Research

Using our inkjet printer system, the goal of this project is to produce a light sensor / solar cell that is composed entirely of 3D printed materials.

We aim to develop a systematic framework for robots to build models of the world and to use these to make effective and safe choices of actions to take in complex scenarios.

Self-driving cars are likely to be safer, on average, than human-driven cars. But they may fail in new and catastrophic ways that a human driver could prevent. This project is designing a new architecture for a highly dependable self-driving car.

Robot scheduling via learning from demonstration for tasks in medicine and more

In order to be able to design synthetic organs that function autonomously, we will need to engineer artificial tissue homeostasis. To control the size of these artificial tissues, two major mechanisms will have to be engineered.

Development of affordable wearable continuous blood pressure monitor based on radial arterial pulse imaged from the skin surface of human wrist

This project aims to let people correct robot mistakes with nothing more than their brain signals - to allow robots to adapt to humans rather than the other way around

Integrating multi-modal clinical data and using recurrent and convolution neural networks to predict when patients will need important interventions.

A system that can help you to design our own drone!

We developed a new algorithm to generate compact sequence sets covering all k-mers using joker characters.

Deep inverse planning for learning from high-dimensional demonstrations

Our goal is to investigate deterministic algorithms for robotic task and motion planning.

The robot garden provides an aesthetically pleasing educational platform that can visualize computer science concepts and encourage young students to pursue programming and robotics.

We are studying how best to implement bilateral control and make it acceptable to drivers

Multi-robot path planning for robot swarms that can both fly and drive

We use computational models of biological pathways in disease to estimate the range of possible responses to various hypothetical drugs, given patient variability and limited pathway-relevant data.

Our goal is to develop collaborative agents (software or robots) that can efficiently communicate with their human teammates. Key threads involve designing algorithms for inferring human behavior and for decision-making under uncertainty.

Our goal is to develop unsupervised or minimally supervised marine learning frameworks that allow autonomous underwater vehicles (AUVs) to explore unknown marine environments and communicate their findings in a semantically meaningful manner.

This project aims to develop an improved alignment algorithm for barcoded short reads as produced by 10X Genomics' sequencing platform.

The goal of the FLA program is to explore non-traditional perception and autonomy methods that could enable a new class of algorithms for minimalistic high-speed navigation in cluttered environments.

Existing methods for cloning and recombination of DNA enable construction of arbitrary sequences. However, the sequential nature of these techniques makes them time-consuming and expensive. Furthermore, while the transformation of an existing plasmid into a host strain can be reliable when a selection marker is used, there are many current limitations: the number of different plasmids that can be co-transformed is limited by the choice of markers and compatible origins of replication; plasmids are less stable than chromosomal DNA and are difficult to maintain indefinitely without mutation; and cistronic interactions cannot be designed since each new nucleotide sequence added is on an unconnected DNA molecule. To overcome these limitations, we are designing reconfigurable chromosomes consisting of both fixed and variable regions. While the fixed region is carefully optimized and tuned ahead of time, the variable region can be modified in the field, at the point-of-use, leading to rapid and on-demand realization of novel biocircuits with many different phenotypes.

We study genetic predictors of statin metabolism, efficacy, and tolerability in a post-cardiac event population.

Our goal is to create an online risk-aware planner for vehicle maneuvers that can make driving safer and less stressful through a “parallel” autonomous system that assists the driver by watching for risky situations, and by helping the driver take proactive, compensating actions before they become crises.

Our goal is to develop computational learning models for agents that intelligently use various types of human feedback to help an agent adapt and transfer knowledge to new situations.