Summer Research Projects

Summer 2024 Projects

Prof. Xiang Ren

Faculty Email: xiangren@usc.edu

Department: Computer Science

Website: Intelligence and Knowledge Discovery Research Lab

Projects:

1. Cultural Fairness Challenge for Large Language Models: Large language models (LLM) have garnered significant attention due to their far-reaching implications. For instance, ChatGPT can effectively respond to a wide range of inquiries, and maintain human-like conversations. Recently, the LLM has also been able to identify explicitly sensitive or offensive requests. ChatGPT’s versatile skills are attributed to its training on a large corpus of human written text and intentional programming to deny inappropriate requests. This, however, begs a few questions: Does ChatGPT learn from any implicit human biases present in the training process? If so, how can we identify and extract these biases? In this project, we look to 1) to create a benchmark dataset by scaling up prompt-based instances of social cultural ambiguity, 2) comparing performances of state-of-the-art LLMs, and 3) proposing a few prompt-based solutions for mitigating implicit cultural bias.

2. Systematic generation of long-tailed knowledge statement for large language models: Since large language models (LLMs) have approached human-level performance on many tasks, it has become increasingly harder for researchers to find tasks that are still challenging to the models. Failure cases usually come from the \lt distribution -- data to which an oracle language model could assign a probability on the lower end of its distribution. Systematically finding evaluation data in the \lt distribution is important, but current methodologies such as prompt engineering or crowdsourcing are insufficient because coming up with \lt examples is also hard for humans due to our cognitive bias. In this project, we look to: (1) build an algorithmic search process to systematically generate challenging knowledge statements for large language models like GPT-4; (2) use the large-scale generated dataset to build a smaller language model that is able to do better reasoning on such long-tailed knowledge than GPT-4.

Prof. Vatsal Sharan

Faculty Email: vsharan@usc.edu

Department: Computer Science

Website: https://vatsalsharan.github.io/

Projects:

1. Area of Research: ML Theory: The student will explore foundational questions regarding machine learning. A focus is on understanding computational-statistical tradeoffs: when computational efficiency might be at odds with statistical requirements (the data needed to learn). Recent work has opened much uncharted territory, particularly with respect to the role of memory in learning, which we will explore.

Prof. Feifei Qian

Faculty Email: feifeiqi@usc.edu

Department: Electrical and Computer Engineering

Research Lab Website: Robot Locomotion And Navigation Dynamics (RoboLAND)

Projects:

1. Obstacle-aided locomotion and navigation: This project explores how robots can exploit different features of their physical environments to achieve desired movements. Can multi-legged robots and snake-like robots intelligently collide with obstacles on purpose to robustly move towards desired directions? Can a robot effectively turn itself by jamming the soft sand with its tail? In this project we will perform robot locomotion experiments to understand the complex interactions between robots and their environments, and use these interaction models to create novel strategies that can enable effective locomotion and navigation through challenging environments.

2. Understanding the world through every step: This project focuses on developing robots that can use their legs as soil or mud sensor to help geoscientists collect and interpret information at high spatial and temporal resolution. To achieve this, we will build robot legs that can sensitively “feel” the responses of desert sand or near-shore mud. We will design different interaction-based sensing protocols for the robot legs, and test these protocols in lab experiments. Once the sensing capabilities are developed and tested, we will take the robots to field trips, where the robots work alongside human scientists and learn how human make sampling decisions and adapt exploration strategies based on dynamic incoming measurements. Going forward, these understandings will help enable our robots with cognitive “reasoning” capabilities to flexibly support human teammates’ scientific objectives during collaborative exploration missions.

