Skip to main content
Electrical and Computer Engineering | UC Davis Engineering
UC Davis Logo
UC Davis Logo
Site Logo
  • Apply
  • Alumni
  • UC Davis Directory
  • Computer Resources
  • Department Resources
  • Emergency Services
  • Job Opportunities
  • Key and Access Request
  • Network Access Request
  • Room Reservations
  • Safety
  • Schedules and Classes
  • Social Media
  • Visit ECE
  • ECE Student Organizations
  • ECE Technology Loan Program
  • Temporary Affiliate Access Request

Main navigation (extended config)

  • About
    • Awards
    • About UC Davis
    • ECE Advisory Board
    • People
      • Faculty
      • Staff
      • Affiliated Staff
      • Graduate Program Faculty
      • Emeriti Faculty
  • Undergraduate
    • Prospective Students
      • Why Study Electrical Engineering?
      • Why Study Computer Engineering?
      • Join UC Davis ECE
      • Frequently Asked Questions
    • Undergraduate Advising
      • B.S./M.S. Integrated Degree Programs
      • Annual Mandatory Advising
      • Forms & Internship Information
      • PTA Request
    • Degrees and Requirements
      • Electrical Engineering Major/B.S.
      • Computer Engineering Major/B.S.
      • Electrical Engineering Minor
      • ABET Accreditation
        • Computer Engineering (BS)
        • Electrical Engineering (BS)
      • Degree Checklists
    • ECE Course Offerings
    • Hands-On Learning
    • Beyond the Classroom
      • Career and Internship Resources
      • Student Clubs and Organizations
      • Student Resources
    • Online Educational Materials
    • Senior Design Projects
    • PTA Request
    • ECE Technology Loan Program
  • Graduate
    • About Our Programs
      • Areas of Research
      • Doctoral Degree
      • Doctoral Degree - Designated Emphases
      • M.S. Degree - Plan I (Thesis)
      • M.S. Degree - Plan II (Exam)
    • Graduate Admissions
    • Admitted Students
    • Current Students
      • Schedules and Classes
      • Graduate Student Association
      • Graduate Student Resources
      • EEC 290C and EEC 299
      • Teaching Assistants
    • Graduate Advising
      • Milestones
      • Forms and Documents
  • Faculty & Research
    • Faculty Directory
    • Computer Engineering
      • Computer Organization and Digital Hardware
      • Computer System and Hardware Security
      • High-Performance and Parallel Computing
      • Cyber-Physical and Embedded Systems
      • Computer Networks
    • Information Systems
      • Information and Networking
      • Decision and Control
      • Signal Processing and Communications
      • Machine Learning and Applications
    • Integrated Circuits and Systems
    • Quantum, Photonic and Electronic Devices
      • Nanoscale Electronics and Photonics
      • Energy Conversion Technologies
      • Biosensing, Biophotonics and Bioelectronics
      • Quantum Devices and Information Processing
      • Photonics in Computing, Communications and Information Processing
      • RF and THz Photonics
      • Non-Traditional Imaging and Displays
    • RF-to-THz Electronics and Waves
    • Bio, Ag and Health Technologies
    • Research Centers and Labs
    • Exploring AI Frontiers
  • News
    • Events
    • Seminar Series
  • Alumni
  • Give
  • Contact Us
hand outstretched with AI web on top
(Designed by Freepik)

Exploring AI Frontiers ECE Research and Educational Initiatives

The UC Davis Department of Electrical and Computer Engineering, or ECE, conducts groundbreaking Artificial Intelligence research with far-reaching applications across diverse fields. The cutting-edge research directly informs the curriculum, creating a vibrant learning environment where the next generation of engineers learn current techniques and theories. Our graduates are well-prepared to enter various industries and contribute to advancing AI technology and its societal impact.

