Electrical & Computer Engineering
Project Titles
** Please note that all lab positions for CURVE have been filled for the 2022-23 academic year. **
Application Specific Intelligent Computing Lab
Faculty / PI: Akhilesh Jaiswal
Research Website: https://sites.usc.edu/asiclab/
Lab Description: Computing platforms for decades have been designed around a basic Boolean switch – ‘The Transistor.’ Over the years, the vision for computing has changed drastically from room-sized computers to handheld devices. However, the underlying hardware principles based on digital switches and synchronous logic have fundamentally remained unaltered. No wonder today’s computing platforms fail to provide the desired power, performance, and area scaling required for the emerging class of applications, including artificial intelligence, edge computing, Internet-of-Things, etc. As such, the vision for enabling ‘Ubiquitous Computing’ needs re-thinking the entire computing stack, including Materials, Devices, Circuits, Architecture, and Software for enabling targeted end-applications. At the Application-Specific Intelligent Computing (ASIC) Lab, we apply “alternate state variables” such as electrons, photons, phonons, and magnetic spins to deliver next-generation hardware fabrics for (1) Artificial Intelligence, (2) Smart Sensors, (3) Smart and Secure Manufacturing, (4) Quantum Information, (5) Novel Device Integration.
Information Session Recording
Project(s):
Project Title: Intelligence at Extreme Edge
Faculty / PI: Akhilesh Jaiswal
Lab Name: Application Specific Intelligent Computing Lab
Department: ECE
Research Website: https://sites.usc.edu/asiclab/
# of Freshmen Positions: 1
# of Continuing Student Positions: 1
Project Description: The project would investigate novel hardware design techniques at circuit and system level to embed artificial intelligence at sensor node (i.e. pushing AI to extreme edge).
Student Responsibilities:
- Students would create models for hardware computations at advanced semiconductor technology node for extreme edge intelligence.
- Use SPICE/python based simulations/modeling on industrial transistor technology for generate simulation results
- Help in writing papers
- Interface with AI algorithm experts for calculation of system-level metric while gaining experience in hardware-algorithm co-design.
Interview Required? No
Skills and Competencies (Preferred)
- Circuit design
- SPICE/Python
- Familiarity with AI algorithms
Cyber-Physical System Design Lab
Faculty / PI: Pierluigi Nuzzo
Research Website: https://descyphy.usc.edu/
Lab Description: 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.
Project(s):
Project Title: Self-Driving Vehicle Testbed
Faculty / PI: Pierluigi Nuzzo
Lab Name: Cyber-Physical System Design Lab
Department: Electrical and Computer Engineering
Research Website: https://descyphy.usc.edu/
# of Freshmen Positions: 1
# of Continuing Student Positions: 2
Project Description: The goal of this project is to build virtual (simulation-based) and experimental testbeds to emulate realistic scenarios for self-driving vehicles and test the effectiveness of different driving algorithms. The testbeds will target traffic intersections and will include, for example, scaled-down autonomous cars, programmable traffic light sequencers to emulate the traffic and pedestrian signals, and robots to emulate pedestrian traffic.
Student Responsibilities: The students will closely collaborate with USC Viterbi faculty and Ph.D. students to define the architecture of the testbeds, define and assemble the different software and hardware components, implement the driving scenarios, and collect data. Activities will include programming the driving algorithms in a simulation environment as well as on embedded microcontrollers (e.g., on Raspberry Pi boards).
Interview Required? No
Khajavikhan Optics and Photonics Lab
Faculty / PI: Mercedeh Khajavikhan
Research Website: https://sites.usc.edu/mklab/
Lab Description: We are interested in the study of open, active, and topological photonic systems. Our research spans from basic science to applications, and from exploring and discovering fundamental phenomena in complex arrangements, to the prediction and realization of novel devices and sub-systems.