Prof. Meisam Razaviyayn

Faculty Email: razaviya@usc.edu

Department: Industrial & Systems Engineering, Electrical Engineering, and Computer Science

Research Lab Website: https://realai.usc.edu

Projects:

1. Training Private Generative Models From a Combination of Public, Synthetic, and Private Data: Training procedures of generative models using individuals' data can pose a risk of the model outputting training, exposing sensitive information, or violating copyrights. To address this concern, Differential Privacy (DP) has emerged as a solution, ensuring that no malicious actor can glean excessive details about any specific individual's data. In addition, DP guarantees that removing any individual training input data does not change the output significantly. DP has been utilized in training small/medium-size models in various companies (such as Google and Apple). However, despite these successes, a significant obstacle to the broader adoption of DP in training large generative models and LLMs is the reduced accuracy of DP models compared to their non-private counterparts. To bridge this accuracy gap between DP and non-private models, one promising approach involves leveraging public data (or models trained based on public data), which is devoid of privacy issues. Additionally, synthetic data generation offers alternative means to access public data. This project develops and explores various methodologies for training private generative models from a combination of private, public, and synthetic datasets.

Prof. Michelle Povinelli

Faculty Email: povinell@usc.edu

Department: Electrical and Computer Engineering

Research Lab Website: Povinelli Nanophotonics Laboratory

Description:

Our lab works on cutting-edge research in Nanophotonics, including the development of new materials for infrared detection and thermal regulation. Students will gain experience in electromagnetic simulation and infrared measurements.

Prof. Souti Chattopadhyay

Faculty Email: schattop@usc.edu

Department: Computer Science

Research Lab Website: ADAPTIVE COMPUTING EXPERIENCES (ACE) LAB

Projects:

1. Exploring the Transformative Influence of AI-Powered Coding Assistants on Software Development: AI-assisted coding tools have transformed the workflow of software development. From the initial stages of coding to final deployment, AI-enabled tools have significantly impacted how developers can efficiently conduct the stages of development life-cycle. In this project, we aim to study how AI tools have changed the process of refactoring, debugging, code review, documentation, and other development activities. We aim to re-think the development workflow, re-imagine capabilities of development tools, and re-evaluate the skills next generation of developers need to build better software.

Prof. Yu-Tsun Shao

Faculty Email: yutsunsh@usc.edu

Department: Chemical Engineering and Materials Science

Research Lab Website: Shao Materials Research Group

Projects:

1. Reconstruction and analysis of multi-modal data acquired by Scanning Transmission Electron Microscopy (STEM): Structural and chemical information can be simultaneously acquired in the STEM, in combination with sub-Angstrom resolution, offering unprecedented precision and resolution for understanding the materials’ structure-property relations.

2. Iterative reconstruction of ptychographic image for achieving super-resolution: Electron ptychography currently holds the Guinness World Record for the highest resolution microscope, and we are actively working on improving this by advancing the experimental acquisition, reconstruction algorithms to beat the current record.

3. Machine learning methods for tackling large datasets: Four-dimensional STEM (4D-STEM) acquired 2D diffraction patterns at each position in real-space, yielding rich information about the materials but also large datasets of >100 GB. Applications of machine learning algorithms help us tackle this challenge and retrieve rich structural information of the materials, such as strain, chirality, polarization, magnetic or electric fields.

Prof. Weihang Wang

Faculty Email: weihangw@usc.edu

Department: Computer Science

Website: https://weihang-wang.github.io

Description:

Dr. Weihang Wang is leading the program analysis and software testing of web applications at USC. The group is broadly interested in software engineering, software security, machine learning, and computer systems. Our vision is to build testing and analysis techniques for improving the reliability, security, and efficiency of complex software systems. Some of our ongoing projects include reverse engineering, static/dynamic bug detection, program analysis for WebAssembly, attack investigation and detection, compiler testing, and performance profiling. We am excited to work with motivated applicants who (1) are committed to top-notch research, (2) have a solid background in system programming, and (3) have experience with building large software systems. Applicants with research experience in software engineering, security, or compilers will be given priority.

Prof. Kallirroi Georgila

Faculty Email: kgeorgila@ict.usc.edu

Department: Computer Science and Institute for Creative Technologies

Research Lab Website: Natural Language Dialogue group

Projects:

1. Exploring synergistic approaches to reinforcement learning and large language models for natural language dialogue modelling: This project seeks to combine the use of reinforcement learning (RL) and large language models (LLMs) in the context of natural language dialogue systems. It will be explored how RL can help LLMs generate dialogue system outputs that are appropriate for a given dialogue context, personalized, and/or convey emotions. It will also be investigated how LLMs can serve as a means to explore various paths for optimal RL-based dialogue system policy learning (e.g., as simulated users), and how combining RL and LLMs can potentially help make dialogue system decisions more interpretable. The visiting students can also work on other topics related to natural language dialogue processing (including spoken language processing).

