Projects

Creating an AI agent capable of independent, rigorous scientific discovery in a laboratory meta-fluid with complex flows.

Using and developing advanced multi-modal generative AI models to map the relationship between protein sequences and their functions, enabling the design of novel proteins with desired properties.

Developing machine learning models for endothelial tissue mechanoresponses to establish a link between force adaptation and heart disease progression.

Developing a dedicated cyberinfrastructure for the application of data-demanding machine learning techniques to rare-event searches, using XENONnT, a dark matter search, as a testbed.

Using a deep-learning approach to overcome the challenges in the sensing accuracy and reliability of field effect transistors sensors and achieve real-time monitoring of multiple nutrient ions simultaneously.

Developing next generation microelectronics with on-chip machine learning for future intelligent pixel detectors.

Establishing a fully automated, research-grade platform for the assembly and evaluation of batteries by integrating AI-driven models for predicting electrolyte properties with a high-throughput (HT) robotic system for battery construction.

Using machine learning to develop a fully automated electron microscopy workflow to greatly increase data acquisition and analysis throughput by automatically acquiring and denoising images and classifying size and shape dispersity.

Using high-plex immunofluorescence to develop meaningful and interpretable spatial features of the tumor-immune microenvironment in breast cancer to improve prediction of patient response to therapy.

Addressing the inherent complexities of materials science by developing a graph neural network to predict material properties.

A machine learning pipeline that extracts the underlying principles of cell behavior from experimental data, offering new possibilities for the application of bioengineering in clinical diagnosis.

Developing new machine learning techniques to improve spectroscopy and discover new molecules present in your body, your microbiome, and the environment.

Using machine learning and statistical modeling to create thousands of synthetic microbiomes that could help safeguard human health and protect the environment.

How can we better detect and remove dangerous contaminants from water?

Explore the mathematical limits of learned emulators for climate, astrophysics, and high-energy physics models and quantify key trade offs related to accuracy and speed

Design new “system-on-a-chip” hardware to help researchers in data-intensive fields monitor data quality and detect promising results without interrupting the flow of data

Use AI to find new, efficient ways of “recycling” CO2 into valuable products such as ethanol and ethylene, using clean electricity generated from the sun and wind

Use AI to automate the design, optimization and forecasting of cosmic survey experiments.

Use machine learning and computer vision to classify cloud textures and patterns, and how those features have changed over time, in order to improve climate projections.

Scientists will use computational techniques to map the neural network of an octopus, the largest invertebrate brain, and better understand the fundamental principles of neuroscience.

Combine machine learning with high-throughput gene synthesis and rapid assay to learn nature’s rules for protein design and create synthetic proteins with elevated or new properties.