Program Overview & 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. Professors are the ones who select SHINE students from the pool of qualified applicants.

IMPORTANT: The online application asks applicants to rank their top three preferences. Applicants should discuss the appeal of three possible fields of engineering in the personal statement submitted as part of the SHINE application:

  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. Pre-requisite: Physics, Calculus and coding (any language) are helpful but not required.Prof. Mitul LuharFluid-Structure Interactions Lab.
  2. COMPUTATIONAL EXPERIMENTAL We develop mechanical folding and buckling-based approaches to make three-dimensional (3D) structures and soft electronic sensors for a wide range of applications, including foldable devices, biomedical sensors and microrobots. The SHINE student would work with a graduate student or postdoc in designing and simulating the formation of various 3D structures using finite element simulation tools. The student would also learn about basic microfabrication techniques including laser cutting, transfer printing, and fabrication of flexible electronics. Experience with CAD (e.g. AutoCAD software) is preferred but not required. Prof. Hangbo Zhao
  3. COMPUTATIONAL EXPERIMENTAL Simple experiments to investigate the properties and perhaps predictability of complex flows. Prerequisite: Physics, algebra, elementary calculus. Prof. Geoff Spedding
  1. COMPUTATIONAL 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. Pre-requisite: Biology, Chemistry. Prof. Eun Ji Chung: The Chung Laboratory-Biomaterials and Nanomedicine Research
  2. 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.  Prof. Megan McCainLaboratory for Living Systems Engineering
  3. COMPUTATIONAL EXPERIMENTAL MEDICAL The Zavaleta lab helps to make and characterize new nanoparticles that would eventually be used for imaging various types of cancer.  Nanoparticles have a lot of advantages over other small molecule imaging contrast agents and our lab is focused on exploiting these advantages to help doctors detect cancer earlier or help guide them during surgery. Prof. Cristina ZavaletaZavaleta 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. Prof. Nicholas Graham: Graham Lab
  2. COMPUTATIONAL EXPERIMENTAL Our research focuses on surfactants (surface active agent, i.e., materials like detergent, shampoo, emulsifier) and creation of surfactant-based nanoparticles and their applications such as gene therapy, drug delivery, nanoreactor, etc. Recommended Courses: Chemistry, Biology. Prof. Ted LeeLee Research Group
  3. COMPUTATIONAL EXPERIMENTAL MEDICAL We are interested in recruiting students who would like to learn a technique called mRNA display It was invented by Professor Richard Roberts, and the technique allows one to select peptides targeting a specific protein, which could be a key protein for a certain disease. Our lab works on many different projects including cancer, Huntington’s disease and Alzheimer’s disease. The technique also can be applied to COVID research to develop therapeutic and diagnosis tools. Students will be exposed to basic recombinant DNA technology as well as expression, purification, and characterization of proteins from bacterial hosts. Our lab requires a lot of bench work (laboratory experiments), and students will obtain those skills and knowledges through remote sessions this year. Our lab would be a good choice with students who are interested in laboratory work and/or are interested in medicinal research. Prof. Rich Roberts: Roberts Research Lab
  4. COMPUTATIONAL We are finding ways to convert carbon dioxide emissions into useful molecules such as pharmaceuticals using chemistry similar to photosynthesis. Prof. Shaama Sharada
  1. COMPUTATIONAL EXPERIMENTAL VR-based ergonomic behavior training for construction workers: As the construction industry has the highest rate of non-fatal injuries among all industries, this project aims to develop a virtual reality (VR) based postural training for construction workers using wearable sensors, virtual reality, and machine learning (ML) techniques. A SHINE student will help track workers' postures during use of construction robots and provide real-time feedback about ergonomic risks using ML. Contributions of the study include the understanding of how effective VR training and real-time feedback can be to training construction workers in safer ergonomic behavior during construction tasks. It is recommended that the students have interest and some experience in basic programming. Past experience with virtual reality and/or machine learning is a plus but not a requirement. Prof. Burcin Becerik-GerberInnovation in Integrated Informatics

