I finished my PhD in Computer Science with a focus on Machine Learning Theory at the University of California, Santa Cruz, in June 2020. My PhD advisor is Manfred K. Warmuth. My research focuses on learning theory, divergence measures, optimization, and their applications in ML. I was formerly a Student Researcher at Google Research, Brain Team. I joined Google Brain 🧠(now GoogleDeepMind) as a Research Scientist in Oct 2021. I am a core member of the Gemini team.
I am serving as an Area Chair for ICLR and ICML 2025.
Our third paper on (Optimal Transport with) Tempered Exponential Measures (TEMs) was accepted to AAAI 2024.
Our second paper on (Boosting with) Tempered Exponential Measures (TEMs) was accepted to NeurIPS 2023.
PhD in Computer Science, University of California, Santa Cruz, CA (2015 - 2020)
Thesis Advisor: Prof. Manfred K. Warmuth
Thesis Title: Tempered Bregman Divergence for Continuous and Discrete Time Mirror Descent and Robust Classification (Best Thesis Award!) pdf
MSc in Machine Learning and Data Mining (with Distinction), Aalto University, Espoo, Finland (2012 - 2014)
Thesis Advisor: Prof. Erkki Oja
Thesis Title: Application of alpha-Divergence for Stochastic Neighbor Embedding in Data Visualization pdf
BSc in Telecommunication, Tehran Polytechnic, Tehran, Iran (2007 - 2012)
Thesis Advisor: Prof. S. M. Ahadi
Thesis Title: Musical Instrument Classification Using Statistical Models
Research Scientist, Google DeepMind, Mountain View (April 2023 - Now)
Research Scientist, Google Research, Brain Team, Mountain View (October 2021 - April 2023)
Research Scientist, Google Mountain View (Aug 2020 - September 2021)
Research Scientist Intern, Google Brain Mountain View (March 2019 - June 2020)
Software Engineering Intern, Google Cloud (June 2018 - September 2018)
Data Scientist Intern, Microsoft (June 2017 - September 2017)
Data Scientist Intern, Adobe (June 2016 - November 2016)
Research Assistant, Helsinki Institute for Information Technology (September 2014 - July 2015)
C Fifty, R G Junkins, D Duan, A Iger, J W Liu, E Amid, S Thrun, C RĂ©. "Restructuring Vector Quantization with the Rotation Trick", ICLR, 2025. Oral - pdfÂ
Gemini Team. "Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context", arXiv preprint arXiv:2403.05530, 2024. pdf
Manfred K Warmuth, Wojciech Kotłowski, Matt Jones, Ehsan Amid. "Noise misleads rotation invariant algorithms on sparse targets", arXiv preprint arXiv:2403.02697, 2024. pdf
Andrew Hard, Antonious M Girgis, Ehsan Amid, Sean Augenstein, Lara McConnaughey, Rajiv Mathews, Rohan Anil. "Learning from straggler clients in federated learning", arXiv preprint arXiv:2403.09086, 2024. pdf
Richard Nock, Ehsan Amid, Frank Nielsen, Alexander Soen, and Manfred K Warmuth. "Hyperbolic Embeddings of Supervised Models", Â NeurIPS 2024. pdf
C Fifty, D Duan, RG Junkins, E Amid, J Leskovec, C RĂ©, S Thrun. "Context-Aware Meta-Learning", ICLR, 2024. pdf
Ehsan Amid, Frank Nielsen, Richard Nock, and Manfred K Warmuth. "Optimal Transport with Tempered Exponential Measures", AAAI, 2024. pdf
Gemini Team. "Gemini: a family of highly capable multimodal models", preprint arXiv:2312.11805, 2023. pdf
Ehsan Amid, Frank Nielsen, Richard Nock, Manfred K Warmuth. "The Tempered Hilbert Simplex Distance and Its Application To Non-linear Embeddings of TEMs", arXiv preprint arXiv:2311.13459, 2023. pdf
Jared Lichtarge, Ehsan Amid, Shankar Kumar, Tien-Ju Yang, Rohan Anil, Rajiv Mathews. "Heterogeneous Federated Learning Using Knowledge Codistillation", arXiv preprint arXiv:2310.02549, 2023. pdf
