Eric Zelikman
Iβm fascinated by how (and whether) algorithms can learn meaningful representations and reason. I'm exploring these questions at xAI. Previously, I was a Ph.D. candidate at Stanford, advised by Nick Haber and Noah Goodman. Perhaps the most daunting exciting gap between human and machine learning is the ease with which we learn concepts about our world from experience: not only do we need much less experience to construct and apply these concepts, but our understanding is flexible in novel situations.
I believe that machine learning can draw lessons from human learning (and vice versa) and that these advances can benefit everyone.
If you're looking for advice or feedback, feel free to schedule a short (non-commercial) research chat. Note I can't give author-level guidance or discuss my current work.
Selected Works
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COLM 2024
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COLM Oral SpotlightCOLM 2024, NeurIPS Workshop on Optimization for Machine Learning 2023
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ICLR 2024
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ICLR 2024
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TMLR 2024TMLR 2024
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NeurIPS Spotlight
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NeurIPS SpotlightNeurIPS 2023
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Applied Energy
- NeurIPS 2022
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TMLR 2022
- ICLR 2021International Conference on Learning Representations 2021
- ICML WorkshopICML Workshop on Uncertainty & Robustness in Deep Learning 2020
- NeurIPS WorkshopNeurIPS Workshop on Tackling Climate Change with Machine Learning 2020
- CVPR WorkshopShort Paper in CVPR Workshop on Adversarial Machine Learning in Computer Vision 2020
- Honors ThesisUndergraduate Honors Thesis 2020
Education
Industry
Member of Technical Staff | xAI | March 2024 - Present | |
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Student Researcher | Microsoft Research | June 2023 β September 2023 | |
Student Researcher | Blueshift @ X & Google Research | June 2022 β September 2022 | |
Deep Learning Engineer | Lazard | July 2020 β September 2021 | |
GANs Curriculum Developer | DeepLearning.AI | June β October 2020 | |
Machine Learning Intern | Argo AI | June β September 2019 | |
Machine Learning Intern | Uncountable | June β September 2018 & April β June 2019 |
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Software Engineer | Philometrics | October 2016 β July 2017 |
Other
Reviewer | Highlighted (8%) @ ICLR 2022, NeurIPS 2022, COLING 2022, EMNLP 2022, ICML 2023, Best Reviewer Award (1-1.5%) @ ACL 2023, NeurIPS 2023, EMNLP 2023, ICLR 2024 | 2022-2024 |
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Editor and 2019 Editor-in-Chief | Stanford Undergraduate Research Journal | 2016 β 2020 |
Teacher | Stanford Splash | 2016 - 2020 |
Section Leader | Stanford Code in Place | April - May 2020 |
TreeHacks, HopHacks, Cal Hacks, etc. | Hackathons | 2016 - 2020 |