Viterbi Summer Undergraduate Research Experience (SURE)

Advanced Composites Design Lab

• Faculty/PI: Bo Jin
• Website: composites.usc.edu
• Research Overview: Established in 1995 and endowed with a generous gift from M.C. Gill in 2002, the mission of the Center is to address problems associated with the manufacture and behavior of composites and composite structures. The scope includes the training of graduate and undergraduate students from chemical, mechanical and materials engineering through sponsored research projects. Personnel within the Center provide a range of expertise that includes postdoctoral associates and research professors with specialized skills in mechanics, polymer science, and manufacturing technology.
• Summer Projects: TBD

Center for Advanced Manufacturing

• Faculty/PI: Yong Chen
• Website: https://viterbi-web.usc.edu/~yongchen/ | https://sites.usc.edu/cam/
• Research Overview: Manufacturing has significantly contributed to improved quality and sustainability of human life. My research goal is to advance the understanding and knowledge of Additive Manufacturing (AM) and to promote its wide applications in future engineering systems. AM is a digital manufacturing process that has become a disruptive manufacturing technology, especially for medical and aerospace industries, in which small-quantity-production is dominant. It is predicted that AM will dominate most manufacturing concerns in future mass customization. To achieve the full potential of AM in cost and time savings, the development of novel AM processes, new process control methods, and a wide selection of functional materials are critical.  In addition, AM enables revolutionary new design by using complex three-dimensional shapes, heterogeneous material properties, and multi-functionality.  Systematic knowledge regarding modeling, analyzing, synthesizing, and optimizing such product designs are required to achieve desired performance.
• Summer Projects:
  • 3D printing processes and applications
    • Description: The students will investigate new 3D printing processes developed in our lab and their applications.
    • Student Responsibilities: Students will work with Ph.D. students in the lab and perform 3D printing process development and experiments.
    • Preferred Majors: Aerospace & Mechanical Engineering, Computer Science, Electrical & Computer Engineering
    • Prerequisites: The students are eager to learn, willing to work with others and enjoy hands-on work.

Dynamic Robotics and Control Lab

• Faculty/PI: Quan Nguyen
• Website: https://sites.usc.edu/quann/
• Research Overview: Our research focuses on developing novel control algorithms for achieving extremely agile and robust locomotion on dynamic robotic systems. Our approach lies at the intersection of nonlinear control, trajectory optimization, and machine learning.
• Summer Projects: TBD

Zhao Research Group

• Faculty/PI: Hangbo Zhao
• Website: https://sites.usc.edu/zhaogroup/
• Research Overview: Our group works at the intersection of advanced manufacturing, materials, and mechanics. We aim to advance the science and technology of manufacturing through a combination of fundamental understanding of materials, mechanics, interfacial science, and multidisciplinary experimental approaches. Our focus areas include micro/nano manufacturing, bio-integrated electronics, engineered surfaces/interfaces, and active/smart materials. Advances in these areas will find applications in biomedical devices, soft robotics, environmental monitoring, tissue engineering, among many others.
• Summer Projects:
  • Stretchable strain sensors for 3D reconstructions of soft robots
    • Description: We develop stretchable sensors to measure large deformations of soft robots, then use the measured data to build 3D digital models of the soft robots.
    • Student Responsibilities: Assist with strain sensor fabrication and testing; 2. Assist with fabrication and actuation of soft robots; 3. Assist with building 3D digital models of soft robots
    • Preferred Majors: Aerospace & Mechanical Engineering, Electrical & Computer Engineering
    • Prerequisites: Experience with AutoCAD, machining, building experimental setup, electrical measurement is preferred
  • Design and simulation of soft robotic arms
    • Description: We develop novel robot manipulators composed of soft materials , which can generate complex deformations
    • Student Responsibilities: Student will work with a PhD student in designing a soft robotic arm with CAD software and simulating its interactions between environment using numerical tools. The student will also assist with the fabrication and testing of the soft robotic arm
    • Preferred Majors: Aerospace & Mechanical Engineering
    • Prerequisites: background in mechanics or finite element simulation, or hands-on experience with robotics will be preferred
  • Fabrication of stretchable microelectrodes using 3D printing
    • Description: We develop highly stretchable microelectrodes using a combination of 3D printing and microfabrication techniques
    • Student Responsibilities: student will work with a PhD student to test and optimize the design parameters and fabrication process
    • Preferred Majors: Aerospace & Mechanical Engineering, Materials Science and Engineering
    • Prerequisites: prior experience in 3D printing or materials processing will be preferred

Applied Movement and Pain Laboratory

• Faculty/PI: Jason Kutch
• Department: Biokinesiology and Physical Therapy
• Website: ampl.usc.edu
• Research Overview: The Applied Movement & Pain Laboratory (AMPL) is directed by Jason J. Kutch. Work in AMPL is at the intersection of chronic pain and movement control. We are particularly interested in how the nervous system controls pelvic floor muscles, as well as how brain dysfunction contributes to chronic pelvic pain. Current research in AMPL is focused on developing non-invasive brain stimulation approaches for augmenting chronic pain treatment.
• Summer Projects: TBD

Biomaterials and Nanomedicine (Chung Laboratory)

• Faculty/PI: Eunji Chung
• Website: https://biomaterials.usc.edu
• Research Overview: The Chung research group focuses on drug delivery, nanomedicine, and regenerative engineering to generate biomaterial strategies to address the limitations of clinical solutions, with a major emphasis on rare and genetic diseases. In particular, we are interested in biomimetic nanoparticles that can be designed to deliver molecular signals to report back on or influence the behavior of diseased tissue for biomedical applications. In addition, we are harnessing our expertise in combining bioactive scaffolds with novel stem cell sources for complex regeneration of hierarchically-ordered tissues and organs. Our group is highly interdisciplinary as our research is positioned at the intersection of engineering, biology, and medicine, and we work with a variety of collaborators to translate our materials towards clinical use.
• Summer Projects: TBD

Biomedical Microsystems Lab

• Faculty/PI: Ellis Meng
• Website: biomems.usc.edu
• Research Overview: The Biomedical Microsystems Laboratory at USC focuses on developing novel micro- and nanotechnologies for biomedical applications. In particular, we are interested in the integration of multiple modalities (e.g. electrical, mechanical, and chemical) in miniaturized devices measuring no more than a few millimeters for use in fundamental scientific research, biomedical diagnostics, and therapy.
• Summer Projects:
  • Summer Research Experience in Biomedical Microdevices
    • Description: The Biomedical Microsystems Lab (https://biomems.usc.edu/) directed by Prof. Ellis Meng seeks an outstanding and highly motivated postdoctoral scholar for a leading role in developing and disseminating implantable microdevices using polymer microelectromechanical systems (MEMS) technology. The Lab is a collaborative and dynamic working environment that leverages the advantages of micro- and nanotechnologies to advance scientific discovery and healthcare.  Projects will either be in the area of neural engineering/neural interfaces and/or biomedical sensor technologies.
    • Student Responsibilities: Coordinate research tasks with graduate student, postdoc, or staff mentor. Learn protocols and standard operating procedures.  Conduct experiments.  Analyze data.  Prepare presentations and reports.  Attend lab meetings and present research progress and findings.
    • Preferred Majors: Aerospace & Mechanical Engineering,Biomedical Engineering,Chemical Engineering,Electrical & Computer Engineering
    • Preferred Skillsets: Prior work experience involving hands-on tasks. This could be research but need not be.  Laboratory experiences from class work is welcome as are hands-on experiences from student clubs, competitions, or work experiences.

