Viterbi Summer Undergraduate Research Experience (SURE)
SURE 2025 Research Opportunities
Labs and research groups are listed by USC departments. Please note that many projects are interdisciplinary in nature and offer opportunities for students across diverse majors and areas of study.
SURE research opportunities and project details will be posted on a rolling basis until the application deadline. Please continue to check back for more updates. Some labs / research groups may not have summer projects listed yet, but applicants are encouraged to review research lab websites for more details about current projects and initiatives. All lab availability and project details are subject to change.
- Faculty/PI: Mitul Luhar
- Website: https://sites.usc.edu/fsi/
- Research Overview: The fluid-structure interactions lab combines physical experiments with reduced-complexity models to address programs in aerodynamic/hydrodynamic design, flow control, soft robotics, and environmental fluid mechanics.
- Summer Projects
- Tunable Porous Surfaces for Turbulence Control
- Project Description: Prior theoretical studies show that porous surfaces that are anisotropic (i.e., provide different resistance to flow in different directions) can reduce fluid dynamic drag relative to smooth, solid walls. This reduction in drag could benefit a range of aerodynamic and hydrodynamic applications. With advances in additive manufacturing, we now have the ability to design and fabricate porous surfaces with complex geometries that provide this anisotropic response. This project aims to design and test such surfaces in laboratory water channel experiments.
- Student Responsibilities: The student will be responsible for designing novel porous geometries, 3D-printing them, and testing their drag reduction performance in benchtop-scale water channel experiments.
- Preferred Majors: Aerospace & Mechanical Engineering, Astronautical Engineering
- Prerequisites and Preferred Skillsets: Background in physics and calculus, computer-aided design, 3D-printing, MATLAB (or similar) are all beneficial but not required.
- Characterizing tsunami propagation and evolution via laboratory experiments
- Project Description: Recent work has led to the development of a novel "jet array wavemaker" that can produce prescribed waves (e.g., long waves resembling tsunamis) and currents (e.g., from a river flow) in lab-scale experiments, while simultaneously accounting for differences in seawater and freshwater density. This project will use this novel flow facility to characterize the evolution of a long saltwater wave (e.g., a tsunami) as it propagates into a freshwater flow (i.e., a river).
- Student Responsibilities: The student will set up and run the experiment, collect state-of-the-art optical measurements (using particle image velocimetry and planar laser-induced fluorescence techniques), and analyze the resulting dataset.
- Preferred Majors: Aerospace & Mechanical Engineering, Civil & Environmental Engineering
- Prerequisites and Preferred Skillsets: Physics and calculus background, hands-on experimental experience (including electronics. imaging) preferred but not required.
- Tunable Porous Surfaces for Turbulence Control
- Faculty/PI: Radha Kalluri
- Website: https://sites.usc.edu/hcn/
- Summer Projects
- Tunable Porous Surfaces for Turbulence Control
- Project Description: We will place students into the laboratory of one of several faculty members affiliated with Viterbi. This includes
Radha Kalluri; Biophysics and neurophysiology
Raymond Goldsworthy; Neural implants and psychophysics
Christopher Shera; Nonlinear mechanics and theoretical modeling of hydromechanical systems
Brian Applegate and John Oghalai; Medical Imaging -
Students will be integrated into a rich ecosystem of trainees including other undergraduate students, graduate students, and medical students who are working in our laboratories. Students will also have opportunities to tour and interact with different research laboratories within the Department of Otolaryngology. Early exposure to our field will strengthen the pipeline of students entering the field of Hearing and Communications Neuroscience and the different career pathways within this area of interest. Please contact me for additional information.
- Project Description: We will place students into the laboratory of one of several faculty members affiliated with Viterbi. This includes
- Tunable Porous Surfaces for Turbulence Control
- Faculty/PI: Qifa Zhou
- Website: https://utrc.usc.edu/team/
- Summer Projects
- High Intensity Frequency Ultrasound Transducer In Therapeutic Applications
- Project Description: Developing single element transducers including fabrication, validation and testing to apply for different clinical application
- Being able to understand the transducer elements, fabrication steps and validate transducer performances
- High Intensity Frequency Ultrasound Transducer In Therapeutic Applications
- Faculty/PI: Ellis Meng
- Website: biomems.usc.edu
- Research Overview: The Biomedical Microsystems Laboratory at USC focuses on developing novel micro- and nanotechnologies for biomedical applications. In particular, we are interested in the integration of multiple modalities (e.g. electrical, mechanical, and chemical) in miniaturized devices measuring no more than a few millimeters for use in fundamental scientific research, biomedical diagnostics, and therapy.
