Program Overview & Research Projects

Students in their labs and SHINE Logo
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.
Please note: This list of projects from 2023 will be fully updated in January 2024 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.
  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 Luhar: Fluid-Structure Interactions Lab 
  1. 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
  2. EXPERIMENTAL COMPUTATIONAL
    Designing a lollipop integrated sensor for acetaminophen detection in saliva
    Acetaminophen is a common over-the-counter pain reliever and fever reducer. The acetaminophen therapeutic window is a safety zone, which is the range of doses where the drug is effective and safe without causing harmful side effects. Below the window, the medicine might not work well, and above it, there is a risk of side effects or toxicity. Acetaminophen toxicity is a critical public health concern in children since its overdose is easily accessible over the counter. When the acetaminophen blood level is above 30 mg/L, it is considered to be toxic. The most common way to measure acetaminophen blood levels includes taking blood samples from patients in a laboratory. However, this method is invasive and time-consuming. On the other hand, it has been reported that salivary concentration of acetaminophen closely correlates to its blood levels. As a result, we can measure acetaminophen in saliva instead of blood. Saliva sampling, as extensively practiced during COVID19 pandemic, is a simple and convenient process.
    We will develop a 3D printed lollipop platform in order to collect saliva. A screen-printed electrochemical sensor is incorporated within the lollipop structure to measure the acetaminophen levels in saliva and alert us if an overdose has occurred.
    SHINE student would be in an experimental and computational research lab Preferably chemistry but it is not necessary, they will lear CAD and 3D printing during the project.

    Prof. Maral Mousavi: MAD Lab (Laboratory for Design of Medical and Analytical Devices)

  3. EXPERIMENTAL
    Advancing Asthma Management: Electrochemical Sensor for the Monitoring of Theophylline Drug Levels in Saliva
    Theophylline, a medication used in managing asthma, requires careful monitoring to remain within its therapeutic range in the bloodstream—typically between 5 to 15 µg/mL. If the levels exceed this range, it can lead to issues such as nausea, vomiting, difficulty sleeping, and even death. This underscores the importance of developing practical and effective methods for monitoring these levels during medical care.
    Saliva is an ideal biofluid for continuous monitoring due to its non-invasiveness and ease of collection. This contrasts with the traditional approach of blood serum sampling and enhances patient compliance. The ease and comfort associated with saliva collection make it a promising medium for routine testing, reducing barriers to regular monitoring. Moreover, saliva provides a real-time reflection of drug levels, making it a valuable resource for personalized medicine and ensuring prompt adjustments to theophylline dosage regimens.
    This project aims at developing a cost-effective, precise, simple, and fast electrochemical sensor for detecting theophylline in saliva. Integrated into a 3D-printed platform, the sensor streamlines testing, ensuring user-friendliness for healthcare professionals and patients. The platform provides a convenient tool for optimizing theophylline therapy while minimizing risks associated with elevated concentrations.
    SHINE student would be in an experimental research lab.
    Recommended: Biology; Chemistry.
    Prof. Maral Mousavi: MAD Lab (Laboratory for Design of Medical and Analytical Devices)

  4. EXPERIMENTAL 
    In the MAD Lab, we like to use creative approaches to solve diagnostic problems. Here, arts and crafts meet rigorous science. Wear your creative hat and buckle up for a fun summer developing new accessible sensors! We use low-cost materials like threat, paper, and plastic to develop sensors for continuous and affordable health and nutrition monitoring. These technologies have the potential to increase access to healthcare and personal diagnostic data by democratizing reliable biological testing. Knowledge is power- and by helping more people know their health status, we enable better healthcare decision making.
    Urine is a powerful and complex diagnostic marker for health. Because it contains waste cleared from the blood, it can be used to monitor diabetes, renal disease, and a host of other medical conditions. It is also one of the easiest biofluids to access for testing. We want to develop an in-toilet sensor that can measure a wide variety of different parameters in urine. SHINE students will help to iterate the model toilet design, fabricate and test individual sensors, and collect training data for machine learning algorithms (and help train the algorithms, depending on the project state).
    SHINE student would be in an     experimental research lab Creativity is always a plus- someone who is good at arts and crafts would be nice as well (manipulating small objects). 3D printing/CAD experience is nice but not required
    Prof. Maral Mousavi: MAD Lab (Laboratory for Design of Medical and Analytical Devices)

