Symposium on Science, Technology and Health 2026

New York City  I  May 19, 2026

Showcasing graduate student research across science, technology and health sciences.

Author: Lei Zhang and Chengyi Liu, Artificial Intelligence
Faculty Mentor: Yucheng Xie, Ph.D.

Lei Zhang and Chengyi Liu presented a robotic health-monitoring system that measures heart rates using millimeter-wave radar instead of wearable devices or cameras. Their system protects privacy and functions even in low-light conditions. The students discovered that radar measurements became much less accurate when devices were placed at poor angles, so they created a robot capable of repositioning itself automatically to improve readings. By combining robotic navigation, body-position tracking and signal analysis, the system more than doubled heart-rate accuracy compared to fixed radar systems, potentially opening new possibilities for hospitals and elder-care facilities.

Author: Tirth Joshi, Artificial Intelligence
Faculty Mentor: Aaron Ross, MBA

Tirth Joshi explored the growing dangers of digital privacy loss in an era of interconnected public data. His project introduced the Re-Identification Pressure Index, or RPI, a system designed to measure how easily supposedly anonymous people can be identified when multiple datasets are combined. By studying public information such as housing records, transportation data and work locations, Joshi demonstrated how small pieces of information can gradually reveal personal identities over time.

Author: Dharani Vedula, Biotechnology Management and Entrepreneurship
Faculty Mentors: Robert Friedman, MBA

Dharani Vedula explored how new brain-repair technologies can move from the laboratory into real medical use in her project, “NEURADAPTIVE: Market and Investor Strategy for an AI-Native Brain Repair Platform.” Her research focused on Neuradaptive, a startup developing technology that combines artificial intelligence, brain-computer interfaces and targeted brain stimulation to help restore function in people with serious neurological conditions.

Author: Srujan Dasari, Cybersecurity
Faculty Mentor: Daniel Galeon, MBA

Srujan Dasari demonstrated an intelligent AI assistant designed to automate many of the repetitive tasks performed by human security analysts. Built using the Model Context Protocol, the system safely allowed large language models to interact with cybersecurity tools while maintaining audit trails and permission controls. In testing, the platform reduced some security analysis tasks from nearly an hour to just minutes, offering a potential solution for increasingly overwhelmed cybersecurity teams.

Author: Emmanuel Olimi Kasigazi, Data Analytics and Visualization
Faculty Advisor: Andrew Catlin, M.S.

Education access stood at the center of Data analytics student Emmanuel Olim Kasigazi’s project, AXAM, an offline artificial intelligence learning platform designed for students without reliable internet access. Built using thousands of lecture transcripts from Massachusetts Institute of Technology OpenCourseWare, the system can answer educational questions in more than 100 languages while running entirely on local devices such as laptops or USB drives. Tests showed that AXAM correctly retrieved educational content roughly 99% of the time in English while also performing strongly in several other languages.

Authors: Chipo Prudence Pasi and Tiyese Kyle Phiri, Digital Marketing and Media
Faculty Mentor: William Wedo, MJ, MBA

Chipo Prudence Pasi and Tiyese Phiri examined how “agentic AI” systems capable of adapting messages in real time could improve digital marketing for faith-based audiences. Their research emphasized the importance of trust, cultural values and religious identity in shaping audience responses. The students argued that AI-generated marketing becomes more effective when it reflects values such as integrity, community and purpose instead of relying on generic advertising language.

Authors: Hangmu Mabuhang, Sebika Bomjan Shahi, Thulani Arnold Nsingo and Aditya Bhandari, Digital Marketing and Media
Faculty Mentor: Shimon Perry, MBA

Aditya Bhandari, Hangmu Mabuhang, Sebika Bomjan Shahi and Thulani Nsingo studied why pharmaceutical research companies are increasingly reluctant to renew costly data-analysis services. Focusing on the company Cytel, the team found that many clients struggle to clearly understand the financial value of the services they receive, especially as lower-cost AI tools and open-source software become more common. Their proposed solution combined clearer return-on-investment reporting with flexible service packages tailored to smaller biotechnology firms.