Project(s):
Project Title: Longitudinal Acoustic Lens
Faculty / PI: Mercedeh Khajavikhan
Lab Name: Khajavikhan Optics and Photonics Lab
Department: ECE
# of Freshmen Positions: 1
# of Continuing Student Positions: 3
Project Description: To build a new type of varifocal lens for applications in biology and diagnostics
Student Responsibilities: To model the device, build a prototype lens. Lab work is needed
Interview Required? Yes
Skills and Competencies (Preferred)
- Mathematical
- Mechanical
- Optics
Project Title: Optoelectronic sensing with ingestibles
Faculty / PI: Yasser Khan
Lab Name: Khan Lab
Department: Electrical and Computer Engineering
Research Website: ykhan.com
# of Freshmen Positions: 0
# of Continuing Student Positions: 2
Project 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.
Interview Required? Yes
Skills and Competencies (Preferred)
- Hands-on lab experience
- Electronics prototyping
- Programming
Project Title: Stretchable MRI receive coils
Faculty / PI: Yasser Khan
Lab Name: Khan Lab
Department: Electrical and Computer Engineering
Research Website: www.ykhan.com
# of Freshmen Positions: 1
# of Continuing Student Positions: 3
Project 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.
Interview Required? Yes
Skills and Competencies (Preferred)
- Basic engineering knowledge
- Hand-on experience of working in a lab
- Experience with electronics
Nanoscale Nonlinear and Quantum Photonics Lab
Faculty / PI: Mengjie Yu
Research Website: https://sites.usc.edu/mjlab/
Lab Description: With ever-increasing demand for processing both classical and quantum information, it is clear that Moore’s law is nearing its end within the next few years. The next technological revolution will be to fully unlock the power of light (low loss, large bandwidth and extreme precision) by nanoscale photonic technology, enabling light-matter interactions at ultralow optical powers and massive integration of optical and electronic devices on a single chip. 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.
Project(s):
Project Title: Characterization of electro-optic material
Faculty / PI: Mengjie Yu
Lab Name: Nanoscale Nonlinear and Quantum Photonics Lab
Department: Electrical and Computer Engineering
Research Website: https://sites.usc.edu/mjlab/
# of Freshmen Positions: 1
# of Continuing Student Positions: 3
Project Description: Spatial light modulator for fan-out in optical neutral network; electrical poling of ferroelectric material; optical interferometer setup for thin film material characterization
Student Responsibilities: program and set up spatial light modulator to realize user-defined output patterns; testing poling quality of materials, optimization, and imaging of the domain walls; optical setup and alignment for testing electro-optic properties of material
Interview Required? Yes
Skills and Competencies (Preferred)
- python
- optical alignment skills
- communication skills
Next Generation Digital Systems
Faculty / PI: Sandeep Gupta
Project(s):
Project Title: Computer-Aided Discoveries: Methods and Applications
Faculty / PI: Sandeep Gupta
Lab Name: Next Generation Digital Systems
Department: Electrical and Computer Engineering
Research Website:
# of Freshmen Positions: 3
# of Continuing Student Positions: 3
Project Description: Developing new methods and algorithms to discover new properties and methods for design of next generation digital systems.
Student Responsibilities: Developing programs to simulate (aspects of digital systems), using these programs to explore ideas (new and old) for solving given problems, and developing inductive methods and tools to discover new properties from the results of these explorations.
Interview Required? No
Skills and Competencies (Preferred)
- Curiosity and desire to explore new ideas
- Logical and mathematical reasoning
- Programming and using programs to solve new problems
Photonics in Complex Systems
Faculty / PI: Chia Wei Hsu
Research Website: https://sites.usc.edu/hsugroup/
Lab Description: Our group, at the USC Ming Hsieh Department of Electrical and Computer Engineering, studies photonics in complex systems through a combination of experiments, numerical simulations, and analytical methods. We actively explore new paradigms for controlling light, overcoming and harnessing light scattering, retrieving information from photons, doing computations with light, and beyond, particularly in complex systems that couple numerous degrees of freedom. Our interests lie both in fundamental questions and in the many applications of optics and photonics such as imaging, sensing, communications, information processing, and energy.
