World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
53
Citations
27146
World Ranking
4679
National Ranking
2171

Overview

Ameet Talwalkar is affiliated with Carnegie Mellon University in the United States, with a focus on research in computer science. Their work spans multiple subfields including artificial intelligence, computer vision and pattern recognition, management science and operations research, genetics, and computer science applications.

The primary fields of study for Talwalkar revolve around computer science, encompassing 117 publications. Their research topics cover a range of areas with particular emphasis on machine learning and data classification, explainable artificial intelligence (XAI), topic modeling, domain adaptation and few-shot learning, adversarial robustness in machine learning, advanced neural network applications, and privacy-preserving technologies in data.

The scientist's publication record includes numerous papers, with research often appearing in well-known venues. Frequent publication platforms include:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • British Journal of Surgery
  • Queue

Some of the recent research papers authored or co-authored by Talwalkar are:

  • Applying interpretable machine learning in computational biology-pitfalls, recommendations and opportunities for new developments, 2024, Nature Methods
  • Interpretable machine learning, 2022, Communications of the ACM
  • Inferring population structure in biobank-scale genomic data, 2022, The American Journal of Human Genetics
  • Interpretable Machine Learning, 2021, Queue
  • A Field Guide to Federated Optimization, 2021, arXiv (Cornell University)

The collaborator network of Talwalkar consists of frequent co-authors, reflecting interdisciplinary partnerships and sustained research collaborations. Key frequent co-authors include:

  • Valerie Chen
  • Mikhail Khodak
  • Gregory Plumb
  • Joon Sik Kim
  • Virginia Smith

Best Publications

  • Federated Learning: Challenges, Methods, and Future Directions

    Tian Li;Anit Kumar Sahu;Ameet Talwalkar;Virginia Smith

  • Foundations of Machine Learning

    Mehryar Mohri;Afshin Rostamizadeh;Afshin Rostamizadeh;Ameet Talwalkar;Ameet Talwalkar

  • Federated Optimization in Heterogeneous Networks

    Tian Li;Anit Kumar Sahu;Manzil Zaheer;Maziar Sanjabi

  • Hyperband: a novel bandit-based approach to hyperparameter optimization

    Lisha Li;Kevin Jamieson;Giulia DeSalvo;Afshin Rostamizadeh

  • MLlib: machine learning in apache spark

    Xiangrui Meng;Joseph Bradley;Burak Yavuz;Evan Sparks

  • A large-scale evaluation of computational protein function prediction

    Predrag Radivojac;Wyatt T Clark;Tal Ronnen Oron;Alexandra M Schnoes

  • Federated multi-task learning

    Virginia Smith;Chao-Kai Chiang;Maziar Sanjabi;Ameet Talwalkar

  • Random Search and Reproducibility for Neural Architecture Search

    Liam Li;Ameet Talwalkar

  • Interpretable Machine Learning

    Unknown

  • LEAF: A Benchmark for Federated Settings

    Sebastian Caldas;Peter Wu;Tian Li;Jakub Konecný

  • A scalable bootstrap for massive data

    Ariel Kleiner;Ameet Talwalkar;Purnamrita Sarkar;Michael I. Jordan

  • MLbase: A Distributed Machine-learning System

    Tim Kraska;Ameet Talwalkar;John C. Duchi;Rean Griffith

  • Non-stochastic Best Arm Identification and Hyperparameter Optimization

    Kevin G. Jamieson;Ameet Talwalkar

  • Sampling methods for the Nyström method

    Sanjiv Kumar;Mehryar Mohri;Ameet Talwalkar

  • The Foundations of Machine Learning

    Mehryar Mohri;Afshin Rostamizadeh;Ameet Talwalkar

  • Large-scale manifold learning

    A. Talwalkar;S. Kumar;H. Rowley

  • Expanding the Reach of Federated Learning by Reducing Client Resource Requirements

    Sebastian Caldas;Jakub Konecný;H. Brendan McMahan;Ameet Talwalkar

  • Divide-and-Conquer Matrix Factorization

    Lester Mackey;Ameet Talwalkar;Michael I. Jordan

  • On the Convergence of Federated Optimization in Heterogeneous Networks.

    Anit Kumar Sahu;Tian Li;Maziar Sanjabi;Manzil Zaheer

  • Joint Link Prediction and Attribute Inference Using a Social-Attribute Network

    Neil Zhenqiang Gong;Ameet Talwalkar;Lester Mackey;Ling Huang

  • A System for Massively Parallel Hyperparameter Tuning

    Liam Li;Kevin G. Jamieson;Afshin Rostamizadeh;Ekaterina Gonina

  • Adaptive Gradient-Based Meta-Learning Methods

    Mikhail Khodak;Maria-Florina F. Balcan;Ameet S. Talwalkar

  • A System for Massively Parallel Hyperparameter Tuning

    Liam Li;Kevin Jamieson;Afshin Rostamizadeh;Ekaterina Gonina

Frequent Co-Authors

Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Afshin Rostamizadeh
Afshin Rostamizadeh Google (United States)
Mehryar Mohri
Mehryar Mohri Google (United States)
Michael J. Franklin
Michael J. Franklin University of Chicago
Sanjiv Kumar
Sanjiv Kumar Google (United States)
Maria-Florina Balcan
Maria-Florina Balcan Carnegie Mellon University
David A. Patterson
David A. Patterson University of California, Berkeley
Eric P. Xing
Eric P. Xing Mohamed bin Zayed University of Artificial Intelligence
Matei Zaharia
Matei Zaharia University of California, Berkeley

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