Jonas Mueller
jonasmueller
á
csail.mit.edu
Director, AI Research @
Handshake
Hiring AI researchers and interns! Email me.
I lead research on Data and Evaluations for frontier AI at
Handshake
, which acquired my previous startup
Cleanlab
(a
Forbes top-50 AI company
). Before Cleanlab, I was a senior scientist at
Amazon Web Services
, devising AutoML & Deep Learning Services used by thousands of enterprises. Before that, I completed my Ph.D. at the
MIT Computer Science & Artificial Intelligence Lab
. I've also co-developed some of the most popular software for
AutoML
(
autogluon
) and for
Data-Centric AI
(
cleanlab
).
Select Publications
[Full list at
Google Scholar
]
Quantifying uncertainty in answers from any language model and enhancing their trustworthiness
J Chen, J Mueller
Proceedings of the Association for Computational Linguistics (ACL)
, 2024
[Featured in
MIT Technology Review
,
MarkTechPost
,
Unite.ai
,
Nvidia Blog
,
LlamaIndex
] [
Blog Post
] [
Code
]
Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges
M Caccia, J Mueller, T Kim, L Charlin, R Fakoor
Conference on Lifelong Learning Agents (CoLLAs)
, 2023
Deep Learning for the Partially Linear Cox Model
Q Zhong, J Mueller, J Wang
Annals of Statistics
, 2022
ResNeSt: Split-Attention Networks
H Zhang, C Wu, Z Zhang, Y Zhu, Z Zhang, H Lin, Y Sun, T He, J Mueller, R Manmatha, M Li, A Smola
IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
, 2022
[Featured in
Synced
,
Analytics India Magazine
,
Pytorch Image Models
,
Top Kaggle Solutions
] [
Code
]
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
C Northcutt, A Athalye, J Mueller
Advances in Neural Information Processing Systems (NeurIPS)
, 2021
[Featured as
Oral Presentation
and in
Wired
,
VentureBeat
,
MIT Technology Review
,
CNN News18
] [
Webpage
] [
Code
] [
Blog
]
Benchmarking Multimodal AutoML for Tabular Data with Text Fields
X Shi
*
, J Mueller
*
, N Erickson, M Li, A Smola
Advances in Neural Information Processing Systems (NeurIPS)
, 2021
[Featured as
Contributed Talk
at
ICML Workshop on Automated Machine Learning
] [
Code
] [
Benchmark Datasets
]
Overinterpretation reveals image classification model pathologies
B Carter, S Jain, J Mueller, D Gifford
Advances in Neural Information Processing Systems (NeurIPS)
, 2021
[Featured in
TechXplore
,
VentureBeat
,
World Economic Forum
,
NY Tech Media
] [
Lecture Notes
] [
Code
]
Continuous Doubly Constrained Batch Reinforcement Learning
R Fakoor, J Mueller, K Asadi, P Chaudhari, A Smola
Advances in Neural Information Processing Systems (NeurIPS)
, 2021
[
Code
]
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation
R Fakoor
*
, J Mueller
*
, P Chaudhari, A Smola
Advances in Neural Information Processing Systems (NeurIPS)
, 2020
[
Lecture Notes
] [
Code
]
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
N Erickson
*
, J Mueller
*
, A Shirkov, H Zhang, P Larroy, M Li, A Smola
ICML Workshop on Automated Machine Learning
, 2020
[Featured in
VentureBeat
,
InfoWorld
,
SiliconAngle
,
Synced
,
SD Times
,
Forbes
,
Top Kaggle Solutions
] [
KDD Tutorial
] [
Code
]
Antibody Complementarity Determining Region Design Using High-Capacity Machine Learning
G Liu
*
, H Zeng
*
, J Mueller, B Carter, Z Wang, J Schilz, G Horny, M Birnbaum, S Ewert, D Gifford
Bioinformatics
, 2020
[Featured in
NYU Center for Data Science
,
Protein & Antibody Engineering Summit Keynote
]
Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing
J Mueller, V Syrgkanis, M Taddy
Advances in Neural Information Processing Systems (NeurIPS)
, 2019
[
Code
]
What made you do this? Understanding black-box decisions with sufficient input subsets
B Carter
*
, J Mueller
*
, S Jain, D Gifford
Artificial Intelligence and Statistics (AISTATS)
, 2019
[Featured as Contributed Talk at
NIPS Workshop on Interpretability and Robustness
] [
Slides
] [
Lecture Notes
] [
Code
]
A peninsular structure coordinates asynchronous differentiation with morphogenesis to generate pancreatic islets
N Sharon
*
, R Chawla
*
, J Mueller, J Vanderhooft, J Whitehorn, B Rosenthal, M Gurtler, R Estanboulieh, D Shvartsman, D Gifford, C Trapnell, D Melton
Cell
, 2019
[Part of effort leading to
a cure for Type 1 Diabetes
] [Featured as a
Research Highlight
in
Nature Reviews Endocrinology
] [
Video
]
Modeling Persistent Trends in Distributions
J Mueller, T Jaakkola, D Gifford
Journal of the American Statistical Association (JASA)
, 2018
[
Journal Version
] [
Code
]
Sequence to Better Sequence: Continuous Revision of Combinatorial Structures
J Mueller, D Gifford, T Jaakkola
International Conference on Machine Learning (ICML)
, 2017
[
Slides
] [
Code
]
Learning Optimal Interventions
J Mueller, D Reshef, G Du, T Jaakkola
Artificial Intelligence and Statistics (AISTATS)
, 2017
[Featured as Contributed Talk at
NIPS Workshop on Bayesian Optimization
] [
Slides
] [
Code
]
Siamese Recurrent Architectures for Learning Sentence Similarity
J Mueller, A Thyagarajan
Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI)
, 2016
[Featured in
Most Influential AAAI Papers
] [
Lecture Notes
] [
Code
]
Principal Differences Analysis: Interpretable Characterization of Differences between Distributions
J Mueller and T Jaakkola
Advances in Neural Information Processing Systems (NeurIPS)
, 2015
[Featured in
IEEE Signal Processing Society
,
Foundations of Geometric Methods in Data Analysis
] [
Slides
] [
Code
]
General Triallelic Frequency Spectrum Under Demographic Models with Variable Population Size
P Jenkins, J Mueller, Y Song
Genetics
, 2014
[Featured as a
Research Highlight
in
Nature Reviews Genetics
]
