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.

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

Our goal is to develop a socially intelligent team coacher agent that helps humans communicate, strategize, and work together efficiently.

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.

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!

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

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.

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.

Help robots learn faster by providing demonstrations when they need help

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.

Models and algorithms to predict in real time the next actions of a human working together with a robot

Our goal is to develop methods for a mobile robot to reason about volumetric spatial uncertainty of objects in its environment.

Our goal is to learn the structure of complex multi-step tasks from demonstration of human experts.

Our goal is to develop better metagenomic binning by origin species of fragments of sequenced environmental DNA.

We develop, parameterize, and validate a model for tumor growth inhibition using in vivo mouse data and study the effects of modeling uncertainty and inter-individual variability on drug candidate efficacy predictions.

Printable Hydraulics allows fluid-actuated robots to be automatically fabricated using 3D printers.

We develop algorithms, systems and software architectures for automating reconstruction of accurate representations of neural tissue structures, such as nanometer-scale neurons' morphology and synaptic connections in the mammalian cortex.

Our goal is to allow planners to exploit the forces and contacts between objects so as to carry out complex manipulation tasks in the presence of uncertainty.

Our goal is to develop algorithms for allowing robots to adapt intelligently to humans when they are working together as a team.

People, including doctors, nurses, and military personnel, often learn their roles in complex organizations through a training and apprenticeship process.

Once a human-machine team has formed a high-quality, flexible plan for working together, the robot must execute its part of the plan and work with its teammate.

How can a mobile-manipulation robot operate robustly and flexibly in complicated uncertain environments, such as households and disaster zones.