Program Overview & Research Projects

Please note: This list of projects from 2021 will be fully updated in January 2022 with additional project descriptions and projects confirmed by returning professors. In the meanwhile, please use this list to view the specificity and type of various projects available in SHINE.

See our FAQ page if you have any questions.
Also see Students' Posters and Videos for additional information on these research projects.

Applicants select their top three choices of engineering fields, and the K-12 STEM Center pairs applicants with an appropriate research team, based on applicant interest, the professor's needs and capacity, etc. The online application asks students to rank their top three preferences for engineering fields, so be prepared to answer this. Also, please be sure to discuss in your personal statement why you feel you are especially well suited for any of the projects you select. Professors are the ones who select SHINE students from the pool of qualified applicants.
  1. COMPUTATIONAL EXPERIMENTAL
    Research in the fluid-structure interactions lab tackles problems ranging from the development of flow control techniques for drag reduction on aircraft and ships to the development of bioinspired robots that can crawl, swim, or fly. This work is largely experimental and takes place in a large-scale water channel facility at USC. For the coming summer, we have a number of potential projects. This includes: (i) 3D-printing sharkskin-inspired drag-reducing surfaces, (ii) testing the performance of flexible flapping wings, and (iii) creating novel flow sensors. (SHINE student would be in an experimental research lab). SHINE student would be in an experimental research lab.
    Pre-requisite: Physics, Calculus, Coding (any language). 3D-Printing/CAD experience are helpful but not required.
    Prof. Mitul LuharFluid-Structure Interactions Lab 
  1. EXPERIMENTAL MEDICAL
    Our lab researches novel water treatment processes for contaminant and energy challenges; systems of desalination and water reuse; colloidal and interfacial aspects of membrane processes; and brine reduction and energy recovery. The SHINE student will collaborate with a PhD student, get hands-on laboratory experience, and participate in research team meetings. We are sure the student will enjoy our close-knit community of students and faculty who are pursuing topics of water, air, energy, food, and climate to solve sustainability challenges. SHINE student would be in an experimental research lab.
    Pre-requisite: Algebra; Chemistry
    Prof. Amy Childress:Childress Research Group
  2. EXPERIMENTAL MEDICAL
    Personalized medicine is the cutting edge of drug delivery innovation. In our lab, we design nanoparticles and biomaterials that can be used to image, treat, and regenerate diseased or damaged tissue. Diseases we study in our lab include atherosclerosis, cancer, and kidney diseases. Over the course of the summer, the SHINE student will assist in designing and synthesizing nanoparticles and biomaterials to target a specific disease, characterizing the material properties, as well as testing their therapeutic or diagnostic potential using diseased cells.
    SHINE student would be in an experimental research lab.
    Pre-requisite: Biology; Chemistry.
    Prof. Eun Ji Chung:The Chung Laboratory-Biomaterials and Nanomedicine Research
  3. EXPERIMENTAL MEDICAL
    We are interested in using conductive fibers to develop wearable sensors for continuous and affordable health monitoring. We develop low-cost electrochemical sensors that can interface with smartphones for at-home testing. These technologies can change the disparities in access to healthcare and diagnostics and bring testing to the disadvantaged communities. We also can create personalized testing through continuous monitoring in biological fluids and providing feedback on the effectiveness of intervention strategies. In the MAD Lab, we like to use creative approaches to solve diagnostic problems. We explore different materials and fabrication tools. Wear your creative hat and buckle up for a fun summer developing new accessible sensors. SHINE student would be in an experimental research lab.
    Recommended: Biology; Chemistry.
    Prof. Mural Mousavi: MAD Lab (Laboratory for Design of Medical and Analytical Devices) 
  4. COMPUTATIONAL EXPERIMENTAL MEDICAL 
    The McCain lab engineers miniature human tissues, known as "Organs on Chips", for studying human diseases and developing new drug targets. The SHINE student would assist a postdoc or graduate student in data analysis using common engineering tools like ImageJ, Matlab, and Tinkercad. In addition, the student would learn about LLSE lab techniques including cell culture, imaging, microcontact printing, and device fabrication.  SHINE student would be in an experimental research lab. Pre-requisite: Biology
    Prof. Megan McCainLaboratory for Living Systems Engineering
  5. COMPUTATIONAL  MEDICAL 
    The Shanechi Lab develops novel neurotechnologies and brain-machine interfaces for treatment of brain disorders like depression. We develop machine learning methods to make sense of brain signals so that we can use them to track someone's mental states such as depressed mood for better therapies. SHINE student would mostly be doing computational work.
    Pre-requisite: Algebra, Calculus, Coding (python), optionally also neuroscience/biology.
    Prof. Mariam Shanechi: The Shanechi Lab
  1. COMPUTATIONAL MEDICAL
    The Graham Lab is pursuing experimental and computational systems biology approaches to develop quantitative models of the cancer and other human diseases. We draw on biology, statistics and engineering to build data-driven, predictive models of tumor phenotypes using quantitative data generated in-house. Through collaboration with physicians and clinicians, we are applying these approaches to questions that impact clinical care. SHINE student would be in an experimental research lab.
    Prof. Nicholas Graham: Graham Lab

