: Prerequisite skills/knowledge:
Project description: This project investigates post-quantum resilient communication techniques for next-generation wireless networks. As quantum computing emerges as a threat to current cryptographic systems, ensuring long-term security for sensitive data transmissions (such as financial transactions, medical records, and mission-critical communications) becomes essential. The research applies quantum-resistant secret sharing codes that encode and protect data for transmission across multiple networks. Students will build experimental network testbeds using distributed cloud infrastructure including AWS, Google Cloud, and Azure. The testbeds will demonstrate how secret sharing codes can encode original data into multiple coded shares, transmit through different routes, and reconstruct the original data securely at the destination, maintaining data security and resiliency. This hands-on research experience addresses critical security challenges for future wireless systems while providing industry-relevant skills in cloud computing, distributed systems, network programming, and post-quantum cybersecurity.Students will: Students will develop methods on commercial-off-the-shelf (COTS) 5G smart phones (Galaxy S24, iPhone 16) for secure data transmission and reception, integrating quantum-resistant secret sharing algorithms. They will implement a multi-cloud distributed network testbed connecting AWS, Google Cloud, and Azure. Tasks include writing Python/C++ code for encoding algorithms that transform data into coded shares and decoding algorithms that reconstruct original data, configuring cloud infrastructure and network topologies, conducting performance evaluations measuring throughput, resiliency, and security metrics, and visualizing results.Prerequisite skills/knowledge: Programming experience in Python, Java, C++, or MATLAB, and basic understanding of communication networks.
Project description: The objective of the research is to apply machine learning (LLMs) techniques to solve challenging problems in chip design.Students will: Develop algorithms and write software for traditional EDA applications for ground truth data generation then apply machine learning techniques to make the algorithm more scalable.Prerequisite skills/knowledge: Knowledge in Python and C++ is required. Course: Algorithms, data structures, circuit design, VLSI design.
Project description: The project consists of developing machine learning algorithms for MRI artifact correction using k-space data.Students will: The student will use MATLAB to generate a large library of synthetic k-space data representing a variety of MRI artifacts. and develop algorithms for artifact removal. They will then test the algorithms on real world data.Prerequisite skills/knowledge: Programming experience in MATLAB.
Project description: Multimodal Adversarial Attacks & Defences for Agents Modern "agents" (LLM/VLM + tools + data pipelines) are highly capable but increasingly vulnerable to multimodal adversarial attacks. This project will build VAD: a standardized evaluation framework to stress-test agentic pipelines with a suite of realistic attacks and to prototype practical defenses. Attacks include document-layer perturbations, image-based transferable black-box attacks, and text/token injection attacks. Defenses emphasize cross-modal verification (OCR + intent alignment checks), input sanitization & parsing hardening, red-teaming harnesses for deployed agents, and downstream corruption tests (e.g., OLTP vs OLAP view divergence). The outcome is a reproducible benchmark, metrics, and a demo that identifies where agents fail and which mitigations work.Students will: Implement attack generators + replay harness for agentic pipelines Design metrics (success rate, stealth, downstream impact, cost) Build defenses (OCR+intent checks, filters, policy gates, monitors) Produce a benchmark report + open-source-style repo structurePrerequisite skills/knowledge: Python required; familiarity with ML/LLMs helpful; interest in security; basic knowledge of PDFs/OCR or willingness to learn."
Project description: The project seeks to develop learning, control-theoretic, or hybrid to solve multi-fingered dexterous manipulation.Students will: learning, control, perception algorithm design, hardware experimentsPrerequisite skills/knowledge: python, hardware experiment
Project description: This project involves developing digital twins and robot learning algorithms for autonomous manufacturingStudents will: The student will work on developing digital twin models of an assembly platform, and leverage high-fidelity simulations to train robot controllers for precise and adaptive actions in pick-and-place, assembly, and inspection tasks in manfuacturingPrerequisite skills/knowledge: Python and/or C++, reinforcement learning and/or control systems, experience in digital twin software is a plus
Project description: This project involves design and prototyping of soft robots for healthcare and infrastructure managementStudents will: The student will work on design and fabticating inflatable soft actuators, and inteagte them into soft wearable robots for human assistance and crawling robots for inspection of civil infrastructure.Prerequisite skills/knowledge: mechanical design, fabrication and prototyping, sensor integartion, control systems, experience in soft robots is a plus
Project description: Hardware-software co-design of under-actuated robots in highly dynamic environments.Students will: Design and development of: 1) hardware controlling an autonomous blimp, 2) control and coordination algorithms for blimps fleetPrerequisite skills/knowledge: Experience with one of several of the following: raspberry pi, ROS system,electronic devices (sensors, DC motor, ESCs, etc.), PCB design and building, control systems
Project description: The research project aims to develope a 10-min VR course "Material Edge: Ceramics for the Extremes".The course is organized into short, engaging segments that transition from a macroscopic engineering system into a fully simulated extreme environment, and then into a 3D fly-through of the materialâ€™s microstructure. Students will explore phenomena such as phase transitions, ion transport, and thermal degradation while guided by narration. The module concludes with an interactive design activity where students apply what they learned to a technology of their choice.Students will: The student will need to work with the PI to develop a short 10 min VR course on ceramics materials under extreme conditions. A student working on this project will begin by reviewing background materials on extreme environments, microstructural behavior, and VR-based STEM learning. They will then storyboard the 10-minute module, outlining each segmentâ€™s macroscopic device, simulated environment, and microstructural fly-through, along with key learning objectives and narration points. Next, the student will gather and prepare visual content to translate the storyboard into accurate VR scenes. They will develop and refine the scientific narration, design the interactive end-of-module activity, and assist with testing to ensure clarity, engagement, and technical accuracy. Finally, the student will prepare supplementary educational materials such as instructor guides, learning objectives, and assessment questions.Prerequisite skills/knowledge: Preferred knowledge on materials science (crystal structures, ceramics defects, etc) and the VR development tools such as Blender, Unity, Unreal Engine, etc
Project description: The project explores applied aspects of program analysis and vulnerability research. We'll work on techniques to find bugs in programs, prove their security applicability, and fix them.Students will: Developing and improving techniques in program analysis, analyzing the security of complex systems.Prerequisite skills/knowledge: Strong programming background, expert-level Python programming, understanding of security issues found in software, assembly language.