Computational Systems Biology Laboratory

• Faculty/PI: Stacey Finley
• Website: https://csbl.usc.edu/
• Research Overview: The Computational Systems Biology Laboratory at USC develops mechanistic models of biological processes and utilizes the models to: gain insight into the dynamics and regulation of biological systems, synthesize and interpret experimental and clinical observations, provide a quantitative framework to test biological hypotheses support the development of novel therapeutics for pathological conditions. We perform experimental studies to obtain quantitative measurements needed to construct computational models that increase our understanding of specific biological processes. We also collaborate closely with experimental and clinical researchers. These fruitful collaborations enable experimental testing of the model predictions.
• Summer Projects:
  • Systems biology modeling of cancer
  • Description: My research group works in the area of mathematical oncology, where we use mathematical models to decipher the complex networks of reactions inside of cancer cells and interactions between cells. Understanding the complexity of these reaction networks requires computational tools and mathematical models. We combine detailed, mechanistic and data-driven modeling to study these networks and predict ways to control tumor growth. Our current projects are aimed at predicting metabolism and signaling in the tumor microenvironment.
  • Student Responsibilities: understand and update MATLAB code, perform new simulations, generate novel model predictions, work with advanced Ph.D. student or postdoctoral fellow to interpret the results
  • Preferred Majors: Biomedical Engineering,Chemical Engineering
  • Preferred Skillsets: proficiency in writing and understanding differential equations (Calculus), interest in coding and computational models, past experience with MATLAB or Python is exceptional, basic understanding of molecular biology and cell physiology

Duncan Lab

• Faculty/PI: Dominique Duncan
• Website: https://sites.usc.edu/duncanlab/
• Research Overview: We are a group of neuroscientists, engineers, mathematicians, computer programmers, chemists, and data scientists who share a common goal of addressing challenges to neuroscience and public health via big data. We do so by working at the intersection of neuroimaging, signal processing, informatics, and machine learning. We leverage computational tools in conjunction with mechanistically oriented neuroimaging to develop analytic tools for multimodal data, build centralized archives for data and algorithms, and promote large-scale collaborative research. Currently, we focus on applications in traumatic brain injury, epilepsy, novel devices for intracranial stimulation, and COVID-19.
• Summer Projects:
  • Epilepsy Bioinformatics Study for Antiepileptogenic Therapy
    • Description: The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4R) Scientific Premise is: epileptogenesis after traumatic brain injury (TBI) can be prevented with specific treatments; the identification of relevant biomarkers and performance of rigorous preclinical trials will permit the future design and performance of economically feasible full-scale clinical trials of antiepileptogenic therapies.
    • Student Responsibilities: The Duncan Lab is specifically contributing to the EpiBioS4Rx Consortium by identifying multimodal biomarkers of epileptogenesis. We apply data science, mathematics, and machine learning to glean insight into what factors may induce the development of seizures after brain injury so we can ultimately predict which patients will develop epilepsy. Our work centers around biomarker identification in scalp and depth electrophysiology and multimodal imaging data.
    • Preferred Majors: Biomedical Engineering,Computer Science,Electrical & Computer Engineering
    • Preferred Skillsets: Python or MATLAB, imaging or time series analysis
  • Data Archive for the BRAIN Initiative
    • Description: Building on decades of experience creating widely-used, large-scale informatics solutions in the neurosciences, the Data Archive for the BRAIN Initiative (DABI) was launched to address the need for a central repository for human invasive neurophysiology. DABI allows researchers to ingest, harmonize, aggregate, store, visualize, and disseminate human invasive neurophysiology data, including EEG, ECoG, LFP, single-unit activity, and more.
    • The repository, which will also house synchronized behavioral, imaging, demographic, and other key data, is specifically designed to help BRAIN Initiative researchers organize and analyze their own data while fulfilling data-sharing directives from federal agencies and their respective institutions. Investigators retain ownership and control of their data through a federated model.
    • Student Responsibilities: Multimodal data analysis of data on DABI.
    • Preferred Majors: Biomedical Engineering, Computer Science, Electrical & Computer Engineering
    • Preferred Skillsets: Python or MATLAB, time series analysis.

Locomotor Control Lab

• Faculty/PI: James Finley
• Department: Biokinesiology and Physical Therapy/Biomedical Engineering
• Website: https://sites.usc.edu/lcl
• Research Overview: In the USC Locomotor Control Lab, we seek to understand how walking is controlled and adapted in both the healthy and injured neuromuscular systems. We develop models and experiments based on principles of neuroscience, biomechanics, engineering, and exercise physiology to identify the factors that guide locomotor learning and rehabilitation. Ultimately, the goal of our work is to design novel and effective interventions to improve walking ability in individuals with damage to the nervous system.
• Summer Projects:
  • Tradeoffs between Risk and Effort during Human Locomotion
  • Description: The risk of falling and the physical effort associated with walking depend on the routes we choose when moving through the environment and the coordination patterns we use to walk through the environment. However, there are currently no theoretical frameworks that explain how we trade off risk and effort when 1) choosing between alternative routes to move through the environment or 2) deciding what strategies we use to reduce risk while walking along a chosen route. Here, we will distinguish between theoretical models of choice by using novel, perturbation-based approaches to modify physical risk during walking, physiological assessments of energy cost, and custom virtual environments.
  • Student Responsibilities: The student will be responsible for scheduling research participants, conducting experiments, and analyzing human behavioral data.
  • Preferred Majors: Aerospace & Mechanical Engineering,Biomedical Engineering
  • Preferred Skillsets: Biomechanics, Dynamics, Matlab Programming