Prof. Peter Yingxiao Wang

Faculty Email: ywang283@usc.edu

Department: Biomedical Engineering

Research Lab Website: Wang Lab

Description:

Chimeric antigen receptor (CAR) T cells show potential as paradigm-shifting therapeutic agents for cancer treatment by eradicating chemotherapy-resistant cancer cells. However, the potential for life-threatening activity against normal, nonmalignant cells (on-target/off-tumor effect) is a major problem that must be overcome to improve the chances of CAR-based immunotherapy for solid tumors. In my lab, we employ synthetic biology and genetic engineering to reprogram immune cells so that they can be controlled by ultrasound remotely and non-invasively for cancer immunotherapy. This spatial and temporal control of CAR expression may not only provide a safety "on" switch but also provide rest periods to the CAR T cells, which is increasingly recognized to reduce CAR T exhaustion and improve cell activity. Therefore, this approach may not only restrict CAR T cells activity to the tumor, but also enhance it within the tumor.

Prof. Andreas Molisch

Faculty Email: molisch@usc.edu

Department: Computer Science

Research Lab Website: Wireless Devices and Systems Group

Projects:

1. Deep Wi-Fi Sensing for Smart Environments: We are excited to introduce a research project that combines Wi-Fi sensing with deep learning to create an innovative and efficient environment. This project offers students a unique opportunity to work at the intersection of wireless technology and artificial intelligence, developing innovative solutions for various applications, including indoor localization, occupancy detection, energy efficiency, and security enhancement. Join our team to contribute to creating safer and more sustainable spaces.

2. Communication Efficient Federated Learning in Wireless Edge Networks for Video Caching: Federated learning (FL) is a potential solution to many machine learning (ML) problems where clients wish to keep their local data private. In FL, the central server broadcasts the ML model to distributed clients. These clients then perform local training on their local datasets and offload their trained models to the server. While this brings a privacy-preserving learning solution as the data stays at the local devices, the communication overhead for these models exchange can be enormous when the links between the server and the clients are wireless. Moreover, the local training and uplink offloading time can significantly slow down the learning process when the clients are the typical wireless user equipment (UE) with limited computation and battery powers. It is necessary to orchestrate the resources in such a resource- constrained environment. As such, we will seek a communication-efficient FL solution with wireless clients for video caching applications in this project.

Prof. Somil Bansal

Faculty Email: somilban@usc.edu

Department: Electrical and Computer Engineering

Research Lab Website: Safe and Intelligent Autonomy Lab

Projects:

1. Detecting and Mitigating Anomalies in Vision-Based Controllers: Autonomous systems, such as self-driving cars and drones, have made significant strides in recent years by leveraging visual inputs and machine learning for decision-making and control. Despite their impressive performance, these vision-based controllers can make erroneous predictions when faced with novel or out-of-distribution inputs. Such errors can cascade to catastrophic system failures and compromise system safety, as exemplified by recent self-driving car accidents. In this project, we aim to design a run-time anomaly monitor to detect and mitigate such closed-loop, system-level failures. Specifically, we leverage a reachability-based framework to stress-test the vision-based controller offline and mine its system-level failures. This data is then used to train a classifier that is leveraged online to flag inputs that might cause system breakdowns. The anomaly detector highlights issues that transcend individual modules and pertain to the safety of the overall system. We will also design a fallback controller that robustly handles these detected anomalies to preserve system safety. In our preliminary work, we validate the proposed approach on an autonomous aircraft taxiing system that uses a vision-based controller for taxiing. Our results show the efficacy of the proposed approach in identifying and handling system-level anomalies, outperforming methods such as prediction errorbased detection, and ensembling, thereby enhancing the overall safety and robustness of autonomous systems. Preliminary experiment videos can be found at: https://phoenixrider12.github.io/failure_mitigation

2. Vision-based navigation in new environments: 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/.

3. Safe assurances for learning and vision-driven robotic systems: 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.

