First-Year Professor Hao Wang Building Smarter, Safer AI Systems for the Real World
Assistant professor in the Department of Electrical and Computer Engineering balances a full research docket with meaningful personal pursuits outside the lab
Hao Wang was drawn to Stevens Institute of Technology due to its rare combination of strong technical research, interdisciplinary collaboration and proximity to industry. He was particularly interested in the university’s strategic focus on artificial intelligence and data science as well as the opportunity to contribute to an institution that values foundational research and real-world impact.
Having just finished his first year as an assistant professor in the Department of Electrical and Computer Engineering following a three-year, eight-month stint at Louisiana State University, Wang has been making the most of the opportunity in Hoboken.
Since arriving, Wang has launched the IntelliSys Lab, where he and his students develop intelligent systems that can operate securely and efficiently in real-world environments. His work focuses on federated learning, serverless computing and AI security – technologies that allow devices to learn collaboratively without sharing sensitive data.
Wang currently leads or co-leads six National Science Foundation (NSF) projects and one National Institutes of Health (NIH) project. These initiatives range from harvesting idle computing resources in high-performance computing systems to assessing bias in opioid prescribing algorithms.
He also advises four Ph.D. students, with plans to expand his team to seven next academic year. Their work spans trustworthy AI, serverless computing and large language model (LLM) systems.
"I’ve been impressed by the strong sense of community and support, both from my colleagues in the Department of Electrical and Computer Engineering and across the broader Stevens campus," said Wang about his first year. "I’ve found the students to be motivated and curious, and the administrative staff incredibly helpful. There’s also a healthy culture of innovation and collaboration here that has only deepened my appreciation for the institution."
Hitting the ground running in the lab
While working on his Ph.D. at the University of Toronto, Wang became particularly intrigued by the idea of federated learning – how people can train intelligent models collaboratively while preserving privacy.
"I saw firsthand how existing solutions struggled with efficiency, security and scalability," he added. "That motivated me to bridge the gap between algorithmic innovation and systems design."
In the IntelliSys Lab, his team focuses on making machine learning more efficient, secure and practical for real-world use, especially in smartphones or edge devices such as routers and switches, in which data is sensitive and computing resources are limited. The lab focuses on federated learning and AI security, creating frameworks that defend against threats such as poisoning attacks and system faults.
In addition to ML/AI, his lab also addresses fundamental challenges in system research, such as mitigating cold-start latency (the time delay between request and response) in serverless computing and optimizing the performance of LLMs using advanced architectures. In interdisciplinary applications, the team applies AI to healthcare and materials science, including projects such as an ML-enhanced opioid risk scoring system and a self-charging autonomous smart car showcased at the Stevens Innovation Expo.
Racking up the grants
As previously mentioned, Wang currently leads or co-leads six National Science NSF projects and one NIH project, each addressing a unique challenge in artificial intelligence systems.
Wang’s NSF-funded work includes optimizing high-performance computing (HPC) by harvesting idle resources, enhancing energy efficiency in federated learning for mobile AI systems, and improving the security and robustness of federated learning through the study of critical learning periods. He is also developing a hybrid CPU/GPU serverless computing platform to accelerate deep learning applications and contributing to the emerging field of AI model forensics, focusing on data integrity and provenance.
Another NSF project under his co-leadership aims to design a resilient NextG network for federated learning on mobile devices. In collaboration with the NIH, Wang is also working on assessing and mitigating bias in machine learning models used for opioid prescribing, applying advanced statistical and deep learning techniques to promote ethical and equitable healthcare AI.
"In the short term, I aim to improve the reliability and efficiency of federated learning and serverless AI systems in mobile and high-performance computing contexts. Long term, I want to establish a more unified framework that bridges AI with large-scale, high-performance computing systems, enabling robust learning across decentralized, dynamic system environments," said Wang of his current research aims. "Ultimately, I hope my work can support scientific discovery, environmental resilience and secure infrastructure for intelligent applications."
A renaissance man off campus
Clearly, Wang has stayed busy in the lab and classroom, where he teaches AAI 595 - Applied Machine Learning for graduate students and senior undergraduates. He also recently hosted DuckAI 2025, a symposium where students presented AI projects and networked with industry leaders from IBM, Google and hedge funds.
But that hasn’t taken him away from his personal interests. Wang founded the Faculty Badminton Club to help faculty de-stress. Wang is also a passionate artist who enjoys watercolor painting and photography. He began painting as a child and continues to find it a meaningful creative outlet, drawing inspiration from nature, architecture and everyday moments. Wang sees a strong connection between art and science, noting that both disciplines rely on observation, abstraction and thoughtful iteration. His photography, often captured during his travels for academic collaborations, allows him to preserve and reflect on significant experiences.
"As a faculty member, I’ve had the opportunity to travel widely to connect and collaborate with researchers around the world," said Wang. "Along the way, photography has allowed me to capture meaningful moments – both fleeting and profound – that reflect the spirit of these experiences."