  5. Development of a Low-Cost Point-of-Care Device for Personalized Depression Management:
    We utilize affordable materials such as thread, paper, and plastic to create sensors for ongoing cost-effective health and nutrition monitoring. These innovative technologies hold the promise of broadening healthcare access and personal diagnostic data by making dependable biological testing more universally available. We are excited to develop a groundbreaking research project aimed at developing a low-cost, point-of-care device for monitoring citalopram levels in patients with depression. Citalopram is a widely used antidepressant, and effective management of its dosage is critical for therapeutic success and minimizing side effects. This project offers high school students a unique opportunity to contribute to the field of mental health. Here, we aim to:

    (i) Design and develop a cost-effective, user-friendly device for monitoring citalopram levels. (ii) Enhance the understanding of personalized medicine in the treatment of depression. (iii) Provide high school students with hands-on experience in biomedical research and development.
    Prof. Maral Mousavi: MAD Lab (Laboratory for Design of Medical and Analytical Devices)

  6. EXPERIMENTAL
    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, 3-D printing
    Prof. Megan McCain
  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: Sharada Lab
  1. COMPUTATIONAL EXPERIMENTAL
    The construction industry has recently experienced a significant increase in the number of robots deployed on site. This project focuses on developing a teleoperation interface to control a demolition robot on a remote construction site. The SHINE student will help build the human-robot interface and install various sensors on the demolition robot. The student will also design and test potential features of the interface using the Unity Game Engine. Potential contributions of this project involve creating a safer environment for robot operations in construction sites.
    Pre-requisite: Experience with programming is a plus but not a requirement.
    Prof. Becerik-Gerber Burcin: iLab
  2. COMPUTATIONAL EXPERIMENTAL
    There has been a distressing escalation in the number of active shooter incidents in the United States in the past few years, presenting a significant threat to public safety and human life. This project focuses on developing a data-driven and artificial intelligence methodology to simulate such critical incidents in various built environments. The students are expected to model different types of buildings, create machine learning-based agents, and simulate real-world scenarios via the Unity game engine. This work is not only a chance to learn valuable technical skills but also contributes to societal well-being by seeking to protect communities.
    Pre-requisite: Programming knowledge and prior experience with 3D modeling is a plus but not a requirement.
    Prof. Becerik-Gerber Burcin: iLab
  3. COMPUTATIONAL EXPERIMENTAL
    In this project, we will examine how the physical aspects of office settings affect equity and inclusiveness in office environments. Using longitudinal data collection, we'll assess psychophysiological, performance, and comfort factors as well as the physical features of the work setting in relation to employee demographics and worksite conditions. Mentee(s) will learn about the fundamentals of research, workplace design, human subject experiments, working with biosensors, statistical analysis, and the integration of the built environment with employee well-being, gaining insights into workspace equity. This multidisciplinary project aims to leverage engineering principles for social good, bridging the gap between technical innovation and societal well-being.
    Prof. Becerik-Gerber Burcin: iLab
  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 Thomason:     GLAMOR Lab
  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
    Artificial Intelligence (AI) is a powerful tool and has been widely used in various areas, including speech recognition, image classification, game playing, and many more. In particular, AI has been successfully applied to playing complicated games such as Go or Poker and is able to beat the best human players. In this project, you will learn to build a very simple "mind-reading" AI to play the classic rock-paper-scissors game, with the goal of beating human most of the time over a sequence of plays. You will learn how this can be done via some sophisticated online decision making algorithms that are fundamental in machine learning. SHINE student would mostly be doing computational work.
    Pre-requisite: coding experience (in any major programming languages such as Python/Java/C) and basic computer science concepts (such as algorithms and data structures).
    Prof. Haipeng Luo
  5. COMPUTATIONAL
    Machine learning (ML) has been exploited in different computing fields where its useful solutions exhibit useful learning behavior autonomously. In this project, we will explore how to use ML to boost the performance of data management and analysis operations. One direction is to improve the processing of different types of progressive user queries where ML is used to refine the query outputs continuously. Example progressive queries will span different domains such as finance (e.g., finding stocks with a consistent growth pattern over the last few hours) and health care (e.g., get me patients at high risk of certain diseases based on progressively detailed medical records). Another direction is to 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. SHINE student would mostly be doing computational work.