Prof. Shaama Mallikarjun Sharada

Faculty Email: ssharada@usc.edu

Department: Chemical Engineering and Materials Science

Research Lab Website: Sharada Lab

Description:

This research group uses computational chemistry and machine learning to find energy-efficient pathways for utilizing carbon dioxide as a source of fuels and chemicals. Please take a look at the above website for more information.

Prof. Daniel Seita

Faculty Email: seita@usc.edu

Department: Computer Science

Research Lab Website: SLURM Lab at USC

Projects:

1. VIsion-Language Models for Deformable Object Manipulation: The last few years has seen incredible growth and interest in vision-language models (VLMs) such as CLIP and GPT-4V. In parallel, the last few years has also seen a huge growth in robotic manipulation, such as with deformable object manipulation, which is inherently challenging due to issues with representing the configuration of the objects and reasoning about complex dynamics. Many recent methods for deformable object manipulation have used imitation learning or reinforcement learning. In this project, we will use pre-trained VLMs for deformable object manipulation, to see if we can sidestep the process of requiring demonstrations or reinforcement learning. We will understand how to use VLMs in a way that takes advantage of their strengths while enabling high-precision deformable object manipulation tasks that involve manipulating rope, cloth, fluids, and other challenging objects.

Prof. Erdem Biyik

Faculty Email: erdem.biyik@usc.edu

Department: Computer Science

Research Lab Website: USC Lira Lab

Projects:

1. Imitation learning from control-constrained demonstrations: This project will explore efficient ways of performing imitation learning and/or inverse reinforcement learning when the expert demonstrations come from a constrained control interfaces, e.g. due to the controller itself or the suboptimality of the expert human. The applications include tabletop manipulation and autonomous driving.

2. Self-supervised improvements over reinforcement learning with large pre-trained models: This project will explore the use of large pre-trained models (e.g., LLMs, VLMs, VQAs, etc.) for creating a self-supervision signal in reinforcement learning. The applications include, but are not limited to, tabletop manipulation.

3. Active querying for reinforcement learning from human feedback: The current implementations of reinforcement learning from human feedback (RLHF) follows the learned policy to generate new queries for the human. In this project, we will explore alternative ways to do it to improve data-efficiency of training. It will involve implementation of active learning techniques.

Prof. Andrei Irimia

Faculty Email: irimia@usc.edu

Department: Gerontology, Quantitative & Computational Biology, Biomedical Engineering and Neuroscience

Research Lab Website: The Irimia Lab

Description:

Please take a look at the above website to identify projects and research areas which you may be interested in.

Prof. Jieyu Zhao

Faculty Email: jieyuz@usc.edu

Department: Computer Science

Website: https://jyzhao.net

Projects:

1. Large language models (LLMs) offer remarkable capabilities and people are incorporating those models into their daily life more than ever. However LLMs can inadvertently learn and perpetuate biases present in the training data, potentially leading to biased outputs. Addressing bias in LLMs is a critical aspect of responsible AI development. Leveraging state-of-the-art machine learning algorithms, the project aims to enhance the transparency and fairness of LLMs by identifying and addressing potential biases in their outputs.

Prof. Viktor K Prasanna

Faculty Email: prasanna@usc.edu

Department: Electrical Engineering and Computer Science

Research Lab Website: https://sites.usc.edu/prasanna/

Description:

Areas of research: Accelerated computing, FPGAs, Accelerators for ML and AI, Data Science applications, Adversarial AI in vision, Applied ML.

Prof. Chongwu Zhou

Faculty Email: chongwuz@usc.edu

Department: Electrical Engineering

Research Lab Website: Nano Lab

Description:

Project: Professor Chongwu Zhou's research lab has projects on the synthesis and device applications of carbon nanotubes and two-dimensional materials such as MoS2 and WSe2. The visiting students will work on the synthesis of nanomaterials and characterization of novel nano-electronic devices. Areas of research: Microelectronics, semiconductor technology, nanotechnology.