Project description: Recovery of metal cations from acidified solutions is crucial for efficient recycling of electronics and batteries, as well as mining processes. Recently, our group has found that inorganic nanoporous ion-exchange materials can be designed for stable and selective ion-exchange. This project will explore the adsorption properties of different metal cations under different conditions to discern the driving forces for selectivity and the reactivity of metal cations in nanoporous environmentsStudents will: The student will measure adsorption isotherms for a range of metals on inorganic oxides of different compositions and pore sizes, screening a wide range of conditions. If successful, the student will also use advanced spectroscopic methods (FTIR, UV-vis, solution and solid-state NMR) to analyze interactions of cations with the sorbent surface, with possible extensions to heterogeneous catalysis.Prerequisite skills/knowledge: Student should have a basic knowledge of chemistry lab procedures and safety. Experience with adsorption phenomena and prior experimental lab experience are a plus.

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Project description: Understand the physical scale-dependent mechanical & dynamic properties of meteoritic materials and gain insight into their deformation mechanisms and fracture & impact dynamics.Students will: Assist in conducting ballistic impact and quasi-static compressive tests on meteorite samples coupled with digital image correlation techniques, and performing microscopy on the fragments from impact.Prerequisite skills/knowledge: Students from mechanical or aerospace engineering; some experience in hands-on experiments is a plus. Junior status would be preferable, but motivated sophomores who meet the requirements will also be considered.
Project description: This project involves applying Large Language Models (LLMs) to hardware verificationStudents will: The student will employ agentic techniques such as RAG, LangGraph, etc. to improve a framework we're developing for hardware verification. The student will benchmark various LLMs and develop problems to benchmark various LLMs. The student will fine-tune LLMs for verification tasks.Prerequisite skills/knowledge: Digital hardware design and verification (equivalent of CSE 320 and EEE 333)
Project description: This project involves developing datasets and ML models for designing FPGA chipletsStudents will: The student will work with open-source FPGA tools. The student will write Verilog and HLS designs. The student will develop ML techniques to model performance of these designs. The student will explore the architecture space of FPGA chiplets.Prerequisite skills/knowledge: Digital hardware design, FPGA concepts
Project description: This project aims to develop and validate a robust numerical model for metal-coated carbon nanotube (CNT) fibers, specifically to characterize their mechanical and electrical properties. By addressing the complexities of their random nano-micro structures, this work will establish a predictive numerical framework for these novel CNT-based electric transmission fibers.Students will: A student researcher will be assigned the task of developing intricate geometry models for the metal-coated CNT fibers, utilizing Representative Volume Element (RVE) principles and Monte Carlo methods to represent their random microstructures. Subsequently, these models will be used to conduct high-fidelity numerical simulations on ASU's supercomputing resources to compute and analyze the key mechanical and electrical properties. This work will directly contribute to identifying the predictive numerical model.Prerequisite skills/knowledge: Knowledge of electric conduction, mechanical tensile test, basic solid mechanics, finite element method, Python
Project description: This project will focus on biosensor design and validation in biological fluids to detect pathogens related to human or animal diseases (to be decided at the project initiation), including infectious diseases, cancer, or neurodegenerative diseases.Students will: Here the students will be mentored to create nanoparticle-supported rapid electronic detection (NasRED) sensors, perform experiments using established sensing protocols, and analyze the data. The results will be summarized.Prerequisite skills/knowledge: Prior experience to work in a BSL-2 setting; basic understanding of biosensors or assay development
Project description: Control systems engineering is being increasingly used as a means to develop "just-in-time" adaptive interventions to improve on healthy behaviors (such as increased physical activity, weight management, and smoking cessation). These control systems, based on dynamic representations of health behavior theories and Model Predictive Control, can be improved through the use of machine learning techniquies such as reinforcement learning (RL). However, developing solutions that are fundamental yet satisfy practical requirements (and can be solved in reasonable time) remains an open area of investigation. The SURI project will contribute to continuing efforts in this topic in ASU's Control Systems Engineering Laboratory.Students will: The student will become familiarized with some of our current computational platforms (based on MATLAB w/Simulink and Python) that implement existing solutions. The nature of the problem, however, implies that many diverse computational approaches can be examined. The student will, drawing from their expertise in numerical methods, data science, dynamical systems, and control engineering, stage and implement approaches that can then be compared to a series of benchmarks. The student is expected to submit a report a the end of the 8 week program.Prerequisite skills/knowledge: Knowledge of differential equations, numerical methods, and dynamical systems. An introductory controls course is strongly recommended. Working knowledge of MATLAB w/Simulink and Python.