USC Neural Modeling and Interface Lab

• Faculty/PI: Dong Song
• Biomedical Engineering
• Website: https://slab.usc.edu/
• Research Overview: The overarching goal of our research is to build biomimetic devices that can be used to treat neurological disorders. Specifically, we develop next-generation modeling and neural interface methodologies to investigate brain functions during naturalistic behaviors in order to (1) understand how brain regions such as the hippocampus perform cognitive functions, and (2) build cortical prostheses that can restore and enhance cognitive functions lost in diseases or injuries.
• Summer Projects:
  • Record and modeling neural signals for building memory prosthesis
    • Description: Our mission is to develop brain-like devices that can mimic and restore cognitive functions. To pursue this goal, we use a combined experimental and computational strategy to (1) understand how nervous systems such as the hippocampus perform higher-order cognitive functions, (2) develop next-generation modeling and neural interface methodologies to investigate brain functions during naturalistic behaviors, and (3) build cortical prostheses that can restore cognitive functions lost in diseases or injuries.  We invite undergraduate students with science or engineering background to participate in these research projects.
    • Student Responsibilities: Each student will have plenty of chances to participant in different aspect of the neuroscience and neural engineering projects described above under the supervision of Dr. Song and graduate students in the lab. Undergraduate researchers will be trained in the following skills:
      • Skill 1. in vivo Recording from Behaving Animals - Starting from preparing animals for MEA implantations, undergraduate researchers will assist graduate students to get the animal familiar with different experimental environments and train the animals to perform certain tasks such as free exploring and the DNMS task. Once the animal is ready for electrode implantation, undergraduate researchers will help to prepare chemicals needed for the implantation and sterilize the surgical area and the surgical instruments.  During the surgery, undergraduate researchers will observe the entire implantation procedure and help to keep notes during the surgery.  They will also learn detailed surgical techniques from graduate students.  During the recovery period, undergraduate researchers will help to monitor the animal and report any abnormal condition to graduate students.  After recovery, undergraduate researchers will assist graduate students to collect neural and behavioral data from implanted animals.
      • Skill 2. Histology of Brain Tissue - Performing histological study to brain tissue collected from implanted animals is a crucial step in neuroscience studies. This step identifies the origin of neural signals and is a necessary procedure to verify immune responses to the implant.  First, undergraduate researchers will learn how to slice brain tissue and stain them with different dyes.  Second, they will be trained to conduct histological works independently.  Finally, they will learn to evaluate brain slices under microscope and keep documentations for each implanted animal.
      • Skill 3. Neural Data Analyze- Undergraduate researchers will learn to analyze neural data using specific software under from graduate students. They will assist in pre-processing neural data such as removal of artifact, counting units, and visualization of neural data using Matlab.  Students with strong mathematic background and programming skills will also be able to participant in the development of computational models.
    • Preferred Majors: Biomedical Engineering,Electrical & Computer Engineering

Armani Lab

• Faculty/PI: Andrea Armani
• Website: https://armani.usc.edu/
• Research Overview: The over-arching mission of the research group is to develop novel nonlinear materials and integrated optical devices that can be used in understanding disease progression and in quantum optics. As part of these efforts, we have numerous collaborations in tool and technology development to enable research and discovery across a wide range of fields.
• Summer Projects: TBD

Geosystems Engineering and Multiphysics Lab

• Faculty/PI: Birendra Jha
• Chemical Engineering and Materials Science
• Website: https://gemlab.usc.edu
• Research Overview: We study the physics and mathematics of geophysical fluid flows in subsurface environments. We create theoretical and computational tools, including machine learning and artificial intelligence tools, to investigate these multi-physics processes and address engineering challenges related to energy and the environment such as geologic carbon storage, induced seismicity, hydraulic fracturing, groundwater remediation, and enhanced oil recovery. Our work is at the intersection of petroleum engineering, geophysics, hydrogeology, and computational mechanics.
• Summer Projects:
  • Fluid flow and mechanical deformation in porous media: experiments and simulations
  • Description: Flow of water, oil and gas through geologic porous media (rocks and soil) is important for extraction of energy from underground, disposal of chemical waste, carbon sequestration to mitigate global warming, and for improving groundwater quality. Fluid flow through a geological, biological, or synthetic porous medium often leads to stress and strain in the medium, which affects the structure and properties of the medium, for example, its capacity to store and transmit fluids. This creates a fascinating world of beautiful patterns that emerge from physical interactions between the fluids and the porous medium. In this project, you will learn to create and observe those patterns in controlled laboratory experiments and computer simulation models. You can choose to sharpen your skills in differential equations, linear algebra, python/matlab programming, or fluid injection experiments. You will receive guidance from other students and researchers working in our lab.
  • Student Responsibilities: For experimental work, you will prepare samples for experiments (rock samples or acrylic plates), prepare fluids (water, glycerol, CO2), setup injection pump and coreholder, run experiments, collect data, make plots, and analyze. For computer simulation work, you will learn different components of a typical finite element simulator (initial and boundary conditions, material properties, mesh), prepare simulation input files, submit them to a computing cluster, collect output files, make plots, and analyze. Depending on your interests and progress, you may write computer codes or create new experiments to improve efficiency or understanding of the process. You will learn to work with a team of researchers, ask insightful questions, make hypotheses, and gather information necessary to test those hypotheses.
  • Preferred Majors: Aerospace & Mechanical Engineering, Chemical Engineering, Civil & Environmental Engineering, Computer Science, Electrical & Computer Engineering
  • Prerequisites: No prerequisite requirement but knowledge in any of these topics--linear algebra, differential equations, solid and fluid mechanics, matlab/python programming--will increase student's learning and satisfaction from the project. For experimental work, if the student has prior experience working with solids and fluids in a lab, that will help.

McCurry Lab

• Faculty/PI: Daniel McCurry
• Website: mccurrylab.com
• Research Overview: The McCurry lab applies the tools of organic and analytical chemistry to solve environmental problems. We primarily work in the areas of wastewater reuse and drinking water treatment.
• Summer Projects:
  • Environmental engineering and chemistry to enable safe sustainable water reuse
  • Description: Our research group currently consists of five PhD students and four undergraduate researchers who all do research in the areas of water treatment and environmental chemistry. Our major research activities include identifying the precursors and formation pathways of disinfection byproducts formed during water treatment and wastewater reuse, developing new chemical technologies for oxidation of trace organic contaminants, and developing new analytical techniques for identification and quantification of chemical pollutants using mass spectrometry. Our research primarily takes place in the BHE Water Lab, and relies heavily on recently purchased analytical instrumentation, including an ion mobility QTOF mass spectrometer, a gas chromatograph/triple quadrupole mass spectrometer, and an inductively-coupled plasma mass spectrometer. These tools allow us to quantify chemicals at extremely low concentrations (e.g., parts per trillion) and to identify previously unknown compounds in water samples.
  • Student Responsibilities: Assisting graduate students with experiments on water treatment, water reuse, and quantification of trace contaminants in water. Specific responsibilities will include preparing for and setting up batch water treatment experiments, sample preparation for mass spectrometry-based identification and quantification of pollutants, and analyzing data produced by analytical instruments. Especially dedicated students may eventually have the opportunity to advance to a fully-independent project advised directly by the PI.
  • Preferred Majors: Chemical Engineering,Civil & Environmental Engineering
  • Preferred Skillsets: General chemistry lab skills; some exposure to analytical chemistry is helpful but not required; chemistry coursework helpful

Muin Research Group

• Faculty/PI: Sifat Muin
• Website: https://www.sifatmuin.com/
• Research Overview: The Muin Research Group at the University of Southern California works at the intersection of structural engineering and data science. The overarching goal of our research is to enhance post-disaster resilience. We are developing a holistic post-disaster monitoring and recovery system that will study the data collected from the structural and human components of a community and improve the resiliency of a city.
• Summer Projects: TBD

Nweke Research Group: The N.E.S.T

• Faculty/PI: Chukwuebuka Nweke
• Website: nwekenest.com
• Research Overview: The Nweke Research Group aims to develop and establish "state of the art" frameworks for the assessment and evaluation of natural hazards. The goal is to improve the engineering resiliency of civil infrastructure and the supporting systems by fostering sustainable foresight and pursuing telluric (well-grounded) collaborative excellence.
• Summer Projects:
  • Tenative: HVSR in Sedimentary Basins as a Site Parameter; and/or Shear Modulus and Damping of biocemented sands
    • Description: TBD
    • Student Responsibilities: assisting in preparing lab specimen, assisting in conducting field data collection, analysis of data on python; literature review
    • Preferred Majors: Civil & Environmental Engineering
    • Prerequisites: Preferable some familiarity with python, willingness to go on field expeditions and to conduct laboratory experiements

