Home Page of Subhro Ghosh

Subhro Ghosh
Dean's Chair Associate Professor
National University of Singapore
Department of Mathematics
Dept of Statistics and Data Science
NUS AI Institute & Institute of Data Science

Email:
subhrowork (**at**) gmail.com
matghos (**at**) nus.edu.sg

About me

I am Dean's Chair Associate Professor at the National University of Singapore and a faculty affiliate at the NUS AI Institute and the Institute of Data Science, NUS. I am broadly interested in stochastics, focussing on problems from statistical physics and the math of data and machine intelligence, and interactions between these. Before joining NUS, I was a post doc at Princeton University, and prior to that I obtained my PhD from the University of California, Berkeley under the supervision of Yuval Peres. Earlier, I received my Bachelor in Statistics and Master in Mathematics degrees from the Indian Statistical Institute. My work has been generously supported in part by the Dean's Chair Associate Professorship from NUS, Singapore MOE Tier II Grant 'Complex structures in Statistical Physics and the Math of Data', and recognised as a Finalist for the Bell Labs Prize.

Research

I am broadly interested in stochastics, focussing on problems from statistical physics and the math of data, and their interactions. These encompass constrained stochastic systems and their applications, including problems of learning under complex structure (e.g., latent symmetries or community structure), dimension reduction, sampling and optimization, statistical networks and signal processing. Key paradigms include determinantal processes (DPP), strong Rayleigh measures and negative dependence, multi reference alignment (MRA), maximum likelihood under constraints, generative priors, Gaussian random fields and stochastic geometry. The investigation of these problems naturally brings together a wide array of tools and techniques, including probability, harmonic and complex analysis, persistent homology and the theory of group representations.

For more on my research, please refer to Selected & recent works and my full list of Publications.

Selected recognitions

Dean's Chair Associate Professorship, NUS, 2025

Finalist for the Bell Labs Prize, 2022

Singapore MOE Tier II Grant, 2022 (~ US$ 500 K +)
Complex structures in Statistical Physics and the Math of Data

Gold Medal for M.Math., Indian Statistical Institute

Gold Medal for B.Stat., Indian Statistical Institute

AMS Simons Travel Grant

Editorial

Advances in Applied Probability

Journal of Applied Probability

Sankhya - The Indian Journal of Statistics (official journal of the Indian Statistical Institute)

Selected and recent works

Gaussian determinantal processes: A new model for directionality in data, with P. Rigollet,
Proceedings of the National Academy of Sciences (PNAS), vol. 117, no. 24, pp. 13207--13213
(Direct Submission)
article

Sparse Multi-Reference Alignment: Phase Retrieval, Uniform Uncertainty Principles and the Beltway Problem, with P. Rigollet
Foundations of Computational Math. (FoCM) 23, no. 5 : 1851-1898
preprint

Small coresets via negative dependence: DPPs, linear statistics, and concentration, with R. Bardenet, H. Simon-Onfroy, H.S. Tran
Spotlight at NeurIPS 2024
Advances in Neural Information Processing Systems , 37: 84329-84349
preprint

Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD, with R. Bardenet, M. Lin
Spotlight at NeurIPS 2021
Advances in Neural Information Processing Systems , 34, 16226-16237
preprint

Approximate Gibbsian structure in strongly correlated point fields and generalized Gaussian zero ensembles, with U. Gangopadhyay, K.A. Tan
Communications in Pure Appl. Math. (CPAM) 77, no. 8 : 3427-3519
preprint

Gaussian complex zeros on the hole event: the emergence of a forbidden region, with A. Nishry,
Communications in Pure Appl. Math. (CPAM), 72, no. 1 : 3-62
preprint

Rigidity and Tolerance in point processes: Gaussian zeroes and Ginibre eigenvalues, with Y. Peres,
Duke Mathematical Journal, 166 (10), 1789-1858
preprint

Determinantal processes and completeness of random exponentials: the critical case,
Probability Theory and Related Fields (PTRF), 163 (3-4), 643-665
preprint