Project description: This project focuses on understanding basic principles of quantum mechanics as it applies to operation of nanoscale devices. A simulation software will be developed for modeling single gate and dual gate MOS capacitors that are integral part of the state-of-the-art fully-depleted SOI device technology. Python programming language will be used for tool implementation and open-source web application Jupyter notebook will be used for the development of the graphical user interface (GUI). The simulation tool will be installed on https://nanohub.org, a well established platform for dissemination of tools and educational modules to the scientific community.Students will: The student will work on device simulator development that involves solution of the 1D Schroedinger and the 1D Poisson equation self-consistently for single-gate and dual gate MOS capacitors with conventional or a stack of high-k dielectrics. Graphical User Interface will be developed for easy selection on structures, models and gate-dielectric stacks.Prerequisite skills/knowledge: Knowledge on numerical analysis, partial differential equations, python language programming and basic Jupyter notebook understanding is necessary.
Project description: My research group leverages nanotechnology to design stimuli-responsive, functional polymeric and hybrid nanocomposite materials at the interface of polymer chemistry, self-assembly, and additive manufacturing. Our overarching goal is to establish fundamental structureâ€“propertyâ€“processing relationships that enable the development of sustainable polymer materials.Students will: Work with a graduate student mentor to learn how to synthesize polymers via controlled polymerization mechanisms and nanoparticles via sol gel methods. Surface-engineering nanoparticles will be assembled with polymers to alter the final behavior of polymers. The student will learn how to characterize polymers, nanoparticles, and polymer nanocomposites using NMR, size exclusion chromatography, UV-Vis spectroscopy, dynamic light scattering, dynamic mechanical analysis, and optical microscopy. The student will also learn 3D printing.Prerequisite skills/knowledge: Knowledge about polymer chemistry, nanoparticle synthesis and functionalization, and materials characterization (mechanical properties, particle size etc)
Project description: This research project focuses on thin-film solar cells and photodetectors.Students will: Thin film deposition and device fabrication, characterization.Prerequisite skills/knowledge: knowledge of materials science and engineering, semiconductor device physics electrical engineering.
Project description: Large language model systems increasingly rely on routing: deciding which model, module, or computation path to use for each input. This project develops statistically grounded routing strategies aimed at reducing inference time while maintaining accuracy. We will explore multi-fidelity bandit approaches, where inexpensive, fast LLMs provide early signals that help determine when a larger model is needed. In parallel, we will study routing inside modular transformer architectures by examining how small models learn to activate specialized components. The project emphasizes principled decision-making, empirical evaluation, and careful experimentation, offering students a structured introduction to scalable and efficient LLM system design.Students will: Students will learn and implement routing algorithms, multi-armed bandit algorithms, and run controlled experiments with open LLMs. There will also be an opportunity to dive into the rigorous mathematical foundations of these methods.Prerequisite skills/knowledge: Python, basic machine learning, probability, linear algebra; familiarity with PyTorch or JAX is helpful but not required.
Project description: This research project focuses on developing agentic AI for education, including material generation, human alignment of LLMs, and progressive learning.Students will: The student will explore LLM Agentic tools, compare the state-of-the-art industry products and conduct experiments for the comparison. The student will also work with faculty and PhD students to enhance new workflows and systems for better education results.Prerequisite skills/knowledge: Knowledge of LLM and agentic AI, python and Linux programming, quick implementation and learning skills, familiarity with AutoGen, LangGraph,CrewAI, etc with be a bonus, experiences with International Olympiad in Informatics is a plusl
Project description: Secure and Trustworthy Human-AI Interaction: Currently, there is a lack of principled methods to construct safe and secure human-ML interaction paradigms. Thus, to prevent harm in ML-based systems, it is paramount that we understand vulnerabilities and apply safeguards now. This set of projects will investigate how human interaction impacts ML security in two ways: How human factors can be 1) exploited to reduce security or 2) harnessed to improve security. Projects topics may include detecting and mitigating the effect of deepfakes (topic 1), or developing ways to prevent vulnerabilities and prevent exploitation in developed machine learning systems (topic 2). The specific topic can be tailored to the interests and proposals of the applicant. Students will: These projects can include developing UI to convey provenance information for media and indicators to reduce the effect of deepfakes, understand how to develop machine learning coursework to include modules on adversarial machine learning topics, or discovering ways that adversarial machine learning exploits occur in practice, and developing methods to mitigate them.Prerequisite skills/knowledge: This topic is extremely interdisciplinary; skill in any of these topics are relevant: Computer security & privacy; Machine learning; Human Factors methodologies (e.g., experimental design, interviews, qualitative coding)
