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Externally Funded Projects

Title: CRII: HCC: RUI: Toward Understanding of Virtual Reality Sickness in Children
Lead PI: Sharif Mohammad Shahnewaz Ferdous, Computer Science
Brief Description: In recent years, Virtual Reality (VR) technologies have become more affordable and accessible to a diverse population, including children. Unfortunately, one of the biggest challenges for VR technologies is virtual reality sickness, also known as cybersickness. The most common cybersickness symptoms include eyestrain, headache, sweating, fullness of head, disorientation, vertigo, nausea, etc. While much research focuses on cybersickness for adults, very few have been conducted for children. In this project, we will focus on understanding cybersickness in children.
The Simulator Sickness Questionnaire (SSQ) is the most popular method of measuring cybersickness, even though some of the questions can be harder for children to comprehend. At the beginning of the project, TCNJ undergraduate student researchers will develop a web application that augments the existing SSQ with animation that makes it easier for children. In addition, they will be trained on recording and analyzing electroencephalography (EEG). In the next step, student researchers will develop a VR roller coaster simulation to study cybersickness using subjective (SSQ) and objective (EEG and heart rate variability) measures in children and compare them with adults. Finally, researchers will investigate the conditions of different virtual environment (VE) conditions (e.g., speed, angular speed, rotation, acceleration, change in lighting, change in contrast etc.) and their effect on cybersickness in children. Ultimately, the researchers will formulate the very first guidelines for developing VR content for children.
Funded By: National Science Foundation (NSF), Division of Information and Intelligent Systems (IIS), NSF Award #2104819.
Funded Period: October 1, 2021 – September 20, 2023

Title: RI: Medium: Learning Joint Crowd-Space Embeddings for Cross-Modal Crowd Behavior Prediction
PI: Sejong Yoon, Computer Science
Project URL
Brief Description: Many societal activities, including air transport, disaster remediation, social events such as concerts and sports, require efficient and effective methodologies for monitoring, understanding, and reacting to behaviors of large concentrations of people, the crowds, that give rise to those events. Simultaneously, the type and evolution of those behaviors are intimately tied to the form and function of the environments where they occur. As crowds increase in size or change their actions in response to intrinsic or extrinsic factors, it is critical for the built environments, including their future designs, to adapt to those changes. Present-day technological tools aim to analyze and predict the link between crowds and environments. However, they rely on rigid, hand-tuned, computationally costly simulation models, severely limiting their practical utility. This project seeks to bridge this gap by devising a novel way of modeling the inherent relationship between the structure and semantics of complex environments, and the presence and behavior of its human occupants, from small groups to dense crowds. The main goal is to predict crowd behavior accurately, from microscopic motion to aggregate crowd dynamics, in novel, never-before-seen environment configurations using Neuro-Cognitive Modeling of Environments and Humans (NUCLEUM) to replace the computationally expensive yet often mismatched-with-reality physical simulations.

To accomplish this goal, this project collaboratively seeks to tackle the problem of predicting crowd behavior in complex environments by learning data-driven models that will seamlessly “translate” between different representations of crowds and their environments. Specifically, this project has three main research thrusts: (Thrust 1) Learning a Joint Crowd-Space Representation. The project will develop a novel multi-concept transfer learning framework to enable coupled learning across three highly heterogeneous concepts: (a) environment layouts (e.g., floor plans), (b) macroscopic crowd properties (e.g., flow), and (c) microscopic crowd trajectories. Once learned, the framework will enable predictions of flow patterns of a crowd, directly from the layout of an environment and vice versa. (Thrust 2) A Hybrid Multi-modal Corpus of Environment Contexts and Crowd Movement. This project will create a novel hybrid multi-modal corpus of environmental contexts and crowd behavior, which will leverage data from field observations, controlled laboratory experiments, crowd simulations, and multi-user virtual reality platforms. This corpus will allow training models that generalize across the space of environment and crowd conditions. (Thrust 3) Model Evaluation, Applications, and Use Cases. Trained models’ robustness will be evaluated in terms of their ability to produce valid crowd trajectories, which are statistically similar to ground truth observations while generalizing to the new, unseen crowd, and environmental contexts. This project will subsequently apply the trained models in a variety of application contexts on real-world built and yet-to-be-built environments to predict crowd behavior in unseen environments, identify vulnerabilities in environments, and reconfigure environment designs to improve crowd behavior.
Funded By: National Science Foundation (NSF), Division of Information and Intelligent Systems (IIS), NSF Award #1955365.
Funded Period: October 1, 2020 – September 30, 2024