    Pre-requisite: Coding in Python and C++ .
    Prof. Sabek Ibrahim
  6. COMPUTATIONAL
    Personal Data for Public Good
    Brief Description: The Integrated Media Systems Center, headquartered in USC's Viterbi School of Engineering, pursues informatics research that delivers data-driven solutions for real-world applications. We find enhanced solutions to fundamental data science problems and apply these advances to achieve major societal impact. One of our most exciting research thrusts is analyzing location and mobility data for various real-world applications while maintaining user privacy. For example, we have an ongoing project to identify anomalous behavior based on GPS tracks. We use advanced techniques such as deep neural networks, differential privacy, and massive location data to build principled solutions for important problems. Student Responsibilities: Gather data from public sources or internal simulations. Implement Python programs to test solutions. Meet with project principle to discuss mutual ideas for new approaches. Create a written and oral presentation summarizing experiments and results. SHINE student would mostly be doing computational work
    Pre-requisite: Linear algebra and Python programing .
    Prof. Cyrus Shahabi
  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
    Understanding alignment between text and speech when describing the cookie theft picture
    Brief description: The cookie theft picture task has been used as a screening tool for predicting dementia in the elderly. For this project, the student will work on understanding and evaluating the alignment between the speech and the text generated when describing the picture. The student also will have the opportunity to build and evaluate ML classifiers to predict early signs of dementia in the elderly.
    Prof. Majal Mataric
  3. COMPUTATIONAL EXPERIMENTAL
    Designing Compelling Robotic Study Companions for Students with ADHD
    Brief description: Past research has shown that college students with ADHD respond positively to in-dorm study companion robots. In feedback from a recent pilot study of in-dorm study companion robots, participants identified increased interactivity and positive rewards as ways for an in-dorm robot to motivate users to spend more time on schoolwork. This project aims to incorporate different modes of interaction (e.g. touch, speech) and positive rewards (e.g. visual light displays, opportunities to customize the robot) on a simple, low-cost robot, in an effort to increase adherence to regular study sessions. We will evaluate these new capabilities by measuring their effect on students' attitude towards the robot and their engagement in study sessions with the robot.
    Prof. Majal Mataric
  4. COMPUTATIONAL EXPERIMENTAL
    Analyzing emotional labor in human interactions
    Brief description: This project focuses on using computational methods to understand and predict emotional labor in human-robot and human-human interactions. Students working on this project will be involved in collecting, processing, and analyzing data from real-life interactions. This work will inform the development of socially assistive robots, systems capable of aiding people through social interactions that combine monitoring, coaching, motivation, and companionship. To address the inherently multidisciplinary challenges of this research, the work draws on theories, models, and collaborations from neuroscience, cognitive science, social science, health sciences, and education.
    Prof. Majal Mataric
  5. COMPUTATIONAL EXPERIMENTAL
    Helping resolve interpersonal conflict using computational tools
    Brief description: Working towards utilizing socially assistive robots to help with interpersonal conflict between two or more people, I am looking to investigate key conflict resolution strategies and how computational tools may be able to help with reinforcing these skills. I would eventually like to work with conflict resolution for children.
    Prof. Majal Mataric
  6. COMPUTATIONAL
    In our research, we specialize in the processing and analysis of Ecog (electrocorticography) and EMG (electromyography) signals, delving into the intricate interplay between the brain and body. By harnessing advanced signal processing techniques like coherence, we aim to unravel the complexities of their collaboration, seeking to decode the rich information embedded in these neural and muscular recordings. Through our work, we contribute to a deeper understanding of the dynamic interactions underlying the seamless coordination between the brain and muscles, with potential implications for enhancing our comprehension of normal motor functions and neurological disorders. Ultimately, our research strives to bridge the gap between neuroscience and technology, paving the way for innovative applications in neural engineering, artificial intelligence, and human-machine interfaces.
    SHINE student would mostly be doing computational work.
    Pre-requisite: Basic algebra, basic knowledge of coding in MATLAB or Python. Interest in signal processing and machine learning is needed.
    Prof. Valero-Cuevas Francisco
  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. (SHINE student would mostly be doing computational work)
    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.
    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
