Doctoral Thesis Proposal - Madhusudhan Reddy Pittu

— 12:30pm

Location:
In Person and Virtual - ET - Traffic21 Classroom, Gates Hillman 6501 and Zoom

Speaker:
MADHUSUDHAN REDDY PITTU , Ph.D. Student, Computer Science Department, Carnegie Mellon University
https://mathrulestheworld.github.io/

Fairness, Diversity, Explainability, and Robustness for Algorithmic Decision-making

Fairness, diversity, explainability, and robustness are key challenges in computational decision-making, impacting machine learning, resource allocation, and data analysis. Balancing these principles with efficiency presents significant computational and structural challenges. This proposal investigates algorithmic approaches for diverse selection, fair allocation, interpretable clustering, constrained subspace approximation, and comparison-based optimization. Together, these directions contribute to more equitable, representative, interpretable, and robust algorithmic decision-making under structural and informational constraints. 

Thesis Committee

David Woodruff (Chair)
Anupam Gupta
Prasad Tetali
Mohit Singh (Georgia Institute of Technology)
Ola Svensson (EPFL)

Additional Information

In Person and Zoom Participation.  See announcement.

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