Author: Fildah Tsuro, Digital Media and Marketing
Faculty Mentor: Joseph Panzerella, M.S.

Housing access was the focus of Fildah Tsuro’s research on newcomers to the United States. Her project proposed replacing traditional credit-score requirements with a “Multi-Factor Reliability Index” that considers school enrollment, employment contracts, international banking records and identity verification. Tsuro argued that the system could reduce fraud while helping immigrants and international workers gain fairer access to rental housing in New Jersey and New York.

Author: Denelsen Dandi, Digital Marketing and Media
Faculty Mentors: Erik Wennerod, MBA and Joseph Panzarella, M.S.

Denelsen Dandi compared challenges facing cattle farmers in Zimbabwe and the United States. While producers in both countries struggled with disease, drought and market pressures, the problems differed substantially by region. Zimbabwean farmers often faced shortages of veterinary services and reliable information, while U.S. farmers focused more heavily on monitoring large herds and detecting disease early.

Author: Ian Phiri Chigada, M.A. in Mathematics
Faculty Mentor: Yuri Katz, Ph.D. 

Ian Phiri Chigada studied whether the language used in Federal Reserve meeting notes could help predict future interest-rate decisions. Comparing traditional word-count methods with AI-generated sentiment analysis, he found that more inflation-focused language often appeared before rate hikes, though the predictive signal remained modest.

Author: Arya Dutta, Ph.D. in Mathematics
Faculty Mentor: Marian Gidea, Ph.D.
Collaborator: Claudio Sierpe, Ph.D., University of San Paolo

Arya Dutta investigated the orbital behavior of comet 39P/Oterma using advanced mathematical models describing the gravitational interactions among the Sun, Jupiter and the comet itself. His work identified additional orbital resonance patterns that may explain why the comet sometimes drifts toward Saturn after close encounters with Jupiter.

Author: Ruslan Gokhman, Ph.D. in Mathematics
Faculty Mentor: Marian Gidea, Ph.D.

Ruslan Gokhman challenged assumptions about modern forecasting systems by comparing complex transformer-based AI models with simpler linear mathematical approaches for long-range temperature prediction. Surprisingly, the simpler models consistently outperformed the more advanced AI systems in accuracy and stability, demonstrating that straightforward methods can still provide the strongest scientific results in certain applications.

Author: Rachel Riemenschneider, Occupational Therapy
Faculty Mentor: Teresa Ludwig, OTD, OTR/L, ASDCS

Rachel Riemenschneider created an occupational therapy guidebook for families navigating frenectomy procedures, helping parents better understand feeding support, exercises and recovery after surgery involving oral tissues.

Author: Vanessa Murad, Occupational Therapy Doctorate
Faculty Mentor: Terrie Ludwig, OTD, OTR/L, ASDCS
Industry Partner: Anne Sinha, MOT, OTR/L, SWC, Art of Living

Vanessa Murad examined how occupational therapists can help children with picky eating and Avoidant Restrictive Food Intake Disorder, or ARFID. Her educational guide for parents explained how occupational therapy can support feeding skills, sensory challenges and family mealtimes.

Author:  Jemma (Shayna) Lifschitz, M.S. in Speech-Language Pathology
Faculty Mentor: Elisabeth Mlawski, Ph.D., CCC-SLP

Jemma (Shayna) Lifschitz studied how asthma affects children’s speech production and found that breathing exercises may improve both asthma control and speech clarity.

Author: Jennifer Schneider, M.S. in Speech-Language Pathology
Faculty Advisor: Marissa A. Barrera, Ph.D., MSCS, CCC-SLP

In Jennifer Schneider's research, she examined how accurately emergency medical workers identify communication problems such as aphasia, a disorder affecting language understanding and expression, and dysarthria, a condition that causes slurred or weakened speech, when evaluating possible stroke patients. By reviewing commonly used ambulance stroke screening tools, she found that communication symptoms are often described too generally or inconsistently, which can lead to missed strokes, incorrect treatment decisions, and delays in life-saving care. Her findings showed that no single screening tool consistently identifies communication problems accurately, highlighting the need for better training and greater involvement of speech-language pathologists in emergency stroke care.