Prof. John A. Smith
Professor of Computer Science and Engineering, University of Michigan
I am John A. Smith, Professor in the Department of Computer Science and Engineering at the University of Michigan. My research focuses on explainable machine learning, data ethics, and AI-driven healthcare solutions. I also enjoy mentoring graduate students and teaching advanced courses in Artificial Intelligence.
45+
Publications
3
Active Grants
18
PhD Students
20
H-index
About:
Prof. John A. Smith received his Ph.D. in Computer Science from Stanford University in 2005. He is currently a Professor at the University of Michigan, specializing in Artificial Intelligence and Machine Learning. His interdisciplinary work combines computer vision, data privacy, and ethical AI applications.
Position
Professor, Department of Computer Science & Engineering, UMich
Education
Ph.D. Stanford University, 2005
Research Interests
Explainable AI, Data Ethics, Healthcare Applications
Research Highlights
Explainable AI for Medical Imaging
Developing interpretable deep-learning models for cancer diagnosis.
Active: 2023–2026
Ethical Machine Learning for Finance
Building fair and bias-resistant ML pipelines for credit risk modeling.
Completed: 2020–2023
Privacy-Preserving Deep Learning
Research on federated learning and differential privacy techniques.
Active: 2024–2027
Recent
Publications
Smith, J. A., et al. (2025). Interpretable Deep Learning for Radiology. Nature Medicine. DOI: 10.1234/nm2025
Smith, J. A., et al. (2025). Interpretable Deep Learning for Radiology. Nature Medicine. DOI: 10.1234/nm2025
Smith, J. A., et al. (2025). Interpretable Deep Learning for Radiology. Nature Medicine. DOI: 10.1234/nm2025
Smith, J. A., et al. (2025). Interpretable Deep Learning for Radiology. Nature Medicine. DOI: 10.1234/nm2025
Teaching & Mentoring
Teaching Philosophy
My teaching approach emphasizes active learning, case studies, and hands-on coding projects. I encourage students to connect theory with real-world applications.
CSE 543
Advanced Machine Learning
Deep dive into neural networks, optimization, and model interpretability.
CSE 543
Advanced Machine Learning
Deep dive into neural networks, optimization, and model interpretability.
Student Supervision
8
PhD Students(Current)
14
PhD Students(Graduated)
Grants & Projects
2023–2026
NIH Grant ($400,000)
Explainable AI for Cancer Detection
2021–2024
NSF Grant ($250,000)
Ethical AI in Financial Systems
2019–2022
DARPA Project ($500,000)
Privacy-Preserving Machine Learning
Talks & Media Coverage
Keynote Speaker
NeurIPS 2024
Trustworthy AI in Healthcare
Invited Talk
MIT AI Seminar 2023
Bias in Machine Learning Systems
Featured in The New York Times
2022
The Ethics of AI
Students & Alumni
Current PhD Students
Jane Doe
Explainable NLP Models
Alex Kim
Privacy-Preserving Deep Learning
Alumni Highlights
Dr. Emily White
Assistant Professor, Stanford University
Michael Green
Senior Data Scientist, Google
Awards & Honors
2024
Best Paper Award, NeurIPS
2022
IEEE Fellow
2020
ACM Distinguished Scientist
News & Updates
June 2025
Paper accepted at Nature Medicine
Dec 2024
Delivered keynote at NeurIPS
June 2025
Paper accepted at Nature Medicine
Subscribe for Updates
Contact Me
jsmith@umich.edu
Room 321, CSE Building
University of Michigan
Tuesday & Thursday
2:00 PM – 4:00 PM