- Summer Project:
- Summer Research Experience in Biomedical Microdevices
- Description: The Biomedical Microsystems Lab (https://biomems.usc.edu/) directed by Prof. Ellis Meng seeks an outstanding and highly motivated postdoctoral scholar for a leading role in developing and disseminating implantable microdevices using polymer microelectromechanical systems (MEMS) technology. The Lab is a collaborative and dynamic working environment that leverages the advantages of micro- and nanotechnologies to advance scientific discovery and healthcare. Projects will either be in the area of neural engineering/neural interfaces and/or biomedical sensor technologies.
- Student Responsibilities: Coordinate research tasks with graduate student, postdoc, or staff mentor. Learn protocols and standard operating procedures. Conduct experiments. Analyze data. Prepare presentations and reports. Attend lab meetings and present research progress and findings.
- Preferred Majors: Aerospace & Mechanical Engineering,Biomedical Engineering,Chemical Engineering,Electrical & Computer Engineering
- Preferred Skillsets: Prior work experience involving hands-on tasks. This could be research but need not be. Laboratory experiences from class work is welcome as are hands-on experiences from student clubs, competitions, or work experiences.
- Summer Research Experience in Biomedical Microdevices
- Faculty/PI: Shaama Mallikarjun
- Website: https://sharada-lab.usc.edu/
- Summer Projects:
- Designing catalysts for light-driven utilization of carbon dioxide
- Description: The project uses quantum chemistry and machine learning methods to understand the mechanisms of light-driven CO2 conversion and design catalysts that are both active and resistant to degradation.
- The student will carry out quantum chemistry simulations, analyze data, develop machine learning models, and contribute to writing articles.
- Designing catalysts for light-driven utilization of carbon dioxide
- Faculty/PI: Thomas Petersen
- Website: https://sites.google.com/usc.edu/petersen-lab
- Summer Projects:
- Transport of clay colloids through heterogeneous porous media
- Description: The project will investigate the impact of electrochemical forces on the mobilization and transport of clay suspensions in microfluidic porous media. Observations will be rationalized with numerical simulations. Specifically, the project wishes to examine 1) the phase behavior of laponite, kaolinite, and clay-stabilized oil droplets, 2) how salt concentration gradients enhance diffusiophoretic motion of the clay aggregates and emulsions, 3) whether osmosis-driven water migration can displace aggregates and droplets out of dead-end pores. We hypothesize that the negative charge of the clay, afforded by the deprotonation of surface hydroxyl groups and/or anionic surfactant, causes colloidal particles to move up NaCl and NaOH concentration gradients and down HCl gradients. Theories will be tested in heterogeneous microfluidic porous media and by simulating the transport phenomena using continuum mechanics.
- Students will focus either on assisting with numerical modeling or conducting microfluidic experiments.
- Transport of clay colloids through heterogeneous porous media
- Faculty/PI: Daniel McCurry
- Website: mccurrylab.com
- Research Overview:The McCurry lab applies the tools of organic and analytical chemistry to solve environmental problems. We primarily work in the areas of wastewater reuse and drinking water treatment.
- Summer Projects:
- Environmental engineering and chemistry to enable safe sustainable water reuse
- Description: Our research group currently consists of five PhD students and four undergraduate researchers who all do research in the areas of water treatment and environmental chemistry. Our major research activities include identifying the precursors and formation pathways of disinfection byproducts formed during water treatment and wastewater reuse, developing new chemical technologies for oxidation of trace organic contaminants, and developing new analytical techniques for identification and quantification of chemical pollutants using mass spectrometry. Our research primarily takes place in the BHE Water Lab, and relies heavily on recently purchased analytical instrumentation, including an ion mobility QTOF mass spectrometer, a gas chromatograph/triple quadrupole mass spectrometer, and an inductively-coupled plasma mass spectrometer. These tools allow us to quantify chemicals at extremely low concentrations (e.g., parts per trillion) and to identify previously unknown compounds in water samples.
- Student Responsibilities: Assisting graduate students with experiments on water treatment, water reuse, and quantification of trace contaminants in water. Specific responsibilities will include preparing for and setting up batch water treatment experiments, sample preparation for mass spectrometry-based identification and quantification of pollutants, and analyzing data produced by analytical instruments. Especially dedicated students may eventually have the opportunity to advance to a fully-independent project advised directly by the PI.
