About

I’m Sid, a CS PhD student in the USC Theory Group, where I am fortunate to be advised by the wonderful Vatsal Sharan and Aleksandra Korolova. I am broadly interested in (1) algorithmic fairness, especially in the presence of uncertainty, finite resources, and ranking / two-sided marketplaces; and (2) theoretical machine learning. I am grateful to have my research supported by a National Defense Science and Engineering Graduate (NDSEG) Fellowship.

Here is my CV (Nov. 2023).

I completed my undergraduate degrees in math and CS at The University of Texas at Dallas (UTD), where I explored research in networking, theory, optimization, and reinforcement learning. Along the way, I was extremely fortunate to be advised by these amazing professors: Nick Ruozzi, Jason Jue, Brendan Juba, and Ben Raichel.

Publications and Preprints

(αβ) indicates alphabetical order, standard in theoretical computer science.

• Regularization and Optimal Multiclass Learning
  (αβ) Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng
  Preprint.
  [arxiv]

• Fairness in Matching under Uncertainty
  Siddartha Devic, David Kempe, Vatsal Sharan, Aleksandra Korolova
  ICML 2023
  Non-archival at ACM conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO 23)
  [arxiv] [poster] [twitter thread] [video]

• Polynomial Time Reinforcement Learning in Factored State MDPs with Linear Value Functions
  (αβ) Zihao Deng, Siddartha Devic, Brendan Juba
  AISTATS 2022
  Also appeared at Neurips 2021 Workshop on Ecological Theory of Reinforcement Learning
  [arxiv]

• Dynamic Bandwidth Allocation for PON Slicing with Performance-Guaranteed Online Convex Optimization
  Genya Ishigaki, Siddartha Devic, Riti Gour, Jason P. Jue
  IEEE GLOBECOM 2021
  [arxiv] [poster]

• Failout: Achieving Failure-Resilient Inference in Distributed Neural Networks
  Ashkan Yousefpour, Brian Q Nguyen, Siddartha Devic, Guanhua Wang, Aboudy Kreidieh, Hans Lobel, Alexandre M Bayen, Jason P. Jue
  ICML 2020 Workshop on Federated Learning for User Privacy and Data Confidentiality
  [arxiv]

• DeepPR: Progressive Recovery for Interdependent VNFs with Deep Reinforcement Learning
  Genya Ishigaki, Siddartha Devic, Riti Gour, Jason P. Jue
  IEEE Journal on Selected Areas in Communications, 2020
  Also appeared at IEEE GLOBECOM 2019
  [arxiv]

Mentorship

• Dutch Hansen and Anish Jayant - Multicalibration of neural networks.
  Undergrads, January 2023-present.
• Jayron Martinez - Bias in Machine Learning Algorithms.
  High school senior, summer 2022. Part of the USC SHINE program.
  [poster]

Student activities and service

The first year of my PhD, I served as a CS department senator within the USC Viterbi Graduate Student Association (VGSA), where I was able to bring goats to campus (amongst other things).

I served as President / VP for my undergrad’s ACM chapter, where I am most proud to have established a perpetual $30k endowed scholarship with club funds. Importantly, the scholarship may also grant eligible students an in-state tuition waiver! Students can apply here.

I also served as the undergrad representative on the UTD CS department head search committee during the 2020-2021 academic year.

If you are a UTD student interested in pursuing a PhD, you may find the grad school section I wrote of this guide helpful. Also check out this research internship / REU masterlist which I put together. You are welcome to reach out to me anytime for more info and/or if you’d like me to review your GRFP or NDSEG applications!

Fun

In my free time, I enjoy learning art, running, climbing/bouldering and hiking, calisthenics progressions, playing classical piano, swimming, skiing (when I can!), and binging npr tiny desk concerts.

Some other stuff from undergrad:

• Clustering in hyperbolic space (with Mike Skinner)
• Short notes on statistical learning (with Dipnil Chakraborty)
• Point packing in hypercubes
• Coarse classification of russian troll tweets

Give, even if you only have a little.

-Siddhartha Gautama