Draft:Birhanu Eshete
| Submission rejected on 31 August 2024 by Sohom Datta (talk). This topic is not sufficiently notable for inclusion in Wikipedia. Rejected by Sohom Datta 2 months ago. Last edited by Sohom Datta 2 months ago. |  |
Birhanu Eshete | |
|---|---|
| Nationality | Ethiopian-American |
| Alma mater | University of Trento (Ph.D.) Addis Ababa University (M.Sc., B.Sc.) |
| Known for | Trustworthy Machine Learning, Adversarial Machine Learning, Privacy-Preserving Machine Learning |
| Awards | NSF CAREER Award (2023)
Fulbright U.S. Scholar Award (2024-2025)USENIX Security Symposium Distinguished Paper Award (2018) |
| Scientific career | |
| Fields | Computer Science |
| Institutions | University of Michigan–Dearborn |
| Website | https://www-personal.umd.umich.edu/~birhanu/ |
Birhanu Eshete (Amharic: ብርሃኑ እሸቴ) is an Ethiopian-American computer scientist and an Associate Professor of Computer Science at the University of Michigan–Dearborn. He is known for his work in trustworthy machine learning, particularly in enhancing the security, privacy, and fairness of machine learning systems in the face of adversarial threats. He is the recipient of the National Science Foundation (NSF) CAREER Award in 2023 and the Fulbright U.S. Scholar Award for 2024-2025.
Early Life and Education
[edit]Birhanu Eshete was born in Ethiopia. He completed his undergraduate and master's degrees in Computer Science at Addis Ababa University. He later pursued his Ph.D. in Computer Science at the University of Trento in Italy. His doctoral research focused on the intersection of cybersecurity and artificial intelligence.
Career
[edit]After earning his Ph.D., Eshete worked as a Postdoctoral Researcher in the Systems Internet Security Lab at the University of Illinois at Chicago. He later joined the faculty at the University of Michigan–Dearborn, where he leads the Data-Driven Security & Privacy Lab (DSPLab).
His research focuses on the robustness and trustworthiness of machine learning systems, particularly in the face of adversarial manipulations. His work has been published in top security and privacy conferences, including IEEE S&P, ACM CCS,USENIX Security, and the Science Magazine.
Eshete's work on an AI risk management framework for autonomous vehicles has been included in NIST’s Trustworthy & Responsible AI Resource Center. His research has been instrumental in advancing the understanding of adversarial machine learning and its implications for real-world applications.
Awards and Honors
[edit]- NSF CAREER Award (2023)[1]
- Fulbright U.S. Scholar Award (2024-2025)[2]
- USENIX Security Symposium Distinguished Paper Award (2018)[3]
- Finalist for the CSAW Best Applied Security Research Award in North America (2018)[4]
Selected Publications
[edit]- Eshete, B. "Making Machine Learning Trustworthy.", Science, 2021.
- Jarin, I., Eshete, B. "MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members. ACM PETS, 2023.
- Amich, A., Eshete, B. "Morphence: Moving Target Defense Against Adversarial Examples.", ACM ACSAC, 2021.
References
[edit]- ^ "NSF CAREER Award Recipients". National Science Foundation. Retrieved 2024-08-15.
- ^ "Fulbright U.S. Scholar Directory". Fulbright Program. Retrieved 2024-08-15.
- ^ "USENIX Security 2018 Distinguished Paper Awards". USENIX Security Symposium. Retrieved 2024-08-15.
- ^ "CSAW Applied Research Finalists". CSAW. Retrieved 2024-08-15.