Please see the CIS blog for news on what’s going on in the Department!
This paper investigates how well people can tell if text they’re reading was written by a machine instead of a human, especially when part of the text is human-written and later transitions to machine-generated. The Read more…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…