Foundations of AI

Applications of AI

AI Education

AI Faculty

Foundations of Artificial Intelligence: A Research Perspective

Graph of AI Fundamentals
Generated by NapkinAI

ECE faculty at UC Davis conduct research across several key areas of AI Fundamentals to enhance trust and accountability, particularly in high-stakes applications; optimize computational efficiency for faster and more energy-efficient AI processing; improve training processes to reduce resource consumption and boost model performance; and advance the field by enabling unsupervised learning by allowing models to discover patterns and generalize knowledge without explicit human guidance.

Fundamentals

  • Unsupervised Learning
  • Unsupervised learning trains AI models on unlabeled data to discover patterns, unlike supervised learning which requires labeled data. ECE faculty demonstrate that unsupervised learning, including unsupervised domain adaptation, can achieve near state-of-the-art performance even with limited labeled data.

    Explore details and research
  • Self-Supervised Learning
  • Self-supervised learning leverages unlabeled data by creating pretext tasks to implicitly learn data structures. ECE faculty's research, including methods like Extreme-Multi-Patch Self-Supervised Learning and temporal trajectory learning, demonstrates the efficiency and effectiveness of this approach in achieving high accuracy and improved 3D representation.

    Explore details and research
  • Reinforcement Learning
  • Reinforcement learning agents learn through environmental interaction and reward signals, with exploration crucial for information gathering. ECE faculty research focuses on improving RL efficiency through techniques like reusable robot actions via multi-task policies and leveraging warm-start policies to enhance online learning.

    Explore details and research
  • Optimization
  • Optimization is critical for enhancing AI model training and overall performance. ECE faculty research explores optimization techniques, like Riemannian Block Coordinate Descent, to improve AI computational efficiency. Their work also investigates forward-forward learning, aiming to create scalable and efficient AI systems for resource-constrained hardware. 

    Explore details and research
  • Hardware Architecture
  • Powerful and efficient hardware is crucial for training and running AI/ML models, enabling the processing of large datasets and achieving optimal performance.  ECE faculty research focuses on hardware acceleration through innovative neural processing engine designs and AI-driven FPGA performance estimation to streamline design and improve efficiency.

    Explore details and research
  • Explainability
  • AI explainability, crucial for trust and accountability, clarifies how AI models reach decisions, especially in high-stakes applications. ECE faculty research focuses on improving the accuracy of saliency maps, a key tool for enhancing AI transparency and responsible use.

    Explore details and research

Innovative Applications and Societal Implications

Graph of AI Applications
Generated by NapkinAI

Artificial intelligence rapidly transforms various sectors, and engineers offer innovative solutions to complex problems and drive significant societal impacts. From healthcare to transportation and fundamental science, ECE faculty at UC Davis enable advancements in disease diagnosis, personalized treatment, traffic flow optimization and discoveries in fundamental science. These applications have the potential to enhance efficiency, improve quality of life and address critical challenges facing society.

Applications and Impacts

  • AI for Science
  • AI significantly accelerates scientific discovery by automating analysis and hypothesis generation, leading to breakthroughs in diverse fields. ECE faculty leverages AI's capabilities in various applications, from economic analysis using satellite imagery to advanced biomedical imaging, high-speed imaging system development and new discoveries in neuroscience.

    Explore details and research
  • AI for Security/Privacy
  • AI strengthens security and privacy through automated threat detection and improved data protection mechanisms. ECE faculty apply AI to enhance cybersecurity in diverse contexts, including malware detection, recycled IC identification, privacy in distributed machine learning and developing robust anonymization techniques.

    Explore details and research
  • AI for Hardware Design
  • AI streamlines and optimizes hardware design by automating tasks and improving efficiency, leading to faster development and better performance. ECE faculty utilize AI to optimize clock skew for energy-efficient designs and accelerate static timing analysis for high-performance hardware.

    Explore details and research
  • AI for 5G Networks
  • AI optimizes 5G network performance and enables new services through real-time data analysis and predictive modeling. ECE faculty apply AI to improve resource allocation, communication efficiency and spatial diversity within 5G networks.

    Explore details and research
  • AI for Health
  • AI enhances healthcare through faster, more accurate diagnoses, personalized treatments and accelerated drug discovery. ECE faculty utilize AI to improve applications such as autism spectrum disorder recognition, neurodegenerative disease analysis, fluid resuscitation prediction and real-time clinical insights.