Prof. Kandis Abdul-Aziz

Faculty Email: kabdulaz@usc.edu

Department: Civil and Environmental Engineering

Research Lab Website: The Sustainable Lab

Description:

Projects: 1. Integrated Carbon Capture and Utilization. 2. Heterogeneous Catalyst Synthesis and Optimization

Prof. Danny JJ Wang

Faculty Email: dannyjwa@usc.edu

Department: Biomedical Engineering

Research Lab Website: Laboratory of Functional MRI Technology

Description:

We develop cutting edge magnetic resonance imaging (MRI) technologies for mapping the function and physiology of the brain and body organs, and translating these new technologies in a range of neurologic disorders. We host the first FDA approved ultrahigh field MRI scanner (Siemens 7T Terra) in North America which provides ultrahigh sensitivity and spatiotemporal resolutions.

Prof. Raymond L. Goldsworthy

Faculty Email: rgoldswo@usc.edu

Department: Biomedical Engineering

Research Lab Website: Bionic Ear Lab

Description:

Dr. Goldsworthy’s lab, The Bionic Ear Lab, studies how hearing loss affects music appreciation. We combine auditory neuroscience, biomedical engineering, and psychology to help people with hearing loss rediscover music. Find out more at the above website.

Prof. Vinay Duddalwar

Faculty Email: Vinay.Duddalwar@med.usc.edu

Department: Clinical Radiology

Research Lab Website: USC Radiomics Lab

Description:

Here are selected areas of interest, but for a comprehensive list of our projects, please reach out to Professor. Renal cell carcinoma, Evaluation of renal Masses, Muscle Invasive Bladder Carcinoma, Contrast Enhanced Ultrasound (CEUS), Prostate cancer, Radiomic QA / Toolkit development.

Prof. Emilio Ferrara

Faculty Email: emiliofe@usc.edu

Department: Computer Science

Research Lab Website: http://www.emilio.ferrara.name

Description:

Looking to host any students who might be interested and qualified to work in the above lab.

Prof. Hossein Hashemi

Faculty Email: hosseinh@usc.edu

Department: Electrical and Computer Engineering

Research Lab Website: Hossein Hashemi Group

Description:

Current research projects include radiofrequency and millimeter-wave integrated circuits for 5G/6G wireless communications, radar, and power beaming; chip-scale lidar for self-driving cars and 3D imaging; optical computing in silicon photonics; computational inverse design and optimization of electromagnetic structures (mm-wave and optical); and implantable biomedical integrated systems.

Prof. Peter A. Beerel

Faculty Email: pabeerel@usc.edu

Department: Electrical and Computer Engineering

Research Lab Website: Energy Efficient Secure Sustainable Computing Group

Description:

Interested in hosting 2 students – in the general areas of Energy-Efficient Trustworthy Machine Learning, covering areas of hardware acceleration, hardware-algorithm co-design, privacy and machine learning, security and machine learning.

Prof. Cyrus Shahabi

Faculty Email: shahabi@usc.edu

Department: Electrical & Computer Engineering

Research Lab Website: Integrated Media Systems Center

Description:

The Integrated Media Systems Center, headquartered in USC’s Viterbi School of Engineering, pursues informatics research that delivers data-driven solutions for real-world applications. We find enhanced solutions to fundamental data science problems and apply these advances to achieve major societal impact. One of our most exciting research thrusts is analyzing location and mobility data for various real-world applications while maintaining user privacy. For example, we have an ongoing project to identify anomalous behavior based on GPS tracks. We use advanced techniques such as deep neural networks, differential privacy, and massive location data to build principled solutions for important problems. We are looking for students with basic experience in Python programming, probability, and machine learning.

Prof. Hangbo Zhao

Faculty Email: hangbozh@usc.edu

Department: Aerospace and Mechanical Engineering

Research Lab Website: https://sites.usc.edu/zhaogroup/

Projects:

1. Develop novel flexible sensors and actuators for closed-loop control of dexterous soft robots: Areas of research: flexible electronics, micro/nano fabrication, sensors and actuators, soft robotics.

