Preetum Nakkiran

I'm a Research Scientist at Apple, working on foundations of machine learning.
My work aims to understand generalization in deep learning, by developing empirical conjectures and theory. Recent topics: calibration [1] [2] [3] [4] [5], and Transformer generalization [6].

I completed my PhD at Harvard, having the unique pleasure of being advised by Madhu Sudan and Boaz Barak. In my postdoc I worked with Misha Belkin. I am grateful for past support from NSF and the Simons Institute. Go Bears!

Interns at Apple (hosted or collaborated):

  • Hattie Zhou. PhD student, Université de Montréal and Mila.
  • Annabelle Carrell. PhD student, University of Cambridge.
  • Lunjia Hu. PhD student, Stanford. (hosted by Parikshit Gopalan)
  • Shivam Garg. PhD student, Stanford. (hosted by Kunal Talwar)
  • Rylee Thompson. MASc student, University of Guelph. (hosted by Shuangfei Zhai)
  • Elan Rosenfeld. PhD student, CMU. (hosted by Fartash Faghri)

Selected Research (one per year)

See [publications] for full list.

2023 What Algorithms can Transformers Learn? A Study in Length Generalization
Hattie Zhou, Arwen Bradley, Etai Littwin, Noam Razin, Omid Saremi, Josh Susskind, Samy Bengio, Preetum Nakkiran
ICLR 2024.
[arXiv] [tweet]
2022 A Unifying Theory of Distance from Calibration
Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran
STOC 2023.
[arXiv] [tweet]
2021 The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers
Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi
ICLR 2021.
[arXiv] [tweet]
2020 Distributional Generalization: A New Kind of Generalization
Preetum Nakkiran*, Yamini Bansal*
[arXiv] [talk] [slides]
2019 Deep Double Descent: Where Bigger Models and More Data Hurt
Preetum Nakkiran, Gal Kaplun*, Yamini Bansal*, Tristan Yang, Boaz Barak, Ilya Sutskever
ICLR 2020.
2018 General Strong Polarization
Jarosław Błasiok, Venkatesan Guruswami, Preetum Nakkiran, Atri Rudra, Madhu Sudan
STOC 2018, JACM 2022.
2016 Near-Optimal UGC-hardness of Approximating Max k-CSP_R
Pasin Manurangsi, Preetum Nakkiran, Luca Trevisan
2015 Compressing Deep Neural Networks Using a Rank-Constrained Topology
Preetum Nakkiran, Raziel Alvarez, Rohit Prabhavalkar, Carolina Parada


2021 Towards an Empirical Theory of Deep Learning
Preetum Nakkiran
Doctoral dissertation, Harvard University Graduate School of Arts and Sciences.
[pdf] [cite]
All papers

Machine Learning Theory


Machine Learning

About Me

For talks, you can use this [bio].

I did my undergrad in EECS at UC Berkeley. I'm broadly interested in theory and science. In the past, I have interned at OpenAI (with Ilya Sutskever) Google Research (with Raziel Alvarez), Google Brain (with Behnam Neyshabur, Hanie Sedghi), and have also done research in error-correcting codes, distributed storage, and cryptography. I am grateful for past support from NSF GRFP and the Google PhD Fellowship. An (outdated) CV is available here. [ORCID]

See also my old website for more. This version borrowed in part from Luca Trevisan and Jon Barron.

What People are Saying

a "high-level" scientist   —colleague (ML)

makes plots and draws lines through them
            —colleague (TCS)

has merits that outweigh flaws   —reviewer 2

Selected Tweets

From The Archives

  • Past successful application materials (fellowships, etc): [drive]
  • Courses I took in undergrad.