- Preferred Majors: Chemical Engineering,Civil & Environmental Engineering
- Preferred Skillsets: General chemistry lab skills; some exposure to analytical chemistry is helpful but not required; chemistry coursework helpful
- Environmental engineering and chemistry to enable safe sustainable water reuse
- Faculty/PI: Shaddin Dughmi
- Website: https://viterbi-web.usc.edu/~cstheory/ and https://viterbi-web.usc.edu/~shaddin/
- Research Overview:
- Summer Projects:
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- Description:
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- Faculty/PI: Leslie Gilliard-Abdul Aziz
- Website: https://the-sustainable-lab.com
- Research Overview: Kandis Leslie Gilliard-AbdulAziz directs the Sustainable Lab, which primarily focuses on developing novel materials for sustainable catalytic processes for low-carbon chemical production. Her primary research focus is novel materials development for CO2 sequestration and utilization using an interdisciplinary toolset from chemistry, material science, chemical, and environmental engineering.
- Summer Project
- Optimization of Novel Materials for Carbon Capture
- Project Description: The development of sorbents is critical for capturing greenhouse gases, such as carbon dioxide, from the air or industrial streams. However, the most prevalent issue is the capability of the sorbent to sustain performance for 100s of cycles with consistent performance. This project seeks to develop novel materials with special properties to maintain carbon capture performance for several cycles.
- Student Responsibilities: Wet lab synthesis of nanoparticles and bulk oxide powders
- Materials characterization using X-ray diffraction, X-ray photoelectron spectroscopy, and Transmission electron microscopy
- Assist with characterization of carbon capture performance
- Preferred Majors: Aerospace & Mechanical Engineering,Astronautical Engineering,Biomedical Engineering,Chemical Engineering,Civil & Environmental Engineering,Computer Science,Electrical & Computer Engineering,Industrial & Systems Engineering
- Prerequisites and Preferred Skillsets: Prior coursework in general chemistry or related course; Good oral and written communication skills; Works well in teams
- Optimization of Novel Materials for Carbon Capture
- Faculty/PI: Ibrahim Sabek
- Website: http://viterbi-web.usc.edu/~sabek/
- Summer Projects:
- Machine Learning Empowered Data-Intensive Systems
- Description: In this project, we will explore using machine learning (ML) to improve data management, processing, and analysis operations. In particular, we will use ML to improve the performance of database system components, such as query optimizers and schedulers, against the dynamic changes in data and user queries. Another direction is to investigate the use of ML to build scalable knowledge-based construction systems.
- Students will be involved in developing algorithms and implementing them in data-intensive systems, mostly using Python and C++ programming languages.
- Machine Learning Empowered Data-Intensive Systems
- Faculty/PI: Evi Micha
- Website: https://evi-micha.github.io/index.html
- Summer Projects:
- Fair and Efficient Collective Decision Making
- Description: Over the past decade, algorithms have had a profound impact on human lives. As a result, it has become more crucial than ever to design decision-making algorithms that treat people fairly, make efficient use of limited resources, and promote social good. These goals lie at the core of our lab's research. Interns will have the opportunity to contribute to projects across this spectrum, with a particular focus on AI alignment challenges involving diverse human values. Specifically, while current methods for aligning large language models (LLMs) with human values often assume a shared societal consensus, this assumption frequently does not hold in practice. Our work focuses on addressing this gap by leveraging the mathematical foundations of computational social choice to design reward functions that account for heterogeneous preferences and effectively capture diverse perspectives.
- Students will be responsible for contributing to the theoretical aspects of the project, such as designing algorithms and conducting worst-case analyses to evaluate their performance. Additionally, they will design and implement experiments to complement and validate the theoretical findings.
- Fair and Efficient Collective Decision Making
- Faculty/PI: Heather Culbertson
- Website: https://sites.usc.edu/culbertson/
- Research Overview: The Haptics Robotics and Virtual Interaction (HaRVI) Laboratory explores how humans interact with our world, robots, and technology through touch. The goal of our research is to create natural and intuitive interactions that realistically mimic the touch sensations experienced during interactions with the physical world. We design novel haptic hardware and rendering algorithms to improve the usability of technology, increasing people’s social connectedness, ability to complete specific tasks, and immersiveness in virtual reality. Our research has a strong focus on integrating human perception into all steps of the design process
- Summer Projects:
- Haptics for Virtual Reality
- Description: This project focuses on the design, building, and control of haptic devices for virtual reality. Current VR systems lack any touch feedback, providing only visual and auditory information to the user. However, touch is a critical component for our interactions with the physical world and with other people. This research will investigate how we use our sense of touch to communicate with the physical world and use this knowledge to design haptic devices and rendering systems that allow users to interact with and communicate through the virtual world. To accomplish this, the project will integrate electronics, mechanical design, programming, and human perception to build and program a device to display artificial touch sensations to a user with the goal of creating a natural and realistic interaction.