Savla Research Group

• Faculty/PI: Ketan Savla
• Website: https://viterbi-web.usc.edu/~ksavla/
• Research Overview: My current research interest is in distributed robust and optimal control, dynamical networks, state-dependent queueing systems, and incentive design, with applications in civil infrastructure (e.g., transportation, energy) and robotic (e.g., automated vehicles, multi-agent) systems.
• Summer Projects: TBD

AI, Language, Learning, Generalization, and Robustness (Allegro) Lab

• Faculty/PI: Robin Jia
• Website: https://robinjia.github.io/
• Research Overview: I am interested broadly in natural language processing and machine learning, with a particular focus on building NLP systems that are robust to distribution shift at test time.
• Summer Projects:
  • Assisting human readers with question answering and fact verification
    • Description: When reading a long and complex document (e.g., a textbook), a human reader will often find that they need to refer back to previously introduced material from earlier in the document. Similarly, when reading a news article or tweet about current events, a reader may want to search through other trusted articles to find evidence for particular claims. However, this process of manual searching is inconvenient and disrupts the reading flow, so readers will often refrain from immediately searching for helpful context or evidence. The idea of this project is to build a system that would assist readers in finding such context and evidence seamlessly. A user can query the system simply by highlighting a span of text in a document they are reading. Based on this, our system will automatically generate questions or claims related to the highlighted span that the user might wish to have answered/verified. It will then search for other passages from the document or related documents that would help answer these questions or provide evidence about these claims.
    • Student Responsibilities: Implementing a question generation and passage retrieval pipeline using python, pytorch, and other existing open-source tools for deep learning; Reading academic papers related to question generation, retrieval, question answering, and fact verification
    • Preferred Majors: Computer Science
    • Prerequisites: Knowledge of python, Coursework in machine learning, Interest in natural language processing and deep learning
  • Generating datasets with large language models
    • Description: Large language models like GPT-3 are powerful deep learning systems that have learned to generate reasonable-sounding natural language text, and have the potential to be used in a very wide variety of language-based applications. One potential use case of
    • Student Responsibilities: Using existing pytorch-based implementations of large language models and running large-scale experiments to generate data with them; Implementing training of smaller models using the generated data; Devising new ideas for filtering data Reading NL
    • Preferred Majors: Computer Science
    • Prerequisites: Knowledge of python, Coursework in machine learning, Interest in natural language processing and deep learning

GLAMOR Lab

• Faculty/PI: Jesse Thomason
• Website: http://glamor.rocks/
• Research Overview: We bring together natural language processing and robotics to connect language to the world (RoboNLP). Our lab is broadly interested in connecting language to agent perception and action, and lifelong learning through interaction.
• Summer Projects:
  • Grounding Language with Robots
    • Description: Our lab is broadly interested in connecting language to agent perception and action, and lifelong learning through interaction. We work primarily at 3 levels of connecting language to the world: language and perception, understanding how words correspond to images, sounds, and feelings; language and actions, understanding how language can be used to instruct an agent about its goals and to communicate constraints; and language as a social mechanism for cooperating and learning.
    • Student Responsibilities: Students will work with PhD students on research-focused projects that involve connecting language to perception, actions, and social behavior. Students will work with large-scale language models, deep learning, simulators, and possibly physical robots, pending their and PhD students' interests by summer 2023.
    • Preferred Majors: Computer Science
    • Prerequisites: Familiarity with Python, preferably also Pytorch and/or ROS frameworks, and solid grasp of AI as covered in undergraduate programs. Familiarity with or background in machine learning preferred.

HaRVI Lab

• Faculty/PI: Heather Culbertson
• Website: https://sites.usc.edu/culbertson/
• Research Overview: The Haptics Robotics and Virtual Interaction (HaRVI) Laboratory explores how humans interact with our world, robots, and technology through touch. The goal of our research is to create natural and intuitive interactions that realistically mimic the touch sensations experienced during interactions with the physical world. We design novel haptic hardware and rendering algorithms to improve the usability of technology, increasing people’s social connectedness, ability to complete specific tasks, and immersiveness in virtual reality. Our research has a strong focus on integrating human perception into all steps of the design process
• Summer Projects:
  • Haptics for Virtual Reality
    • Description: This project focuses on the design, building, and control of haptic devices for virtual reality. Current VR systems lack any touch feedback, providing only visual and auditory information to the user. However, touch is a critical component for our interactions with the physical world and with other people. This research will investigate how we use our sense of touch to communicate with the physical world and use this knowledge to design haptic devices and rendering systems that allow users to interact with and communicate through the virtual world. To accomplish this, the project will integrate electronics, mechanical design, programming, and human perception to build and program a device to display artificial touch sensations to a user with the goal of creating a natural and realistic interaction.
    • Student Responsibilities: Reading and analyzing research papers. Designing, prototyping, and programming haptic devices. Conducting human subject studies.
    • Preferred Majors: Aerospace & Mechanical Engineering,Computer Science
    • Preferred Skillsets: Programming (C++ preferred), CAD/3D printing, electronics and circuits (helpful, but not required)

Language Understanding and Knowledge Acquisition (LUKA) Lab

• Faculty/PI: Muhao Chen
• Website: https://luka-group.github.io/
• Research Overview: Our research focuses on robust and accountable machine learning methods for natural language understanding, structured data processing, and knowledge acquisition from unstructured data. We are also interested in knowledge-driven intelligent systems that handle interdisciplinary tasks (for example, biology, medicine, software engineering, and geoinformatics). Our long-term goal is to develop robust, generalizable and minimally supervised knowledge-aware learning systems that help machines understand nature.
• Summer Projects:
  • Indirect Supervision for Knowledge Acquisition
    
    • Description: Knowledge acquisition tasks (e.g., relation extraction, entity and event extraction and typing, consolidation) face challenges including extreme label spaces, insufficient annotations and out-of-distribution prediction. To this end, we study the method for leveraging indirect supervision signals from auxiliary tasks (e.g., natural language inference, abstractive summarization, etc.) to foster robust and generalizable inference for (open-domain) knowledge acquisition or information extraction. In the same context, study the method for generating semantically rich label representations based on either gloss knowledge or structural knowledge from a well-populated lexical knowledge base, in order to better support learning with limited labels.
    • Student Responsibilities: Work with a PhD student towards a publishable work.
    • Preferred Majors: Computer Science
    • Preferred Skillsets: PyTorch, Huggingface