Manfred K Warmuth and Ehsan Amid. "Open Problem: Learning sparse linear concepts by priming the features", The Thirty Sixth Annual Conference on Learning Theory (COLT), 2023. pdf
George E Dahl, Frank Schneider, Zachary Nado, ..., Ehsan Amid et al. "Benchmarking Neural Network Training Algorithms", arXiv preprint arXiv:2306.07179, 2023. pdf
Richard Nock, Ehsan Amid, Manfred K. Warmuth. "Boosting with Tempered Exponential Measures", NeurIPS, 2023. pdf
Christopher Fifty, Joseph M. Paggi, Ehsan Amid, Jure Leskovec, Ron Dror. "Harnessing Simulation for Molecular Embeddings", arXiv preprint arXiv:2302.02055, 2023. pdf
Ehsan Amid, Rohan Anil, Christopher Fifty, and Manfred K. Warmuth. "Layerwise Bregman Representation Learning of Neural Networks with Applications to Knowledge Distillation", Transactions on Machine Learning Research (TMLR), 2023. pdf
Ehsan Amid, Richard Nock, Manfred Warmuth. "Clustering above Exponential Families with Tempered Exponential Measures", International Conference on Artificial Intelligence and Statistics (AISTATS), 2023. pdf
Jiaheng Wei, Harikrishna Narasimhan, Ehsan Amid, Wen-Sheng Chu, Yang Liu, Abhishek Kumar. "Distributionally Robust Post-hoc Classifiers under Prior Shifts", International Conference on Learning Representations (ICLR), 2023. pdf
Jiaheng Wei, Zhaowei Zhu, Tianyi Luo, Ehsan Amid, Abhishek Kumar, Yang Liu. "To Aggregate or Not? Learning with Separate Noisy Labels", Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023. pdf
Yatong Chen, Abhishek Kumar, Yang Liu, Ehsan Amid. "Fast Implicit Constrained Optimization of Non-decomposable Objectives for Deep Networks", Has it Trained Yet? NeurIPS 2022 Workshop, 2022. pdf
Abel L. Peirson*, Ehsan Amid*, Yatong Chen, Vlad Feinberg, Manfred K. Warmuth, Rohan Anil. "Fishy: Layerwise Fisher Approximation for Higher-order Neural Network Optimization", Has it Trained Yet? NeurIPS 2022 Workshop, 2022 (*Equal Contribution). pdf
Ehsan Amid*, Om Thakkar*, Arun Narayanan, Rajiv Mathews, Françoise Beaufays. "Extracting Targeted Training Data from ASR Models, and How to Mitigate It", INTERSPEECH, 2022 (*Equal Contribution). Oral - pdf
Ehsan Amid, Rohan Anil, Wojciech Kotłowski, Manfred K Warmuth. "Learning from Randomly Initialized Neural Network Features", arXiv preprint arXiv:2202.06438, 2022. pdf
Ehsan Amid*, Rohan Anil*, and Manfred K. Warmuth. "LocoProp: Enhancing BackProp via Local Loss Optimization", International Conference on Artificial Intelligence and Statistics (AISTATS), 2022 (*Equal Contribution). pdf
Ehsan Amid, Arun Ganesh*, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M Suriyakumar, Om Thakkar, Abhradeep Thakurta. "Public Data-Assisted Mirror Descent for Private Model Training", International Conference on Machine Learning (ICML), 2022 (*Corresponding Author). Oral - pdf
Abhishek Kumar and Ehsan Amid. "Constrained Instance and Class Reweighting for Robust Learning under Label Noise", arXiv preprint arXiv:2111.05428, 2021. pdf
Christopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn. "Efficiently Identifying Task Groupings for Multi-Task Learning", NeurIPS, 2021. Spotlight - pdf
Negin Majidi, Ehsan Amid, Hossein Talebi, and Manfred K. Warmuth. "Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond", arXiv preprint arXiv:2104.01493, 2021. pdf
Sina Rezaei Aghdam, Ehsan Amid, Marija Furdek, and Alexandre Graell i Amat. "Privacy-Preserving Wireless Federated Learning Exploiting Inherent Hardware Impairments", International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 2021. pdf