- Student Responsibilities: Reading and analyzing research papers. Designing, prototyping, and programming haptic devices. Conducting human subject studies.
- Preferred Majors: Aerospace & Mechanical Engineering, Computer Science
- Preferred Skillsets: Programming (C++ preferred), CAD/3D printing, electronics and circuits (helpful, but not required)
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- Feedback control for a robotic walker with human user
- Project Description: Feedback control for a robotic walker with human user
- Student Responsibilities: Reading and analyzing research papers. Designing, prototyping, and programming a physical system. Conducting human subject studies.
- Preferred Majors: Aerospace & Mechanical Engineering,Biomedical Engineering,Computer Science
- Prerequisites and Preferred Skillsets: Programming (C++ preferred), CAD/3D printing, electronics and circuits (helpful, but not required)
- Haptics for Virtual Reality
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- Faculty/PI: Erdem Biyik
- Website: https://liralab.usc.edu/
- Research Overview: USC Learning and Interactive Robot Autonomy Lab (LiraLab) develops algorithms for robot learning, safe and efficient human-robot interaction and multi-agent systems. Our mission is to equip robots, or more generally agents powered with artificial intelligence (AI), with the capabilities that will enable them to intelligently learn, adapt to, and influence the humans and other AI agents.
- Summer Projects
- Imitation learning from control-constrained demonstrations
- Project Description: This project will explore efficient ways of performing imitation learning and/or inverse reinforcement learning when the expert demonstrations come from a constrained control interfaces, e.g. due to the controller itself or the suboptimality of the expert human. The applications include tabletop manipulation and autonomous driving.
- Preferred Majors: Computer Science, Electrical & Computer Engineering
- Prerequisites and Preferred Skillsets: Python programming language; Fundamentals of machine learning; Some machine learning libraries in Python
- Self-supervised improvements over reinforcement learning with large pre-trained models
- Project Description: This project will explore the use of large pre-trained models (e.g., LLMs, VLMs, VQAs, etc.) for creating a self-supervision signal in reinforcement learning. The applications include, but are not limited to, tabletop manipulation.
- Preferred Majors: Computer Science, Electrical & Computer Engineering
- Prerequisites and Preferred Skillsets: Python programming language; Basic machine learning knowledge; Familiarity with machine learning libraries in Python
- Active querying for reinforcement learning from human feedback
- Project Description: The current implementations of reinforcement learning from human feedback (RLHF) follows the learned policy to generate new queries for the human. In this project, we will explore alternative ways to do it to improve data-efficiency of training. It will involve implementation of active learning techniques.
- Preferred Majors: Computer Science, Electrical & Computer Engineering
- Prerequisites and Preferred Skillsets: Python programming language; Basics of machine learning and reinforcement learning; Familiarity with machine learning libraries in Python; Basics of information theory
- Developing testbeds for reinforcement learning
- Project Description: This project is about creating testbeds that offer plug-and-play feature for reinforcement learning. These testbeds will be for the other projects in the lab that are about domain transfer in reinforcement learning, reinforcement learning with human feedback, etc. Depending on the needs, the environments may be simple OpenAI Gym environments or more sophisticated algorithms built on Unity, MuJoCo, etc.
- Preferred Majors: Computer Science, Electrical & Computer Engineering
- Imitation learning from control-constrained demonstrations
- Faculty/PI: Daniel Seita
- Website: https://slurm-lab-usc.github.io/
- Research Overview: The Sensing, Learning, and Understanding for Robotic Manipulation (SLURM) Lab at the University of Southern California studies how we can enable robots to interact with and manipulate geometrically and perceptually challenging objects. Our long-term goal is to develop robots that can reliably manipulate objects in less-structured and messier real world settings. To this end, we develop novel methods for improving robotic manipulation by incorporating state of the art machine learning algorithms, perception methods, and hardware platforms. Professor Daniel Seita directs the SLURM Lab.
- Summer Project
- Manipulating Mixtures of Liquids and Solids
- Project Description: Humans use spoons and similar tools to transport and manipulate liquids and solids. For example, we might use spoons to transfer sauces from one bowl to another when cooking, or use spoons to retrieve food items when eating soup, stew, breakfast cereal, chopped almonds, etc. We wish to train robots to similarly manipulate tools in these contexts, with generalization to different mixtures of liquids and solids.To start, we may build upon a simulation environment that can simulate mixtures of liquids and solids. Long-term, we hope that this work can lead the groundwork for robotic assistive feeding, and we aim to collaborate with other labs to work on physical feeding experiments.