    Explore details and research
  • AI for Transportation
  • AI improves transportation through autonomous vehicles, optimized traffic management, and predictive maintenance, enhancing efficiency, safety and sustainability. ECE faculty apply AI to address privacy in GPS data, optimize traffic flow using reinforcement learning and create realistic autonomous driving scenarios.

    Explore details and research
  • AI for Equity
  • Understanding and mitigating biases in large language models (LLMs) is critical for ethical AI. ECE faculty use AI to visualize and analyze LLM internal workings, identifying potential biases and promoting responsible development.

    Explore details and research

ECE Education Drives AI Innovation

The UC Davis ECE department's artificial intelligence and machine learning curriculum begins in upper-division undergraduate courses and continues into graduate studies. The curriculum emphasizes core concepts such as data preparation, sensor design, algorithm development, model explainability, hardware-software integration, and security and privacy. Students benefit from UC Davis's diverse academic environment, applying AI/ML to real-world problems in fields like agriculture, veterinary medicine and health sciences. This approach integrates practical application constraints and ethical considerations, drawing on expertise across the university.

Coding Skills

Upper-division ECE students are expected to possess foundational coding skills (including Python for data science) to readily acquire competence in Python libraries (e.g., scikit-learn for ML and PyTorch for deep learning) in AI/ML-focused courses to comprehend cutting-edge AI/ML models and develop/train models of their own. CE and EE undergraduates fulfill coding requirements through parallel tracks. CE majors complete a three-course sequence focused on in-depth programming and algorithm design, while EE majors complete a two-course sequence emphasizing practical applications and microcontroller programming.

  • Computer Engineering (CE) Coding Track

  • Course 1: Programming & Problem Solving (ECS 036A): Algorithm design, debugging, programming style, UNIX tools.

    Course 2: Software Development & Object-Oriented Programming in C++ (ECS 036B): Data structures, program complexity and efficiency, debugging, verification.

    Course 3: Data Structures, Algorithms, & Programming (ECS 036C): Data structure design and analysis (trees, heaps, searching, sorting, hashing, graphs).

  • Electrical Engineering (EE) Coding Track
  • Course 1: Engineering Problem Solving (ENG6): Procedural and object-oriented programming, data structures, graphical user interface (GUI) programming.

    Course 2: Introduction to Programming and Microcontrollers (EEC7): C/C++ languages, basic hardware programming.

These foundational coding courses provide comprehensive coverage, preparing students to learn additional languages throughout their studies. Further specialized language training is also available through the UC Davis Department of Computer Science and the UC Davis DataLab. 

ECE AI Curriculum

  • EEC 174ABY | Applied Machine Learning Senior Design
  • Design, development and evaluation of components that are critical to artificial intelligence (AI) driven control systems.
  • EEC 174AY | Applied Machine Learning Senior Design
  • Applied machine learning (ML) and deep learning (DL) in engineering systems. Design and evaluation of components that are critical to artificial intelligence (AI) driven control systems, including but not limited to sensor fusion, feature engineering, computer vision (semantic segmentation, objection detection), ML based classification and learning-based control systems.
  • EEC 175AB | Internet of Things Senior Design Project
  • Internet of Things engineering course with focus on use of ML applications in supporting the IOT solution’s decision-making process.
  • EEC 289A | Introduction to Unsupervised Learning
  • A fundamental and advanced machine learning course focused on building machines that can learn from the world and data automatically driven by intrinsic objectives without external supervision.
  • EEC 289Q | Practical AI
  • Comprehensive introduction to practical AI, focusing on the application of AI techniques in real-world scenarios.
  • EEC 289Q | Deep Learning Hardware
  • Comprehensive understanding of the hardware aspects of machine learning.
  • EEC289QV | Machine Learning for Health Applications
  • Introduction to the key AI/ML tools and methodology for health applications.

Undergraduate AI Senior Design Projects

Professor Chen-Nee Chuah taught EEC174ABY, in which the following senior projects were completed in the 2023-24 academic year.  

