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Professor of Computer Science

Dr. Sarah Mitchell

Director of Artificial Intelligence Research Center | IEEE Fellow
Distinguished Scholar in Machine Learning & Neural Networks

s.mitchell@mbust.edu.np +977-1-234-5678 Room 405, Research Building

5

Publications

5,400

Citations

18

PhD Students

42

H-Index

Biography

Dr. Sarah Mitchell is a distinguished Professor of Computer Science and the Director of the Artificial Intelligence Research Center at MBUST. With over 20 years of experience in machine learning and neural networks, she has become a leading authority in the field of artificial intelligence.

Her groundbreaking research in deep learning architectures and natural language processing has been published in top-tier conferences and journals, garnering over 5,400 citations. Dr. Mitchell’s work on adaptive neural networks has been instrumental in advancing both theoretical understanding and practical applications of AI systems.

Prior to joining MBUST, Dr. Mitchell held research positions at MIT and Stanford University, where she collaborated with industry leaders and contributed to several breakthrough AI technologies. She is an IEEE Fellow and serves on the editorial boards of multiple prestigious journals in computer science and artificial intelligence.

Beyond her research, Dr. Mitchell is passionate about mentoring the next generation of AI researchers and has supervised 18 doctoral students, many of whom have gone on to prominent positions in academia and industry.

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Education

Ph.D. in Computer Science
Massachusetts Institute of Technology (MIT)
Machine Learning & Neural Networks
2005
M.S. in Computer Science
Stanford University
Artificial Intelligence
2001
B.S. in Computer Engineering
University of California, Berkeley
Summa Cum Laude
1999
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Honors & Awards

  • IEEE Fellow (2020)
  • Distinguished Scholar Award, MBUST (2019)
  • ACM SIGAI Autonomous Agents Research Award (2018)
  • Best Paper Award, NeurIPS Conference (2016)
  • NSF CAREER Award (2012)

Research Interests

Exploring cutting-edge technologies and methodologies to advance the field of artificial
intelligence

Machine Learning

Deep learning architectures, neural network optimization, and transfer learning

Natural Language Processing

Language models, semantic analysis, and conversational AI systems

Computer Vision

Image recognition, object detection, and visual understanding systems

AI Ethics & Fairness

Responsible AI development, bias mitigation, and ethical frameworks

Courses Taught

Current and previous courses spanning undergraduate, graduate, and doctoral levels

Graduate CS601
Advanced Machine Learning
Mon & Wed, 2:00 PM - 3:30 PM
35 Students
PhD CS801
Deep Learning Seminar
Thu, 4:00 PM - 6:00 PM
12 Students
Undergraduate CS450
Introduction to Artificial Intelligence
Tue & Fri, 10:00 AM - 11:30 AM
68 Students
Graduate CS620
Neural Networks
Mon, 1:00 PM - 3:00 PM
42 Students
Undergraduate CS310
Data Structures
Tue, 9:00 AM - 11:00 AM
80 Students
PhD CS890
Advanced AI Research
Wed, 3:00 PM - 5:00 PM
10 Students

Selected Publications

Recent high-impact publications in top-tier journals and conferences

2021 IF: 9.2

Explainable AI: Bridging the Gap Between Performance and Interpretability

Mitchell, S.
412 citations View Paper