- Student Responsibilities: The student would be developing and using a simulator for simulating complex deformables and their interactions. A stretch goal would be to show applicability on a physical robot system (or to collaborate with a team which does this).
- Preferred Majors: Computer Science
- Prerequisites and Preferred Skillsets: The student would need to be familiar with Python and the basics of machine learning (or be willing to learn as they work on the project). Past experience in robotics is not required.
- Manipulating Mixtures of Liquids and Solids
- Faculty/PI: Shinyi Wu
- Summer Project
- Data Integration and Model Development for Problematic Cannabis Use
- Project Description: Our research focuses on understanding the patterns of Problematic Cannabis Use (PCU) among young adults in the dynamic environment of Los Angeles County, one of the nation's largest markets for liberalized cannabis use. We aim to develop a mathematical model by integrating the longitudinal behavioral survey data, theory-driven conceptual frameworks, and inputs from policy and community stakeholders. Our objective is to respond to national calls for advanced analytical techniques to identify risk and protective factors of PCU, examining how various elements influence the progression of cannabis use. These insights are crucial for shaping future cannabis policies and treatment practices.
- Student Responsibilities: Participants will gain hands-on experience in processing real-world longitudinal data and developing simulation models grounded in their theoretical understanding. This opportunity offers engagement in interdisciplinary research at the intersection of public policy, community health, and engineering approaches.
- Preferred Majors: Industrial & Systems Engineering
- Prerequisites and Preferred Skillsets: Strong foundation in mathematics and comfort with learning mathematical modeling techniques. Proficiency in Python programming languages is preferred.
- Data Integration and Model Development for Problematic Cannabis Use
- Faculty/PI: Meisam Razaviyayn
- Website: https://sites.usc.edu/razaviyayn/group/
- Research Overview: Designing and studying of efficient large scale data analysis algorithms for machine learning tools and models. Led by Professor Meisam Razaviyayn
- Summer Projects:
- Scalable fair and private learning in the presence of distribution shift
- Project Description: Machine learning models are increasingly used in high-stakes decision-making systems. In such applications, a major concern is that these models sometimes discriminate against certain demographic groups such as individuals with a certain race, gender, or age. Another major concern in these applications is the violation of the privacy of users. While fair learning algorithms have been developed to mitigate discrimination issues, these algorithms can still leak sensitive information, such as individuals' health or financial records. On top of these challenges, there can be changes in data distribution when we move from training data to test data. In this project, we aim at developing scalable algorithms for tackling these challenges. This project is a part of a bigger collaborative project with the involvement of students from USC and researchers from industry (Meta, Amazon, Google Research).
- Student Responsibilities: TBD
- Preferred Majors: Computer Science
- Prerequisites: Understanding the basics concepts in machine learning, optimization
- Knowledge of Python and PyTorch/TensorFlow
- Scalable fair and private learning in the presence of distribution shift
- Faculty/PI:Christopher Torng
- Website: https://sites.usc.edu/acorn-research/
- Summer Projects
- Enabling Rapid Chip Design with Agile Flow Tools
- Project Description: Achieving high code reuse in physical design flows is challenging but increasingly necessary to build complex systems. We present a vision and framework based on modular flow generators that encapsulates coarse-grained and fine-grained reusable code in modular nodes and assembles them into complete flows. These agile flow tools are being designed to enable students across the country in R1/R2 universities to successfully build chips in advanced technologies.
- Student Responsibilities: Depending on the student's interests and abilities, the candidate can work with physical design flow tools using Tcl and Python, or experiment with designing small hardware blocks pushed from RTL to GDS in a commercial digital ASIC tool flow.
- Preferred Majors: Electrical and Computer Engineering
- Prerequisites and Preferred Skillsets: Candidates should: - Have initiative and curiosity - Work well in teams - Have prior programming experience in Python - Have prior programming experience in any hardware description language (e.g., Verilog) - Have taken some VLSI coursework
- Rapid Runtime Reconfigurable Arrays for Wideband Spectrum Sensing and Machine Learning
- Project Description: Commercial and military demands on the electromagnetic spectrum are driving RF systems to operate in increasingly congested and complex environments. These systems must analyze large volumes of continuously streaming data, detect and characterize waveforms, and wake up downstream decision-making applications, all within unknown environments. We will build and ask fundamental questions about how to build compute-dense runtime reconfigurable arrays with fast and flexible program switching controlled by embedded real-time schedulers.
