Sid Devic

Email: sid.devic [at] gmail.com Twitter/X

CV (Dec. 2025) Google Scholar

I am currently a Member of Technical Staff at Bespoke Labs in Mountain View, CA. These days, I am broadly interested in data and understanding / improving frontier capabilities.

I completed my PhD in the USC Theory Group, where I was very fortunate to be advised by the wonderful Vatsal Sharan and Aleksandra Korolova. My PhD was broadly focused on trustworthy, fair, and reliable machine learning and AI. I also benefited greatly from the sage guidance and collaboration of other Theory Group professors, including David Kempe, Shang-Hua Teng, and Shaddin Dughmi.

My research was supported by a National Defense Science and Engineering Graduate (NDSEG, 2021-2024) Fellowship and a USC Viterbi Graduate Fellowship (2024-2025).

I was lucky to spend some time doing fun research at various places outside of USC during my PhD.

Summer 2025: Research internship at Apple hosted by Aravind Gollakota and Preetum Nakkiran.
Fall 2024: Visiting student at the UC Berkeley Simons Institute modern paradigms in generalization research program.
Summer 2024: Research internship at Amazon with Gaurush Hiranandani.

Prior to graduate school, 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, who greatly shaped the way I think about problems: Nick Ruozzi, Jason Jue, Brendan Juba, and Ben Raichel.

Rejections

Here is a list of some of my rejections (e.g. job hunt, fellowships, papers, etc.). You can also find a bunch of materials for fellowships applications and job searching (interview tips) on that page. I think it is important to share these to help normalize the fact that most things don't work out :D

Publications and Preprints

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

Selected Publications

Uncertainty Quantification and Calibration

Machine-learned models and systems which "know what they don't know" is a critical requirement of trustworthy AI. How should we measure and quantify such model uncertainty in practice? How does the problem change when models are deployed in the wild, especially in settings with diverse and non-homogenous populations? Finally, what does uncertainty quantification mean for large language models, and how is it impacted by reasoning post-training?

Trustworthy Algorithmic Marketplaces and Recommender Systems

Modern algorithmic platforms like LinkedIn, ride-sharing, or content delivery are increasingly complex aggregations of multiple machine-learned systems. How can we ensure that these "composite" systems are trustworthy, fair, and reliable? How does the problem change when pieces of these systems have intrinsic uncertainty tied to their predictions? Furthermore, how do we evaluate the fairness of production-grade systems in the wild?

Foundations of Machine Learning

Our work in this area explores some basic questions in (efficient) learnability, the power of unlabeled data, and the role of regularization. A lot of this work was motivated by a beautiful object called the one-inclusion graph, which reframes supervised learning as a bipartite matching problem. Our collaborator Shaddin Dughmi wrote a wonderful expository article overviewing this perspective and some of our work.

Other

Additional research spanning networking, reinforcement learning, and black-box optimization.

Mentorship and Interns

I have been lucky to mentor a couple students, all of whom are much quicker and smarter than I am!

Alex Reyes Aranda - Phenomenological deep learning.
Undergrad, May 2025 - Oct. 2025. Part of USC CURVE program.
Nathan Derhake - Auditing for multicalibration.
Undergrad, Oct. 2024 - Dec. 2025.
→ Next position: NSF GRFP Fellow and Mathematics PhD student at Georgia Tech!
Kuan Liu - Auditing for multicalibration.
Undergrad, Oct. 2024 - Dec. 2025. Part of USC CURVE program.
→ Next position: Computer Science PhD student at Northeastern University!
Dutch Hansen - Multicalibration of neural networks.
Undergrad, Jan. 2023 - May 2025. Part of USC CURVE program.
→ Next position: NSF GRFP Fellow and Computer Science PhD student at the University of Washington!
Anish Jayant - Multicalibration of neural networks.
Undergrad, Jan. 2023 - May 2023.
→ Next position: Georgia Tech PhD student in Algorithms, Combinatorics, and Optimization (ACO)!
Jayron Martinez - Bias in Machine Learning Algorithms.
High school student, summer 2022. Part of the USC SHINE program.
→ Next position: Undergraduate student in CS and Business Administration at USC!

Service and Teaching

Reviewer: Neurips 2024, ICLR 2025, ICML 2025, Neurips 2025, Reliable ML workshop @ Neurips 2025, ICLR 2026.
Emergency reviewer: COLM 2025, FORC 2025.
TA: Discrete Methods in CS (Spring 2025)

Student Activities

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.

In my free time I enjoy climbing/bouldering, surfing, hiking, and skiing (the latter two less frequently!). I have (at some point in the past) also attempted the following: learning art, running, calisthenics progressions, playing classical piano, swimming, and binging npr tiny desk concerts.

Some other stuff from undergrad:

Give, even if you only have a little.

-Siddhartha Gautama

Siddartha Devic