Project description: This project focuses on designing high-performance and reconfigurable computer architectures tailored for AI and machine learning (AI/ML) workloads, addressing the growing demand for efficient and flexible computation. The goal is to co-optimize hardware and compiler toolchains to achieve superior speed, energy efficiency, and adaptability across diverse neural network models and data types. By leveraging reconfigurable fabrics, such as FPGAs or coarse-grained reconfigurable arrays (CGRAs), the system will dynamically adapt to varying algorithmic requirements, minimizing performance bottlenecks. The compiler framework will automate hardware-software co-design, translating high-level AI models into optimized hardware configurations and executable code. This holistic approach enables seamless integration of emerging AI techniques while maintaining scalability and programmability. Ultimately, the project seeks to advance the frontier of domain-specific architectures and intelligent compilation, enabling next-generation AI systems that deliver higher performance, lower power consumption, and improved flexibility for rapidly evolving machine learning applications.Students will: Architectural simulation, hardware design, ML development, and compiler designPrerequisite skills/knowledge: C/C++, Verilog, Pytorch
Project description: We target two problems arising from decoupling databases and deep learning systems in the existing architectures: (1) The lack of a consistent design for data and model privacy mechanisms poses obstacles to achieving optimal privacy and security for integrated use cases. (2) The lack of verification mechanisms for the compliance of vendor's privacy policies defined over its data/model with privacy regulations such as Health Insurance Portability and Accountability Act (HIPAA), Payment Card Industry Data Security Standard (PCI-DSS), and General Data Protection Regulation (GDPR). These regulations place strict limitations on how this information can be collected and shared. Thus, it is critical for organizations to take steps to protect sensitive information and prevent inadvertent disclosure following the related regulations. We will develop a novel database system that closes the above gaps.Students will: Task 1 proposes a unified privacy model for data and model management; Task 2 leverages LLM to extract knowledge from privacy regulation documentation and vendor's privacy policy documentation, and add such knowledge to the knowledge graph constructed in our preliminary work; and Task 3 leverages LLM to retrieve information from the knowledge graph and verify the compliance with target regulations. The student is expected to contribute to one or multiple of the tasks.Prerequisite skills/knowledge: Strong coding skills.
Project description: fabricate and evaluate organic electronic and optical devicesStudents will: fabricate and evaluate organic electronic and optical devicesPrerequisite skills/knowledge: basic understanding of organic semiconductors and skills for device fabrications
Project description: This project aims to develop reinforcement learning (RL) controllers for a custom-built, high-degree-of-freedom (DOF) quadruped robot capable of agile and dynamic behaviors such as leaping and turning. The focus is on leveraging the robotâ€™s extra body DOFs to achieve enhanced maneuverability and stability during rapid motion. A key challenge is bridging the simulation-to-reality (sim-to-real) gap to ensure that policies trained in simulation transfer effectively to the physical robot. The student will design, train, and test RL policies using high-fidelity simulation environments and deploy them on the real robot for validation.Students will: The student will work on reinforcement learningâ€“based control, including environment setup, policy training and optimization, sim-to-real transfer strategies, and hardware implementation.Prerequisite skills/knowledge: Knowledge of reinforcement learning and robotics (kinematics and dynamics); proficiency in Python/C++; experience with Isaac Lab and/or MuJoCo.Prior experience implementing learning-based controllers on hardware is prefered.
Project description: AI-based methods for chip design, especially LLMs, for hardware design and hardware security. Design of AI accelerator architectures.Students will: The student(s) will develop AI models (CNNs, DNNs, LLMs) for hardware synthesis and design-for-test solutions. There are also oppprtunities available to study AI-based attacks on hardware and corresponding defenses.Prerequisite skills/knowledge: Basic understanding of CNNs, DNNs, LLMs, knowledge of Python and Pytorch (or similar tools), familiarity with EDA tools for chip design.
Project description: Built-in self-test and built-in self-repair for advanced packaging and chip/package co-designStudents will: The student will design test solutions for fanout wafer-level packaging and hybrid bonding. The test methods will include fault detection, diagnosis (localization), and repair.Prerequisite skills/knowledge: Basic knowledge of VLSI design and integrated circuits, circuit simulation and timing analysis; familarity with EDA tools. Design and analysis of algorithms.
Project description: AI-based healthcare solutions and privavcy preserving AI solutions for healthcareStudents will: The student will use various AI methods (transformers LLMs,...) to analyze anonymized patient data (EEG, ECG, etc.) for security vulnerabilities and identify encoding techniques to preserve privacy without impacting diagnostic accuracy.Prerequisite skills/knowledge:
Project description: Evaluate the corrosion behavior of structural materials in liquid metals and molten saltsStudents will: Assist in analyzing the experimental data from corrosion tests conducted in liquid metals and molten salts, perform image analyses to identify depths of attack and phase fractions, compile the results for a journal publicationPrerequisite skills/knowledge: Students from mechanical or materials science engineering; some experience in hands-on experiments is a plus.
Project description: Evaluate the corrosion behavior of structural materialsStudents will: Analyze the role of different water vapor contents on the oxidation behavior of Fe- and Ni-based alloys, evaluate microstructural characterization data, perform image analyses to measure oxide thicknesses, compile the results for a journal publicationPrerequisite skills/knowledge: Students from mechanical or materials science engineering; some experience in hands-on experiments is a plus.