- Student Responsibilities: Depending on the student's interests and abilities, the candidate can design hardware blocks in Verilog, write flow scripts in Python/Tcl, and more generally have an opportunity to explore compiler-level work (how to run software on this accelerator) or VLSI-level work (how to build a chip for this accelerator)
- Preferred Majors: Electrical and Computer Engineering
- Prerequisites and Preferred Skillsets: Candidates should: - Have initiative and curiosity - Work well in teams - Have prior programming experience in Python - Have prior programming experience in any hardware description language (e.g., Verilog)
- Specializing Communication at the Inter-Chiplet Boundary for Energy-Efficient Machine Learning
- Project Description: An open chiplet ecosystem would allow heterogeneous integration of chips in a compact, advanced package. Building a chiplet ecosystem represents a tremendous paradigm shift in the time and cost of assembling future computing systems and constitutes a key thrust in the CHIPS and Science Act research strategy. How can we build chiplet-based systems to be simpler, faster, and more efficient when domain-specific hardware accelerators are communicating across inter-chiplet interfaces?
- Student Responsibilities: Depending on the student's interests and abilities, the candidate can design hardware blocks in Verilog, write flow scripts in Python/Tcl, and more generally have an opportunity to explore compiler-level work (how to run software on this accelerator) or VLSI-level work (how to build a chip for this accelerator)
- Preferred Majors: Electrical and Computer Engineering
- Prerequisites and Preferred Skillsets: Candidates should: - Have initiative and curiosity - Work well in teams - Have prior programming experience in Python - Have prior programming experience in any hardware description language (e.g., Verilog)
- Enabling Rapid Chip Design with Agile Flow Tools
- Faculty/PI: Justin Haldar
- Website: https://mr.usc.edu/
- Summer Project
- Project Description: Magnetic resonance imaging (MRI) technologies provide unique capabilities to probe the mysteries of biological systems, and have enabled novel insights into anatomy, metabolism, and physiology in both health and disease. However, while MRI is decades old, is associated with multiple Nobel prizes (in physics, chemistry, and medicine), and has already revolutionized fields like medicine and neuroscience, current MRI methods are still very far from achieving the full potential of the MRI signal. Specifically, modern MRI methods suffer due to long data acquisition times, limited signal-to-noise ratio, high monetary costs, and various other practical and experimental limitations — this limits the amount of information we can extract from living human subjects, and often precludes the use of advanced experimental methods that could otherwise increase our understanding by orders-of-magnitude. Our research group addresses such limitations from a signal processing perspective, developing novel methods for data acquisition, image reconstruction, and parameter estimation that combine: (1) the modeling and manipulation of physical imaging processes; (2) the use of novel constrained signal and image models; (3) novel theory to characterize signal estimation frameworks; and (4) fast computational algorithms and hardware. Methods we developed have enabled substantial acceleration of routine modern MRI exams, and have also enabled the development of highly-informative next-generation MRI experiments that were previously impractical. Our approaches are often based on jointly designing data acquisition and image reconstruction methods to exploit the inherent structure that can be found within high-dimensional data, and we do our best to take full advantage of the "blessings of dimensionality" while mitigating the associated "curses."
- Preferred Majors: Electrical & Computer Engineering
- Prerequisites and Preferred Skillsets: Signal processing, linear algebra, programming
- Faculty/PI: Hossein Hashemi
- Website: https://hhlab.usc.edu/
- Research Overview: We analyze, design, and implement integrated circuits and systems for communications, sensing, and imaging applications.
- Specifically, we envision high-performance sensing and communication devices that can be embedded in the environment to increase our awareness, improve the quality of life, and create an intelligent and responsive ambient.
- Summer Projects:
- Millimeter-Wave Integrated Circuits
- Description: Modern and future wireless communication systems (5G, 6G), automotive radars, and high-resolution 3D imagers operate at millimeter wave frequencies. This project involves simulations and design of millimeter-wave integrated circuits.
- Student Responsibilities: The student will design and simulate millimeter-wave integrated circuits.
- Preferred Majors: Electrical & Computer Engineering
- Preferred Skillsets: Required background- Basic electromagnetics (Maxwell equations), Basic transistor-level analog circuits; Additional Preferred Knowledge (not necessary) - Cadence design tool, Python programming
- Silicon Photonics Integrated Circuits
- Description: The project involves application of silicon photonics integrated circuits for lidar, optical computing, and other applications.
