Visión General del Programa y Proyectos de Investigación

Tenga en cuenta: Esta lista de proyectos de 2022 se actualizará completamente en enero de 2023 con descripciones de proyectos adicionales y otros proyectos confirmados por profesores que repiten en el programa. Mientras tanto, revise esta lista para ver la especificidad y los tipos de varios de los proyectos disponibles en SHINE.

Vea nuestra página de preguntas frecuentes si tiene alguna duda.
Vea también los Carteles y Videos de los Estudiantes para información adicional sobre estos proyectos de investigación. 

Los solicitantes escogen tres campos de la ingeniería que les resulten afines, y el K-12 STEM Center los asigna a un equipo de investigación apropiado, basándose en el interés del solicitante, las necesidades y capacidad del profesor, etc. En la solicitud en línea se les pide a los estudiantes que escojan y ordenen sus tres preferencias entre los campos de la ingeniería, así que prepárese para responder esta pregunta. Además, asegúrese de explicar en su declaración personal por qué cree que usted es especialmente adecuado para los proyectos que seleccione. Los profesores son quienes eligen a los alumnos que consideran adecuados para SHINE entre el grupo de solicitantes calificados.
  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 bio-inspired 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, Coding (any language), 3D-Printing/CAD experience are helpful but not required. (SHINE student would be in an experimental research lab)
    Prof. Mitul Luhar: Laboratorio de Interacciones Fluido-Estructura
  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. Pre-requisite: Biology and Chemistry. (SHINE student would be in an experimental research lab)
    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 McCain: Laboratory 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 Zavaleta: Zavaleta 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 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 Lee: Lee 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
    At i-lab we’re all about human-centric design and how we can shape dynamic built environments that assist people by making them comfortable, healthier, and more productive. For that, this project aims at creating an office that can be aware of workers’ stress levels and help them cope with their increasingly job demands. While stress is almost always treated as negative, when proper work conditions are established, positive stress experiences can also occur. Thus, we plan to develop machine learning algorithms to differentiate between positive and negative stress during work hours. A shine student will help current Ph.D. students with basic data analysis and will explore applying various machine learning algorithms for positive-negative stress detection. It is recommended that the students have interest and some experience in basic programming. Experience with machine learning is a plus but not a requirement.
    Prof. Burcin Becerik-Gerber: Innovation in Integrated Informatics

  2. COMPUTATIONAL EXPERIMENTAL
    Automated Postural Assessment of Office Workers using Computer Vision and Machine Learning:     Current developments in artificial intelligence have allowed an increased deployment of remote-controlled and teleoperated robots in construction sites. This project proposes an automated postural assessment of office workers teleoperating construction robots using Virtual Reality (VR), Computer Vision (CV), and Machine Learning (ML). The SHINE student is expected to help with the development of the VR and ML models of the proposed tool. Contributions of this study include the understanding of how VR can benefit real-time postural assessments. Past experience with programming is a plus but not a requirement.
    Prof. Burcin Becerik-Gerber: Innovation in Integrated Informatics

  3. COMPUTATIONAL EXPERIMENTAL
    VR-Based Emergency Training for Office Workers: Emergencies can take place in built environments at any time resulting in injuries or deaths. Important considerations for developing an optimal emergency preparedness strategy are to thoroughly understand the human response in various emergency scenarios, and to train building occupants on how to respond properly and rapidly to building emergencies. This project aims to develop virtual reality (VR) based emergency training for office workers. A SHINE student may help develop the training environment in Unity, conduct experiments with participants, and finally analyze data. Programming knowledge and prior experience with virtual reality can be a plus but not necessary.
    Prof. Burcin Becerik-Gerber: Innovation in Integrated Informatics

