Data Science Environments that Promote Reuse
The past decade has brought a sea change in the availability of data. Instead of a world in which we have small number of carefully curated data sources — instead we have a plethora of Read more…
Modern Machine Learning Algorithms for Ranking and Choice Data
Problems involving learning from ranking and choice data arise in several domains, ranging from sports rankings to elections and from recommender systems to marketing. Indeed, algorithms for these problems have been developed in many different Read more…
DELPHI: Deep Logical Reasoning via Neural Learning
The overarching goal of the DELPHI project is to simplify the development of correct and reliable software. Existing methodologies require significant human expertise, such as in the design of appropriate abstract domains and environment models, Read more…
StreamQRE: Programming Abstractions for Data Stream Processing Systems
The Internet of Things (IoT) promises to change the way we acquire and handle data — as Internet-connected devices (wearable or implantable medical sensors, environmental sensors, devices) “stream” data back to the cloud for processing, Read more…
Developing Real-Time Virtualization
Recent years have witnessed two major trends in the development of complex real-time systems. First, they are moving from physically isolated hosts towards common computing platforms shared by multiple systems. Using common platforms can bring Read more…
Design and Analysis of Closed-Loop Medical Cyber-Physical Systems
Critical care involves the use of two common types of medical devices: monitoring devices, such as bedside monitors; and actuation devices, such as infusion pumps. Traditionally, caregivers need to collect information from the monitoring devices Read more…
Development of Attack-Resilient Cyber-Physical Systems
The project aims to develop control systems for ground vehicles that are resilient to a variety of external attacks. Our approach is to combine control-level techniques and code-level techniques, resulting in the approach comprises of Read more…
Network Provenance
Operators of distributed systems often find themselves needing to answer a diagnostic or forensic question. Some part of the system is found to be in an unexpected state; for example, a suspicious routing table entry Read more…
Theory and Practice of Differential Privacy
Large, diverse datasets hold tremendous promise, if only we can derive statistical insights from them. But often, these datasets are siloed and withheld, because of privacy concerns. Differential privacy can mitigate these concerns — it Read more…
Fairness in Machine Learning
Important decisions such as police deployment, loan approvals, hiring, and parole from incarceration are beginning to be made by machine learning algorithms. This has lent urgency to the question of whether these algorithms are fair. Read more…
Foundations of Adaptive Data Analysis
Classical tools for rigorously analyzing data make the assumption that the analysis is static: the models to be fit, and the hypotheses to be tested are fixed independently of the data, and preliminary analysis of Read more…
Improving GPU Performance and Programmability
In recent years, graphics processing units (GPUs) have thoroughly permeated consumer processor designs. It is now essentially impossible to find a smartphone, tablet or laptop without a substantial integrated GPU on the processor die. Utilizing Read more…
Measuring the World’s Well-Being
The World Well-Being Project (WWBP) is pioneering scientific techniques for measuring psychological well-being and physical health based on the analysis of language in social media. As a collaboration between computer scientists, psychologists, and medical researchers, Read more…
Facilitating Trustworthy Data Science
When a data analysis result is published or shared, how do we know whether it is trustworthy? Conceptually, we must show that each step of the analysis (and each set of parameters used in the Read more…
DeDOS: Declarative Dispersion-Oriented Software
The goal of this project is to create fundamentally new defenses against distributed denial-of-service (DDoS) attacks that can provide far greater resilience to these attacks compared to existing solutions. Today’s responses to DDoS attacks largely Read more…
The Science of Deep Specification
In our interconnected world, software bugs and security vulnerabilities pose enormous costs and risks. The Deep Specification project addresses this problem by showing how to build software that does what it is supposed to do, Read more…