Research Overview

Prof. Heather Zheng at the white board

Computer Engineering Research at UCSB

CE research lies not only at the interface of computer science and electrical engineering, but increasingly ties computing together with biology, medicine, chemistry, physics, mechanical engineering, and even environmental engineering.

Our research is ideally positioned to help solve societal problems through the construction of practical systems composed of emerging technologies. We live in a time of both opportunity and crisis. Rising carbon emissions and energy costs are a global problem. Aging populations increasingly strain healthcare resources. Computing technologies are at the heart of many potential solutions to these problems. Emerging technologies in nanoscale and bio-compatible materials hold the promise to increase energy-efficiency and revolutionize healthcare. We also see opportunities in massive information gathering and large-scale computing resources to exploit that information.

We must also address increasing challenges to continued scaling of conventional silicon and to maintaining the dramatic performance growth of past computing systems.

CE Areas of Research

  • Bioinspired Computing
  • Circuit and System Design
  • Computer Architecture
  • Electronic Design Automation & Testing
  • Emerging Technologies for Computing

  • Energy-efficient Computing
  • Nanotechnology
  • Networking
  • Operating and Distributed Systems
  • Software and Language

Faculty Lab Spotlight


Intelligent and Predictive Systems Laboratory
Alberto Giovanni Busetto, Assistant Professor


ips lab logo

The research work of the IPS Lab primarily concerns the development of methods to reliably extract useful knowledge from raw data. The ultimate goal of our interdisciplinary activity is to provide users with tools able to automatically select the most useful bits of information for the purpose of improving the performance of a specific task (for instance, solving an optimization problem).


Our research program consists of three main aspects:

  1. Extracting information to algorithmically generate hypotheses and models, to explore data with the purpose of identifying causal relationships, as well as to detect outliers, atypical and anomalous behavior;
  2. Validating predictions to select the correct model class from a set of candidates, to statistically validate how appropriate is the available class for a specific application, to adaptively approximate input-output functions and quantify uncertainty;
  3. Deciding optimally to design crucial experiments, to select interventions, inputs or actions to maximize the expected utility by taking advantage of validated information.

In our vision, these steps will help building computer systems that are able to operate well even under heterogeneous disturbances, general uncertainty, and in particular to perform efficiently when dealing with noisy big data. A key aspect of our research is the emphasis on strategies designed to achieve justified tradeoffs between exploration and exploitation, and between model simplicity and predictive power.

Our activity exhibits the following methodological priorities:

  • Information-theoretic validation of unsupervised learning (clustering, in particular), big data, time-series analysis and causal inference.
  • Statistical system identification of discrete (automata, Boolean networks), continuous (ordinary, stochastic, partial differential equations) and hybrid systems (cyber-physical, digital-dynamic controllers).
  • Reinforcement learning with multi-armed bandit models, sequential and parallel design of experiments.

Our contributions impact a wide spectrum of application domains, and our research is motivated by concrete problems from personalized medicine, synthetic biology, nanotechnology, computational physics, wind power, and human learning. We welcome and encourage further collaborations with other groups!

Faculty Research Profiles

Fred Chong & Co-PI awarded $480K NSF Grant

photo of Fred ChongProfessor Fred Chong and co-principal investigator receive a National Science Foundation research grant to study quantum error correction schemes.

See the Computer Science News release

Q & A with Professor Luke Theogarajan

Photo of Luke TheogarajanMain Research Areas: biomedical engineering & high-speed communications
Recognitions: NIH New Innovator (2010) and NSF Career (2011) awards
Q & A — UCSB Convergence Magazine

Faculty Research Profile: Dr. Tim Cheng

photo of Tim Cheng

Tell Us About Your Research:
My research encompasses two major areas: (1) Design automation and test for electronic systems and (2) Mobile computer vision and embedded systems. In the first area, the research addresses challenges of ensuring correctness and reliability of complex integrated systems. Throughout my career, I've developed a number of techniques that have led to the availability of low-cost and high-quality solutions for validation and testing of high-performance and robust silicon integrated circuits as well as of flexible electronics. In the second area, my group developed a number of solutions for fast and accurate content analysis in images and videos, with an increasing focus on mobile applications that have limited hardware and energy resources.

Why Did You Get Into Your Area of Research?:
Electronic design automation (EDA) and test are critical areas for the semiconductor industry and their research involves hardware and software, as well as algorithms and methodologies, and has strong "systems" components. These attributes are very attractive to me and thus helped provide me a long-lasting research career in this field. Mobile embedded systems, which have a broad spectrum of applications and are technically sophisticated, is multidisciplinary in nature and also interests me.

What do Find Particularly Rewarding about your Research?: I have been offering a course on mobile embedded systems, cross-listed for both undergraduate and graduate students, which I found particularly rewarding. The course covers the latest smartphone and tablet technologies including hardware platforms, operating systems, emerging apps, and low-power design techniques. The growth of smartphone and tablet functionalities, and advances in their technologies, have come about far more rapidly than most people’s ability to comprehend. There is not a textbook or any publicly available teaching materials for a comprehensive coverage of this fast-moving technical area. I have spent a significant amount of time and efforts developing the course materials to help students, as well as myself, gain a broad and sufficiently deep knowledge of the key technologies behind these devices. Due to the timeliness of the course materials, many students taking the course successfully land an internship or a full-time job in the smartphone/Android industry right after receiving their B.S. or M.S. degrees and I have found this very rewarding.

(More about Professor Cheng and other faculty's research...)