Continuum Percolation for Gaussian zeroes and Ginibre eigenvalues, with M. Krishnapur, Y. Peres,
Annals of Probability, 44 (5), 3357-3384
preprint

Maximum Likelihood under constraints: Degeneracies and Random Critical Points, with S. Chaudhuri, U. Gangopadhyay
IEEE Transactions on Information Theory , vol. 69, no. 12, pp. 7976-7997
preprint

Signal Analysis via the Stochastic Geometry of Spectrogram Level Sets , with M. Lin, D. Sun
IEEE Transactions on Signal Processing, 70 : 1104-1117.
preprint

Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos, with K. Balasubramanian, X. Yamg,
IEEE Transactions on Information Theory, 68 (5), 3234-3252
International Conference on Machine Learning (ICML), PMLR Vol. 119 3545--3555
preprint
Disordered complex networks: energy optimal lattices and persistent homology, with N. Miyoshi, T. Shirai,
IEEE Transactions on Information Theory, 68 (8), 5513-5534
preprint

Generative Principal Component Analysis , with Z. Liu, J. Liu, J. Han, J. Scarlett,
International Conference on Learning Representations (ICLR), 2022
preprint

Generalized stealthy hyperuniform processes: maximal rigidity and the bounded holes conjecture, with J.L. Lebowitz,
Communications in Mathematical Physics (CMP), 363, no. 1 : 97-110
preprint

Fluctuation and Entropy in Spectrally Constrained random fields, with K. Adhikari, J.L. Lebowitz,
Communications in Mathematical Physics (CMP), 386, 749–780 .
preprint

Rigidity hierarchy in random point fields: random polynomials and determinantal processes, with M. Krishnapur,
Communications in Mathematical Physics (CMP), 388, no. 3 : 1205-1234.
preprint

Filtering through a topological lens: homology for point processes on the time-frequency plane, with J. Miramont, K.A. Tan, S.S. Mukherjee, R. Bardenet
preprint

Negative Dependence as a toolbox for machine learning: review and new developments, with H.S. Tran, V. Petrovic, R. Bardenet
preprint

Research Grants and major organisational activities

My research work and organisational activities have been generously supported by :

Complex structures in Statistical Physics and the Math of Data (~ US$ 500 K +) [Singapore MOE Tier II Grant; 2022-25]

Randomness and structure through a topological lens [Singapore MOE Tier I Grant; 2025-27]

Stochastic models with latent symmetries [Singapore MOE Tier I grant; 2024-26]

Stochastics beyond Independence : A Math of Data Perspective [Singapore MOE Tier I Grant; 2020-24]

Resilience and Growth Programme [NUS, 2020-21]

Rigidity Phenomena in Random Processes [NUS Start Up Grant; 2017-23]

The Mathematics of Data (with A. Bandeira, P. Rigollet) [Institute for Mathematical Sciences, Singapore; 02 Jan–26 Jan 2024]

Mathematics for Machine Intelligence [One day Woskshop, Dept of Math (NUS); Apr 29, 2025]

Invited Session at 2025 INFORMS International Meeting [Singapore; Jul 20-23, 2025]

Statistical Physics of interacting particle systems (with D. Dereudre, A. Hardy, M. Maida) [Institute for Mathematical Sciences, Singapore; Aug 29 -- Sep 9 2022]

Random matrices, Stochastic geometry and Related topics (with T. Shirai, K. Suzaki) [J.S.P.S., IMS Singapore & Dept. of Math. (NUS); Mar 14--16, 2019]

Conformal Field Theories: Randomness and Geometry (with T.C. Dinh, S. Ganguly) [Institute for Mathematical Sciences, Singapore; May 18--29, 2026]

Symposium on Mathematics (with T.C. Dinh) [Dept of Math (NUS); Aug 5, 2022]

A Day of Randomness: in honour of Percy Deift (with H. Huang) [Dept of Math (NUS); May 24, 2024]

