Upcoming Doctoral Dissertations
School of Engineering and Science
DISSERTATIONS IN JULY
July 17, 2026 - Hanfei Yu
Candidate | Hanfei Yu |
Date | Friday, July 17, 2026 |
Time | 09:00 AM (Eastern) |
Title | Co-Designing Infrastructure, Runtime, and Model Serving for Efficient AI Systems |
Location |
"Artificial Intelligence (AI) has rapidly transformed scientific research, industry, and everyday life. The rapid evolution of AI models, particularly large language models (LLMs), has dramatically increased the computational and memory demands of modern AI workloads. As AI models continue to grow in scale and complexity, performance bottlenecks increasingly arise across multiple layers of the AI systems stack" Read more
July 7, 2026 - Guanqun Yang
Candidate | Guanqun Yang |
Date | Tuesday, July 7, 2026 |
Time | 02:00 PM (Eastern) |
Title | Scalable Software Testing and Analysis with NLP Methods |
Location | GN 303 |
"Building reliable and secure software increasingly depends on work that grows faster than the people available to do it: sorting and prioritizing security vulnerability reports, testing machine learning models for hidden failures, and judging the AI assistants that now help write code. Each of these tasks is still done largely by hand. This dissertation demonstrates that natural language processing (NLP) methods can handle much of this manual work. " Read more
July 13, 2026 - Luke McEvoy
Candidate | Luke McEvoy |
Date | Monday, July 13, 2026 |
Time | 11:00 AM (Eastern) |
Title | Physics-Informed Sparse Single Photon 3D Imaging |
Location | Buchard 715 |
"Single-photon light detection and ranging (LiDAR) offers exceptional sensitivity for 3D imaging but faces fundamental barriers: sparse signal photons, high noise, and slow acquisition speeds due to point-by-point scanning. These limitations are especially severe in photon-starved environments or when imaging through scattering media. This dissertation overcomes these challenges through a unified hardware-software framework that co-designs physical data acquisition with computational reconstruction." Read more
To view past Doctoral Dissertations, please visit this website.