ML Theory Group

• Faculty/PI: Vatsal Sharan
• Website: https://vatsalsharan.github.io/
• Research Overview: I work on machine learning and theoretical computer science. My research aims to understand how to solve learning and inference tasks in the face of various computational and statistical constraints, such as limited memory or too little data. I am also broadly interested in the desiderata in practical applications beyond these constraints as well---particularly in notions of robustness. My interests are both in developing theoretical frameworks to understand the fundamental limits in these modern settings, and in designing practical algorithms to tackle these problems.
• Summer Projects:
  • Machine Learning Mysteries
  • Description: Students will explore foundational questions regarding machine learning, using a combination of systematic experiments and theoretical analysis. Both students interested in performing systematic experiments to tease out phenomenon in practice, and those interested in using theoretical tools to prove novel guarantees are welcome. The exact questions and their scope is broad, but the following are some potential options: 1. Understanding deep learning: One particular question of interest here is to understand why neural networks generalize despite having the capacity to overfit. We've been exploring what role the data itself has to play in this mystery, exploring connections to the amazing self-supervised learning capability of neural networks in the process. 2. Data augmentation and amplification: In some recent work ("Sample Amplification"), we showed that it is often possible to generate new samples from a distribution without even learning it. We will explore how this ties into various data augmentation techniques, and develop new frameworks to increase dataset size. 3. Fairness and robustness: We will explore how to train models which are robust in many ways, such as to changes in the data distribution, or to do well on minority sub-populations (and not just on average over the entire data). 4. Computational-statistical tradeoffs: This is a more theoretical direction, to understand when computational efficiency might be at odds with statistical requirements (the data needed to learn). Recent work has opened by much uncharted territory, particularly with respect to the role of memory in learning, which we will explore.
  • Preferred Majors: Computer Science, Electrical & Computer Engineering
  • Preferred Skillsets: Familiarity with probability, linear algebra, calculus, and analysis of algorithms. Some basic understanding of machine learning would be very helpful for certain projects.

Networked Systems Laboratory

• Faculty/PI: Ramesh Govindan
• Website: https://nsl.usc.edu
• Research Overview: Founded in 2002, our laboratory conducts research on the design and implementation of a wide range of networked computing systems.
• Summer Projects: TBD

Analog/RF Integrated Circuits, Microsystems, and Electromagnetics (ACME) Laboratory

• Faculty/PI: Constantine Sideris
• Website: https://sites.usc.edu/acmelab/
• Research Overview: We design, model, and implement integrated circuits and systems for biomedical applications, wired and wireless communications, and emerging technologies. We also develop and leverage computational techniques to efficiently model and optimize new electromagnetic devices and sensors with cutting-edge performance.
• Summer Projects: TBD

Autonomous Networks Research Group

• Faculty/PI: Bhaskar Krishnamachari
• Website: https://anrg.usc.edu
• Summer Projects:
  • Networked Distributed Systems and Applied Machine Learning
    • Description: The Autonomous Networks Research Group directed by Prof. Bhaskar Krishnamachari, seeks bright undergraduate students with backgrounds in electrical engineering, computer science and mathematics for research into a broad range of topics including Internet of Things, robotic networks, connected vehicles, blockchain technology and applied machine learning. Projects will involve a mix of mathematics, simulation and testbed experiments, tailored to student background and interest, and provide a solid exposure to graduate-level research. Previous summer interns with this group have gone on to Ph.D. programs at top places including UC Berkeley, Columbia, MIT, Princeton, UCLA, USC, UT Austin, Stanford, U. Michigan, and UIUC.
    • Preferred Majors: Computer Science,Electrical & Computer Engineering
    • Prerequisites: Programming ability in Python is strongly preferred, interest in mathematical modeling is a plus

Cyber-Physical Systems

• Faculty/PI: Paul Bogdan
• Website: https://cps.usc.edu/studs.html
• Research Overview: Our research is dedicated to understanding and designing Cyber Physical Systems. CPS is a highly interdisciplinary area and our group works on a variety of topics. In general, we are interested in the fundamental and applied questions relating to the structure and dynamics of networks.
• Summer Projects: TBD

Cyber-Physical System Design (DesCyPhy) Lab

• Faculty/PI: Pierluigi Nuzzo
• Website: https://descyphy.usc.edu/
• Research Overview: I am interested in methodologies and tools for the design of cyber-physical systems and embedded systems, including analog and mixed-signal integrated circuits. My research aims at combining design methodology, formal methods, and scalable verification, synthesis, and optimization-based algorithms, to build a high-assurance system engineering framework that improves design quality, cost, and productivity, while providing strong guarantees of correctness and dependability.
• Summer Projects: TBD

Data Science Lab

• Faculty/PI: Viktor Prasanna
• Website: https://dslab.usc.edu
• Research Overview: The Data Science Lab focuses on applying machine learning, data mining, and network analysis to real-world problems in society and industry.
• Summer Projects:
  • Graph Machine Learning
    • Description: Defining Graph ML models for applications. Predictive modeling. Optimization. Evaluation.
    • Preferred Majors: Computer Science,Electrical & Computer Engineering
    • Prerequisites: Algorithms and data structures. ML

Energy-Efficient Secure Sustainable Computing Group (EESSC)

• Faculty/PI: Peter Beerel
• Website: https://sites.usc.edu/eessc/
• Research Overview: The E2S2C group, led by Professor Peter A. Beerel, has active research efforts spanning circuits, micro-architecture, and algorithms that target a variety of emerging areas in energy-efficient, secure, and sustainable computing. The group is guided by academic curiosity, integrity, and the spirit of collaboration to solve real-world problems using the wide array of mathematics that make up the foundation of Electrical and Computer Engineering. The group’s current research projects include topics in machine-learning algorithm hardware co-design, superconducting electronics, hardware security, and asynchronous VLSI design. The group is also collaborating on multidisciplinary problems including mitigating wild-fires using a network of drones.
• Summer Projects: TBD

FPGA/Parallel Computing Lab

• Faculty/PI: Viktor Prasanna
• Website: https://usc.edu
• Research Overview: The FPGA/Parallel Computing Lab is focused on solving data, compute and memory intensive problems in the intersection of high speed network processing, data-intensive computing, and high performance computing. We are exploring novel algorithmic optimizations and algorithm-architecture mappings to optimize performance of parallel and heterogeneous architectures including Field-Programmable Gate Arrays (FPGA), general purpose multi-core (CPU) and graphics (GPU) processors
• Summer Projects:
  • FPGA Acceleration of ML
    • Description: Design and analysis of accelerators for ML tasks
    • Preferred Majors: Computer Science,Electrical & Computer Engineering
    • Prerequisites: Algorithms, Architecture, Hardware design, ML

HHLab

• Faculty/PI: Hossein Hashemi
• Website: https://hhlab.usc.edu/
• Research Overview: We analyze, design, and implement integrated circuits and systems for communications, sensing, and imaging applications.
• Specifically, we envision high-performance sensing and communication devices that can be embedded in the environment to increase our awareness, improve the quality of life, and create an intelligent and responsive ambient.
• Summer Projects:
  • Millimeter-Wave Integrated Circuits
    • Description: Modern and future wireless communication systems (5G, 6G), automotive radars, and high-resolution 3D imagers operate at millimeter wave frequencies. This project involves simulations and design of millimeter-wave integrated circuits.
    • Student Responsibilities: The student will design and simulate millimeter-wave integrated circuits.
    • Preferred Majors: Electrical & Computer Engineering
    • Preferred Skillsets: Required background- Basic electromagnetics (Maxwell equations), Basic transistor-level analog circuits; Additional Preferred Knowledge (not necessary) - Cadence design tool, Python programming
  • Silicon Photonics Integrated Circuits
    • Description: The project involves application of silicon photonics integrated circuits for lidar, optical computing, and other applications.
    • Student Responsibilities: The student will work on design and simulations of photonic components and photonic integrated circuits.
    • Preferred Majors: Electrical & Computer Engineering
    • Preferred Skillsets: Required Knowledge - Basic electromagnetic (solving Maxwell equation with boundary value)