The Singapore-Bangalore "What is ...?" Workshop on Mathematics (with W.T. Gan, D. Yogeshwaran, V. Pingali, S. Mukherjee, S. Lahiry) [Indian Institute of Science, Bangalore, Aug 23-34, 2025]

Oppenheim Lecture and Workshop (by Martin Hairer) (with W.T. Gan, R. Sun) [Dept of Math (NUS) & IMS, Singapore; Oct 16-26, 2024]

Research Fellows

I have had the pleasure to mentor :

Satyaki Mukherjee (PhD UC Berkeley) [current]

Xiaoyu Dong (PhD University of Michigan) [current]

Samriddha Lahiry (PhD Cornell University) [current]

Akshay Hegde (PhD Oxford University) [Fall 2025 -- ]

Soumendu Sundar Mukherjee (PhD UC Berkeley) [next position : Assistant Professor, Indian Statistical Institute]

Meixia Lin (PhD National University of Singapore) [next position : Assistant Professor, Singapore University of Technology and Design]

Hong T.M. Chu (PhD National University of Singapore) [next position : Assistant Professor, VinUniversity, Vietnam]

Ujan Gangopadhyay (PhD University of Southern California) [next position : Postdoc, University of Luxembourg]

Hao Wu (PhD National University of Singapore) [next position : Assistant Professor, Nanjing University, China]

Graduate students

I have had the pleasure to supervise :

Hoang Son Tran (PhD, 2020 -- )

Kin Aun Tan (Phd, 2022 -- )

Clement Ng (PhD, 2025 -- ; Masters by research, 2023-24 )

Clarence Chew (PhD, 2024 -- )

Chew Shia See (PhD, 2025 -- )

Rathindra Karmakar (Research assistant, 2024 -- )

Manh Hung Tran (PhD, 2020--24 )

Xinran Bian (PhD, 2020--24 )

Gim Siang Chia (Masters by research, 2023--24)

Vladimir Petrovic (Masters by research, 2023--24)

Jingbin Pan (Masters by research, 2023--24)

Jeremiah Thomson (Masters by research, 2020 -- 2022)

Dongfang Sun (Masters by research, 2020)

Jnaneshwar Baslingekar (Research assistant, 2022)

Hugo Simon (Research assistant, 2022)

Teaching

Curriculum Design :

Mathematics for Artificial Intelligence (MA3270) (undergraduate course on math for AI)

Mathematical Underpinnings of Data Science (MA5272) (graduate course on math of ML)

Stochastic Processes (S) (MA3238S) (advanced undergrad course for special talents programme)

Stochastic Processes and Algorithms (MA5249) (graduate course)

Taught courses:

The Mathematics of Data (MA6251) (graduate topics course on applied mathematics)

Topics in Undergraduate Mathematics : The Mathematics of Data (MA4292) (advanced undergraduate)

Stochastic Calculus (ORF527) (graduate)

Stochastic Processes (MA3238/ST3236) (undergraduate)

Living with Mathematics (GEH1036) (introductory undergraduate)

Lecture series / Mini-course:

Tutorial at Data Science: Probabilistic and Optimization Methods, ICTS-TIFR, Bangalore [Aug 2025]

Course at the Autumn School and Workshop on Dynamics of point processes and particle systems, Münster, Germany [Nov 2024]

Course at the Darwin “Summer” School on the Mathematical Aspects of Data Science (org. ANU), Darwin, Australia [Jun 2024]

Course at Topics in High Dimensional Probability, ICTS-TIFR, Bangalore [Jan 2023]

Course at DPP-Fermions 2022, ENS de Lyon, France [Jun 2022]

Seminar

I organise the Stochastics Seminar at NUS Math. If you are interested in giving a talk, please reach out via e-mail.

We organise the following reading seminars lead by my postdocs and students :

Optimal Transport and Sampling

Quantum Stochastics and Quantum Learning

Boolean Fourier Analysis

Mathematics of Deep Learning

If you are interested in participating, please reach out via e-mail.