Project description: Prediction of failures in real-world robotic systems either requires accurate model information or extensive testing. Moreover, obtaining such demonstrations is expensive, and it could be risky for the robotic system to fail during data collection repeatedly. In this work, we will investigate the sim-to-real gap for various robotic platforms such as manipulator arm, ground vehicles as well as aerial vehicles, from the perspective of falsification, using existing testing pipelines for simulation while working with limited data from the true system to enable better prediction of failures.Students will: The student will be working with PhD student to design experiments for a given robotic platform for data collection, perform experimetns for data collection and retrain or fine-tune policies to fix the predicted failure modes.Prerequisite skills/knowledge: Strong coding background, a good math background (control theory, optimization, probability, statistics), working knowledge of ML and NN training, hands-on experience with ROS/ROS2
Project description: Shape-morphing structuresâ€”unlike conventional deployable systems based on discrete hinges and springsâ€”can continuously deform into complex 3D shapes, enabling transformative applications in soft robotics, biomedical devices, and advanced manufacturing. Their virtually infinite degrees of freedom allow for precise control of the final shape; however, this same high dimensionality makes the inverse design process extremely challenging, requiring extensive trial-and-error exploration of a large design space. This research project seeks to develop a data-driven, physics-informed framework to reduce the dimensionality of the inverse design problem and accelerate the discovery of optimal designs. Students will have the opportunity to compare traditional finite elementâ€“based design methods with a newly developed physics-informed neural network approach. By the end of the project, students will fabricate and experimentally validate their optimized shape-morphing structures using 3D printing technology.Students will: Students will develop and apply a physics-informed hypergraph neural network to model the dynamics of shape-morphing structures for applications in soft robotics and biomedical devices. The neural network will incorporate both geometric and nonlinear material properties, and will then be integrated into a topology optimization framework. By implementing gradient-based optimization, students will design shape-morphing structures tailored for specific mechanical performance targets. They will compare the neural-network-generated designs with results from traditional finite element simulations and, finally, validate their optimized designs through 3D printing and experimental testing.Prerequisite skills/knowledge: Matlab and Python programming. Knowledge in machine learning and finite element simulations.
Project description: This summer project examines how small interstitial atoms affect the structure and mechanical behavior of high-entropy alloys by modifying local chemical ordering, dislocation interactions, and grain-boundary cohesion. The project offers hands-on experience in computational materials mechanics and supports a broader effort to design next-generation alloys with improved strength, ductility, and fracture resistance.Students will: The student will use computational tools, such as density functional theory, molecular dynamics, and machine-learning potentials, to study microstructural evolution and mechanical stability.Prerequisite skills/knowledge: Programming experience in MATLAB and/or Python, familiarity with HPC workflows, and a basic understanding of materials mechanics.
Project description: Mu group is dedicating in designing sustainable materails for rechargeable batteries, and developping electrochemical method to directely extract critical minerals from various water resources. Leveraging by comprehensive characterizations methods, Mu group aims to design high-performance and low-cost materials for Li/Na batteries.Students will: Students will learn how to use electrochemical method to direct extract critical minerals from different water resources. Students will be trained on how to make a battery and evaluate the battery performance. Students will be evolved in different type of X-ray characterization methods to undersand working principles and degradation behaviors.Prerequisite skills/knowledge: Materials science engineering, chemsitry, characterization methods
Project description: We are constructing the most efficient solar electrolyzer in the world. It delivers 99% of the solar energy it receives to the electrolyzer, while reduing the system cost by 30%. this leads to a 50% lower cost of solar electricity for green hydrogen production. The project involves control electronics and algorithm, system integration and debugging, system modification and optimization, and performance verification.Students will: This opportunity is open American students only (citizen or green card). You will work with PhD students on system development. You will also work on a simpler version of the system. The simpler system is much easy to construct, operate, and trouble shoot. It is intended for remoate communities with little access to technical support.Prerequisite skills/knowledge:
Project description: The development of miniaturized and high-performance power electronic converters relies on a host of critical infrastucture, including performing finite element analysis (FEA) simulations to design power magnetics and high speed power converters, programming FPGAs and microcontrollers to synthesize gating signals, and assisting in the development of hardware prototypes. In this project, the student will assist a graduate student in undertaking one or more of these tasks for the development of a miniaturized, high performance power electronic converter.Students will: (1) Finite element analyis simulations; (2) programming of microcontrollers and/or FPGAs; (3) PCB design and hardware prototyping.Prerequisite skills/knowledge: Experience with power electronics is ideal.
Project description: This project aims to fabricate 316L+NbC metal matrix composites (MMCs) for high-temperature applications using the laser powder bed fusion (LPBF) additive manufacturing process. 316L stainless steel is widely used in energy, biomedical, chemical, and marine industries due to its excellent corrosion resistance and ductility. However, its relatively low strength, particularly at elevated temperatures, limits its suitability for extreme environments involving high stress and high temperatures. To address this limitation, 316L+NbC MMCs will be fabricated under various LPBF processing conditions. Their high-temperature mechanical performance will be evaluated through elevated-temperature tensile testing. Ultimately, the project seeks to establish process-structure-property relationships for these additively manufactured MMCs.Students will: (1) Fabrication of 316L+NbC with LPBF machine. (2) Microstructure characterization with scanning electron microscopy. (3) Tensile testing.Prerequisite skills/knowledge: Knowledge of metal additive manufacturing. Hand-on skills on microstructure characterization and tensile testing.
