Ameet Talwalkar
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I am an assistant professor in the Machine Learning Department at Carnegie Mellon University. I am also co-founder and Chief Scientist at Determined AI. My primary interests are in the field of statistical machine learning, including problems at the intersection of systems and learning, and applications in computational genomics. My current work is motivated by the goal of democratizing machine learning, with a focus on topics related to the scalability, automation, and interpretability of learning algorithms and systems. Here is my CV, Google scholar page, and formal bio. |
Selected Work
- Foundations of Machine Learning, 2nd Edition (hardcopy,
pdf,
html)
M. Mohri, A. Rostamizadeh, A. Talwalkar
MIT Press, 2018 - Federated Multi-task Learning
(pdf)
V. Smith, C. Chiang, M. Sanjabi, A. Talwalkar
Neural Information Processing Systems (NIPS), 2017 - Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter
Optimization
(pdf,
blog1,
blog2)
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, A. Talwalkar
ICLR, 2017 - MLlib: Machine Learning in Apache Spark (pdf)
X. Meng, J. Bradley, B. Yuvaz, E. Sparks, S. Venkataraman, D. Liu, J. Freeman, D. Tsai, M. Amde, S. Owen, D. Xin, R. Xin, M. Franklin, R. Zadeh, M. Zaharia, A. Talwalkar
JMLR, 2016 - Distributed Matrix Completion and Robust Factorization (pdf)
L. Mackey, A. Talwalkar, M. I. Jordan
JMLR, 2015 - MLbase: A Distributed Machine Learning System (pdf)
T. Kraska, A. Talwalkar, J. Duchi, R. Griffith, M.J. Franklin, M.I. Jordan
CIDR, 2013 - A Scalable Bootstrap for Massive Data (pdf)
A. Kleiner, A. Talwalkar, P. Sarkar, M.I. Jordan
JRSS-B, 2013
Recent News
- March 2019: In advance of SysML19, we've written a whitepaper to describe both the field and the conference.
- March 2019: I'm excited to receive a J.P. Morgan A.I. Research Faculty Award and a Carnegie Bosch Institute Research Award.
- December 2018: We just started a new CMU blog focused on machine learning. I'm excited for CMU students to showcase their amazing research!
- June 2018: I am honored to receive a Okawa Research Grant and Google Faculty Award!
- May 2018: I am the General Chair for SysML19. Please consider submitting a paper and/or attending!
- April 2018: Check out my recent blog post and podcast in which I discuss some of the key challenges hindering the wider adoption of machine learning technology.
- Nov 2017: I am the Program Chair for SysML, a new conference targeting research at the intersection of systems and machine learning. Please consider submitting a paper and/or attending!
- June 2017: I will be be joining CMU's Machine Learning Department as an Assistant Professor in January 2018!
- Nov 2016: Congrats to Lisha Li for winning best student presentation at the Socal ML Symposium for our work on Hyperband! Also, congrats to Hang Qi on being runner-up for best real-world application for our work on Paleo!
- July 2016: Anthony Joseph and I are finalists for the edX Teaching Prize! Relatedly, I'm launching an updated version of my MOOC this month.
- June 2016: Check out these blog posts (one, two) that nicely summarize some of our recent work on hyperparameter optimization.
- Oct 2015: Thank you Amazon, Bloomberg, and Google for generous research awards.
- Jan 2015: I taught a three day course at the Berkeley Institute of Data Science (BIDS) on Distributed Analytics and Machine Learning with Apache Spark.
- Nov 2014: I'm teaching a MOOC next Spring. Check out the edX site to learn more and/or to register (it's free!): Scalable Machine Learning.
- Sept 2014: I am co-organizing a NIPS workshop on Distributed Machine Learning and Matrix Computations.
- June 2014: I am joining UCLA's Computer Science Department as an assistant professor. For the next year, I will be working at Databricks.
- Oct 2013: We released SMaSH, our benchmarking toolkit for genomic variant calling algorithms.
- Sept 2013: MLlib, a distributed low-level machine learning library and a component of MLbase, was featured as part of the Spark 0.8.0 release.
- Aug 2013: We released the developer's preview of MLI, a user-friendly machine learning API and a part of MLbase, and presented a demo of it at AMPCamp.
- Aug 2012: Our textbook, Foundations of Machine Learning (MIT Press), has been published.
- Sept 2011: I am co-organizing a NIPS workshop on Sparse Representation and Low-rank Approximation.
- Aug 2011: I was awarded a three-year NSF OCI postdoctoral fellowship.
- Mar 2011: I am a recipient of the 2011 Janet Fabri Prize for best doctoral dissertation in NYU's Computer Science Department.
- Sept 2010: I am co-organizing a NIPS workshop on Low-rank Methods for Large-scale Machine Learning.