Faculty: R. Vidal

Enabling rapid point-of-care diagnostics by using AI to analyze lens free images of a drop of blood and produce complete blood count (CBC)


Faculty: R. Vidal and Q. Long

AI methods can be used to detect a brain tumor, assess the skill of a surgeon, count and classify blood cells, segment neurons, assess motor imitation

Faculty: C. Davatzikos and Team

AI methods can offer precision treatment guidance, treatment decision, and trial enrichment

Faculty: E. Eaton Collaborator: D. Hashimoto, MD

Reduce inadvertent injuries in minimally invasive surgery via computer vision and lifelong machine learning

Faculty: S. Goel

Design new optimization algorithms and architecture fixes to enable current large language models (LLMs) to solve logical “reasoning” tasks robustly and not “hallucinate”

Faculty: B. Lee

Understand and optimize the complex, interdependent dynamics in distributed computer systems, improving performance, efficiency, and reliability 

Faculty: S. Guntuku

Measure and intervene to improve individual and community level health behaviors across cultures and languages using natural language processing and machine learning methods

Faculty: M. Posa

Understand and optimize the complex, interdependent dynamics in distributed computer systems, improving performance, efficiency, and reliability 

Faculty: D. Lee, B. Moniri, X. Huang, E. Dobriban, H. Hassani

Conducted the first comprehensive theoretical analysis of disagreement-on-the-line and discovered a variety of new phenomena that were not observed in empirical studies

Faculty: D. Metaxa

Designing human-centered, AI-powered tools that improve people’s experiences with algorithmic media like targeted ads: We propose AdPlaylists, a browser-based system for end users to collectively control their online ad content

Faculty: C. Callison-Burch

Large language models (LLMs) are rapidly becoming adopted by users across the world. However, their performance and factual knowledge differs across languages. For geopolitical questions, these different responses are especially problematic, as they amplify differences in cultural viewpoints

Faculty: R. Vidal

Build AI systems that not only make predictions but also provide a sequence of questions and answers that explains why they made that prediction

Faculty: L. Ungar

Explain deep learning models that predict everything from politeness in different languages to outcomes in surgery

Faculty: E. Wong and R. Alur

Develop model wrappers that endow black-box models (i.e. deep networks) with formally verifiable explanations

Faculty: C. Callison-Burch

Self-interpretability with faithfulness guarantee, along with performance boost

Faculty: O. Bastani

Software systems are incorporating deep learning to solve diverse tasks including robotics control, medical diagnosis, code generation, protein synthesis, and question answering

Faculty: P. Chaudhari

Would like to build diagnostic and prognostic models using multi-source data (imaging, clinical, genetic factors) that can work across broad populations

Faculty: K. Danilidis and P. Chaudhari

Design a system that can detect other moving vehicles using minimum bandwidth and latency and under challenging illumination conditions

Faculty: K. Johnson and E. Eaton

Create a repository of multi-modal recorded patient encounters, while ensuring multi-level privacy and deidentification 

Faculty: I. Lee, E. Dobriban, and O. Sokolsky

Design a framework that bounds the error rates in anomaly detection

Faculty: A. Roth and M. Kearns

Huge amounts of sensitive data are used to train ML models (in industry) or produce aggregate statistical tables (in government: e.g. Census). But aggregation is not sufficient to protect privacy

Faculty: C. Callison-Burch

Text generated by large language models has proliferated with unprecedented, viral speed. How susceptible are people to being duped by machine-generated text? Can human users be trained to detect when text they are reading did not originate with a human writer? Research-based answers will help us understand the potential threats (fraud, misinformation) and how to mitigate them

Faculty: K. Johnson

What if, instead of spending more time documenting a clinical encounter than engaging with the patient, health care providers could leverage AI-based technology to generate documentation? Could such tools enable us to think of the computational system as a partner in ensuring the safety and quality of each visit?

Faculty: A. Roth

How can we derive meaningful and actionable uncertainty estimates to individuals from ML models?

Faculty: G. Pappas and H. Hassani

Use the insights from adversarial ML to attack problems in AI safety; tools from probabilistic and adversarial machine learning 

Faculty: M. Naik, R. Alur, and E. Wong

Disrupting model development in a fast-growing industry by putting neurosymbolic AI in the hands of every developer

Faculty: D. Jayaraman and O. Bastani

Shared representations facilitate flexible teaching, trust, and explanation between robots and humans 

Faculty: R. Alur, O. Bastani, and D. Jayaraman

To synthesize control policies for robotic tasks using RL, user must specify rewards as numerical values associated with states. Such reward engineering requires expertise and is error prone

Faculty: H. Bastani

Allocate a limited daily budget of COVID-19 tests to screen a subset of international travelers to Greece. Targeted testing of “high-risk” travelers can maximize the number of active infections identified

Faculty: Z. Ives, J. Liang, L. Cao, S. Madden, and G. Li

Can we train AI to detect important diagnostic or predictive events in time, across multi-modal and multi-channel time series data? Can we automatically cluster related phenomena in real-time? Can we build databases and digital biobanks of time series symptoms, and instantly find similar patterns?

Faculty: I. Lee

Develop a detector that classifies input images to DNN-based perception systems as clean or adversarial

Faculty: R. Alur, I. Lee, and G. Pappas

Design a controller mapping raw sensory data to control actions by training a neural network based on simulation data