Dr. Jane Smith
Associate Professor
Education
Ph.D. in Computer Science
2015 - 2019Stanford University
Thesis: "Advanced Neural Networks for Computer Vision Applications"
Advisor: Prof. Michael Johnson
M.S. in Computer Science
2013 - 2015Massachusetts Institute of Technology
Focus: Artificial Intelligence and Machine Learning
GPA: 4.0/4.0
B.S. in Computer Science
2009 - 2013University of California, Berkeley
Minor: Mathematics
Summa Cum Laude, Phi Beta Kappa
Experience
Associate Professor
2022 - PresentUniversity of Research, Department of Computer Science
- Leading the AI Research Lab with a focus on computer vision and natural language processing
- Teaching graduate courses in Advanced Machine Learning and Deep Learning
Assistant Professor
2019 - 2022University of Research, Department of Computer Science
- Established a new research group focused on AI applications
- Secured $1.2M in research grants from NSF and industry partners
Research Scientist
2019Tech Innovation Labs
- Led a team of 4 researchers working on computer vision applications
- Developed novel algorithms for real-time object detection on mobile devices
Papers
Efficient Transformer Models for Resource-Constrained Devices
2023Smith, J., Johnson, A., Williams, B.
Proceedings of the Conference on Neural Information Processing Systems (NeurIPS)
A Survey of Vision-Language Models: Capabilities, Limitations, and Future Directions
2022Smith, J., Chen, C., Garcia, M.
ACM Computing Surveys
Novel Approaches to Few-Shot Learning in Computer Vision
2022Smith, J., Park, L., Thompson, R.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Best Paper Award
News
New Research Grant Awarded
May 2023Our lab has been awarded a $2.5M grant from the National Science Foundation to study advanced AI techniques for climate change modeling. This project will involve collaboration with the Earth Sciences department and several industry partners.
Paper Accepted at NeurIPS 2023
April 2023Our paper "Efficient Transformer Models for Resource-Constrained Devices" has been accepted for publication at NeurIPS 2023. This work introduces novel techniques to optimize transformer architectures for deployment on edge devices.
Invited Talk at AI Conference
March 2023I will be giving an invited talk on "The Future of AI in Healthcare" at the International Conference on AI in Medicine in September 2023. The talk will cover our recent work on medical image analysis and predictive healthcare.