Prof. Qiang Huang

Faculty Email: qianghua@usc.edu

Department: Industrial and Systems Engineering

Research Lab Website: https://huanglab.usc.edu

Projects:

1. Domain-information machine learning for smart manufacturing: Students will explore research to develop quality control theory and methods for personalized manufacturing and small-sample machine learning in for quality control in additive manufacturing.

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Summer 2023 Projects

Prof. Dong Song

Faculty Email: dsong@usc.edu

Name of Research Lab/Center: USC Neural Modeling and Interface Lab

Department: Biomedical Engineering

Research Lab Website: https://slab.usc.edu/

Projects:

1. Record and modeling neural signals for building memory prosthesis: We are the USC Neural Modeling and Interface Lab (https://slab.usc.edu) led by Dr. Dong Song. 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. Each undergraduate researcher is expected to spend 8 to 10 hours per week (20 - 25 hours per week for the summer semester) for each semester on the project. 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.

Prof. Birendra Jha

Faculty Email: bjha@usc.edu

Name of Research Lab/Center: Geosystems Engineering and Multiphysics Lab

Department: MFD Chemical Engineering and Materials Science

Research Lab Website: https://gemlab.usc.edu

Projects:

1. Fluid flow and mechanical deformation in porous media: experiments and simulations: 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. 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.

Prof. Jesse Thomason

Department: Computer Science

Research Lab Website: http://glamor.rocks/

Projects:

1. Grounding Language with Robots: 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. 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.

Prof. Meisam Razaviyayn

Faculty Email: razaviya@usc.edu

Name of Research Lab/Center: Optimization for Data Driven Science

Department: ISE

Research Lab Website: https://sites.usc.edu/razaviyayn/group/

Projects:

1. Scalable fair and private learning in the presence of distribution shift: 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).

Prof. Somil Bansal

Faculty Email: somilban@usc.edu

Name of Research Lab/Center: Safe and Intelligent Autonomy Lab

Department: ECE

Research Lab Website: https://smlbansal.github.io/sia-lab/

Projects:

1. Vision-based navigation in new environments: 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/.

2. Safe assurances for learning and vision-driven robotic systems: 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.

Prof. Bhaskar Krishnamachari

Faculty Email: bkrishna@usc.edu

Name of Research Lab/Center: Autonomous Networks Research Group

Department: Electrical and Computer Engineering, and Computer Science

Research Lab Website: https://anrg.usc.edu

Projects:

1. Networked Distributed Systems and Applied Machine Learning: 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.

Prof. Sifat Muin

Faculty Email: muin@usc.edu

Name of Research Lab/Center: Main Research Group

Department: Civil and Environmental Engineering

Research Lab Website: https://www.sifatmuin.com/

Projects:

1. Printed strain sensors

Prof. Ajitesh Srivastava

Faculty Email: ajiteshs@usc.edu

Name of Research Lab/Center: Srivastava Group

Department: Ming Hsieh Department of Electrical and Computer Engineering

Research Lab Website: www.ajitesh-srivastava.com

Projects:

1. Data Science for Health: 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 duties include Literature review, coding, preparing presentations, weekly meetings.

Prof. Quan Nguyen

Faculty Email: quann@usc.edu

Name of Research Lab/Center: Dynamic Robotics and Control Lab

Department: Aerospace and Mechanical Engineering

Research Lab Website: https://sites.usc.edu/quann/

Prof. Ketan Savla

Faculty Email: ksavla@usc.edu

Name of Research Lab/Center: Savla Research Group

Department: Civil and Environmental Engineering

Research Lab Website: https://viterbi-web.usc.edu/~ksavla/

Prof. Luis Garcia

Faculty Email: lgarcia@isi.edu

Name of Research Lab/Center: STEEL: Security Research Lab

Department: Information Sciences Institute

Research Lab Website: https://steel.isi.edu/

Projects:

1. Cyber-physical Fuzzing Framework: 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. 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.

2. Decoding how humans encode memory: 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 approximations of the brain’s complex functional relationships with the external environment–-in particular, a mapping between targeted stimulation and naturalistic responses of different regions of the brain. However, existing approaches fail to capture the environmental context of neuronal biomarkers. Students will explore mechanisms to reason about complex sensor data from our recording platform, NeurIoT, that leverages a comprehensive set of Internet-of-Things (IoT) sensors to capture the human experience and environmental context, i.e., a subset of human sensory channels, in order to estimate the state of the human brain and provide the foundation for smarter, context-dependent DBS.