Project description: Creating new pathways for scalable, fast production of precise crystals, leveraging raw materials from non-conventional mixtureStudents will: Students get to learn and contribute to advanced flow chemistry platformPrerequisite skills/knowledge: Basic chemistry lab, or basic process modeling
Project description: This research project develops advanced technologies for subsurface exploration using robotics, acoustic waves, and artificial intelligence. The goal is to create high-resolution, non-invasive methods for detecting underground features such as voids, utilities, buried infrastructure, contaminated zones, and geotechnical hazards, challenges important to defense, space, and energy applications. The student will work with a multidisciplinary team to design robotic sensing platforms, integrate acoustic and ultrasonic tools, and develop AI models that improve subsurface imaging and anomaly detection. Activities will include laboratory testing, field data collection, signal processing, and participation in team discussions to interpret findings and plan next steps. Through this work, the student will gain hands-on experience in robotics, sensing systems, and data-driven geophysical analysis. The project supports DoD infrastructure resilience, NASA planetary exploration needs, and DOE priorities in environmental monitoring and underground storage safety.Students will: The student will help design, assemble, and operate robotic sensing platforms; collect acoustic and ultrasonic data in both laboratory and field settings; and assist in developing machine-learning models that improve subsurface imaging and anomaly detection. The studentâ€™s assigned tasks will include conducting experiments, performing signal processing, coding for AI workflows, documenting results, and participating in group discussions to interpret findings and plan next steps. Through this experience, the student will gain hands-on skills in robotics, sensing technologies, data analytics, and applied geophysics.Prerequisite skills/knowledge: Basic experience with Matlab or Python programming, General understanding of physics or signals, Interest in robotics, sensing, or AI tools
Project description: A compelling opportunity for higher energy density batteries is solid-state electrolytes (SSEs), which offer a host of advantages over the liquid electrolytes that dominate the market today: they are leak-proof, energy-dense, flame-resistant, contain no toxic organic solvents, and can charge faster. A challenge to the commercialization of solid-state batteries is the development of a stable SSE that can support the film stresses that develop from significant expansion during cycling and can be processed with low-cost manufacturing processes. The objective of this work is to two-fold: to improve the thermomechanical reliability of SSEs and to subsequently produce safe, durable, and high-specific energy solid state batteries with a robust thin film SSE. The overarching questions that will be investigated are the material (ionic and electronic conductivity) from thin-film processing of ceramic-based SSEs and mechanical properties that develop in SSEs for understanding of chemomechanical degradation modes.Students will: Students will: Learn how to make battery materials and devices from solution using printing processes and characterize them with electronic and ionic conductivity measurements. There is also funding for additional proejcts to work on perovskite-based photovoltaics materials and devices.Prerequisite skills/knowledge: Knowledge of electrochemistry, materials science, and mechanical properties is a plus (although not required)
Project description: This project will focus on design and fabrication of gallium oxide power semiconductor devicesStudents will: The student will design gallium oxide lateral power devices in Silvaco and fabricate transistors in the ASU Nanofab. These devices will then be characterized to evaluate device characteristicsPrerequisite skills/knowledge: Prior cleanroom experience with handling of lithography tools is required.
Project description: This research project develops and evaluates advanced materials and processes to remove emerging contaminants such as nutrients, PFAS and toxic metals from water. The student will work alongside a multidisciplinary team to synthesize novel adsorbents, develop sensors for atmospheric water harvesting, and test photochemical methods that enhance treatment efficiency and sustainability. Activities will include laboratory experiments, data analysis, and participation in group discussions to interpret results and guide next steps. Through this work, the student will gain hands-on experience in materials synthesis, water quality analysis, and environmental process design. The project bridges environmental chemistry, materials science, and process engineering, offering valuable preparation for graduate study or careers focused on clean water technologies and sustainable infrastructure.Students will: Students will assist in laboratory experiments focused on developing and testing advanced materials for water treatment. They will prepare and characterize samples, conduct water quality analyses, collect and interpret data, and contribute to experimental design discussions. Students will also participate in weekly research meetings, maintain lab notebooks, and help summarize findings for presentations or reports.Prerequisite skills/knowledge: Students should have a basic understanding of chemistry, environmental engineering, or materials science principles - Chemical, environmental, civil or mechanical engineers work in my lab; some biology or bio-engineering students are involved too. Prior laboratory experienceâ€”such as preparing solutions, measuring water quality parameters, or handling analytical instrumentsâ€”is helpful but not required. Familiarity with data analysis tools (e.g., Excel, MATLAB, or Python) and good documentation practices are desirable. Most importantly, students should demonstrate curiosity, attention to detail, and a willingness to learn new experimental and analytical techniques in a collaborative research environment.