  1. COMPUTATIONAL EXPERIMENTAL We plan to develop algorithms to enable assistive robotic arms help people with every day tasks, such as cooking and hair brushing! The SHINE students will work with current PhD students on basic data analysis, implementation and testing techniques of software algorithms for assistive robotic arms. There are no formal pre-requisites but some coding experience in Python would be desirable. Prof. Stefanos NikolaidisInteractive and Collaborative Autonomous Robotic Systems (ICAROS) lab
  2. 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. Coding experience in C++ or Python desirable. Prof. Ramesh Govindin, USC Networked Systems Laboratory
  3. COMPUTATIONAL We focus on theoretical computer science, which is an interplay between computer science and mathematics. For computational problems, we solve them by designing algorithms. On one hand, we need to ensure the algorithms are correct, i.e., we hope to obtain the expected answer from the algorithms. On the other hand, we'd like to design efficient algorithms. In this project, we focus on analyzing the correctness and efficiency of algorithms. In particular, we would like to design algorithms solving problems related to artificial intelligence. Students who are interested in both mathematics and computer science are strongly encouraged to join. Prof. Jaipeng Zhang
  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. Pre-req: Linear algebra; coding experience with python. Prof. Xiang Ren.                                                                                                                                                                                                                                                                                                                                                                                                                           SHINE-Affiliated Program in CS: Information Theory and Machine Learning over Tactical Edge Networks -- This is a 10-week research program to join Professor Salman Avestimehr’s team to improve tactical battlefield networks. Coded Computing is a novel approach to inject and leverage computational redundancy to overcome battlefield bottlenecks in communication, resiliency and security. Funded by the Army Educational Outreach Program, this project is tuition free and pays the student a stipend of $3,000 to encourage students structurally under-represented in engineering to develop STEM skills. APPLY BY MARCH 15.
  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. Pre-requisite: Coding (C,C++ or Python), circuity (a plus, but not required). Prof. Heather Culbertson: HARVI Lab
  2. COMPUTATIONAL We develop socially assistive robots aimed at helping people through social rather than physical interaction; our robots are tested with children with autism, stroke patients, Alzheimer's patients, healthy elderly, and other real-world beneficiary populations. The robots help people to learn, train, and recover, in order to enhance wellness and quality of life. Recommend that students have some background in computer programming. Prof. Maja Matarić Interaction Lab
  3. COMPUTATIONAL The Automatic Coordination of Teams (ACT) Lab works on topics in multi-robot systems. Multi-robot systems can provide many benefits, including robustness and cost effectiveness, but bring a host of new challenges. Some questions the ACT Lab is currently pursuing are: How can we plan for for many robots moving safely through a confined space? Can we learn from the ways that humans coordinate to improve the ways that robots coordinate? SHINE students will collaborate with their mentor to develop and implement new algorithms, process data, and analyze results. Previous experience coding, python preferred. Prof. Nora Ayanian:  ACT Lab
  4. Developing control and machine learning algorithms to achieve extremely robust and agile locomotion on legged robots. There are no formal pre-requisites but prior experience in coding (C, C++, Python, MATLAB...) or particpation in robotics clubs is preferable. Prof. Quan Nguyen: Dynamic Robotics and Control Laboratory
  1. COMPUTATIONAL Students will work to fabricate, characterize and model electronic and photonic devices for next-generation high-performance computing and telecommunication systems. Recommended that students have experience in programming and/or a chemistry lab; some algebra is also desirable. Prof. Rehan KapadiaLaboratory for Photons, Electrons, & Materials
  2. 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. Pre-req: Basic Math (Algebra, Calculus), Coding (Python) Prof. Pierluigi NuzzoNuzzo Lab
  3. 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. Pre-req: Basic Math (Algebra, Calculus), Coding (Python) Prof. Pierluigi NuzzoNuzzo Lab
  4. COMPUTATIONAL Magnetic field gradient generation for localization and tracking of MRI-inspired miniature medical devices: The function of miniature wireless medical devices such as capsule endoscopes, biosensors, and drug-delivery systems critically depends on their location inside the body. We have recently developed a new class of microchips for localization of microscale devices by mimicking the behavior of nuclear spins in MRI, allowing high resolution localization by application of magnetic field gradients. In this project, the SHINE students will work closely with faculty and Ph.D. students to design, simulate, and test custom electromagnetic systems for the generation of magnetic field gradients. Recommended that students have taken courses in algebra, physics and some coding (e.g., in Python or Arduino). Prof. Manuel Monge: Integrated Medical Electronics Lab

