Congratulations to Our New PhDs – Machine Learning – CMU

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!