Research

Faculty members, undergraduates, and graduate students are integral to cutting-edge research under way not only in core computer science, but also in interdisciplinary areas. Our groundbreaking research is supported by agencies such as the National Science Foundation, the National Institutes of Health, the U.S. Department of Education, U.S. Army, Office of Naval Research, National Security Agency, IBM, and Google. See the latest Department SIGBITS issue for recent research happenings by faculty and students.

Fabricio Murai, Assistant Professor of CS, DS, AI

Fairer, faster and more Accurate AI Models with Graph Data

Roee Shraga, Assistant Professor of CS, DS, AI

Better Data for Better Science

Xiangnan Kong, Associate Professor of CS, DS, AI

Towards Understanding Brain Networks using Deep Learning

Craig Shue, Professor and CS Department Head

Operating System Isolation: Enabling Tailored, Asset-Centric Security through VMs

Morgan Lee, CS PhD Student

Expert Features for a Student Support Recommendation Contextual Bandit Algorithm

Abdulsalam Almadani, CS PhD Student

HCM-Echo-VAR-Ensemble: Advanced Echocardiogram Analysis Using Deep Learning

Reza Saadati Fard, CS PhD Student

Multimodal Neural Networks for Chronic Wound Decision Support

Oluseun Olulana, CS PhD Student

Hidden or Inferred: Fair Learning-to-Rank with Unknown Demographics

Adam Beauchaine, Cyber Security PhD Student

Clustering for Confidentiality: An Exploration of Unsupervised Learning for Security of Data Assets

Eric Warnemunde Vertina, DS PhD Student

Predicting Material Properties via Artificial Intelligence

Maryam Atai Kachooei, CS PhD Student

Improving TCP Slow Start Performance in Wireless Networks with SEARCH

Research Groups

Many research groups exist within the department. These groups hold regular meetings of faculty, grad students and undergraduate students to discuss current research topics and results. Departmental research groups include Applied Logic and Security (ALAS), Database Systems Research Group (DSRG), Performance Evaluation and Distributed Systems (PEDS) and the Tutor Research Group (TRG). Visit faculty member profiles to learn more about the research groups that individual faculty are involved in as well as when these research groups meet.

Research Labs

The Daisy Lab

Elke Rundensteiner - Professor

Data-Driven Intelligent Systems group is excited to solve the world's most pressing problems. We tackle a wide range of challenges from detecting fraud in systems, uncovering bias in AI models, discovering new materials, screening for mental illness, ensuring fairness in ranked AI decisions, making AI systems transparent and responsible, to extracting valuable insights from social media data.

Professor Rundensteiner Faculty Profile

ASSISTments Lab

Neil Heffernan - Professor

The ASSISTments lab conducts research using Prof. Heffernan's online math learning platforms ASSISTments. We study artificial intelligence in education, educational data mining, and intelligent learning systems. Our most recent work is funded by IES to develop an AI tutor (called CAIT) to help middle-school students learn math while doing homework. As part of this work, we are developing our own Large Language Model (called GOAT) to provide a safe AI experience to our student users in general and to provide tutoring support to lower-income students who have fallen behind. The ASSISTments Lab at WPI is located in Room 320 in Unity Hall with the Learning Sciences & Technology (LS&T) graduate program of which Prof. Heffernan is the program director.

Professor Heffernan Faculty Profile

The VIEW Lab

Lane Harrison - Associate Professor

In the VIEW Lab (the Visualization and Information Equity lab at WPI), Professor Harrison and students leverage computational methods to understand and shape how people engage with interactive data visualizations and visual analytics systems. VIEW lab work has been supported by the NSF, the US DoD, and industry.

Professor Harrison Faculty Profile

Clinical AI Research (CARE) Lab

Emmanuel Agu - Professor

The CARE Lab is focused on the intersection of AI and healthcare to tackle critical challenges. Our projects include Smartphone Wound Assessment (SmartWAnDS) to support nurses in analyzing wounds, and Cardiovascular Disease Detection using deep learning to identify conditions. We also focus on Gait Analysis via smartphones to detect intoxication and neurological issues, and on DARPA’s Warfighter Analytics, which assesses health condition. Other research areas include Wearable Infectious Disease Monitoring for early illness detection (e.g., COVID-19) and Neurolinguistic Assessment for mental health tracking. Additionally, our Chronic Pain Management study explores biomarkers to optimize mindfulness interventions for underserved populations with low back pain.

CARE Lab Website

The Cake Lab

Rose Bohrer - Assistant Professor, Mark Claypool – Professor, Jun Dai – Associate Professor, Tian Guo – Associate Professor, Craig Shue – Professor, Sherry Sun – Associate Professor, Robert Walls – Associate Professor, Craig Wills – Professor and Harmony Zhan – Assistant Professor

The Cake Lab Website

The Interaction Lab

Erin Solovey – Associate Professor

Professor Solovey Faculty Profile