Project Title: Nonlinear dynamics of lasers near exceptional points
Faculty / PI: Chia Wei Hsu
Lab Name: Photonics in Complex Systems
Department: Electrical Engineering
Research Website: https://sites.usc.edu/hsugroup/
# of Freshmen Positions: 0
# of Continuing Student Positions: 1
Project Description: Two modes of a cavity can merge into one at a so-called "exceptional point". Exceptional points exhibit unique properties, with applications such as sensors with better sensitivity. However, the properties of lasers operating near exceptional points remain poorly understood, because the two modes have close-by frequencies, the beating of which invalidate the stationary-inversion approximation commonly used to describe lasers at equilibrium. The goal of this project is to explore -- analytically and numerically -- the properties of lasers near exceptional points and their unique nonlinear dynamics.
Student Responsibilities: Perform simulations of exceptional-point lasers using the finite-difference time-domain (FDTD) method. Work with a PhD student to compare the FDTD results with analytic predictions.
Interview Required? Yes
Skills and Competencies (Preferred)
- Wave equations and PDEs
- Fourier transforms
- Programming & scripting experience
- Laser theory
Robot Locomotion And Navigation Dynamics (RoboLAND)
Faculty / PI: Feifei Qian
Research Website: https://sites.google.com/usc.edu/roboland
Lab Description: 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.
Project(s):
Project Title: Collective legged locomotion in obstacle-cluttered environments
Faculty / PI: Feifei Qian
Lab Name: Robot Locomotion And Navigation Dynamics (RoboLAND)
Department: Electrical and Computer Engineering
Research Website: https://sites.google.com/usc.edu/roboland
# of Freshmen Positions: 1
# of Continuing Student Positions: 1
Project Description: The selected candidate will work closely with Dr. Feifei Qian’s group to develop a multi-agent legged robot system, where individual robot agents can geometrically connect with each other to collectively achieve desired trajectory in an obstacle-cluttered environment.
Student Responsibilities: In this role, the candidate will perform the following tasks:
(1) design and control multi-agent legged robot with wireless communication ability
(2) programming microcontroller to implement the desired leg movement (a “gait”)
(3) test robot collective strategies in both locomotion experiments and python simulation, to identify strategies for robots to collectively achieve desired locomotion task in obstacle-cluttered environments.
Interview Required? No
Skills and Competencies (Preferred)
- mechanical design
- programming (python)
- communication / signal processing
- physics (especially force analysis)
- (optional) network analysis
Project Title: Robot locomotion in sandy and muddy environments
Faculty / PI: Feifei Qian
Lab Name: Robot Locomotion And Navigation Dynamics (RoboLAND)
Department: Electrical and Computer Engineering
Research Website: https://sites.google.com/usc.edu/roboland
# of Freshmen Positions: 0
# of Continuing Student Positions: 1
Project Description: The selected candidate will work closely with Dr. Feifei Qian’s group to support our mission in (1) develop high-mobility legged robots with embodied sensing capabilities, to help human scientists in exploration of complex natural environments such as deserts, forests, and muddy terrains; (2) enable the robot to infer human exploration objectives and aid human experts with adaptation of sampling strategies in response to incoming information.
Student Responsibilities: In this role, the candidate will perform the following tasks: design and control multi-legged robots for sand and mud traversal; perform systematic experiments to characterize robot leg force sensing capabilities and measure terrain reaction forces; use MATLAB to perform simple analysis and create plots to communicate results
Interview Required? No
Skills and Competencies (Preferred)
- mechanical design
- programming
- data analysis
Safe and Intelligent Autonomy Lab
Faculty / PI: Somil Bansal
Research Website: https://smlbansal.github.io/LB-WayPtNav/
Lab Description: Model-based control is a popular paradigm for robot navigation because it can leverage a known dynamics model to efficiently plan robust robot trajectories. However, it is challenging to use model-based methods in settings where the environment is a priori unknown and can only be observed partially through onboard sensors on the robot. In this work, we address this short-coming by coupling model-based control with learning-based perception. The learning-based perception module produces a series of waypoints that guide the robot to the goal via a collision-free path. These waypoints are used by a model-based planner to generate a smooth and dynamically feasible trajectory that is executed on the physical system using feedback control. Our experiments in simulated real-world cluttered environments and on an actual ground vehicle demonstrate that the proposed approach can reach goal locations more reliably and efficiently in novel environments as compared to purely geometric mapping-based or end-to-end learning-based alternatives. Our approach does not rely on detailed explicit 3D maps of the environment, works well with low frame rates, and generalizes well from simulation to the real world.