  2. COMPUTATIONAL
    We are finding ways to convert carbon dioxide emissions into useful molecules such as pharmaceuticals using chemistry similar to photosynthesis.
    SHINE student would mostly be doing computational work.
    Pre-requisite: Chemistry; Python Programming.
    Prof. Shaama Sharada
  1. COMPUTATIONAL EXPERIMENTAL
    At the Networked Systems Lab, we create and build networked software for autonomous vehicles to help them navigate the environment safely and for drones to enable them to survey the environment. This software sends data from LiDAR sensors on cars and drones to the cloud, which processes the data and instructs the car or drone on how best to perform the task. Our lab combines expertise in computer networks and operating systems, and uses tools from computer vision and robotics. SHINE student would mostly be doing computational and experimental work.
    Recommended: Coding experience in C++ or Python
    Prof. Ramesh Govindan: USC Networked Systems Laboratory
  2. COMPUTATIONAL EXPERIMENTAL
    Natural language technologies like GPT-3 and ChatGPT have surprised the public and AI researchers alike in their ability to generate fluent, (semi-)coherent text. However, the output from these models can frequently be self-contradictory, factually incorrect, or physically implausible. Part of the reason for these behaviors is that such systems are, for the most part, trained only on static text on the web. In this project, we will explore ways to include additional sensory signals into natural language processing systems, such as ways of tying language to vision and actions in the real world. It is recommended that the student have some experience in Python programming and an interest in and willingness to learn deep learning frameworks like PyTorch. Interest in operating a robot is a bonus, but not required. SHINE student would be in an experimental research lab. Pre-requisite: Python programming; interest in deep learning/PyTorch; interest in robotics (bonus).
    Prof. Jesse Thompson: USC Networked Systems Laboratory
  3. COMPUTATIONAL
    My research is at the intersection of programming languages, software engineering and automated reasoning. I draw on techniques from machine learning and formal methods to solve problems in program synthesis, verification, and static analysis. My goal is to build theoretically well-understood, rigorously evaluated, and practically useful tools to help programmers create better software with less effort. SHINE student would mostly be doing computational work.
    Pre-requisite: Familiarity with programming in any language.
     
    Prof. Mukund Raghothaman
  4. COMPUTATIONAL
    Commonsense reasoning aims to empower machines with the human ability to make presumptions about ordinary situations in our daily life. In this project, we aim to develop novel machine algorithms and programs for answering commonsense questions, which can effectively utilize external commonsense knowledge graphs to perform explainable predictions. SHINE student would mostly be doing computational work.
    Pre-requisite: Algebra; Coding with Python.
    Prof. Xiang Ren
  5. COMPUTATIONAL
    Artificial intelligence & machine learning (AI & ML) are increasingly being used in applications which involve significant societal implications and require high levels of trust in the system (such as in self-driving cars, or in deciding whether someone gets a loan). In this project we will explore how to make AI & ML systems fair (such as not being biased against any subpopulations or demographics of individuals) and robust (such as still making good predictions when encountering different kinds of data). It is recommended that the student have interest and some experience in Python programming. SHINE student would mostly be doing computational work. Pre-requisite: Calculus, algebra, coding in Python (or any other language).
    Prof. Vatsal Sharan
  6. COMPUTATIONAL
    The project will be based on using nanophotonic technology to probe and understanding light-matter interaction for solving real-life problems in classical and non-classical optical communication, computing, sensing, ranging and metrology. SHINE student would mostly be doing computational and experimental work.
    Pre-requisite: Physics, 3-D printing, CAD, coding (Python); Photography.
    Prof. Mengjie Yu: Yu Group