Khan Lab

• Faculty/PI: Yasser Khan
• Website: khan.usc.edu
• Research Overview: The Khan Lab at the University of Southern California focuses on sensors and systems for precision health and psychiatry. We are part of the USC Institute for Technology and Medical Systems (ITEMS), a joint Keck-Viterbi initiative on medical devices, with presence in both USC Viterbi School of Engineering and Keck School of Medicine of USC. Our vision is to make medical devices accessible to everyone!
• Summer Projects:
  • Optoelectronic sensing with ingestibles
    • Description: We are developing a smart capsule that will go in the gut and measure chemical markers.
    • Student Responsibilities: In this project, you will be aiding the electronic prototyping as well data collection of the capsule.
    • Preferred Majors: Biomedical Engineering,Electrical & Computer Engineering
    • Preferred Skillsets: Hands-on lab experience
    • Electronics prototyping
    • Programming
  • Stretchable MRI receive coils
    • Description: We will be developing a fabrication process for manufacturing MRI receive coils using liquid metal conductors. USC has one of the world's three 0.55T low-field MRI systems. This MRI receive coil will be the first-ever flexible MRI received coil designed for a low-field MRI system.
    • Student Responsibilities: Fabrication and characterization of liquid metal electronics.
    • Preferred Majors: Biomedical Engineering,Electrical & Computer Engineering
    • Preferred Skillsets: Basic engineering knowledge
    • Hand-on experience working in a lab
    • Experience with electronics
  • Printing and characterization of organic electrochemical transistors
    • Description: We are developing next-generation printed sensors and circuits with organic electrochemical transistors.
    • Student Responsibilities: Students will fabricate and characterize printed OECT-based sensors and circuits.
    • Preferred Majors: Biomedical Engineering,Chemical Engineering,Electrical & Computer Engineering
    • Preferred Skillsets: Basic engineering knowledge
    • Hand-on experience of working in a lab
    • Experience with electronics

Photonics in Complex Systems Group

• Faculty/PI: Wade Hsu
• Website: https://sites.usc.edu/hsugroup/
• Research Overview: Our group works on the control of light in complex systems—systems that couple numerous degrees of freedom in space and in time, across angles and frequencies. They can be naturally occurring such as in biological tissue and colloidal suspension, or artificially engineered such as in optical metasurfaces and integrated photonic circuits. We actively explore opportunities enabled by fully addressing the complexity of these systems, for example to image deeper with higher resolution in a scattering medium, to operate a metasurface across a wider angular range, or to generate frequencies in new ways. We also develop numerical methods to efficiently model these complex systems.
• Summer Projects:
  • Non-invasive 3D Imaging inside Biological Tissue
    • Description: We are developing optical imaging methods that can reconstruct high-resolution 3D images even inside an opaque scattering medium like biological tissue that typically cannot be seen through.
    • Student Responsibilities: The student will take part in sample preparation, building the measurement setup, data acquisition, and computational image reconstruction.
    • Preferred Majors: Computer Science,Electrical & Computer Engineering
    • Preferred Skillsets: Experience with some experiments and with MATLAB. Knowledge of the wave equation.
  • Fast multi-source nanophotonic simulations
    • Description: Maxwell's equations describe phenomena over the full electromagnetic spectrum from visible light to radio waves. Numerous problems, such as optical computing, metasurface design, inverse-scattering imaging, and stealth aircraft design, require computing the scattered wave given a very large number of distinct incident waves. However, existing Maxwell solvers scale poorly--either in computing time or in memory--with the number of input states of interest. We are developing highly efficient multi-source solvers for Maxwell's equations.
    • Student Responsibilities: The student will take part in code development, testing, and preparing tutorials for our MESTI solver (https://github.com/complexphoton/MESTI.m).
    • Preferred Majors: Computer Science,Electrical & Computer Engineering
    • Preferred Skillsets: Programming experience. Knowledge of the wave equation.
  • Ab-initio theory of lasers near exceptional points
    • Description: "Exceptional points" are unique states where two optical modes coalesce into one mode. Lasers operating near exceptional points exhibit enhanced sensitivity to perturbations, making them promising as ultra-sensitive sensing devices such as gyroscopes. However, existing laser theories cannot describe lasers near EP, because such lasers have a non-stationary population inversion. We are developing an ab-initio laser theory that can describe such complex lasers.
    • Student Responsibilities: The student will take part in the theoretical and numerical modeling of the nonlinear dynamics and noise properties of such unique lasers.
    • Preferred Majors: Computer Science,Electrical & Computer Engineering
    • Preferred Skillsets: Programming experience. Knowledge of the wave equation.
  • Inverse design of high-speed spatial-light modulators
    • Description: A high-speed spatial light modulator (SLM) can be useful for a wide range of applications such as LiDAR, virtual reality and mixed reality, optical communications, biomedical imaging, and optical computing. However, existing SLMs based on liquid crystals are slow (at kHz speed). We are developing a new type of SLMs that can operate 100,000 times faster, at the 100 MHz range. This requires a computational inverse design.
    • Student Responsibilities: The student will take part in code development and performing simulations and the numerical inverse design of the high-speed SLM.
    • Preferred Majors: Computer Science,Electrical & Computer Engineering
    • Preferred Skillsets: Programming experience. Knowledge of the wave equation.

Robot Locomotion And Navigation Dynamics (RoboLAND) Lab

• Faculty/PI: Feifei Qian
• Website: https://sites.usc.edu/qian/
• Research Overview: Animals -- lizards, snakes, insects -- often exhibit novel strategies in effectively interacting with their physical environments and generating desired responses for locomotion. In our lab, we are interested in creating robots that can do the same. Our approach integrates engineering, physics, and biology to discover the general principles governing the interactions between bio-inspired robots and their locomotion environments. For example, how do legged animals and robots use solid-like and fluid-like responses from soft sand and mud to produce effective movement? How can insect-like and snake-like robots take advantage of obstacle collisions to navigate within cluttered environments? We use these principles to create novel sensing and control strategies that can allow robots to perceive and intelligently elicit environment responses to achieve desired motion, even from traditional-considered "undesired" environments such as flowing sand, yielding mud, and cluttered obstacle fields.
• Summer Projects:
  • Robot Locomotion and Navigation on Complex Terrains
    
    • Description: Physical environments can provide a variety of interaction opportunities for robots to exploit towards their locomotion goals. However, it is unclear how to even extract information about - much less exploit - these opportunities from physical properties (e.g., shape, size, distribution) of the environment. This project integrates mechanical engineering, electrical engineering, computer science, and physics, to discover the general principles governing the interactions between bio-inspired robots and their locomotion environments, and uses these principles to create novel control, sensing, and navigation strategies for robots to effectively move through non-flat, non-rigid, complex terrains. For example, with a simple interaction model of robot-obstacle interactions, a bio-inspired multi-legged robot can intelligently exploit obstacle disturbances to generate desired locomotion dynamics. With a better understanding of sand responses to robot leg interactions, we are developing robots with direct-drive or quasi direct-drive legs that can sensitively "feel" the stability and erodibility of desert soil, to help geoscientists collect invaluable measurements on desertification through every step.
    • Student Responsibilities: Selected candidate will assist in development of robotic platforms (Arduino, Raspberry Pi, SolidWorks, 3D Printing), locomotion experiment data collection and analysis (motion capture tracking, force measurements), modelling and simulation (MATLAB, Python, or C++).
    • Preferred Majors: Aerospace & Mechanical Engineering, Astronautical Engineering, Biomedical Engineering, Computer Science, Electrical & Computer Engineering
    • Preferred Skillsets: Mechanical design (SolidWorks) and fabrication (hand and power tools), programming (Python or C++), a good understanding of physics or mechanics. Experiences with Arduino, Rasp Pi, and motors, would be a plus.

