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OppyAI, an NJIT spinout led by Assoc. Prof. Hai Phan and co-founders Prof. Ruoming Jin from Kent State University and Prof. My T. Thai from the University of Florida, have tackled the challenge of using powerful cloud AI without handing over readable prompts, confidential context, or generated responses. The solution, Neural Crypto, is a technology designed to combine cloud-scale intelligence with customer-controlled privacy. Now, industry collaboration, research recognition, and new investment are helping to move it forward for commercial use.
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| Distinguished Professor David Bader, a pioneering scientist whose work helped reshape modern supercomputing and continues to expand what researchers can learn from massive, interconnected data sets, is this year’s winner of NJIT’s Excellence in Research Award.
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Ph.D. candidate Erfan Arvan, along with NJIT assistant professor Martin Kellogg and collaborators at William & Mary, published their work On the Reliability of Code Comprehension Proxies and won a Distinguished Paper Award at the IEEE/ACM Automated Software Engineering conference. The paper investigates correcting flawed approaches in the study of code comprehension that will teach programmers to write code more easily.
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Distinguished Professor Julie Ancis provided her expertise in cyberpsychology and online hate on Sept. 1 before the New Jersey Advisory Committee to the U.S. Commission on Civil Rights. The committee invited her to address “cyberpsychology and the impacts of antisemitic rhetoric found on the internet,” particularly how online content influences young people and enters college environments.
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High school robotics teams from Vancouver, B.C., Irvine, Calif. and Flushing, N.Y. won the 2026 Highlander Summit at New Jersey Institute of Technology’s Wellness and Events Center this fall.
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AWS Student Builder Group at NJIT AI Chatbot Workshop Attracts 80 Sign Ups Prior to Approval |
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On September 30, the Amazon Web Services (AWS) Student Builder Group at NJIT hosted "Build Your Own AI Chatbot with Amazon Bedrock & Claude Code." Using the AWS Workshop Studio sandbox, students built their own AI chatbots in a single afternoon, no AWS account or paid subscription needed.
Over 80 students signed up before AWS Student Builder Group were even an officially approved club, and more than 40 participants completed the lab and earned their certificates. The feedback they received was overwhelmingly positive, and many students are already asking when the next one is. This indicates that there's a real appetite for hands-on cloud and AI learning at New Jersey Institute of Technology .
AWS Student Builder Group extends a huge thank you to everyone who joined, to their officers for the dry runs and late nights, to the faculty advisor Dr. Cristian Borcea for his support, to the AWS Student Builder Group Manager Isabella Atchina and Core team members Asmar Hasanova, Shippy Singh Serli Cihangül Ruchir Patel Dorukhan Cakir Ferit (Farid) Altay and Anvar Islamov.
The next event will be announced soon. Stay tuned!
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Capstone Sponsors Meet Their Fall 2026 Team Candidates |
Capstone sponsors from all areas of industry presented their project pitches and were matched with interested students at the annual September open house meeting session. The chosen teams are tasked with identifying an organizational challenge and producing a viable solution to move that business forward. This senior experiential learning requirement is a signature final step in developing top tier professionals who will transform today's technology - and tomorrow's as well.
Interested in becoming a sponsor or future student? Learn more.
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Md Rakibul Hasan and Dimitri Theodoratos Won Best Paper at DaWaK 2026.
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Congratulations to Ph.D. candidate Md Rakibul Hasan and his advisor, Associate Professor Dimitri Theodoratos, for winning the Best Paper Award at the 28th International Conference on Big Data Analytics and Knowledge Discovery in Graz, Austria. Their paper, Database Tuning via Distributional Reinforcement Learning for Hybrid Transactional/Analytical Processing Workloads, introduces a machine learning technique that automates database system tuning—an increasingly critical capability in modern data platforms.
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