His primary area of research is robust optimization in convex and nonconvex problems. Nohadani’s applied work addresses a variety of settings: cancer therapy, statistics, machine learning, nano-technology, ultrafast optics, high-performance computing, and vehicle routing. More recently, his interests have focused on the area of healthcare engineering, both from the methodological as well as treatment design aspect. He received his Diploma degree in mathematical physics from the University of Bonn, Germany and a Ph.D. in theoretical physics