Steven Zanganeh

Title: Assistant Professor

Department: Bioengineering

Campus: Long Island

Area(s) of Expertise: Cancer Immunoengineering and Iron Immunometabolism

Education Credentials: Ph.D.

Joined New York Tech: 2024

Steven Zanganeh, Ph.D., is a bioengineering expert and assistant professor at New York Institute of Technology’s College of Engineering and Computing Sciences. His lab integrates immunoengineering, advanced biomaterials, and cellular imaging to develop human-relevant platforms for precision oncology. Current efforts span 3-D bioprinting of functional, organ-mimicking tissues for preclinical drug evaluation, immune-informed therapeutic design, and the discovery of diagnostic and predictive cancer biomarkers. A central theme of his research is understanding and manipulating the immune microenvironment, particularly the roles of macrophages, iron metabolism, and inflammatory signaling in cancer progression and treatment response. By incorporating these mechanistic insights into engineered, human-relevant testbeds that reduce dependence on conventional cell and animal models, his team aims to accelerate the translation of cancer immunotherapy into clinically meaningful diagnostics and therapies.

Zanganeh earned his Ph.D. in Biomedical Engineering from the University of Connecticut and completed postdoctoral training at Stanford University School of Medicine, followed by research appointments at Memorial Sloan Kettering Cancer Center. He has authored numerous seminal peer-reviewed publications in Nature Portfolio and Science family journals, as well as Advanced Science, multiple books, more than 20 book chapters, and 42 U.S. and international patents in bioengineering and translational cancer research.

He also actively mentors undergraduate and graduate trainees and is deeply engaged in the entrepreneurship domain, translating academic innovations into practical biomedical and healthcare solutions. At New York Tech, Zanganeh teaches and leads a multidisciplinary research program focused on building robust, clinically meaningful tools to evaluate therapies and guide treatment decisions.