Prof. Ramesh Govindan

Faculty Email: ramesh@usc.edu

Name of Research Lab/Center: Networked Systems Laboratory

Department: Computer Science

Research Lab Website: https://nsl.usc.edu

Prof. Muhao Chen

Faculty Email: muhaoche@usc.edu

Name of Research Lab/Center: Language Understanding and Knowledge Acquisition Lab

Department: Computer Science

Research Lab Website: https://luka-group.github.io/

Projects:

1. Indirect Supervision for Knowledge Acquisition: 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.

Prof. Viktor Prasanna

Faculty Email: prasanna@usc.edu

Name of Research Lab/Center: Data Science Lab FPGA Lab

Department: ECE

Research Lab Website: 1. dslab.usc.edu   2. fpga.usc.edu

Projects:

1. Graph Machine Learning: Defining Graph ML models for applications. Predictive modeling. Optimization. Evaluation.

2. FPGA Acceleration of ML: Design and analysis of accelerators for ML tasks.

Prof. Hangbo Zhao

Faculty Email: hangbozh@usc.edu

Name of Research Lab/Center: Zhao Research Group

Department: Aerospace and Mechanical Engineering

Research Lab Website: https://sites.usc.edu/zhaogroup/

Projects:

1. Stretchable strain sensors for 3D reconstructions of soft robots: 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 duties include: 1. 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.

Prof. Peter Beerel

Faculty Email: pabeerel@usc.edu

Name of Research Lab/Center: Energy-Efficient Secue Sustainable Computing Group (EESSC)

Department: Electrical and Computing Engineering

Research Lab Website: https://sites.usc.edu/eessc/

Prof. Jelena Mirkovic

Faculty Email: mirkovic@isi.edu

Name of Research Lab/Center: STEEL

Department: USC Information Sciences Institute

Research Lab Website: steel.isi.edu

Prof. Mayank Kejriwal

Faculty Email: kejriwal@isi.edu

Name of Research Lab/Center: Artificial Intelligence and Complex Systems

Department: Information Sciences Institute

Research Lab Website: New website is under construction and will be available in early January. At present please use this: https://viterbi.usc.edu/directory/faculty/Kejriwal/Mayank

Prof. Mengjie Yu

Faculty Email: mengjiey@usc.edu

Name of Research Lab/Center: The Yu lab: integrated nonlinear and quantum photonics lab

Department: ECE

Research Lab Website: https://sites.usc.edu/mjlab/

1. Integrate photonics

Prof. Dominique Duncan

Faculty Email: duncand@usc.edu

Name of Research Lab/Center: Duncan Lab

Department: Neuroimaging and Informatics

Research Lab Website: https://sites.usc.edu/duncanlab/

Prof. Paul Bogdan

Faculty Email: pbogdan@usc.edu

Name of Research Lab/Center: Cyber-Physical Systems

Department: ECE

Research Lab Website: https://cps.usc.edu/studs.html

Prof. BO Jin

Faculty Email: bochengj@usc.edu

Name of Research Lab/Center: Advanced Composites Design Lab

Department: Aerospace and Mechanical Engineering

Research Lab Website: composites.usc.edu

Prof. Jason Kutch

Faculty Email: kutch@usc.edu

Name of Research Lab/Center: Applied Movement and Pain Laboratory

Department: Biokinesiology and Physical Therapy

Research Lab Website: ampl.usc.edu

Projects:

Looking for students from these departments: Aerospace & Mechanical Engineering,Biomedical Engineering,Computer Science,Electrical & Computer Engineering

Prof. Vatsal Sharan

Faculty Email: vsharan@usc.edu

Name of Research Lab/Center: ML Theory Group

Department: Computer Science

Research Lab Website: https://vatsalsharan.github.io/

Projects:

1. Machine Learning Mysteries: he student 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.

Familiarity with probability, linear algebra, calculus, and analysis of algorithms. Some basic understanding of machine learning would be very helpful for certain projects.

