Computer-designed organisms (top) with their their real-life cell analogs called xenobots (bottom). Credits: Douglas Blackiston

Artificial intelligence (AI) is the ability of a computer program or machine to learn from and adopt to data. At the University of Vermont, we are developing AI for a large variety of applications, such as to enable computer-designed organisms, to create context-aware robots, to use deep learning to diagnose biological diseases, and much more. Explore this page to learn more about our world-renowned faculty and their AI research. Our faculty encompasses experts from a variety of backgrounds, including Computer Science, Electrical & Biomedical Engineering, and Civil & Environmental Engineering, as well as centers specifically dedicated to AI research.

The applications for Artificial Intelligence are endless and continuously evolving to benefit many different industries and society as a whole. We invite you to join us in discovering how AI can dramatically improve our lives.

Core Faculty

Josh Bongard, Computer Science

Dr. Bongard is the Veinott Professor of Computer Science at the University of Vermont and director of the Morphology, Evolution & Cognition Laboratory. His work involves automated design and manufacture of soft-, evolved-, and crowdsourced robots, as well as computer-designed organisms. He is the co-author of the book How The Body Shapes the Way we Think, the instructor of a reddit-based evolutionary robotics MOOC, and director of the robotics outreach program Twitch Plays Robotics.
Faculty Profile, Lab Website, Faculty Website

Research Spotlight: Xenobots
Here, a method that designs completely biological machines from the ground up is presented: computers automatically design new machines in simulation, and the best designs are then built by combining together different biological tissues. This suggests others may use this approach to design a variety of living machines to safely deliver drugs inside the human body, help with environmental remediation, or further broaden our understanding of the diverse forms and functions life may adopt. Read more.

Nick Cheney, Computer Science

Dr. Cheney is an Assistant Professor of Computer Science and a core faculty in Complex Systems and Data Science. His research is in bio-inspired artificial intelligence. His research lab, the UVM Neurobotics Lab, draws inspiration from natural systems, and especially biological learning processes to design machine learning algorithms which create more flexible, scalable, and context-aware robots and decision-making systems.

Faculty Profile, Lab Website, Faculty Website

Research Spotlight: Learning to Continually Learn
Inspired by neuromodulatory processes in the brain, we propose A Neuromodulated Meta-Learning Algorithm (ANML). It differentiates through a sequential learning process to meta-learn an activation-gating function that enables contextdependent selective activation within a deep neural network. Read more.

Byung Lee, Computer Science

Dr. Lee is a Professor of Computer Science at the University of Vermont. His expertise lies in developing and comparing computational methods for modeling, understanding, and processing large data efficiently, intelligently, and adaptively.

 

Faculty Profile

Safwan Wshah, Computer Science

Dr. Wshah is currently investigating machine learning algorithms to be applied in Energy, Transportation and Healthcare fields. He joined UVM CEMS from PARC (Palo Alto Research Center), where he worked as a research scientist in the fields of machine learning/deep learning, computer vision, and image/video processing. 


Faculty Profile, Lab Website

Research Spotlight: Deep Learning for Endoleak Recognition
This project was primarily concerned with the automated binary classification of Endoleaks, defined as perigraft flow into the residual aneurysm sac, within computerized tomography angiography (CTA) volumes of patients post-EVAR. We proposed a set of cascaded deep convolutional neural network architectures to localize an aneurysm region and subsequently predict the presence of an Endoleak within this region. Read more.

 

Affiliated Research

Ryan McGinnis, Electrical & Biomedical Engineering

Dr. McGinnis' research interests focus on the development of digital biomarkers and therapeutics. His work relies on technical expertise in biomedical signal processing, machine learning, biomechanics, and computational dynamics developed during past positions in both academia and industry. He is passionate about developing new technology-based solutions to pressing problems facing society.

Faculty Profile, Lab Website

Donna Rizzo, Civil & Environmental Engineering

Dr. Rizzo's research focuses on the development of new computational tools to improve the understanding of human-induced changes on natural systems and the way we make decisions about natural resources. Since joining UVM in fall 2002, she has worked on a number of computational approaches to multi-scale environmental problems


Faculty Profile, Faculty Website


Affiliated Centers

                                                                                  

Complex Systems Center           The Institute for Computationally Designed Organisms (ICDO)

 

Selected Courses in Artificial Intelligence

CS 206 Evolutionary Robotics

Exploration of the automated design of autonomous machines using evolutionary algorithms. Coursework involves reading of research papers, programming assignments and a final project. Prerequisites: Junior standing and programming experience, or Instructor permission.

CS 254 Machine Learning

Introduction to machine learning algorithms, theory, and implementation, including supervised and unsupervised learning; topics typically include linear and logistic regression, learning theory, support vector machines, decision trees, backpropagation artificial neural networks, and an introduction to deep learning. Includes a team-based project. Prerequisites: STAT 151 or STAT 251; MATH 122 or MATH 124.

CS 288 Statistical Learning

Statistical learning methods and applications to modern problems in science, industry, and society. Topics include: linear model selection, cross-validation, lasso and ridge regression, tree-based methods, bagging and boosting, support vector machines, and unsupervised learning. Prerequisites: STAT 143, STAT 183 or STAT 211.

CS 352 Evolutionary Computation

Theory and practice of biologically-inspired search strategies, including genetic algorithms, genetic programming, and evolution strategies. Applications include optimization, parameter estimation, and model identification. Significant project. Students from multiple disciplines encouraged. Pre/co-requisites: Familiarity with programming, probability, and statistics.

CS 354 Deep Learning

Introduction to Deep Learning algorithms and applications, including basic neural networks, convolutional neural networks, recurrent neural networks, deep unsupervised learning, generative adversarial networks and deep reinforcement learning. Includes a semester team-based project. Prerequisite: CS 254.

CE 359 Applied Artificial Neural Networks

Introduction to artificial neural networks. A broad range of example algorithms are implemented in MATLAB. Research applications to real data are emphasized. Prerequisites: CS 021, STAT 223 or equivalent.

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