Project(s):
Project Title: Vision-based navigation in new and unknown environments
Faculty / PI: Somil Bansal
Lab Name: Safe and Intelligent Autonomy Lab
Department: ECE
Research Website: https://smlbansal.github.io/LB-WayPtNav/
# of Freshmen Positions: 2
# of Continuing Student Positions: 2
Project Description: Autonomous robot navigation is a fundamental and well-studied problems 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, on-board 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 the onboard perception sensors. These architectures use machine learning for high-level planning based on the 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/.
Student Responsibilities: Experience and background (if any) in ML, control, and/or robotics. Experience (if any) with MATLAB/Python, training deep networks, ROS, and/or working with real hardware. Note that experience is not strictly necessary.
Interview Required? Yes
Signal Transformation Analysis and Compression (STAC)
Faculty / PI: Antonio Ortega
Research Website: https://sites.google.com/usc.edu/stac-lab/home
Lab Description: Our current research interests are focused on the theory of graph signal processing (GSP) and its applications, including 3D point clouds, image and video compression, sensor networks and machine learning. The main goal of GSP is to extend conventional signal processing operations such as filtering and sampling to data associated with graphs. In some cases, graphs can represent physical networks (the Internet, sensor networks, electrical grids or the brain) or information networks (the world wide web, Wikipedia or online social networks). We are also interested in applications where there is no graph and one has to be selected first. Examples include image, video and 3D point cloud processing (graph nodes are pixels), as well as machine learning (each node is a data point in a data set).
Project(s):
Project Title: Applications of Graph Signal Processing
Faculty / PI: Antonio Ortega
Lab Name: Signal Transformation Analysis and Compression (STAC)
Department: Electrical and Computer Engineering
Research Website: https://sites.google.com/usc.edu/stac-lab/home
# of Freshmen Positions: 1
# of Continuing Student Positions: 3
Project Description: We are interested in developing applications of graph signal processing (GSP), a new set of methods that can be used to analyze signals defined on graphs. Example applications include 3D point cloud processing (AR/VR, self driving cars), human activity analysis (skeleton graphs), image and video processing, remote sensing (satellite and in-situ) and machine learning.
Student Responsibilities: Students will work with PhD students in the lab to contribute to our ongoing projects. I expect them to contribute new ideas, participate in regular meetings, develop code (Matlab or Python) and co-author publications.
Interview Required? Yes
Skills and Competencies (Preferred)
- Linear Algebra
- Programming (e.g., Matlab, Python)
- Independence
Srivastava Group
Faculty / PI: Ajitesh Srivastava
Research Website: http://www-scf.usc.edu/~ajiteshs/
Project(s):
Project Title: Improving Ensemble methods for Epidemic Forecasting
Faculty / PI: Ajitesh Srivastava
Lab Name: Srivastava Group
Department: Ming Hsieh Department of Electrical and Computer Engineering
Research Website: http://www-scf.usc.edu/~ajiteshs/
# of Freshmen Positions: 1
# of Continuing Student Positions: 1
Project Description: Understanding the epidemiological situation and generating short-term forecasts and long-term scenario projections are important to drive public health decisions. For epidemics of interest, collaborative efforts take place worldwide between experts, government agencies, and stakeholders and have generated a vast amount of data that can be leveraged to evaluate forecasts. In this project, the candidate will conduct research to address how to improve epidemic forecasting by developing better data pre-processing techniques, fast regression models, and better ensemble techniques. The candidate may also work on exposing the dynamics of competing variants, and learning the dynamics of the imperfect vaccines.