Safe and Intelligent Autonomy Lab

• Faculty/PI: Somil Bansal
• Website: https://smlbansal.github.io/sia-lab/
• Research Overview: We seek to design autonomous and robotic systems that operate with guaranteed safety and performance in new settings and environments. In short-term, our research emphasis is on bridging machine learning with classical, model-based planning and control methods to achieve this endeavor.
• Summer Projects:
  • Vision-based navigation in new environments
    • Description: Autonomous robot navigation is a fundamental and well-studied problem in robotics. However, developing a fully autonomous robot that can navigate in a priori unknown environments is difficult due to challenges that span dynamics modeling, onboard perception, localization and mapping, trajectory generation, and optimal control. Classical approaches such as the generation of a real-time globally consistent geometric map of the environment are computationally expensive and confounded by texture-less, transparent or shiny objects, or strong ambient lighting. End-to-end learning can avoid map building, but is sample inefficient. Furthermore, end-to-end models tend to be system-specific. In this project, we will explore modular architectures to operate autonomous systems in completely novel environments using onboard perception sensors. These architectures use machine learning for high-level planning based on perceptual information; this high-level plan is then used for low-level planning and control via leveraging classical control-theoretic approaches. This modular approach enables the conjoining of the best of both worlds: autonomous systems learn navigation cues without extensive geometric information, making the model relatively lightweight; the inclusion of the physical system structure in learning reduces sample complexity relative to pure learning approaches. Our preliminary results indicate a 10x improvement in sample complexity for wheeled ground robots. Our hypothesis is that this gap will only increase further as the system dynamics become more complex, such as for an aerial or a legged robot, opening up new avenues for learning navigation policies in robotics. Preliminary experiment videos can be found at: https://smlbansal.github.io/LB-WayPtNav/ and https://smlbansal.github.io/LB-WayPtNav-DH/
    • Preferred Majors: Aerospace & Mechanical Engineering,Astronautical Engineering,Computer Science,Electrical & Computer Engineering
    • Prerequisites: Some familiarity (through past research or coursework) with machine learning, control theory, or robotics would be desirable (but not necessary); Comfort with coding; Experience with hardware is a plus.
  • Safe assurances for learning and vision-driven robotic systems
    • Description: Machine learning-driven vision and perception components make a core part of the navigation and autonomy stacks for modern robotic systems. On the one hand, they enable robots to make intelligent decisions in cluttered and a priori unknown environments based on what they see. On the other hand, the lack of reliable tools to analyze the failures of learning-based vision models make it challenging to integrate them into safety-critical robotic systems, such as autonomous cars and aerial vehicles. In this project, we will explore designing a robust control-based safety monitor for visual navigation and mobility in unknown environments. Our hypothesis is that rather than directly reasoning about the accuracy of the individual vision components and their effect on the robot safety, we can design a safety monitor for the overall system. This monitor detects safety-critical failures in the overall navigation stack (e.g., due to a vision component itself or its interaction with the downstream components) and provides safe corrective action if necessary. The latter is more tractable because the safety analysis of the overall system can be performed in the state-space of the system, which is generally much lower-dimensional than the high-dimensional raw sensory observations. Preliminary results on simulated and real robots demonstrate that our framework can ensure robot safety in various environments despite the vision component errors (the videos of some of our preliminary experiments can be found at https://smlbansal.github.io/website-safe-navigation/). In this project, we will extend the proposed framework to more complex and high-dimensional robotic systems, such as drones and legged robots. Other than ensuring robot safety, we will also explore using the proposed framework to mine critical failures of the system at scale, and using this failure dataset to improve the robot perception over time.
    • Preferred Majors: Aerospace & Mechanical Engineering,Astronautical Engineering,Computer Science,Electrical & Computer Engineering
    • Prerequisites: Comfort with coding and math; Past experience (through research work or coursework) in either machine learning, control theory, or robotics is a plus; Past experience of working with real robots is a plus

Srivastava Group

• Faculty/PI: Ajitesh Srivastava
• Website: ajitesh-srivastava.com
• Research Overview: My research interests include Machine Learning, Modeling, and Graph Algorithms applied to epidemics, social good, and social networks
• Summer Projects:
  • Data Science for Health
    • Description: The project will involve applications of machine learning, mathematical modeling, and data engineering to address problems in health, including epidemics and mental disorders. This may include time-series, networks, and ensemble techniques.
    • Student Responsibilities: Literature review, coding, preparing presentations, weekly meetings
    • Preferred Majors: Computer Science,Electrical & Computer Engineering
    • Prerequisites: Programming (preferably Python / Matlab); Basic Linear Algebra, Probabilities and Statistics Bonus: Prior experience with Machine Learning

Wireless Devices and Systems Group (WiDeS)

• Faculty/PI: Andreas Molisch
• Website: wides.usc.edu
• Research Overview: The WiDeS group performs ground-breaking research in the area of wireless propagation, system design, communication theory, and cross-layer design. WiDeS is dedicated to research that is both scientifically challenging and practically relevant.
• Summer Projects:
  • Ray tracing for wireless propagation research
    • Description: In this project we investigate the design of new ray tracing/ ray launching algorithms for efficient prediction of wireless propagation in 5G/6G scenarios. One of the aspects we consider is the application of cutting-edge computer science methods for the acceleration of ray tracing. Another is the design of ray launching simulations that can handle movement of not only the receiver (where movement can be incorporated easily by traditional approaches), but also transmitter and environmental objects.
    • Student Responsibilities: Two students will be working on particular on the topic of ray visibility and object edge detection, making use of computer science approaches (these positions are thus best suited for CS students). They will research, implement, and test these algorithms in Python or a similar language. Another student (ECE preferred) will be performing ray tracing simulations with a commercial ray tracer, designing the simulation setups to accommodate movement of transmitter and objects in an efficient way.
    • Preferred Majors: Computer Science,Electrical & Computer Engineering
    • Preferred Skillsets: Experience with ray tracing algorithms, edge detection, etc., are ideal. For the two CS-oriented positions, extensive programming experience/skill is required. For the ECE-oriented position, interest in (and preferable experience in) wireless propagation
  • Wireless propagation channel measurements
    • Description: In this project we will perform measurements of wireless propagation channels with various channel sounders. These can range from measurements based on existing WiFi signals to measurements in the higher frequency ranges.
    • Student Responsibilities: Students are expected to help with the measurement setup, execution of the measurements (all under supervision), and first evaluations of the measurement results.
    • Preferred Majors: Electrical & Computer Engineering
    • Preferred Skillsets: For the incoming student, interest in the topic, and attention to detail, are required. For the international student, ideally courses on wireless communications/wireless propagation channels/radar have been done. Experience with RF measurement equipment.

