John W. Fisher III     (fisher@csail.mit.edu)

I am a Senior Research Scientist at the Massachusetts Institute of Technology where I run the Sensing, Learning, and Inference Group in the Computer Science and Artificial Intelligence Laboratory. I started the group in 2004 following the merger of the Artificial Intelligence Laboratory and the Laboratory for Computer Science. We analyze complex, high-dimensional data sources in a variety of application domains. We develop scalable algorithms with theoretical performance guarantees utilizing physics-based sensor models. Towards that end we combine elements of Bayesian inference, information theory, optimization, and machine learning in our research. Formulating problems in this context has led to interesting questions in probabilistic inference, information planning, scene understanding, and Bayesian structural inference (to name a few). Applications expose our ideas to the challenges and complexity of real world data and environments. Examples include multi-modal data fusion, distributed inference under resource constraints, multi-object tracking, and accelerated materials design. We are interested in all aspects of the process of going from sensing to information to understanding to action.

Rujian Chen     (rjchen@mit.edu)

I am interested in Bayesian statistics, inference methods, machine learning and information theory. My current research interests include understanding sensing and information gathering tasks and developing algorithms for estimation and decision making, especially in complex systems such as those with large parameter spaces or uncertain dynamics. Prior to joining SLI, I completed my undergraduate at Cambridge University.

Harry Qiaohao Liang     (hqliang@mit.edu)

I am a PhD student working in the SLI group and the MIT Photovoltaics Laboratory. My research interests are Bayesian statistics, machine learning, and how they can be adapted to solve materials science related problems. I’m currently working on using Bayesian Optimization to guide decision-making and experimental planning during optimization of new energy materials in large parameter spaces. Before joining MIT, I’ve lived in Finland and China, and obtained my undergraduate degree in Materials Science, Electrical Engineering and Computer Science from UC Berkeley.

David S. Hayden     (dshayden@mit.edu)

My research enables scientists to collect, analyze and react to observations at scale. By working towards automated interventions, the tools I build aim to make new experimental designs possible from what once were observational studies. More broadly, my research interests are in interpretable machine learning and computer vision, with special focus on Bayesian nonparametrics applied to time-series, distributions on manifolds and using uncertainty to guide decision making, analysis and experimental design.

Christopher L. Dean     (cdean@csail.mit.edu)

My current research interests are in probabilistic data fusion, Bayesian nonparametric models, and Bayesian deep learning. I received my SM in Electrical Engineering and Computer Science from MIT in 2015 and my BS in Computer Science and Engineering from The Ohio State University in 2012.

Sue Zheng     (zhengs@mit.edu)

I am interested in computationally tractable algorithms for planning and decision making in Bayesian models. Applications include sensor planning for detection of special nuclear materials in cargo containers and experimental design for structure learning in gene regulatory networks.

Felipe Oviedo     (foveido@mit.edu)

I am a PhD candidate working in the SLI group and the MIT Photovoltaics Laboratory. My research interests are in machine learning, Bayesian statistics, Bayesian Optimization and information theory, applied to the discovery and development of new energy materials.

Genevieve E. Flaspohler     (geflaspo@mit.edu)

My research focuses on decision-making in partially observable and dynamic environments. I develop planners that leverage structured models and reason about uncertainty; thus my research also focuses on Bayesian statistics and scientific machine learning. Applications of interest include robotics and sensor placement for large-scale environmental monitoring. I am in the MIT/Woods Hole Oceanographic Institution (WHOI) joint program and am co-advised by Prof. Nicholas Roy in the Robust Robotics Group (RRG).

Daniel Yang     (dxyang@mit.edu)

I am interested in Bayesian inference, perception, and robotics and aim to enable autonomous systems to make intelligent decisions in complex, real-world environments. I am also in the MIT/Woods Hole Oceanographic Institution (WHOI) joint program. Previously, I received a BSE in Electrical Engineering from Princeton University in 2017 and spent time at Oculus and Samsung.

Shiqi Yang     (yangsq96@mit.edu)

I am interested in Bayesian modeling and computer vision. Before joining SLI, I received my BS in Electrical and Computer Engineering from The Ohio State University.

Michael A. Wallace     (mwallace@csail.mit.edu)

I am an M.Eng. student in the SLI group. Prior to the M.Eng., I received my B.S. in Mathematics and Computer Science from MIT and worked in consulting for a couple of years.

Stephen J. Lee     (leesj@csail.mit.edu)

I am developing spatio-temporal models to inform large-scale infrastructure planning endeavors in countries with low electricity access rates and unreliable service. My research builds on methods for probabilistic data fusion, deep learning, and applied economics to infer characteristics of electricity supply and demand at large scales. Before starting a PhD in EECS, I completed a SM in the Technology and Policy Program. I am co-advised by John and by Ignacio Perez-Arriaga with the MIT-Comillas Universal Energy Access Lab.