Student Responsibilities: - Preprocess and integrate datasets
- Implement estimation of vaccine efficacy from breakthrough datasets
- Implement new ensemble methods
- Attend group meetings and present their work
Interview Required? Yes
Skills and Competencies (Preferred)
- Good coding skills (Python/MATLAB)
- Math skills (probability, regression, optimization)
- Presentation skills - writing and communication
Wireless Devices and Systems Group (WiDeS)
Faculty / PI: Andreas Molisch
Research Website: https://wides.usc.edu/
Lab Description: WiDeS is dedicated to research that is both scientifically challenging and practically relevant. The main emphasis of our research is on the physical layer of wireless communications, but we also deal with overall wireless systems integration, networking, and cross-layer design. Our philosophy is to bridge the main chasms in today's wireless research. The WiDeS group was founded in January 2009 by Andy Molisch, when he joined the USC Viterbi School of Engineering, after a career in both industry (AT&T Bell Labs, Mitsubishi Electric Research Labs), and academia (Lund University, Technical University Vienna). WiDeS is a part of the Communications Sciences Institute of the Department of Electrical Engineering at USC, and is affiliated with the ULTRA-Lab at USC.
Project(s):
Project Title: Channel Sounding and Measurement
Faculty / PI: Andreas Molisch
Lab Name: Wireless Devices and Systems Group
Department: Electrical and Computer Engineering
Research Website: https://wides.usc.edu/
# of Freshmen Positions: 0
# of Continuing Student Positions: 1
Project Description: This project involves an undergraduate student to learn to use RF measurement devices including network analyzer, waveform generator, and digitizer, and conduct indoor/outdoor measurement using the devices, for wireless channel modeling purposes.
Student Responsibilities: The student will work on LabVIEW based programming, verification of the code, documentation of the programs, and measurement campaigns.
Interview Required? Yes
Skills and Competencies (Preferred)
- LabVIEW
- MATLAB
- RF
- Antenna
- Wireless
Project Title: Machine Learning for localization
Faculty / PI: Andreas Molisch
Lab Name: WiDeS
Department: Electrical and Computer Engineering
Research Website: wides.usc.edu
# of Freshmen Positions: 0
# of Continuing Student Positions: 1
Project Description: In this project we aim to collect extensive data of signal quality and other parameters from WiFi access points and use those as input to machine-learning based localization algorithms.
Student Responsibilities: The student will finalize and test the measurement equipment (which will require Linux programming skills and some knowledge of WiFi cards), perform field measurements of the WiFi signals (on campus), post-process the data, and program neural networks (in Pytorch or similar) to analyze the suitability of various data types for localization.
Interview Required? Yes
Skills and Competencies (Preferred)
- Enthusiasm for the topic and the associated work
- Programming skills in Linux and knowledge of hardware
- Experience in Machine Learning; ideally programming experience with Pytorch
Additional Details: This project is part of an ongoing cutting-edge research project. Based on the technical contribution, participants will be co-authors of peer-reviewed publications related to this project.
Project Title: Ray tracer design and optimization for wireless channel simulations
Faculty / PI: Andreas Molisch
Lab Name: WiDeS
Department: Electrical and Computer Engineering
Research Website: https://wides.usc.edu/
# of Freshmen Positions: 1
# of Continuing Student Positions: 2
Project Description: We aim to design and optimize a ray tracer for wireless channels simulations. Ray tracers are a computer graphics tool for rendering and visualization of three-dimensional scenes, that are used in gaming (e.g., Unreal Engine), movies and engineering simulations. Scenes vary from small indoor to large urban outdoor environments. We aim to use ray tracing to simulate the propagation of electromagnetic waves in those environments, which will help in the planning of wireless tower deployments for 5G and 6G networks. The ray tracer needs to be optimized to run fast on GPUs and use advanced functionalities such as edge detection, building recognition and visibility pre-processing.
Student Responsibilities: 1- Design and optimize an edge detection algorithm that works on point cloud data.
2- Design and optimize a building recognition algorithm that classifies objects in a scene in a hierarchical fashion, e.g., building, first wall, second wall, window, triangular meshes.
3- Help in the implementation of a scene visibility processing algorithm.
Interview Required? Yes
Skills and Competencies (Preferred)
- Familiarity with computer graphics and/or computer vision
- Good MATLAB coding skills and familiarity/interest in GPU coding
- Familiarity with data structures and algorithms with an eye for optimization
- Familiarity/interest in C/C++ programming
Additional Details: Student must be available through emails and available for meetings (in-person or zoom) and interested in discussion of ideas
. Please check our publications page for our recent research progress.