The Yu Group: integrated nonlinear and quantum photonics lab

• Faculty/PI: Mengjie Yu
• Website: https://sites.usc.edu/mjlab/
• Research Overview: Our lab will lead efforts in integrated nonlinear photonics for classical and quantum applications. We aim to advance the fundamental understanding of nonlinear sciences at nanoscale, as well as realize next-generation optoelectronic circuits which could sit on our finger tips and solve real-life problems in classical and non-classical optical communication, computing, sensing, ranging and metrology.
• Summer Projects: TBD

Optimization for Data Driven Science

• Faculty/PI: Meisam Razaviyayn
• Website: https://sites.usc.edu/razaviyayn/group/
• Research Overview: Designing and studying of efficient large scale data analysis algorithms for machine learning tools and models. Led by Professor Meisam Razaviyayn
• Summer Projects:
  • Scalable fair and private learning in the presence of distribution shift
    • Description: Machine learning models are increasingly used in high-stakes decision-making systems. In such applications, a major concern is that these models sometimes discriminate against certain demographic groups such as individuals with a certain race, gender, or age. Another major concern in these applications is the violation of the privacy of users. While fair learning algorithms have been developed to mitigate discrimination issues, these algorithms can still leak sensitive information, such as individuals' health or financial records. On top of these challenges, there can be changes in data distribution when we move from training data to test data. In this project, we aim at developing scalable algorithms for tackling these challenges. This project is a part of a bigger collaborative project with the involvement of students from USC and researchers from industry (Meta, Amazon, Google Research).
    • Student Responsibilities: TBD
    • Preferred Majors: Computer Science
    • Prerequisites: Understanding the basics concepts in machine learning, optimization
    • Knowledge of Python and PyTorch/TensorFlow

Artificial Intelligence and Complex Systems

• Faculty/PI: Mayank Kejriwal
• Website: https://viterbi.usc.edu/directory/faculty/Kejriwal/Mayank
• Research Overview: Kejriwal's research focuses on knowledge graphs (KG), an exciting area of Artificial Intelligence and data analytics research that has found widespread applications in industry (including in e-commerce giants like Amazon, and search providers like Google), academia (health informatics and social sciences) and for social causes (fighting human trafficking and mobilizing resources in the aftermath of crises). Simply put, knowledge graphs are a means to getting a machine to retain and 'understand' knowledge, rather than just raw data. Today, we live in an era when the World Wide Web provides us with an ever-expanding repository of data, yet we are still far from building machines that can process and understand this data in the way that domain experts (or in many cases, ordinary humans) can. Dr. Kejriwal has built and deployed knowledge graph-based systems that have been used by law enforcement and other subject matter experts to fight human trafficking, in addition to tackling other important domains like e-commerce and natural disaster response. Dr. Kejriwal draws on interdisciplinary research inspired by multiple communities in AI and data science, including human-computer interaction, social media, computational social science and Web sciences.
• Summer Projects: TBD

Knowledge Capture and Discovery

• Faculty/PI: Yolanda Gil
• Website: https://knowledgecaptureanddiscovery.github.io/
• Research Overview: We develop Artificial Intelligence (AI) approaches that use knowledge to accelerate and innovate scientific discovery processes that are unnecessarily carried out manually and inefficiently today. We work on a variety of AI research areas, such as semantic workflows, human-guided machine learning, interdisciplinary model integration, knowledge networks, controlled crowdsourcing of metadata, and automated hypothesis-driven discovery. A key theme in our projects is the use of AI technologies for different aspects of data science processes in order to make them more efficient. We collaborate with scientists in diverse areas including Earth sciences, neuroscience, genomics and proteomics, agriculture, and economics.
• Summer Projects:
  • Automating data science
    • Description: When scientists analyze data they do a lot of tasks that could be automated. We are developing intelligent systems that help scientists and data analysts to make sense of their data faster but also to surprise them with new interesting methods automatically synthesized through AI.  These AI systems will also help people who have no expertise in data analysis but have a lot of data, by automating data analysis for them and offering explanations of the findings.  We work with neuroscience, biomedicine, climate, and social sciences data.
    • Student Responsibilities: Students will apply existing AI systems to new types of data and domains, create user interfaces to explain how the AI system works, and generate visualizations that best illustrate new discoveries.
    • Preferred Majors: Computer Science,Electrical & Computer Engineering,Industrial & Systems Engineering
    • Prerequisites: Programming skills in Python; some experience using data for projects; and curiosity and interest in AI.

STEEL: Security Research Lab

• Faculty/PI: Jelena Mirkovic, Luis Garcia
• Information Sciences Institute
• Website: https://steel.isi.edu/
• Research Overview: USC/ISI's STEEL lab was founded in 2012 under supervision of Dr Jelena Mirkovic. It is currently led jointly by Dr Mirkovic, Dr Genevieve Bartlett , Dr Luis Garcia and Dr Christophe Hauser. Its members perform cutting-edge research in cybersecurity and testbed experimentation.
• Summer Projects:
  • Cyber-physical Fuzzing Framework
    • Description: Fuzzing is a standard automated technique for testing software for unwanted or unexpected behavior against different software inputs. When we put software on cyber-physical systems, i.e., systems that interact with the physical world through sensors and actuators, fuzzing the software is not sufficient for triggering unwanted behaviors. The sense-to-actuate pipeline is vulnerable to cyber-physical side-channels, such as sensor spoofing or false data injection. Conversely, we cannot just subject the physical systems to various signal generators as the systems would be subject to damage and could be costly. If we opt for simulation, modeling these behaviors would require capturing the sensor and actuator physical dynamics with high fidelity. The models would also need to understand how the sensor and actuator signals interact with the software. Students will study existing techniques to capture sensor and actuator dynamics in simulation and design and implement a prototype on an existing cyber-physical system testbed that can incorporate these models.
    • Student Responsibilities: Students will work with interfacing game-engine-based drone simulators with cybersecurity program analysis tools. This project is part of ongoing efforts and, thus, by the time the program starts, the student will work alongside PhD students to progress the state of the project.
    • Preferred Majors: Computer Science,Electrical & Computer Engineering
    • Prerequisites: Ability to work dynamically with various programming languages; Python; Basic control systems/signal processing; AI for sensor data; Good software engineering/development practices
  • Decoding how humans encode memory
    • Description: Recent advancements in closed-loop deep brain stimulation (DBS) have enabled more intelligent autonomy for therapeutic intervention across a wide range of neurologic and psychiatric disorders. The predominant approach relies on control-theoretic approxima
    • Student Responsibilities: The project is an ongoing effort and, thus, the student will work alongside an existing team to progress the research project.
    • Preferred Majors: Computer Science,Electrical & Computer Engineerin
    • Preferred Skillsets: Ability to work dynamically with various programming languages
    • Python; Basic control systems/signal processing; AI for sensor data; Good software engineering/development practices