- Student Responsibilities: The student will work on design and simulations of photonic components and photonic integrated circuits.
- Preferred Majors: Electrical & Computer Engineering
- Preferred Skillsets: Required Knowledge - Basic electromagnetic (solving Maxwell equation with boundary value)
- Millimeter-Wave Integrated Circuits
- Faculty/PI: Yasser Khan
- Website: khan.usc.edu
- Research Overview: We use additive manufacturing and hardware AI to produce skin-like wearables, implantables, and ingestibles. These medical devices are being used for precision health and psychiatry.
- Summer Projects
- Wearables for Mental Health
- Project Description: Developing multi-modal wearables for brain and behavior study involves creating devices that collect diverse data types like physiological and neurological signals. These wearables, used in daily life, help analyze complex aspects of human cognition and behavior, leveraging advanced data analysis for applications in healthcare, research, and technology.
- Student Responsibilities:
- Wearable Sensor Design: Designing the sensors that will be integrated into the wearables, ensuring they are suitable for capturing the required data (like physiological and neurological signals).
- Fabrication: Physically creating the components of wearable devices, which may involve working with different materials and manufacturing techniques.
- Electronics and Embedded System Programming: Developing the electronic systems that power the wearables, including programming the microcontrollers or embedded systems that manage sensor data collection and device operation.
- Printed Circuit Board (PCB) Design: Designing PCBs that connect the electronic components of the wearables, including sensors, processors, and power supplies.
- Experimental Setup: Setting up experiments to test and validate the wearables, which includes preparing the environment, the devices, and any supporting technology.
- Data Collection: Operating the wearables during experiments to collect data.
Data Processing: Analyzing the collected data to extract meaningful information, which may involve cleaning the data, performing statistical analyses, and using AI algorithms.
- Fabrication: Physically creating the components of wearable
- Preferred Majors: Aerospace & Mechanical Engineering, Biomedical Engineering, Chemical Engineering, Electrical & Computer Engineering
- Prerequisites and Preferred Skillsets: Electronics design, fabrication experience, coding, embedded systems, prototyping, AI/ML tools use.
- Wearables for Mental Health
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- MRI coils and wearables
- Project Description: The project involves developing wearable devices and flexible coils specifically designed for use in MRI environments. These advanced tools are capable of concurrently collecting physiological data and MRI images, enabling simultaneous monitoring of physiological responses and detailed imaging of internal body structures. This dual functionality enhances the depth and quality of data collected during MRI scans, providing valuable insights for medical research and diagnosis.
- Student Responsibilities:
- Wearable Sensor Design: Designing the sensors that will be integrated into the wearables, ensuring they are suitable for capturing the required data (like physiological and neurological signals).
- Fabrication: Physically creating the components of the wearable devices, which may involve working with different materials and manufacturing techniques.
- Electronics and Embedded System Programming: Developing the electronic systems that power the wearables, including programming the microcontrollers or embedded systems that manage sensor data collection and device operation.
- Printed Circuit Board (PCB) Design: Designing PCBs that connect the electronic components of the wearables, including sensors, processors, and power supplies.
- Experimental Setup: Setting up experiments to test and validate the wearables, which includes preparing the environment, the devices, and any supporting technology.
- Data Collection: Operating the wearables during experiments to collect data.
- Data Processing: Analyzing the collected data to extract meaningful information, which may involve cleaning the data, performing statistical analyses, and using AI algorithms.
- Preferred Majors: Aerospace & Mechanical Engineering, Biomedical Engineering, Electrical & Computer Engineering
- Prerequisites and Preferred Skillsets: Electronics design, fabrication experience, coding, embedded systems, prototyping, AI/ML tools use.
- Ingestible electronics
- Project Description: A pill-sized electronic module is being developed to detect chemicals and gases within the gut. This innovative device is designed for ingestion and aims to analyze the internal chemical environment of the gastrointestinal tract, providing insights into digestive health and potentially aiding in diagnosing various gut-related conditions.
- Student Responsibilities:
- Sensor Design: Designing the sensors that will be integrated into the ingestible.
- Fabrication: Physically creating the components of ingestible devices, which may involve working with different materials and manufacturing techniques.
- Electronics and Embedded System Programming: Developing the electronic systems that power the ingestible, including programming the microcontrollers or embedded systems that manage sensor data collection and device operation.
- Printed Circuit Board (PCB) Design: Designing PCBs that connect the electronic components of the ingestible, including sensors, processors, and power supplies.
- Experimental Setup: Setting up experiments to test and validate the ingestible, which includes preparing the environment, the devices, and any supporting technology.
