I direct the Artificial Intelligence and Systems Laboratory (AISys). AISys is located at 2212 Storey Innovation Center.

The AISys lab welcomes people of any race, religion, national origin, gender identity, family commitments, political affiliation, sexual orientation, and eligible age or ability.

We investigate a variety of open problems that sit at the intersection of artificial intelligence, machine learning, and computer systems. We investigate the development of novel algorithmic and theoretically principled methods that are grounded in mathematics for systems problems with the ultimate goal of building reliable and high-performance machine learning systems. On the application side, we aim to develop the next generation of autonomous systems (on-device, embedded, heterogeneous, cloud, robotics) that can perceive, reason, and react to complex real-world environments and users with high levels of precision and efficiency. Overall, we aim to conduct cutting-edge and high-impact research through full-stack approaches that encourage lab members with skills in algorithms, systems, statistics, mathematics, and software to work closely together to solve critical and practical challenges in the areas at the intersection of AI+Systems.
Core technical areas: Transfer learning, representation learning, deep learning, non-convex optimization, causal inference, reinforcement learning, concept learning, and robust optimization.

At AISys, fruitful collaborations and constant learning matter a lot to all of us. We have a culture where students frequently collaborate. We typically combine theoretical and empirical insights to build a principled and thorough understanding of key techniques in machine learning, such as deep learning, as well as the challenges we face in this context. Currently, a major theme in our lab is to develop secure, robust, reliable, and performant machine learning systems.

Current Members

Ying Meng

Ying Meng phd

Ying is a PhD student in computer science. She is interested in adversarial machine learning and software testing.

Jianhai Su

Jianhai Su phd

Jianhai is a PhD student in computer science. He is interested in transfer learning, autonomic computing, and reinforcement learning.

Shahriar Iqbal

Shahriar Iqbal phd

Shahriar is a PhD student in computer science. He is interested in multi-objective optimization of deep neural networks in embedded systems.

Peter Mourfield

Peter Mourfield phd

Peter is a PhD student in computer science. He is interested in autonomic computing and reinforcement learning.

Mohammad Ali Javidian

Mohammad Ali Javidian research associate

Mohammad Ali is a postdoctorate student in computer science. He is interested in probabilistic graphical models, Bayesian networks, and transfer learning.

Blake Edwards

Blake Edwards undergrad

Blake is a honors college undergraduate student in computer science and math. He is interested in deep neural network model compression and distillation.

Stephen Baione

Stephen Baione undergrad

Stephen is a honors college undergraduate student in computer science and math. He is interested in deep neural network model compression and distillation.