Project description: The goal of this project is to develop new biobased materials to mitigate shrinkage in 3D printed concreteStudents will: The student will be responsible for synthesizing new biobased materials that absorb and release water in to the concrete matrix in a regulated manner. The students will perform shrinkage studies and prepare a report at teh ened of the 8 weeks program.Prerequisite skills/knowledge: Civil Engineering student with previous lab experience and a strong interest in MS/ PhD programs
Project description: The goal of this project is to predict fracture in Metal additive manufactured samplesStudents will: The student will be responsible for generating fracture data and training ML models to predict fracture initiation in additively manufactured steel components. The student is expected to submit a report a the end of the 8 weeks program.Prerequisite skills/knowledge: MS student in structural engineering or related area with an intention to join our PhD program
Project description: Use topological data analysis and AI to enhance time series forecasting; use multimodal data to build knowledge graphs for ADStudents will: Methodology developemtn, Python coding, and data analysisPrerequisite skills/knowledge: Knowledge in statistical modeling, machine learning, and the use of AI.
Project description: The project explores will develop techniques for software understanding of binary code. Binary code is extremely resistant to understanding by humans and AI techniques, and a number of promising approaches, which we will explore, hint at the potential to alleviate this problem.Students will: Understanding system security, developing new techniques in Software Understanding for both humans and AI, improving binary decompilation approaches.Prerequisite skills/knowledge: Binary reverse engineering, strong programming background, expert-level Python programming.
Project description: Electrification of some industrial processes are already economical in the state of Arizona (e.g., electrifying forklifts) while others need further analysis and development. This SURI project will use techno-economic models to assess the feasibility of current electrification strategies for industrial clients.Students will: Evaluation of multiple electrification strategies that are applicable in industry and assessment of the current capital costs and energy savings to evaluate the return on investment.Prerequisite skills/knowledge: Basic concepts from thermodynamics and energy conversion are beneficial
Project description: This project will consist of Aerosol Jet Printing experiments, in-situ and ex-situ data collection from these experiments, and advanced data processing and AI algorithm design and training for AJP prognostic analysis. Students will have the chance of getting trained for operating an OPTOMEC Aerosol Jet machine. The lab environment and experiments will be safe and nonhazadous.Students will: (1) AJP operations and experiments; (2) data collection with inline sensor and microscope; (3) data processing and analytics with Python/Matlab/R codingPrerequisite skills/knowledge: Interest in 3D printing; proficiency in coding
Project description: The project focuses on combining self-assembly and advanced 3D nanoprinting to create next-generation optical materials and on-chip photonic structures. Students will gain hands-on experience in nanofabrication, optical characterization, and computational design of photonic devices. Ideal candidates are curious, detail-oriented, and eager to learn interdisciplinary techniques at the intersection of materials science, optics, and nanotechnology. This position offers an excellent opportunity to contribute to emerging research in lightâ€“matter interaction and metamaterial engineering.Students will: Solution based self-assembly/3D printing, optical simulation and charaterizationPrerequisite skills/knowledge: ideally some experiences in "solution phase based self-assembly aand printing", or "basic optics knowledge". But not required
Project description: TBDStudents will: Prerequisite skills/knowledge:
Project description: This project investigates post-quantum resilient communication techniques for next-generation wireless networks. As quantum computing emerges as a threat to current cryptographic systems, ensuring long-term security for sensitive data transmissions (such as financial transactions, medical records, and mission-critical communications) becomes essential. The research applies quantum-resistant secret sharing codes that encode and protect data for transmission across multiple networks. Students will build experimental network testbeds using distributed cloud infrastructure including AWS, Google Cloud, and Azure. The testbeds will demonstrate how secret sharing codes can encode original data into multiple coded shares, transmit through different routes, and reconstruct the original data securely at the destination, maintaining data security and resiliency. This hands-on research experience addresses critical security challenges for future wireless systems while providing industry-relevant skills in cloud computing, distributed systems, network programming, and post-quantum cybersecurity.Students will: Students will develop methods on commercial-off-the-shelf (COTS) 5G smart phones (Galaxy S24, iPhone 16) for secure data transmission and reception, integrating quantum-resistant secret sharing algorithms. They will implement a multi-cloud distributed network testbed connecting AWS, Google Cloud, and Azure. Tasks include writing Python/C++ code for encoding algorithms that transform data into coded shares and decoding algorithms that reconstruct original data, configuring cloud infrastructure and network topologies, conducting performance evaluations measuring throughput, resiliency, and security metrics, and visualizing results.Prerequisite skills/knowledge: Programming experience in Python, Java, C++, or MATLAB, and basic understanding of communication networks.
Project description: The objective of the research is to apply machine learning (LLMs) techniques to solve challenging problems in chip design.Students will: Develop algorithms and write software for traditional EDA applications for ground truth data generation then apply machine learning techniques to make the algorithm more scalable.Prerequisite skills/knowledge: Knowledge in Python and C++ is required. Course: Algorithms, data structures, circuit design, VLSI design.
Project description: The project consists of developing machine learning algorithms for MRI artifact correction using k-space data.Students will: The student will use MATLAB to generate a large library of synthetic k-space data representing a variety of MRI artifacts. and develop algorithms for artifact removal. They will then test the algorithms on real world data.Prerequisite skills/knowledge: Programming experience in MATLAB.
