[Quantum-ms] [Colloquium] Joint CS/EE Faculty Candidate Lecture on 3/26 at 11:40am, Sadhika Malladi

[Xintian Tina Wang]

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*Please join us for a joint CS/EE faculty candidate lecture next week!*

*When:* Wednesday, March 26th
*Where:* CSB 451
*Who:* Sadhika Malladi

*Title: *
Deep Learning Theory in the Age of Generative AI

*Abstract:*
Modern deep learning has achieved remarkable results, but the design of
training methodologies largely relies on guess-and-check approaches.
Thorough empirical studies of recent massive language models (LMs) is
prohibitively expensive, underscoring the need for theoretical insights,
but classical ML theory struggles to describe modern training paradigms. I
present a novel approach to developing prescriptive theoretical results
that can directly translate to improved training methodologies for LMs. My
research has yielded actionable improvements in model training across the
LM development pipeline — for example, my theory motivates the design of
MeZO, a fine-tuning algorithm that reduces memory usage by up to 12x and
halves the number of GPU-hours required. Throughout the talk, to underscore
the prescriptiveness of my theoretical insights, I will demonstrate the
success of these theory-motivated algorithms on novel empirical settings
published after the theory.

*Bio:*
Sadhika Malladi is a final-year PhD student in Computer Science at
Princeton University advised by Sanjeev Arora. Her research advances deep
learning theory to capture modern-day training settings, yielding practical
training improvements and meaningful insights into model behavior. She has
co-organized multiple workshops, including Mathematical and Empirical
Understanding of Foundation Models at ICLR 2024 and Mathematics for Modern
Machine Learning (M3L) at NeurIPS 2024. She was named a 2025 Siebel Scholar.
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