- Data Collection: Operating the ingestible during experiments to collect data.
- Data Processing: Analyzing the collected data to extract meaningful information, which may involve cleaning the data, performing statistical analyses, and using AI algorithms.
- Preferred Majors: Aerospace & Mechanical Engineering, Biomedical Engineering, Chemical Engineering, Electrical & Computer Engineering
- Prerequisites and Preferred Skillsets: Electronics design, fabrication experience, coding, embedded systems, prototyping, AI/ML tools use.
- Organic electrochemical-transistor based sensors and circuits
- Project Description: The objective of this project is to utilize sensors and circuits based on printed organic electrochemical transistors to develop wearables equipped with Artificial Intelligence (AI). These advanced wearables are intended to harness AI capabilities for enhanced performance and functionality in various applications.
- Student Responsibilities:
- Wearable Sensor Design: Designing the sensors that will be integrated into the wearables, ensuring they are suitable for capturing the required data (like physiological and neurological signals).
- Fabrication: Physically creating the components of the wearable devices, which may involve working with different materials and manufacturing techniques.
- Electronics and Embedded System Programming: Developing the electronic systems that power the wearables, including programming the microcontrollers or embedded systems that manage sensor data collection and device operation.
- Printed Circuit Board (PCB) Design: Designing PCBs that connect the electronic components of the wearables, including sensors, processors, and power supplies.
- Experimental Setup: Setting up experiments to test and validate the wearables, which includes preparing the environment, the devices, and any supporting technology.
- Data Collection: Operating the wearables during experiments to collect data.
- Data Processing: Analyzing the collected data to extract meaningful information, which may involve cleaning the data, performing statistical analyses, and using AI algorithms.
- Preferred Majors: Aerospace & Mechanical Engineering, Biomedical Engineering, Chemical Engineering, Electrical & Computer Engineering
- Prerequisites and Preferred Skillsets: Electronics design, fabrication experience, coding, embedded systems, prototyping, AI/ML tools use.
- MRI coils and wearables
- Faculty/PI: Stephen Tu
- Website: https://stephentu.github.io
- Research Overview: My lab is interested in the interplay between statistical learning, dynamical systems, and control theory. Our goal is to rigorously understand the effects of integrating machine learning components into complex autonomous systems. While learning allows feedback systems to react to diverse sources of inputs (vision, speech, natural language, etc.), enabling many new and exciting capabilities, it also introduces complexity into the system which must be reasoned about in novel ways. At our core, we develop mathematical tools and practical algorithms to characterize the closed-loop behavior of dynamical systems with uncertainty.
- Summer Project:
- Neural Galerkin Methods for Solving PDEs
- Description: Numerical solutions to partial differential equations (PDEs) allow one to answer many important questions about the dynamics of closed-loop systems, including reachability, stability, and safety properties. Since PDEs are also important in various aspects of science, engineering, and mathematics, many numerical methods have been invented to solve them. One such method is the classic Galerkin method, which chooses a fixed basis and enforces the PDE condition on the subspace spanned by the basis. A typical choice for this fixed basis are localized functions gridding the domain of the PDE; this gives rise to finite-element algorithms for solving PDEs. However, as is well-known, these methods suffer from the curse of dimensionality. Can we overcome this in certain cases by using learnable basis functions, much as we do in machine learning? One idea which has come up recently in the literature is to use random basis elements; this is very similar to using random Fourier features from the machine learning literature. However, because the Galerkin method is based on linear algebraic arguments, the random basis is fixed and not trained to adapt to the data. The goal of this project is to design and implement algorithms to jointly learn both the features and the Galerkin weights. The selected candidate will first come up to speed on the background material, and then propose and evaluate new algorithms for numerical solutions to classic benchmark PDEs. Depending on the efficacy of the proposed algorithms, the project can then progress in various directions based on the candidate’s interest: either the candidate works on scaling up their solution to much higher dimensional problems, or the candidate embarks on a mathematical analysis to understand the theoretical properties of their proposed algorithm.
- Student Responsibilities: Computational work and mathematical derivations.
- Preferred Majors: Aerospace & Mechanical Engineering, Computer Science, Electrical Engineering, Mathematics, Physics, Statistics
- Preferred Skillsets: Strong foundations in linear algebra, calculus, and probability theory. Background knowledge in machine learning and PDEs. Programming proficiency in Python and its scientific computing libraries (numpy, scipy, etc).
- Neural Galerkin Methods for Solving PDEs
Published on October 19th, 2022
Last updated on January 6th, 2025
