illustration of of metallic wires connecting memristors
Illustration: Brian Long, UCSB
Courtesy: The Strukov Group

CE Research Activities

Activities & faculty include but are not limited to:

Bio / Brain Inspired Computing

Emerging approach utilizing principles of biology and neuroscience to develop intelligent computing systems — Faculty

Computer Arch. / System Level Design

Science and engineering of interconnecting digital systems blocks to design next generation computing systems — Faculty

Electronic Design Automation / System Verif.

 OhCombines theories in computation and modeling to develop automation tools for designing the next gen. of complex electronic design — Faculty

Emerging Technologies for Computing

Exploring the next generation of computing utilizing non Von Neumann architectures, in memory computing and computing using novel memory devices — Faculty

Machine Learning / Neuromorphic Computing / AI

Novel architectures, algorithms, devices and techniques for AI including machine learning and neuroscience inspired architectures — Faculty


Exploiting the physics at the nanoscale for developing advanced devices for computing, sensing and signal processing — Faculty

VLSI Design / Circuits & Sys. / Signal Process.

Design principles and techniques for developing complex integrated circuits and systems — Faculty

CE Research Overview

Computer engineering research at UCSB spans a wide spectrum of topics, from devices and integrated circuits to software systems and applications.  Computer architecture, which is central to the efforts aimed at improving the performance, cost-effectiveness, and energy efficiency of computing systems, is a key focal point.

The computer engineering faculty at UCSB conduct collaborative, multidisciplinary projects that allow students and other participants to engage in practically motivated, state-of-the-art research problems. There is an intense focus on industrial relevance and on the impact of emerging technologies on computing. 

Additional Information about the Computer Engineering and its affiliated faculty can be found at the Computer Engineering Program website. For more detailed descriptions of faculty research and activities, please follow the links to the various research centers, labs, and groups indicated on this page.

CE Faculty Groups / Labs

Name Group / Lab Research Interests
Kaustav Banerjee Nanoelectronics; Physics, Technology, and Applications of Low-Dimensional Nanomaterials; 2D Materials and their Heterostructures; Bioelectronics; Quantum Electronics; Ultra-Low Power Devices, Circuits and Sensors
Forrest Brewer VLSI Design and Architecture, System Level Tools and Specification, Electronic Design Automation, Low-Power Sigma-Delta Control and Signal Processing
Kerem Çamsarı Nanoelectronics, Spintronics, Emerging Technologies for Computing, Digital and Mixed-signal VLSI, Neuromorphic and Probabilistic Computing, Quantum Computing, Hardware Acceleration
Haewon Jeong Machine Learning, Ethical AI, Responsible Computing, Information Theory, Large-scale Distributed Computing
Bongjin Kim Integrated Circuits and Systems, Memory-Centric Computing, Analog Mixed-Signal and Digital VLSI, Hardware Accelerator, Alternative Computing, Brain-Inspired and Neuromorphic computing, Machine Learning Hardware, Design Automation
Peng Li Integrated Circuits and Systems, Brain-Inspired Computing, Machine Learning Enabled Electronic Design Automation, Hardware Machine Learning Systems, Robust Machine Learning
Behrooz Parhami Computer Arithmetic, Parallel Processing, Dependable Computing, Computer Architecture
Dmitri Strukov Solid-State Nanoionics, Emerging Electron Devices and Circuits, Non-Volatile Memories, Neuromorphic Computing
Luke Theogarajan Low-Power Analog VLSI, High Speed Electronics for Photonics, Biomimetic Nanosystems, Neural Prostheses, Biosensors, Block Polymer Synthesis, Self-Assembly, and Microfabrication
Li-C. Wang Artificial Intelligence for Design and Test, Data Analysis, Machine Learning
Zheng Zhang Electronic and Photonic Design Automation; Mathematical Data Science; Uncertainty quantification: Tensor-based Machine Learning and Hardware