Center for Undergraduate Research in Viterbi Engineering (CURVE) Fellowship
Electrical & Computer Engineering
** 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
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.
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.
Faculty / PI: Yasser Khan
Research Website: https://viterbi-web.usc.edu/~yasserkh/
Lab Description: 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 . Please check our publications page for our recent research progress.
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.
Next Generation Digital Systems
Faculty / PI: Sandeep Gupta
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.
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.
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.
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).
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.
Published on June 8th, 2021
Last updated on October 10th, 2022