Prof. Wade Hsu

Faculty Email: cwhsu@usc.edu

Name of Research Lab/Center: Photonics in Complex Systems Group

Department: Electrical and Computer Engineering

Research Lab Website: https://sites.usc.edu/hsugroup/

Projects:

1. Non-invasive 3D Imaging inside Biological Tissue: 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.

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. 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 (electromagnetics) is desirable.

2. Fast multi-source nanophotonic simulations: 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. The student will take part in code development, testing, and preparing tutorials for our MESTI solver (https://github.com/complexphoton/MESTI.m).

3. Ab-initio theory of lasers near exceptional points: "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. The student will take part in the theoretical and numerical modeling of the nonlinear dynamics and noise properties of such unique lasers.

4. Inverse design of high-speed spatial-light modulators: The student will take part in code development and performing simulations and the numerical inverse design of the high-speed SLM.

Prof. Andreas Molisch

Faculty Email: molisch@usc.edu

Name of Research Lab/Center: WiDeS

Department: Electrical and Computing Engineering

Research Lab Website: wides.usc.edu

Projects:

1. Ray tracing for wireless propagation research: 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.

2. Wireless propagation channel measurements: 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.

Prof. Hossein Hashemi

Faculty Email: hosseinh@usc.edu

Name of Research Lab/Center: HHLab

Department: Electrical and Computing Engineering

Research Lab Website: https://hhlab.usc.edu/

Projects:

1. Millimeter-Wave Integrated Circuits: The student will design and simulate millimeter-wave integrated circuits.

2. Silicon Photonics Integrated Circuits: The project involves application of silicon photonics integrated circuits for lidar, optical computing, and other applications.

Prof. Pierluigi Nuzzo

Faculty Email: nuzzo@usc.edu

Name of Research Lab/Center: Cyber-Physical System Design (DesCyPhy) Lab and Center for Autonomy and Artificial Intelligence (CAAI)

Department: Electrical and Computer Engineering and Computer Science

Research Lab Website: https://descyphy.usc.edu/

Projects:

Looking for students from the following departments: Aerospace & Mechanical Engineering,Computer Science,Electrical & Computer Engineering.

Prof. Stefanos Nikolaidis

Faculty Email: nikolaid@usc.edu

Name of Research Lab/Center: Interactive and Collaborative Autonomous Robotic Systems

Department: Computer Science

Research Lab Website: http://icaros.usc.edu

Projects:

1. Human-Robot Collaboration in Shared Workspaces: We focus on enabling robots to assist people in collaborative tasks, such as assembling an IKEA bookcase: https://youtu.be/wDHQXBua4OI
This requires robot assistants to perceive parts in the environment, grasp the parts while avoiding obstacles and hand the parts safely to human teammates. Robots also need to observe the actions of their human counterparts, infer their intent and preemptively take actions to help them in the task, e.g., deliver a screw driver when the user is about to fasten a screw. The tight integration of perception, inference and action results in seamless collaboration between a robot assistant and a human user. The students will work on programming the robot behaviors in simulation environments and in the real world. They will develop, implement and improve algorithms for robot learning and planning. They will then deploy the algorithms to our robotic arms in the lab and test them in a variety of collaborative tasks. Required skill: Programming ability in Python. Required coursework: Probability Theory, Calculus.
Preferred (but not required) coursework: Algorithms, Machine Learning, Robotics.

Prof. Yasser Khan

Faculty Email: yasser.khan@usc.edu

Name of Research Lab/Center: Khan Lab

Department: Electrical and Computer Engineering

Research Lab Website: khan.usc.edu

Projects:

1. Optoelectronic sensing with ingestibles: We are developing a smart capsule that will go in the gut and measure chemical markers. In this project, you will be aiding the electronic prototyping as well data collection of the capsule.

2. Stretchable MRI receive coils: 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. Fabrication and characterization of liquid metal electronics.

3. Printing and characterization of organic electrochemical transistors: We are developing next-generation printed sensors and circuits with organic electrochemical transistors. Students will fabricate and characterize printed OECT-based sensors and circuits.