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 * Aadirupa Saha
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Research Scientist, Apple Machine Learning Research

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ABOUT ME

Hi, thanks for your interest!



I am currently a research scientist at Apple MLR, broadly working in the area of
Machine Learning theory. I just finished a short-term research visit at Toyota
Technological Institute at Chicago (TTIC), and completed my postdoc stinct at
Microsoft Research New York City before that. I obtained my PhD from the
department of Computer Science, Indian Institute of Science, Bangalore, advised
by Aditya Gopalan and Chiranjib Bhattacharyya. I was fortunate to intern at
Microsoft Research, Bangalore; Inria, Paris; and Google AI, Mountain View.

Research Interests: Machine Learning (esp. Online Learning theory, Bandits,
Reinforcement Learning), Optimization, Federated Learning, Differential Privacy,
Mechanism Design.
 

My research focuses on developing large-scale robust algorithms for sequential
decision-making tasks under restricted and unconventional feedback, for e.g.,
preference information, click data, proxy rewards, partial ranking, etc. Some of
my past ventures also include handling complex prediction environments, like
combinatorial decision spaces, dynamic regret, multiplayer games, distributed
optimization, etc. Recently, I have also been interested in the
interdisciplinary fields of prediction modeling with algorithmic fairness,
assortment optimization and strategic mechanisms. Please feel free to reach out
if you are interested in brainstorming any of these related directions!

Short Bio (in third person) Aadirupa is currently a research scientist at Apple
ML research, broadly working in the area of Machine Learning theory. She did a
short-term research visit at Toyota Technological Institute, Chicago (TTIC),
after finishing her postdoc at Microsoft Research New York City. Aadirupa
obtained her Ph.D. from IISc Bangalore under Aditya Gopalan and Chiranjib
Bhattacharyya.
Her research primarily focuses on designing Efficient Human Aligned Prediction
Models: Few specific research areas include Online learning theory, Bandits &
RL, Federated Optimization, and Differential Privacy. Of late, she has also been
working on some problems at the intersection of Mechanism Design, Game Theory
and Algorithmic Fairness. Aadirupa has organized several workshops and tutorials
in recent years, including a [NeurIPS, 2023] tutorial on Preference Learning, a
[UAI, 2023] ] tutorial on Federated Optimization, two tutorials at [ECML, 2022]
, [ACML, 2021], two ICML workshops [ICML, 2023] and [ICML, 2022], and two TTIC
workshops [TTIC, 2023] and [TTIC, 2022]. She has also served in different panel
discussions and reviewing committees.
[Selected Papers]   [Full List]   [Google Scholar]   [DBLP]   [arXiv]