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Publications

Filtering through a topological lens: homology for point processes on the time-frequency plane, with J. Miramont, K.A. Tan, S.S. Mukherjee, R. Bardenet
preprint

Negative Dependence as a toolbox for machine learning: review and new developments, with H.S. Tran, V. Petrovic, R. Bardenet
preprint

Small coresets via negative dependence: DPPs, linear statistics, and concentration, with R. Bardenet, H. Simon-Onfroy, H.S. Tran
Spotlight at NeurIPS 2024
preprint

On an Empirical Likelihood based Solution to the Approximate Bayesian Computation Problem, with S. Chaudhuri, K.C. Pham
Statistical Analysis and Data Mining: The ASA (American Statistical Association) Data Science Journal 17, no. 5 (2024): e11711.
preprint

Implicit regularization via Spectral Neural Networks and non-linear matrix sensing, with H.T.M. Chu, C.T. Lam, S.S. Mukherjee
preprint

Minimax-optimal estimation for sparse multi-reference alignment with collision-free signals, with S.S. Mukherjee, J.B. Pan
preprint

Hole event for random holomorphic sections on compact Riemann surfaces, with T.C. Dinh, H. Wu,
preprint

Approximate Gibbsian structure in strongly correlated point fields and generalized Gaussian zero ensembles, with U. Gangopadhyay, K.A. Tan
Communications in Pure Appl. Math., 77, no. 8 (2024): 3427-3519
preprint

Learning Networks from Gaussian Graphical Models and Gaussian Free Fields, with S.S. Mukherjee, H.S. Tran, U. Gangopadhyay,
Journal of Statistical Physics , 191, no. 4 (2024): 45
preprint

Sparse Multi-Reference Alignment: Phase Retrieval, Uniform Uncertainty Principles and the Beltway Problem, with P. Rigollet
Foundations of Computational Math 23, no. 5 (2023): 1851-1898
preprint

Maximum Likelihood under constraints: Degeneracies and Random Critical Points, with S. Chaudhuri, U. Gangopadhyay
IEEE Transactions on Information Theory , vol. 69, no. 12, pp. 7976-7997 (2023)
preprint

Dictionary Learning under Symmetries via Group Representations, with A. Low, Y.S. Soh, Z. Feng, B. Tan,
preprint

Efficient clustering through the lens of Gaussian Determinantal Processes, with J.S. Baslingekar, S.S. Mukherjee, H. Talukdar,
Draft available upon request

Disordered complex networks: energy optimal lattices and persistent homology, with N. Miyoshi, T. Shirai,
IEEE Transactions on Information Theory, 68 (8), 5513-5534 (2022)
preprint

Signal Analysis via the Stochastic Geometry of Spectrogram Level Sets , with M. Lin, D. Sun
IEEE Transactions on Signal Processing, 70 (2022): 1104-1117.
preprint

Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos, with K. Balasubramanian, X. Yang,
IEEE Transactions on Information Theory, 68 (5), 3234-3252 (2022)
International Conference on Machine Learning (ICML), 2020
preprint
Generative Principal Component Analysis , with Z. Liu, J. Liu, J. Han, J. Scarlett,
International Conference on Learning Representations (ICLR), 2022
preprint

Learning with latent group sparsity via heat flow dynamics on networks, with S.S. Mukherjee,
preprint

Rigidity hierarchy in random point fields: random polynomials and determinantal processes, with M. Krishnapur,
Communications in Mathematical Physics, 388, no. 3 (2021): 1205-1234.
preprint

Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD, with R. Bardenet, M. Lin
Spotlight paper at NeurIPS 2021
at Advances in Neural Information Processing Systems, 34 (2021) 16226-16237
preprint

Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors, with Z. Liu, J. Scarlett,
Neural Information Processing Systems (NeurIPS), 2021
preprint

Fluctuation and Entropy in Spectrally Constrained random fields, with K. Adhikari, J.L. Lebowitz,
Communications in Math. Physics, 386, 749–780 (2021).
preprint