    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 Nuzzo: Nuzzo Lab
  4. 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 Nuzzo: Nuzzo Lab
  5. COMPUTATIONAL 
    Machine Learning for Electro-magnetics: 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 Auto-encoders, 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
  6. EXPERIMENTAL 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: Coding (either MATLAB, Python, or C++).
    Prof. Constantine Sideris: ACME Lab
  7. EXPERIMENTAL COMPUTATIONAL
    Working with an emerging memory device, called memristor, the fourth passive circuit element. Fabricate the device, test the device electrical properties. Working with an integrated circuit chips based on memristor and use such chip to perform machine learning and artificial intelligence in a much more energy efficient way than the traditional digital computers.
    Pre-requisites: entrance level of physics, electrical, and computer science classes.
    SHINE student would mostly be doing computational and experimental work.
    Prof. Yang J. Joshua
  8. EXPERIMENTAL COMPUTATIONAL
    A mixture of computational and experiemental work in nanotechnology
    Pre-requisite: Basic Physics
    Prof. Wei Wu
  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. COMPUTATIONAL EXPERIMENTAL
    Biocemented Sands and Microscale Investigations to its Behavior
    This project is focused on understanding how microscale feature changes due to cementation and other factors affect the macro scale behavior. We will use Micro CT Scanning to help in this investigation. SHINE student would mostly be doing computational and experimental work.
    Pre-requisite: Physics, Algebra, some coding
    Prof. Nweke Chukwuebuka
  3. COMPUTATIONAL EXPERIMENTAL
    Physics-Based Earthquake Simulation Assessment and Validation in Frequency Space
    The project is focused on validating physics-based ground motions using real data as a reference. SHINE student would mostly be doing computational and experimental work.
    Pre-requisite: Physics, Algebra, some coding
    Prof. Nweke Chukwuebuka
  4. COMPUTATIONAL EXPERIMENTAL
    Quantifying Cause of Uncertainties in Horizontal-to-Vertical Spectral Ratio (HVSR) Measurements
    This project is focused on quantifying/characterizing the cause of uncertainties in HVSR estimates from measurements collected using temporary and permanent sensors. SHINE student would mostly be doing computational and experimental work.
    Pre-requisite: Physics, Algebra, some coding
    Prof. Nweke Chukwuebuka
  5. COMPUTATIONAL EXPERIMENTAL
    Directional (Path) Dependencies of Sedimentary Basin Site Response
    This project is focused on quantifying the effects of directionality (Path Bias) on site response in large or small sedimentary basins using observed data and empirical relationships. SHINE student would mostly be doing computational and experimental work.
    Pre-requisite: Physics, Algebra, some coding
    Prof. Nweke Chukwuebuka
  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 Sahimi: Laboratory for Complex Materials
  2. COMPUTATIONAL
    The Li Group develops and uses advanced computational methods to study quantum phenomena in nanomaterials. We are interested in how electrons behave when they see each other and when they interact with the atoms. We also study how to use light to control exotic behavior of quantum materials at a very small length scale for future device design. We look at broad phenomena including superconductivity, optical excitations, magnetism, and dynamics. We convert quantum physics equations into highly efficient computer codes.
    SHINE student would mostly be doing computational work.
    Pre-requisite: Physics, Algebra, Python
    Prof. Zhenglu Li: Li Research Group 
  3. COMPUTATIONAL EXPERIMENTAL
    Electron microscopes are powerful tools that enable us to look at the microscopic details of the materials, hence provide insights into the fundamental structure-property relations. This applies to all materials -- including energy materials, quantum materials, etc. Thus, developing new imaging modes is analogous to putting on a new pair of glasses that allows us to see things that weren't possible before, including the crystal symmetry, strain, polarity, electric or magnetic fields within the materials. Through this project, we will develop new imaging modes by combining hands on experimental and data mining/machine learning efforts, to explore the materials microstructures down to the nanometer scale. SHINE student would mostly be doing computational and experimental work.
    Pre-requisite: Matlab or python programming, experience with electronic circuits for automated control. Most importantly, curiosity!
    Prof. Yu-Tsun Shao
  1. COMPUTATIONAL 
    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.
    SHINE student would mostly be doing computational work.
    Prof. Paul Plucinsky
  2. 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 several research projects for SHINE students including 3D printing of wearable electrophysiological sensors, development of wireless brain-machine interfaces, and dexterous soft robots. The SHINE students will work closely with PhD students and learn basic knowledge and skills for design, fabrication, and testing 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
  3. COMPUTATIONAL EXPERIMENTAL
    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
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Published on December 14th, 2016

Last updated on February 22nd, 2024