Title: Collaborating Across Boundaries to Engage Undergraduates in STEM Learning (CAB).
Lead PI: S. Monisha Pulimood, Computer Science
Co-PI: Kim Pearson (Journalism) and Diane Bates (Sociology)
Project URL:
Brief Description: This project aims to serve the national interest by studying how interdisciplinary collaborations in the classroom can improve STEM learning for all undergraduates. The increasingly interdisciplinary and complex issues facing our society requires diverse, STEM-literate experts from a range of fields who can work and solve problems in collaboration. Addressing this national need requires innovative, research-based teaching practices that retain students and improve STEM learning. This project will expand, improve, and study an innovative curricular model in which two undergraduate courses from different disciplines are taught in coordination. The instructors, goals, and outcomes of each course will be distinct, but the courses will be connected by a science-focused project that is developed through an active collaboration with a community partner. By the end of the project, 750 students will have experienced this model, allowing for a comprehensive evaluation of its effectiveness. Furthermore, over a dozen faculty members in different disciplines will be trained in using effective strategies for teaching STEM concepts. This project will contribute to educational strategies that can produce the STEM-literate workforce needed to tackle the pressing interdisciplinary problems of our time.
Funded By: National Science Foundation (NSF), Division of Undergraduate Education (DUE), Improving Undergraduate STEM Education (IUSE), NSF Award #1914869.
Funded Period: Academic Years 2019 – 2022.

Title: MRI: Acquisition of Hardware for the Enhancement of the ELSA High Performance Computing Cluster to Enable Computational Research at The College of New Jersey
Lead PI: Joseph Baker, Chemistry
Co-PIs: Michael Ochs (Mathematics and Statistics), Wendy Clement (Biology), Paul Wiita (Physics), Michael Bloodgood (Computer Science)
Project URL:
Brief Description: The College of New Jersey (TCNJ) will acquire equipment to significantly upgrade and enhance the Electronic Laboratory for Science and Analysis (ELSA) High Performance Computing cluster. TCNJ is a primarily undergraduate institution promoting a deep engagement of undergraduate students in research. Many of TCNJ’s School of Science faculty members are working at the cutting edge of computational research in their fields, which include a broad range of areas including biochemistry/biophysics, genetics, bioinformatics, astrophysics, machine learning, and mathematical biology. In order to maintain a diverse and state of the art resource that meets the current and future computational needs of TCNJ’s faculty and undergraduate students the current ELSA cluster requires targeted hardware enhancements. The new instrument will (1) enhance the research capacity and resulting scientific discovery of TCNJ’s School of Science faculty members and their undergraduate research teams; (2) expose a greater number of undergraduate students and researchers to this powerful computational infrastructure through a series of newly developed High Performance Computing and data visualization short courses and workshops; and (3) improve access to the ELSA cluster for students traditionally underrepresented in STEM, as well as to researchers beyond TCNJ through a new collaboration with Open Science Grid.
Funded By: National Science Foundation (NSF), Office of Advanced Cyberinfrastructure (OAC), NSF Award #1828163
Funded Period: September 1, 2018 – August 31, 2021


Title: FIRSTS (Foundation for Increasing and Retaining STEM Students) Program: A bridge program to study the sociological development of science identities.
Lead PI: Sudhir Nayak, Biology
Co-PI: Benny Chan (Chemistry), Lynn Gazley (Sociology & Anthropology), S. Monisha Pulimood (Computer Science), Su Van der Sandt (Mathematics & Statistics)
Project URL:
Brief Description: The overall goals of this program are to help students from underserved populations transition to the rigors of the STEM curriculum and gather both qualitative and quantitative data on the reasons financially needy students elect to leave STEM disciplines. Rather than simply filling content gaps, FIRSTS will use the remediation of study skills in a interdisciplinary summer course, extensive mentoring, and the development of a science identity to improve success in STEM disciplines. In addition to student-focused strategies, the program also will incorporate faculty development in the improvement of teaching methods and mentoring through participation, training, interdisciplinary interactions, and discussions.
Funded By: National Science Foundation (NSF), Division of Undergraduate Education (DUE), Improving Undergraduate STEM Education (IUSE), NSF Award #1525109.
Funded Period: Academic Years 2016 – 2019.

Title: Scholarships for Success in Computational Science.
Lead PI: Thomas Hagedorn, Mathematics & Statistics
Co-PI: Monisha Pulimood, Computer Science
Evaluator: Diane Bates, Sociology
Project URL:
Brief Description: This grant forms the basis of a sustainable initiative to recruit, retain and graduate more students in computer science and mathematics at TCNJ. The project will fund approximately 27 scholarships per year for computer science and mathematics students who will be organized into learning communities and engage in research focused on a common theme of computational science. The project will also provide significant advising, mentoring, and tutoring services that supplement those already provided by the college.
Funded By: National Science Foundation (NSF), Division of Undergraduate Education (DUE), Scholarships for Science, Technology, Engineering and Mathematics (S-STEM), NSF Award #1356235.
Funded Period: Academic Years 2014 – 2018.

