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
See the latest Department SIGBITS issue for recent research happenings by faculty and students.
Craig Shue, Professor and CS Department Head
Operating System Isolation: Enabling Tailored, Asset-Centric Security through VMs
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
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
Faculty Research Interests
Our faculty have diverse research interests. Here is a table of interests and faculty doing work in those areas:
Graduate Student Research
Maryam Atai Kachooei, CS PhD Student
Improving TCP Slow Start Performance in Wireless Networks with SEARCH
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
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.
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.
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.
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.
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 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.
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.
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.
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.
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
Highlights & Accomplishments of our Award Winning Faculty
The Most Recent Edition of SIGBITS: A summary of department happenings involving faculty and students.