Project description: Multimodal Adversarial Attacks & Defences for Agents Modern "agents" (LLM/VLM + tools + data pipelines) are highly capable but increasingly vulnerable to multimodal adversarial attacks. This project will build VAD: a standardized evaluation framework to stress-test agentic pipelines with a suite of realistic attacks and to prototype practical defenses. Attacks include document-layer perturbations, image-based transferable black-box attacks, and text/token injection attacks. Defenses emphasize cross-modal verification (OCR + intent alignment checks), input sanitization & parsing hardening, red-teaming harnesses for deployed agents, and downstream corruption tests (e.g., OLTP vs OLAP view divergence). The outcome is a reproducible benchmark, metrics, and a demo that identifies where agents fail and which mitigations work.Students will: Implement attack generators + replay harness for agentic pipelines Design metrics (success rate, stealth, downstream impact, cost) Build defenses (OCR+intent checks, filters, policy gates, monitors) Produce a benchmark report + open-source-style repo structurePrerequisite skills/knowledge: Python required; familiarity with ML/LLMs helpful; interest in security; basic knowledge of PDFs/OCR or willingness to learn."
Project description: The project seeks to develop learning, control-theoretic, or hybrid to solve multi-fingered dexterous manipulation.Students will: learning, control, perception algorithm design, hardware experimentsPrerequisite skills/knowledge: python, hardware experiment
Project description: This project involves developing digital twins and robot learning algorithms for autonomous manufacturingStudents will: The student will work on developing digital twin models of an assembly platform, and leverage high-fidelity simulations to train robot controllers for precise and adaptive actions in pick-and-place, assembly, and inspection tasks in manfuacturingPrerequisite skills/knowledge: Python and/or C++, reinforcement learning and/or control systems, experience in digital twin software is a plus
Project description: This project involves design and prototyping of soft robots for healthcare and infrastructure managementStudents will: The student will work on design and fabticating inflatable soft actuators, and inteagte them into soft wearable robots for human assistance and crawling robots for inspection of civil infrastructure.Prerequisite skills/knowledge: mechanical design, fabrication and prototyping, sensor integartion, control systems, experience in soft robots is a plus
Project description: Hardware-software co-design of under-actuated robots in highly dynamic environments.Students will: Design and development of: 1) hardware controlling an autonomous blimp, 2) control and coordination algorithms for blimps fleetPrerequisite skills/knowledge: Experience with one of several of the following: raspberry pi, ROS system,electronic devices (sensors, DC motor, ESCs, etc.), PCB design and building, control systems
Project description: The research project aims to develope a 10-min VR course "Material Edge: Ceramics for the Extremes".The course is organized into short, engaging segments that transition from a macroscopic engineering system into a fully simulated extreme environment, and then into a 3D fly-through of the materialâ€™s microstructure. Students will explore phenomena such as phase transitions, ion transport, and thermal degradation while guided by narration. The module concludes with an interactive design activity where students apply what they learned to a technology of their choice.Students will: The student will need to work with the PI to develop a short 10 min VR course on ceramics materials under extreme conditions. A student working on this project will begin by reviewing background materials on extreme environments, microstructural behavior, and VR-based STEM learning. They will then storyboard the 10-minute module, outlining each segmentâ€™s macroscopic device, simulated environment, and microstructural fly-through, along with key learning objectives and narration points. Next, the student will gather and prepare visual content to translate the storyboard into accurate VR scenes. They will develop and refine the scientific narration, design the interactive end-of-module activity, and assist with testing to ensure clarity, engagement, and technical accuracy. Finally, the student will prepare supplementary educational materials such as instructor guides, learning objectives, and assessment questions.Prerequisite skills/knowledge: Preferred knowledge on materials science (crystal structures, ceramics defects, etc) and the VR development tools such as Blender, Unity, Unreal Engine, etc
Project description: The project explores applied aspects of program analysis and vulnerability research. We'll work on techniques to find bugs in programs, prove their security applicability, and fix them.Students will: Developing and improving techniques in program analysis, analyzing the security of complex systems.Prerequisite skills/knowledge: Strong programming background, expert-level Python programming, understanding of security issues found in software, assembly language.
Project description: Recovery of metal cations from acidified solutions is crucial for efficient recycling of electronics and batteries, as well as mining processes. Recently, our group has found that inorganic nanoporous ion-exchange materials can be designed for stable and selective ion-exchange. This project will explore the adsorption properties of different metal cations under different conditions to discern the driving forces for selectivity and the reactivity of metal cations in nanoporous environmentsStudents will: The student will measure adsorption isotherms for a range of metals on inorganic oxides of different compositions and pore sizes, screening a wide range of conditions. If successful, the student will also use advanced spectroscopic methods (FTIR, UV-vis, solution and solid-state NMR) to analyze interactions of cations with the sorbent surface, with possible extensions to heterogeneous catalysis.Prerequisite skills/knowledge: Student should have a basic knowledge of chemistry lab procedures and safety. Experience with adsorption phenomena and prior experimental lab experience are a plus.

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Brief statement describing your interest in participating in SURI which may include your research interests, any relevant research, academic, or work experience, your goals and how SURI will help you achieve them (up to 500 words).

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Summer 2026