Title: EAGER: Algorithms for Synthetic Gene Library Design.
Lead PI: Dimitris Papamichail, Computer Science
Co-PI: J. Rob Coleman, SUNY – Farmingdale State College
Brief Description:This grant will enable exploration of the combinatorial design of synthetic gene variants to aid the construction of large scale, purposed libraries. The aim is to assay the most important sequence features which determine gene expression, while minimizing experimental cost and maximizing the exploration of the coding landscape.
Funded By: National Science Foundation (NSF). CCF Division of Computing and Communication Foundations. NSF Award #1418874.
Funded Period: Academic Years 2013 – 2015.

Title: TUES: Collaborating Across Boundaries to Engage Undergraduates in Computational Thinking.
Lead PI: Monisha Pulimood, Computer Science
Co-PI: Kim Pearson, Journalism
Evaluator: Diane Bates, Sociology
Project URL:
Brief Description: To develop a model for students and faculty to collaborate across diverse disciplines and with a community organization to develop technology-based solutions to address complex real-world problems. As a proof-of-concept, this project focuses on collaboration between computer science and journalism faculty and students, and the Habitat for Humanity to address the problem of pollution in targeted neighborhoods of Trenton, NJ.
Funded By: National Science Foundation (NSF), Division of Undergraduate Education. NSF (DUE), Award #1141170.
Funded Period: Academic Years 2012 – 2015.

Title: Giving the Maestro a Human Heart: Fostering Creativity in a Multi-Disciplinary Undergraduate Environment
Lead PI: Andrea Salgian, Computer Science
Co-PIs: Chris Ault (IMM), Teresa Nakra (Music), Jennifer Wang (Mech. Engineering)
Project URL:
Brief Description:The “Conducting Robots” project at The College of New Jersey is a platform for teaching interdisciplinary teamwork and creative problem solving to undergraduate students in Engineering, Interactive Multimedia, Music, and the Sciences. Students work collaboratively to design and build human-scale robots and abstract animations that conduct the TCNJ Orchestra at the end of each semester. The students develop expertise in building real-time systems that perform functions in music listening, pitch and tempo estimation, beat tracking, emotion/gesture generation, and score following. The student-designed and student-built robots interact directly with musicians and receive feedback that is then applied toward iterative design and revision of the musician-robot interaction.
Funded By: National Science Foundation (NSF). Award #0855973.

Title: COMTOR: Enabling Students and Educators to Automatically Assess Software Documentation and Source Code Comments
Lead PI: Peter DePasquale, Computer Science
Co-PI: Miroslav (Mike) Martinovic, Computer Science
Brief Description: COMment MenTOR (COMTOR) is a toolset for automatically assessing the quality of source code comments. Since comments possess a more free-form nature than most constructs in traditional programming languages, the process of grading this type of documentation requires a significant amount of manual effort. COMTOR will automate and reduce the effort of grading comments. At the same time, the use of COMTOR will give students a feedback process which allows them to self-assess the quality of their comments before submitting assignments for grading. A comprehensive assessment will determine whether the quantity and quality of commenting improves with the use of COMTOR as well as establishing whether advanced or introductory programming students benefit more from its use.
Use of COMTOR may lead to improvements in the frequency with which students write and modify comments, and the quality of the comments themselves. Comments and other documentation which encourage the practice of reflective design and continuous evaluation during development have the potential for a transformative effect on the software industry.
Funded By: National Science Foundation (NSF).

Title: Broadening Participation in Computing via Community Journalism
Lead PI: Ursula Wolz, Computer Science
Co-PIs: Kim Pearson (English) and Monisha Pulimood (Computer Science)
Project URL:
Brief Description: The Interactive Journalism Institute for Middle Schoolers (IJIMS) was designed to introduce middle schoolers from underrepresented populations to opportunities in computing by following the shift of journalism onto the Web. Through the institute, middle school students and their teachers create an online magazine to learn computational thinking via digital media, interactive graphics, animation, video and database design in a collaborative setting. They gain confidence in their computational and writing skills and to share their online magazine with family, friends and teachers.
Funded By: National Science Foundation (NSF). DUE-CISE Division of Undergraduate Education. Award #0739173.
Funded Period: Academic Years 2007 – 2011.