May 08, 2020
We are Incredibly Proud of Our Researchers on Successfully Defending their Dissertations
PITTSBURGH, PA — As with many things in the lives of doctoral researchers, defending their Ph.D. theses has gone virtual during the COVID-19 pandemic. Prospective Ph.D.s presented their research and fielded questions from a thesis committee before receiving their degrees this spring.
Earning a Ph.D. is no easy task, and for our researchers it has required continuous hard-work in their field of specialty.
Therefore, the Machine Learning Department at Carnegie Mellon University would like to congratulate the following doctoral researchers on successfully defending their dissertations:
Chieh Lin
Probabilistic Single Cell Lineage Tracing
Liam Li
Thesis: Towards Efficient Automated Machine Learning
Han Zhao
Thesis: Towards a Unified Framework for Learning and Reasoning
Anthony Platanios
Thesis: Learning Collections of Functions
Micol Marchetti-Bowick
Thesis: Structured Sparse Regression Methods for Learning from High-Dimensional Genomic Data
Calvin Murdock
Thesis: Data Decomposition for Constrained Visual Learning
Eric Wong
Thesis: Provable, Structured, and Efficient Methods for Robustness of Deep Networks to Adversarial Examples
Hyun Ah Song
Thesis: Reconstructing and Mining Signals: Algorithms and Applications
William Herlands
Thesis: Change modeling for Understanding Our World and the Counterfactual One(s)
Maria De-Arteaga
Thesis: Machine Learning in High-Stakes Settings: Risks and Opportunities
We are looking forward to what our doctors will do in their respective fields. Once again, congratulations!
Credit: Google News