Manfred K. Warmuth, Wojciech Kotłowski, and Ehsan Amid. "A case where a spindly two-layer linear network whips any neural network with a fully connected input layer",  ALT, 2021. pdf
Christopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, and Chelsea Finn. "Measuring and Harnessing Transference in Multi-Task Learning", arXiv preprint arXiv:2010.15413, 2020. pdf
Ehsan Amid, Rohan Anil, Christopher Fifty, and Manfred K. Warmuth. "Step-size Adaptation Using Exponentiated Gradient Updates", Workshop on "Beyond first-order methods in ML systems" at the 37th International Conference on Machine Learning (ICML), 2020. pdf
Ehsan Amid and Manfred K. Warmuth. "Winnowing with Gradient Descent", Conference on Learning Theory (COLT), 2020. pdf
Ehsan Amid and Manfred K. Warmuth. "Reparameterizing Mirror Descent as Gradient Descent", NeurIPS, 2020. pdf
Hossein Talebi, Ehsan Amid, Peyman Milanfar, and Manfred K. Warmuth. "Rank-smoothed Pairwise Learning in Perceptual Quality Assessment", IEEE International Conference on Image Processing (ICIP), 2020. pdf
Ehsan Amid and Manfred K. Warmuth. "Divergence-based motivation for online EM and combining hidden variable models", Â UAI, 2020. pdf
Ehsan Amid and Manfred K. Warmuth. "An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint", AAAI, 2020. pdf
Ehsan Amid, Manfred K. Warmuth, Rohan Anil, and Tomer Koren. "Robust Bi-Tempered Logistic Loss Based on Bregman Divergences", Â Neurips, 2019. pdf, code, demo
Ehsan Amid, Manfred K. Warmuth, and Sriram Srinivasan. "Two-temperature logistic regression based on the Tsallis divergence", AISTATS, 2019. pdf
Ehsan Amid and Manfred K. Warmuth. "TriMap: Large-scale Dimensionality Reduction Using Triplets", arXiv preprint arXiv:1910.00204, 2019. pdf, code (incorporated into the scanpy package, FlowJo implementation by Ian Taylor)
Ehsan Amid and Manfred K. Warmuth. "A more globally accurate dimensionality reduction method using triplets", arXiv preprint arXiv:1803.00854, 2018. pdf
Ehsan Amid, Nikos Vlassis, Manfred K. Warmuth. "Low-dimensional data embedding via robust ranking", arXiv preprint arXiv:1611.09957, 2016. pdf, code
Ehsan Amid, Aristides Gionis, and Antti Ukkonen. "Semi-supervised kernel metric learning using relative comparisons." arXiv preprint arXiv:1612.00086, 2016. pdf
Ehsan Amid and Antti Ukkonen. "Multiview triplet embedding: Learning attributes in multiple maps." International Conference on Machine Learning (ICML), 2015. pdf, code
Ehsan Amid, Aristides Gionis, and Antti Ukkonen. "A kernel-learning approach to semi-supervised clustering with relative distance comparisons." Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 2015. pdf, code
Ehsan Amid, Onur Dikmen, and Erkki Oja. "Optimizing the Information Retrieval Trade-off in Data Visualization Using alpha-Divergence." arXiv preprint arXiv:1505.05821, 2015. pdf, code
Ehsan Amid, et al. "Unsupervised feature extraction for multimedia event detection and ranking using audio content." 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. pdf
Ehsan Amid. "Bayesian Non-parametric Image Segmentation with Markov Random Field Prior", Scandinavian Conference on Image Analysis, 2013. pdf
S. Ishikawa, M. Koskela, M. Sjöberg, J. Laaksonen, E. Oja, E. Amid, K. Palomaki, A. Mesaros, and M. Kurimo. "Picsom experiments in TRECVID 2013", in Proc. of the TRECVID 2013 Workshop, 2013. pdf
Sina R. Aghdam and Ehsan Amid. "A Fast Method of Steel Surface Defect Detection Using Decision Trees Applied to LBP based Features", IEEE Conference on Industrial Electronics and Applications, 2012.
