A pdf version of my CV can be found here.

Employment History

Research Scientist
Polymathic-AI, NYU
2023 - present

Leading the development of the next generation foundation models for scientific analysis at Polymathic AI. Our domains of interest vary from astrophysics to general purpose foundation models based on language.

Machine Learning Consultant
Airhop Comm
2021 - present

Helping the development of ML based algorithms that can improve performance and efficiency of 4G and 5G radio access networks in real world environments. The tools we employ range from reinforcement learning to time-series analysis with Transformers.

Associate Research Scientist
Flatiron Institute
2019 - 2023

Developed novel machine learning algorithms inspired by neuroscience and cognitive experiments. Published a reproducibility report on cancer detection in collaboration with domain experts.

Postdoctoral Fellow
New York University
2016 - 2019

Recipient of the James Arthur fellowship award. In collaboration with the scientists at CCPP and CDS, developed novel algorithms in machine learning (e.g. continual learning and transfer learning) as well as applied ML to the sciences (quantum tomography).

Postdoctoral Fellow
University of Cambridge
2015 - 2016

Continued research in application of theoretical high energy physics methods to condensed matter, including the fractional quantum hall effect. Explored possibilities of applying machine learning for a data-driven analysis of physical experiments.

Education

Doctor of Philosophy
University of Chicago
2009 - 2015

Research in High Energy Physics with a focus on String Theory, Effective Field Theory.
Thesis: Geometry, topology and anomalies in condensed matter Effective Field Theories.

Master of science
University of Toronto
2007 - 2009

Research in High Energy Physics and String Theory. Masters Thesis: Conformal windows of SP(2N) and SO(N) gauge theories from topological excitations on R3 x S1.

Bachelor of Science
University of Toronto
2003 - 2007

Double major in Math and Physics, Honours with high distinction.

Invited talks

  • Jan 2024 – Serialization for Heterogenous Data Challenges

    AI-driven discovery in physics and astrophysics
    Center for Data-Driven Discovery, Kavli Institute, The University of Tokyo, Japan

  • Feb 2024 – Prospects of LLMs in Fundamental Physics

    Large Language Models in Physics Symposium (LIPS)
    DESY, Hamburg, Germany

  • Mar 2024 – (Plenary seminar) Towards Foundation Models for Science

    International Workshop on Advanced Computing and Analysis Techniques in Physics Research
    Stonybrook, New York, United States

Miscellaneous

  • Author of over 30 publications in leading journals and conferences.
  • Area Chair and Top Reviewer for NeurIPS and other top machine learning conferences.
  • NVIDIA Certified in Data and Model Parallelism: How to Build, Train, and Deploy Large Neural Networks.
  • Recipient of the James Arthur Fellowship award (2016-2019).
  • Organizer of the Machine Learning Seminar Series at Flatiron Institute (2020 – 2023).
  • Fluent in English and Farsi, advanced French, and intermediate Japanese proficiency.
  • Visual artist experienced in both traditional and AI-assisted art siavashgolkarart.com.

Publications

For an up-to-date list of publications see my Google Scholar profile.