  4. COMPUTATIONAL EXPERIMENTAL
    IOT-Enabled Occupant Thermal Comfort Prediction and Data Mining:     With recent advances in the Internet of Things (IoT), it is possible to monitor the occupant’s surrounding environment (microclimate) and their behavior at a much granular level, reflecting their individual preferences. The abundance of data collected from indoor sensing technologies such as wrist-worn sensors, thermal cameras, and ambient sensors can only be harnessed via established data science methodology. Well-analyzed indoor sensing streams can provide invaluable insight into occupant comfort while enabling effective energy optimization strategies for the building sector. In this project, students will first get familiar with the area by reviewing selected research projects and literature. Previously collected data and related analyses will be presented to students to ensure their adequate understanding of the problem. Students will then be introduced to fundamental concepts of statistics and data mining (e.g., associations, regression, and classification) to obtain hands-on experience of working with a real-world stream of data. Basic Python programming will be used to implement different machine learning algorithms.
    Prof. Burcin Becerik-Gerber: Innovation in Integrated Informatics

  5. COMPUTATIONAL EXPERIMENTAL
    The construction industry is one of the biggest industries globally; yet, it is challenged by productivity rates, safety concerns, labor shortages, etc. Construction robotics is introduced as one of the opportunities to overcome these challenges. However, workers need to learn how to interact with these new technologies on job sites. This project aims to investigate the effectiveness of a training program based on virtual reality to prepare construction workers for collaboration with robots. The research group will explore the specific mechanisms of VR-based training in building/improving human-related factors in interaction with robots. A VR-based training developed with Unity3D game engine integrated with VR equipment will be used in the methodology of this project. Required skills: C#/Java Programming skill, Understanding of Machine Learning algorithms. SHINE students will mostly be doing computational work.
    Prof. Burcin Becerik-Gerber: Innovation in Integrated Informatics
  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. Coding experience in C++ or Python is desirable. (SHINE student would mostly be doing computational and experimental work)
    Prof. Ramesh Govindan: USC Networked Systems Laboratory
  2. 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. Prof. Mukund Raghothaman
  3. 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
  4. 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.
    Prof. Vatsal Sharan
  5. 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. Jiapeng Zhang
  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: Programming (C,C++ or Python), circuity (a plus, but not required) and electronics (a plus but not required ). (SHINE student would mostly be doing computational and experimental work)
    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 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. (SHINE student would be in an experimental and computational research lab)
    Prof. Stefanos Nikolaidis: Interactive and Collaborative Autonomous Robotic Systems (ICAROS) Lab

  4. Also, please scroll down to Mechanical Engineering to see Prof. Quan Nguyen's project involving robots
  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. 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 Kapadia: Laboratory for Photons, Electrons, & Materials
  3. 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
  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. Pre-req: Basic Math (Algebra, Calculus), Coding (Python)     Prof. Pierluigi Nuzzo: Nuzzo 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. Pre-req: Basic Math (Algebra, Calculus), Coding (Python)
    Prof. Pierluigi Nuzzo: Nuzzo Lab

  6. 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

  7. 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

  8. 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
  9. COMPUTATIONAL
    The SHINE students will involve in various aspects of electronic system design, such as Artificial Intelligence (AI) hardware design, 5G communication systems, low power internet of things (IoT) devices, etc. This internship can provide research opportunities, ranging from software programming to practical hardware design experiences. In terms of software programming, the students may be involved in Python, and C/C++ coding. For hardware design, the students may be involved in printed circuit board (PCB) level design and testing. 
    Prof. Mike Shuo-Wei Chen

  1. EXPERIMENTAL 
    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. EXPERIMENTAL 
    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 McCurry: McCurry Lab

  1. COMPUTATIONAL 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. Professor Sze-chuan Suen
  1. EXPERIMENTAL
    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. (SHINE student would be in an experimental research lab.)
    Prof. Jayakanth Ravichandran: Laboratory for Complex Materials
  2. COMPUTACIONAL - Please go to Aerospace to see Prof. Ananya Renuka Baladrishna's work in Solids & Materials
  1. COMPUTACIONAL 
    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. COMPUTACIÓN MEDICAL 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
  3. COMPUTACIÓN EXPERIMENTAL 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 participation in robotics clubs is preferable. Prof. Quan T. Nguyen: Dynamic Robotics and Control Lab
  4. COMPUTACIÓN TEÓRICA 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
  5. COMPUTACIÓN 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
  6. 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 blood pressures. Prof. Niema Pahlevan: Medical Flow Physics Laboratory
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Published on October 6th, 2022

Last updated on November 10th, 2022