Ehsan Amid, Sina R. Aghdam and Hamidreza Amindavar. "Enhanced Performance for Support Vector Machines as Multiclass Classifiers in Steel Surface Defect Detection", International Conference on Computer Vision and Image Processing, 2012.
Ehsan Amid and Sina R. Aghdam. "Musical Instrument Classification Using Embedded Hidden Markov Models", International Conference on Computer Vision and Image Processing, 2012.
Ehsan Amid, Om Thakkar, Rajiv Mathews, Francoise Beaufays. "Phrase Extraction for ASR Models", US Patent US20230178094A1, 2023. pdf.
Om Thakkar, Ehsan Amid, Arun Ganesh, et al. "Leveraging Public Data in Training Neural Networks with Private Mirror Descent", US Patent US20230103911A1, 2023. pdf.
Abhishek Kumar, Ehsan Amid. "Unified Sample Reweighting Framework for Learning with Noisy Data and for Learning Difficult Examples or Groups", US20230044078A1, 2023. pdf
Ehsan Amid, Manfred K. Warmuth, Rohan Anil. "Training neural networks using layer-wise losses", US 20220253713A1, 2022. pdf
Nikos Vlassis and Ehsan Amid. "Enhanced triplet embedding and triplet creation for high-dimensional data visualizations", US Patent US10127694B2, 2018. pdf
A Dualistic View of Activations in Deep Neural Networks
July 2023 - DP4ML workshop, ICML 2023
TriMap: Large-scale Dimensionality Reduction Using Triplets
Feb, 2018 - Google Brain, Mountain View, CA
Nov 2019 - UC Santa Cruz Genomics Institute
Robust Bi-tempered Logistic Loss
July 2019 - Google Mountain View, CA
Nov 2019 - Google New York City, NY
April 2020 - PARC
GitHub: https://github.com/google/bi-tempered-loss
Google AI blog post: https://bit.ly/35GOSQ3
Instructor for UCSC Tools of the Trade Bootcamp Series (topics: editors, git, LaTeX, Make, Shell, etc.)
Website: https://sites.google.com/a/ucsc.edu/bootcamps/home
Python, TensorFlow, Maple, MATLAB, R
C, Java, Bash Scripting
Numba, Familiar with Apache Spark and JAX
SQL, LATEX , git, Unix, Linux
Fall 2015 - UCSC Regent’s Fellowship University of California, Santa Cruz
Summer 2014 - Awarded Master’s Degree with Distinction in Machine Learning and Data Mining, Aalto University
2012-2014 - Honours Programme Grant in the Department of Information and Computer Science, Aalto University (ics.aalto.fi/en/studies/honours_programme)
Summer 2007 - National Physics Olympiad: Silver Medal (www.ysc.ac.ir)
Professor Emeritus
Department of Computer Science, University of California, Santa Cruz
Email: [email protected]
Principal Engineer
Google Brain, Mountain View, CA
Email: [email protected]
Professor
Department of Computer Science, Aalto University, Finland
Email: [email protected]
Professor Emeritus
Department of Computer Science, Aalto University, Finland
Email: [email protected]