  1. COMPUTATIONAL EXPERIMENTAL
    We create and program haptics devices that provide artificial touch sensations to the user. These devices can be used in virtual reality, gaming, medical simulation, communication, or to display simple alerts. Many computer and technology systems lack any touch feedback and provide only vision and sound to the user. However, touch is a very important component of how we interact with the world and with other people. Our lab combines human perception, electronics, mechanical design, and programming to create devices that can recreate the sensations you feel when touching real objects. SHINE student would mostly be doing computational and experimental work.
    Pre-requisite: Programming (C,C++ or Python), circuity (a plus, but not required) and electronics (a plus but not required ).
    Prof. Heather Culbertson: HARVI Lab
  2. COMPUTATIONAL EXPERIMENTAL
    We create and program haptics devices that provide artificial touch sensations to the user. These devices can be used in virtual reality, gaming, medical simulation, communication, or to display simple alerts. Many computer and technology systems lack any touch feedback and provide only vision and sound to the user. However, touch is a very important component of how we interact with the world and with other people. Our lab combines human perception, electronics, mechanical design, and programming to create devices that can recreate the sensations you feel when touching real objects. SHINE student would mostly be doing computational and experimental work.
    Pre-requisite: Programming (C,C++ or Python), circuity (a plus, but not required) and electronics (a plus but not required ).
    Prof. Rahul Jain: AI for Dynamic Systems
  1. COMPUTATIONAL
    Research in the Cyber-Physical Systems lab tackles interdisciplinary problems, ranging from the modeling and analysis of biological systems to developing new machine learning and artificial intelligence algorithms and architectures. This work is mainly of analytical and computational nature. It involves learning about various mathematical models and algorithms used for studying complex systems from a wide range of areas like biology, neuroscience, energy, and traffic/transportation. For the coming summer, we have a number of potential projects on the following topics: (1) Analysis of complex networks; (2) Quantifying trustworthiness in various AI algorithms and architectures; (3) Analysis of various biological systems (e.g., neuronal networks, cancer cells); (4) Modeling, analysis and forecasting of pandemic events.
    Pre-requisites: Willingness to learn mathematical models, algorithms and coding (Python, Matlab, C). Looks for students who have a passion for mathematics, learning, reading, dreaming and research. (SHINE student would mostly be doing computational work)
    Prof. Paul Bogdan: Cyber-Physical Systems Lab
  2. EXPERIMENTAL COMPUTATIONAL THEORETICAL

    Distributed Systems, Robotics, and Networks: At the autonomous networks research group, we undertake research on a wide range of topics including wireless networks, distributed robotics, blockchain and cryptocurrency, and multi-agent reinforcement learning. Working with a Ph.D. student, the SHINE student will gain knowledge about one or more of these topics, assist in conducting experiments and numerical analysis, and write research summaries for a broad audience. The student must be excited to learn about engineering and willing to work hard; no other specific prior experience is needed, we will teach the student whatever knowledge, skills, or tools are needed for the project.
    Prof. Bhaskar Krishnamachari

  3. COMPUTATIONAL
    The project will be related to machine learning and neural network inference. SHINE student would mostly be doing computational work.
    Pre-requisite: Software programming, good math skills.
    Prof. Massoud PedramS.P.O.R.T. Lab
  4. COMPUTATIONAL
    Securing Next-Generation Microelectronics Chips: A modern chip combines many components that are designed and integrated by different companies. Moreover, due to the increase in fabrication costs, many chip design companies have outsourced the fabrication of their devices to third-party foundries. Overall, modern computing systems contain modules from potentially untrusted players, which may pose serious security threats and produce irreparable damages. In this project, the SHINE students will work closely with faculty and Ph.D. students to develop, simulate, and test optimized hardware obfuscation methods, i.e., methods that can obfuscate and protect the designs from threats such as piracy, reverse engineering, and hardware Trojans. SHINE student would mostly be doing computational work.
    Pre-req: Basic Math (Algebra, Calculus), Coding (Python)

    Prof. Pierluigi NuzzoNuzzo Lab
  5. COMPUTATIONAL
    Design of Safe and Intelligent Self-Driving Vehicles: The goal of this project is to design and test control strategies for self-driving vehicles. Autonomous vehicles use machine learning to achieve certain levels of intelligence and operation in uncertain environments. However, they must also provide performance and safety guarantees to avoid undesired outcomes or incidents. The SHINE students will work closely with faculty and Ph.D. students to simulate realistic driving scenarios, program different driving algorithms, and test them on a testbed including a set of scaled-down autonomous cars and robots.  SHINE student would mostly be doing computational work. Pre-req: Basic Math (Algebra, Calculus), Coding (Python)
    Prof. Pierluigi NuzzoNuzzo Lab
  6. COMPUTATIONAL 
    Machine Learning for Electromagnetics: In this project, the student will explore applying various machine learning algorithms for designing new electromagnetic devices. They will learn about Deep Neural Networks, specifically Generative Adversarial Networks and Variational Autoencoders, and use them to design antennas and photonic devices. SHINE student would mostly be doing computational and experimental work. Pre-requisites: Coding (either MATLAB, Python, or C++).
    Prof. Constantine Sideris: ACME Lab