Conference proceedings
  • S. Golkar, D. Lipshutz, T. Tesileanu, and D. B. Chklovskii, “An online algorithm for contrastive principal component analysis,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2023, pp. 1–5.
  • A. Genkin, D. Lipshutz, S. Golkar, T. Tesileanu, and D. Chklovskii, “Biological learning of irreducible representations of commuting transformations,” in Advances in Neural Information Processing Systems, 2022.
  • S. Golkar, T. Tesileanu, Y. Bahroun, A. Sengupta, and D. Chklovskii, “Constrained predictive coding as a biologically plausible model of the cortical hierarchy,” in Advances in Neural Information Processing Systems, vol. 35, 2022, pp. 14 155–14 169.
  • P. Karimi, S. Golkar, J. Friedrich, and D. Chklovskii, “Learning a biologically plausible linear controller for nonlinear systems,” in APS March Meeting Abstracts, vol. 2022, 2022, N00–269.
  • J. Friedrich, S. Golkar, S. Farashahi, A. Genkin, A. Sengupta, and D. Chklovskii, “Neural optimal feedback control with local learning rules,” in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 16 358–16 370.
  • S. Golkar, D. Lipshutz, Y. Bahroun, A. Sengupta, and D. Chklovskii, “A simple normative network approximates local non-hebbian learning in the cortex,” in Advances in neural information processing systems, vol. 33, 2020, pp. 7283–7295.
  • S. Golkar, D. Lipshutz, Y. Bahroun, A. M. Sengupta, and D. B. Chklovskii, “A biologically plausible neural network for local supervision in cortical microcircuits,” in NeurIPS 2020 Workshop ’Beyond backpropagation’, 2020.
  • D. Lipshutz, C. Windolf, S. Golkar, and D. B. Chklovskii, “A biologically plausible neural network for slow feature analysis,” in Advances in Neural Information Processing Systems 33, 2020.
  • K. Cranmer, S. Golkar, and D. Pappadopulo, “Inferring the quantum density matrix with machine learning,” in ICML 2019 Workshop on Theoretical Physics for Deep Learning, 2019.
  • S. Golkar, “Emergent structures and lifetime structure evolution in artificial neural networks,” in NeurIPS 2019 Workshop on Real Neurons & Hidden Units, 2019.
  • S. Golkar and K. Cho, “Task-driven data verification via gradient descent,” in KDD 2019 Workshop on Data Collection,Curation, and Labeling for Mining and Learning (DCCL), 2019.
  • S. Golkar, M. Kagan, and K. Cho, “Continual learning via neural pruning,” in NeurIPS 2019 Workshop on Real Neurons & Hidden Units, 2019.
  • S. Golkar and K. Cranmer, “Backdrop: Stochastic backpropagation,” in ICML 2019 Workshop on Theoretical Physics for Deep Learning, 2018.
Journal Articles
  • S. Golkar, A. Bietti, M. Pettee, et al., “Contextual counting: A mechanistic study of transformers on a quantitative task,” 2024. arXiv: 2406.02585.
  • S. Golkar, M. Pettee, M. Eickenberg, et al., “Xval: A continuous number encoding for large language models,” arXiv preprint arXiv:2310.02989, 2023.
  • F. Lanusse, L. Parker, S. Golkar, et al., “Astroclip: Cross-modal pre-training for astronomical foundation models,” arXiv preprint arXiv:2310.03024, 2023.
  • D. Lipshutz, Y. Bahroun, S. Golkar, A. M. Sengupta, and D. B. Chklovskii, “A normative framework for deriving neural networks with multi-compartmental neurons and non-hebbian plasticity,” arXiv preprint arXiv:2302.10051, 2023.
  • M. McCabe, B. R.-S. Blancard, L. H. Parker, et al., “Multiple physics pretraining for physical surrogate models,” arXiv preprint arXiv:2310.02994, 2023.
  • C. Pedersen, T. Tesileanu, T. Wu, et al., “Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures,” arXiv preprint arXiv:2309.16645, 2023.
  • T. Tesileanu, S. Golkar, S. Nasiri, A. M. Sengupta, and D. B. Chklovskii, “Neural circuits for dynamics-based segmentation of time series,” Neural Computation, vol. 34, no. 4, pp. 891–938, 2022.
  • D. Lipshutz, Y. Bahroun, S. Golkar, A. M. Sengupta, and D. B. Chklovskii, “A biologically plausible neural network for multichannel canonical correlation analysis,” Neural Computation, vol. 33, no. 9, pp. 2309–2352, 2021.
  • D. X. Nguyen, S. Golkar, M. M. Roberts, and D. T. Son, “Particle-hole symmetry and composite fermions in fractional quantum hall states,” Physical Review B, vol. 97, no. 19, p. 195 314, 2018.
  • S. Golkar, D. X. Nguyen, and D. T. Son, “Spectral sum rules and magneto-roton as emergent graviton in fractional quantum hall effect,” Journal of High Energy Physics, vol. 2016, no. 1, pp. 1–15, 2016.
  • S. Golkar, D. X. Nguyen, M. M. Roberts, and D. T. Son, “Higher-spin theory of the magnetorotons,” Physical review letters, vol. 117, no. 21, p. 216 403, 2016.
  • S. Golkar and S. Sethi, “Global anomalies and effective field theory,” Journal of High Energy Physics, vol. 2016, no. 5, pp. 1–20, 2016.
  • S. Golkar and M. M. Roberts, “Viscosities and shift in a chiral superfluid: A holographic study,” arXiv preprint arXiv:1502.07690, 2015.
  • S. Golkar, M. M. Roberts, and D. T. Son, “The euler current and relativistic parity odd transport,” Journal of High Energy Physics, vol. 2015, no. 4, pp. 1–22, 2015.
  • S. Golkar and D. T. Son, “(non)-renormalization of the chiral vortical effect coefficient,” Journal of High Energy Physics, vol. 2015, no. 2, p. 169, 2015.
  • M. Geracie, S. Golkar, and M. M. Roberts, “Hall viscosity, spin density, and torsion,” arXiv preprint arXiv:1410.2574, 2014.
  • S. Golkar, M. M. Roberts, and D. T. Son, “Effective field theory of relativistic quantum hall systems,” Journal of High Energy Physics, vol. 2014, no. 12, pp. 1–10, 2014.
  • S. Golkar and D. T. Son, “Operator product expansion and conservation laws in non-relativistic conformal field theories,” Journal of High Energy Physics, vol. 2014, no. 12, pp. 1–11, 2014.
  • S. Golkar, “Conformal windows of sp (2n) and so (n) gauge theories from topological excitations on R3 × S1,” Journal of High Energy Physics, vol. 2009, no. 11, p. 076, 2009.