  5. 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. Pre-requisite: computer programming (Python, MATLAB, or C) knowledge is required. Prof. Constantine Sideris: ACME Lab
  6. 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. Pre-requisite: computer programming (Python, MATLAB, or C) knowledge is required. Prof. Constantine Sideris: ACME Lab
  7. COMPUTATIONAL The Hsu Group works on photonics in complex systems, exploring new paradigms for controlling light, overcoming and harnessing light scattering, retrieving information from photons, and beyond. One of our current interests is developing computational imaging methods that can reconstruct volumetric 3D images inside an opaque scattering medium that typically cannot be seen through. As part of this research effort, the SHINE student will perform data analysis of experimentally measured images and retrieve the phase information from pairs of intensity-only images. Prerec: Physics, Calculus, and Coding (any language) Prof. Chia Wei (Wade) Hsu:Photonics in Complex Systems
  1. The SHINE student will aid PhD students on environmental engineering applications focusing on resource recovery (water, energy, and nutrients) from waste streams such as food waste.Environmental engineering is inherently interdisciplinary (engineering, chemistry, and biology) and our research tackles immense societal challenges such as water shortage, energy sustainability, and climate change. The student will gain hands-on experience with analytical chemistry, process engineering, and molecular biology. Prof. Adam Smith: Smith Research Group

  2. Our lab studies chemical treatment technologies and transformation processes to enable the safe recycling of wastewater. With California and many other parts of the world faced with chronic water shortages due to increasing populations and declining natural supply reliability because of climate change, water reuse will continue to expand. However, wastewater contains a number of microbial and chemical hazards that must be mitigated to ensure public health if it is to be recycled for potable use. The SHINE student will work with a current PhD student on one of several projects in this area. The student will gain experience with basic water treatment techniques (e.g., chlorination) and mass spectrometry instrumentation for measuring chemical concentrations in water. Prof. Daniel McCurryMcCurry Lab

  1. The student will study new materials that my group is exploring for developing next generation solar cells and for developing advanced electronics. Basic knowledge of Physics, and Chemistry. Coding and Math knowledge (calculus) will be an optional bonus. Prof. Jayakanth RavichandranLaboratory for Complex Materials
  1. COMPUTATIONAL This reserach group uses Machine Learning tools to improve the delivery of healthcare for diseases like COVID-19 and cancer. Prerequisites: coding (Python), algebra, statistics. Prof. Assad Oberai: Computation and Data Driven Discovery Group
  1. Over the last year, policy makers at the county, state, and national levels have struggled with how to manage the spread of COVID-19. Extensive efforts have been performed to computationally model the spread of the disease at these levels which has aided in strategic policy decision making. However, what these models often lack is consideration for health safety behaviors at the local level. This is crucial as policy is enacted at the local level where there can be substantial behavioral variation even within the same county. We are currently working to 1) develop a COVID-19 computational model at the LA County level, and 2) develop a value measure for health safety climate that can be used to assess the behavior trends related to COVID spread in different communities. By integrating these two pieces, we hope to develop a local level model for LA County that more accurately considers the behaviors of different communities. This type of model will help local policy makers make more informed decisions for the county such as establishing testing or vaccination locations, where to focus information dissemination, and where to prioritize resources. The SHINE student will work with a current PhD student and be supervised by two faculty members in Industrial and Systems Engineering department. The scope of work includes data analysis and development of the health safety measure. Additionally, the student may assist with calibration of the modified COVID-19 model and analysis of outcomes. Pre-requisites (recommended but not required): Exposure to a scientific programming language (MATLAB, Python, R) and completion of a course in calculus. Professors Shinyi Wu and Sze-chuan Suen

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Also see Students' Posters and Videos for additional information on these research projects.