One Faculty Fellow is appointed from each of the three colleges; CAHSS, CNMS and COEIT and serve a two-year term. Nominations are sought from faculty who exemplify the entrepreneurial mindset inside and outside the classroom and who are recognized champions of entrepreneurship initiatives on the UMBC campus.
Dr. Michael Andrews CAHSS
| Phone |
443-812-4538
|
|---|---|
| mandrews@umbc.edu | |
| Education |
PhD, Economics, The University of Iowa
B.A. Economics, University of Maryland
|
About
Dr. Andrews is an assistant professor in the economics department. His research and teaching focus on the economics of innovation and entrepreneurship, as well as economic history. He is co-editor of The Role of Innovation and Entrepreneurship in Economic Growth (University of Chicago Press, 2022).
To read more about Dr. Andrews, click on the link here.
Dr. Stephen Miller CNMS
| Phone |
410-455-3381
|
|---|---|
| stmiller@umbc.edu | |
| Education |
PhD, Massachusetts Institute of Technology (1991)
BS, Case Western Reserve University (1984)
|
About
Dr. Miller is a professor in the department for biological sciences, having teaching interests in Introductory Biology, Genetics, RNAi, Gene Expression.
To read more about Dr. Miller, click on the link here.
Dr. Tim Oates COEIT
| Phone |
(410) 455-3082
|
|---|---|
| oates@cs.umbc.edu | |
| Education |
Ph.D., Computer Science, U.Massachusetts, Amherst, 2000
M.S., Computer Science, U. Massachusetts, Amherst, 1997 B.S., Computer Science, B.S., Electrical Engineering, 1989 |
About
Dr. Oates’ recent work has explored deep neural networks for weakly supervised EEG denoising for brain-machine interfaces, human-in-the-loop deep reinforcement learning to train robots using video demonstrations, and learning declarative representations of the functionality of “found” hardware using black box methods. Ongoing efforts include novel methods for learning semantically rich compositional sentence embeddings, learning policies for monitoring and updating deployed deep learning models to maintain performance in the face of domain shifts, and unsupervised methods for learning grounded, relational policies for understanding and control of real and simulated environments.
To learn more about Dr. Oates’ research pursuits, read his research profile.