  7. COMPUTATIONAL
    Magnetic Biosensing: In this project, the student will learn about Point-of-Care biosensors, how they work, and will help measure and characterize new magnetic biosensor chips that we are designing in our group. Point-of-Care biosensing seeks to achieve personalized healthcare by miniaturizing and bringing diagnostic sensors that only require small biological samples to peoples’ homes. These sensors require only a small biological sample for analysis such as a drop of blood from a finger prick or a saliva sample and will be capable of diagnosing a variety of diseases and health conditions including infections and heart disease.  SHINE student would mostly be doing computational and experimental work. Pre-requisites: Pre-requisites: Coding (either MATLAB, Python, or C++).
    Prof. Constantine Sideris: ACME Lab

  1. COMPUTATIONAL
    In my group we use artificial intelligence and machine-learning methods to understand the properties of complex systems. This is done by carrying out numerical simulations of fluid flow and reaction, training a neural network with the results and then use the trained network to make prediction.
    SHINE student would mostly be doing computational work.
    Pre-requisite: Some computer skills.

    Prof. Muhammad SahimiLaboratory for Complex Materials
  1. COMPUTATIONAL 
    Focus on Materials Science in Aerospace: Our research group uses a combination of solid mechanics, materials science, and mathematical modeling to investigate how microstructures evolve in materials, and how we can engineer microstructural patterns to enhance material properties. Research members develop physics-based models to solve open problems in a wide range of materials including battery materials, magnets, and ferroelectrics. Our theoretical studies provide insights into fundamental material behavior that guides the development of next-generation energy storage and energy-conversion materials.
    Prof.Ananya Renuka Balakrishna: Solids & Materials Lab
  2. COMPUTATIONAL EXPERIMENTAL
    Developing control and machine learning algorithms to achieve extremely robust and agile locomotion on legged robots. SHINE student would mostly be doing computational and experimental work.
    Pre-requisites: Physics, algebra, calculus; basic programming skills.
     Prof. Quan T. Nguyen: Dynamic Robotics and Control Lab
  3. COMPUTATIONAL THEORETICAL We are a research group studying the mechanics of materials and structures— with an overarching theme of examining the interplay between material and structure. Particularly, we aim to understand and predict the interesting mechanics and functionality that emerges when complex materials — such as active and architectured materials — are  incorporated into thin or slender structures. We work at the intersection of engineering mechanics, materials science and applied mathematics.
    Prof. Paul Plucinsky
  4. COMPUTATIONAL EXPERIMENTAL
    Our group works on developing advanced manufacturing technologies to create flexible and stretchable structures and functional devices for a broad range of applications, such as healthcare and robotics. Specifically, we have 3 research projects for SHINE students: (1) testing of flexible strain sensors for motion tracking; (2) design and simulation of soft robotic arms; (3) 3D printing of wearable electrophysiological sensors. For each project, the SHINE students will work closely with a PhD student and learn basic knowledge and skills for design and fabrication of flexible and stretchable devices. SHINE student would be in an experimental research lab.
    Recommended: Experience with CAD (e.g. AutoCAD software) is preferred but not required.
    Prof. Hangbo Zhao
  5. EXPERIMENTAL MEDICAL 
    In Medical Flow Physics Laboratory, we are studying mechanics of the heart diseases (e.g., heart failure) to design equipment that improve diagnostics and treatment. We perform experimentations in the lab using our mock human heart-vessel system. SHINE students will be closely guided and trained by PhD students on a project related to the fabrication of artificial blood vessels and measurements of blood pressure in our artificial heart-vessel system. SHINE students will be trained to operate blood pressure measurement devices and to analyze and apply machine learning techniques to blood pressures. SHINE student would mostly be doing computational and experimental work. 
    Prof. Niema Pahlevan: Medical Flow Physics Laboratory

Published on December 14th, 2016

Last updated on January 23rd, 2023