People

Machine learning, speech/language/bioinformatics/music, submodularity & discrete optimization

Computer vision, machine learning

Convex optimization; systems and control theory

Robotics, artificial intelligence, activity recognition

Machine learning, active learning, reinforcement learning

Computational biology - precision medicine, network biology, genetics of complex traits; Machine learning - interpretability, feature selection, structure learning

Statistical learning algorithms

Development of machine learning techniques for molecular biology

Scientific-discovery games, games for learning, computer graphics, animation, optimal control, natural locomotion, optimization

Computational neuroscience, artificial intelligence, brain-computer interfaces

Computer vision, computer graphics

Computer vision, multimedia retrieval, biomedical informatics

Robotic manipulation, motion planning, human-robot interaction, assistive robotics

Liveness in programming environments, programming for virtual reality, educational technology, collaborative problem-solving environments

Data management, artificial intelligence

Adaptive systems and interaction

Knowledge representation and reasoning systems.

Robotics, brain-machine interface

Human activity recognition, sensor-based reasoning

Human-Computer Interaction and Brain-Computer Interfaces

Intelligent control in biology and engineering

Mitchell Gordon is a postdoc working with professors Jeff Heer and Yejin Choi. He designs interactive systems and evaluation approaches that bridge principles of human-computer interaction with the realities of machine learning. He recently completed his Ph.D. in computer science at Stanford, advised by Michael Bernstein and James Landay. He will join MIT EECS/CSAIL as an assistant professor starting fall 2024.

Markus Grotz currently works with Dieter Fox in the UW Robotics and State Estimation Lab and Tamim Asfour in the High Performance Humanoid Technologies Lab (H2T). His research focuses on visual perception for robotic manipulation tasks.

3D reconstruction and mapping with RGB-D (Kinect-style) cameras.

Natural language processing, artificial intelligence, natural language semantics

I'm interested in natural language understanding. My research is aimed at learning to automatically map natural language sentences to graph representations of their meaning, ideally in a way that works well for a broad variety of domains and languages.

Computer vision