  • ARMS: Automated Rover for Mobile Support
  • Vigil-Eye: Enhancing Road Safety for Distracted Drivers
  • EmergenSee
  • Intelligent Traffic Management System
  • Vision-Guardian
  • NavigateNCount

View more details

Text book cover

Professor Houman Homayoun is a co-author of a textbook on Applied Machine Learning for Computer Scientists and Data Analysts: From an Applied Perspective, Springer Nature

The textbook thoroughly explains the theory behind machine learning algorithms, progressing from simple neuron fundamentals to complex neural networks such as generative adversarial networks and graph convolution networks. The book's main goal is to help readers understand these concepts and develop the ability to choose the right algorithm for any given problem. The book uses numerous case studies, ranging from simple time-series forecasting to object recognition and recommendation systems using large databases, along with practical implementation examples and exercises.

ECE Faculty Advancing AI

Profile Picture: Chen-Nee Chuah

Chen-Nee Chuah

Professor

Chen-Nee Chuah employs advanced data science and AI/ML techniques to healthcare delivery, digital neuropathology and intelligent transportation systems, while tackling the associated security and privacy issues. 

image of Yubei

Yubei Chen

Assistant Professor

Yubei Chen’s research is at the intersection of computational neuroscience and deep unsupervised (self-supervised) learning, enhancing our understanding of the computational principles governing unsupervised representation learning in both brains and machines.

image of Dr. Ding

Zhi Ding

Distinguished Professor

Zhi Ding specializes in data and signal analysis, federated learning and networked intelligent systems. His team develops AI/ML techniques to innovate next-generation communication networks driven by generative AI. His research also addresses channel impairment and coverage issues wireless communication. 

Houman Homayoun

Houman Homayoun

Professor

Houman Homayoun is a leader in cybersecurity creating ML-based solutions for cyber-attacks at the hardware level. He is also co-author of a textbook titled Applied Machine Learning for Computer Scientists and Data Analysts: From an Applied Perspective.  

Prof Lai sitting

Lifeng Lai

Professor

Lifeng Lai is developing robust algorithms that yield fair and trustworthy results in ML decision-making applications. 

image of Prof. Munday

Jeremy Munday

Professor

Jeremy Munday uses AI/ML techniques to design optical structures for energy and climate applications and explores how AI/ML could play a role in developing new physical laws.

Middle aged white man with a blue polo shirt and bald head

John Owens

Professor

John Owens specializes in the Graphics Processing Unit (GPU) systems that power today’s ML applications. 

Profile Picture: Avesta Sasan

Avesta Sasan

Professor

Avesta Sasan is developing efficient ML hardware, ML-assisted electronic design automation tools, lightweight ML solutions for edge processing, ML solutions for model and hardware security and explainable AI models 

Thomas

Anthony Thomas

Assistant Professor of Teaching

Anthony Thomas is developing a curriculum at the intersection of computer engineering and AI/ML.

 

Weijian Yang

Weijian Yang

Associate Professor

Weijian Yang is developing and applying AI/ML in optical imaging and microscopy, bioimaging and biosensing, and image processing. 

Clean cut Asian man with glasses and a suit and tie

Junshan Zhang

Professor

Junshan Zhang studies foundations for AI/ML, including self-supervised learning, reinforcement learning and continual learning. His group had developed software platforms for self-driving vehicles that leverages AI for safety enhancement and energy efficiency.

 

 

UC Davis AI Center in Engineering

The Department of Electrical and Computer Engineering is a proud contributor to the UC Davis AI Center in Engineering, 
which is leading the way in foundational AI research as well as translational applications and AI education efforts.

Explore the UC Davis AI Center in Engineering

College of Engineering logo
UC Davis footer logo

University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011

  • Questions or comments?
  • Privacy & Accessibility
  • Principles of Community
  • University of California
  • Sitemap
  • Last update: March 20, 2025

Copyright © The Regents of the University of California, Davis campus. All rights reserved.

This site is officially grown in SiteFarm.