Quantitative Marcinkiewicz's theorem and central limit theorems: applications to spin systems and point processes, with T.C. Dinh, H.S. Tran, M.H. Tran
preprint

Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priors, with Z. Liu, J. Scarlett
IEEE Information Theory Workshop, 2021
preprint

Learning from DPPs via Sampling: Beyond HKPV and symmetry, with R. Bardenet
preprint

Transmission and Navigation on Disordered Lattice Networks, Directed Spanning Forests and Brownian Web, with K. Saha,
Journal of Statistical Physics, vol. 180, no. 1 (2020): 1167-1205
Special issue in honour of J.L. Lebowitz
preprint

Gaussian determinantal processes: A new model for directionality in data, with P. Rigollet,
Proceedings of the National Academy of Sciences, vol. 117, no. 24 (2020), pp. 13207--13213
article

Gaussian complex zeros on the hole event: the emergence of a forbidden region, with A. Nishry,
Communications in Pure Appl. Math., 72, no. 1 (2019): 3-62
preprint

Generalized stealthy hyperuniform processes: maximal rigidity and the bounded holes conjecture, with J.L. Lebowitz,
Communications in Math. Physics, 363, no. 1 (2018): 97-110
preprint

Point processes, hole events, and large deviations: random complex zeros and Coulomb gases, with A. Nishry,
Constructive Approximation, 48, no. 1 (2018): 101-136.
Special Issue: Approximation and Statistical Physics – Part I
preprint

Fluctuations, large deviations and rigidity in hyperuniform systems: a brief survey, with J.L. Lebowitz
Indian Journal of Pure and Applied Mathematics , vol. 48 no. 4 (2017), 609-631
Special issue in honour of B.V. Rao
preprint

Rigidity and Tolerance in point processes: Gaussian zeroes and Ginibre eigenvalues, with Y. Peres,
Duke Mathematical Journal , 1166, no. 10, 1789-1858 (2017)
preprint

Number rigidity in superhomogeneous random point fields, with J. L. Lebowitz,
Journal of Statistical Physics , vol. 166 (3-4), 1016-1027 (2017)
Special issue in honour of Y. Sinai
preprint

Multivariate CLT follows from strong Rayleigh property, with T. Liggett,R. Pemantle,
Proceedings of ANALCO, SIAM, 2017
preprint

Continuum Percolation for Gaussian zeroes and Ginibre eigenvalues, with M. Krishnapur, Y. Peres,
Annals of Probability, 44 (5), 3357-3384 (2016)
preprint

Palm measures and rigidity phenomena in point processes,
Electronic Communications in Probability , vol. 21 (2016)
preprint

Large deviations for zeros of random polynomials with i.i.d. exponential coefficients, with O. Zeitouni,
Int. Math. Res. Not., (5), 1308-1347 (2016)
preprint

Determinantal processes and completeness of random exponentials: the critical case,
Probability Theory and Related Fields, 163 (3-4), 643-665 (2015)
preprint

Rigidity and Tolerance in Gaussian zeroes and Ginibre eigenvalues: quantitative estimates,
preprint

Exact quantum algorithm to distinguish Boolean functions of different weights, with S.L. Braunstein, B.S. Choi, S. Maitra,
Journal of Physics A: Mathematical and Theoretical, Volume 40, Number 29 (2007)
journal

Symmetry of Bound and Antibound States in the Semiclassical Limit for a General Class of Potentials, with S. Dyatlov,
Proc. Amer. Math. Soc. 138 (2010), 3203-3210
preprint

Rigidity Phenomena in random point sets
PhD Thesis, UC Berkeley (2013)
Thesis

A Random Gallery

Left to right: Surface plot of normalised GAF; and Fractal Gaussian networks

Left to right: Gaussian DPP for clustering Fisher's Iris data; and stochastic geometry of spectrogram level sets

Left to right: Laplace transform based sampling for UK retail data; and DPP based minibatch sampling for Stochastic Gradient Descent

Left to right: Conditional intensity for a Gaussian matrix and for a Gaussian polynomial

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