Peng Chen
Assistant Professor
Develops scientific machine learning, uncertainty quantification and Bayesian inference for complex models.
Peng Chen's lab at Georgia Tech works on probabilistic & causal inference and efficient training & inference. 25 papers since 2023, 6 at top venues, mostly NeurIPS and ICLR. 8 PhD students and 1 master's student. 16 alumni, most often to Amazon. Applications close .
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 24%
- PhD students
- 8
- MS students
- 1
- alumni listed
- 16
Papers by yearfrom the lab homepage, 2023–2026
- 7 papers2026 · T1 3 · collecting
- 5 papers2025 · T1 2
- 6 papers2024 · T1 1
- 7 papers2023 · T1 0
T1 venuesOtherVenues: NeurIPS, ICLR, JMLR
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference7 papers
- Efficient training & inference4 papers
- develops scientific0 papers
- scientific machine0 papers
- bayesian inference0 papers
Paper list
From the lab homepage.
20267 papers
- JMLR (T1)Dimension reduction for derivative-informed operator learning: An analysis of approximation errors (opens in a new tab)
- SIAM/ASA Journal on Uncertainty QuantificationGaussian mixture Taylor approximations of risk measures constrained by PDEs with Gaussian random field inputs (opens in a new tab)
- ICLR (T1)LD-EnSF: Synergizing latent dynamics with ensemble score filters for fast data assimilation with sparse observations (opens in a new tab)
- NeurIPS (T1)LEVDA: Latent ensemble variational data assimilation via differentiable dynamics (opens in a new tab)
- Acta Materialia, 318, 122597PolyMicros: A foundation model for polycrystalline material structure (opens in a new tab)
- Journal of Hydrology, 680, 136461Toward AI-driven digital twins for metropolitan floods: A conditional latent dynamics network surrogate of the shallow water equations (opens in a new tab)
- Computer Methods in Applied Mechanics and Engineering, 461, 119228Variationally correct operator learning: Reduced basis neural operator with a posteriori error estimation (opens in a new tab)
20255 papers
- Computer Methods in Applied Mechanics and Engineering, 438, 117845Accurate, scalable, and efficient Bayesian optimal experimental design with derivative-informed neural operators (opens in a new tab)
- SIAM Journal on Scientific Computing, 47(4), C899-C931Efficient PDE-constrained optimization under high-dimensional uncertainty using derivative-informed neural operators (opens in a new tab)
- ICLR (T1)Latent-EnSF: A latent ensemble score filter for high-dimensional data assimilation with sparse observation data (opens in a new tab)
- NeurIPS (T1)PDPO: Parametric density path optimization (opens in a new tab)
- Journal of Computational Physics, 532, 113976Sequential infinite-dimensional Bayesian optimal experimental design with derivative-informed latent attention neural operator (opens in a new tab)
20246 papers
- Journal of Computational Physics, 498, 112599A greedy sensor selection algorithm for hyperparameterized linear Bayesian inverse problems with correlated noise models (opens in a new tab)
- NeurIPS (T1)Derivative-enhanced deep operator network (opens in a new tab)
- Journal of Computational Physics, 496, 112555Derivative-informed neural operator: An efficient framework for high-dimensional parametric derivative learning (opens in a new tab)
- SIAM Journal on Mathematics of Data Science, 6(4), 978-999Optimal neural network approximation of Wasserstein gradient direction via convex optimization (opens in a new tab)
- Journal of Open Source Software, 9(99), 6101SOUPy: Stochastic PDE-constrained optimization under high-dimensional uncertainty in Python (opens in a new tab)
- Physical Review D, 110(10), 104037Tempered multifidelity importance sampling for gravitational wave parameter estimation (opens in a new tab)
20237 papers
- SIAM/ASA Journal on Uncertainty Quantification, 11(1), 235-261A fast and scalable computational framework for large-scale and high-dimensional Bayesian optimal experimental design (opens in a new tab)
- SIAM Journal on Scientific Computing, 45(1), B57-B77An offline–online decomposition method for efficient linear Bayesian goal-oriented optimal experimental design: Application to optimal sensor placement (opens in a new tab)
- Computer Methods in Applied Mechanics and Engineering, 417, 116349Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data and machine learning surrogate (opens in a new tab)
- Journal of Scientific Computing, 95(1)Large-scale Bayesian optimal experimental design with derivative-informed projected neural network (opens in a new tab)
- Journal of Computational Physics, 485, 112101Optimal design of chemoepitaxial guideposts for directed self-assembly of block copolymer systems using an inexact-Newton algorithm (opens in a new tab)
- SIAM Journal on Optimization, 33(3), 1828-1854Performance bounds for PDE-constrained optimization under uncertainty (opens in a new tab)
- Vietnam Journal of Mathematics, 51, 151-175Sparse polynomial approximations for affine parametric saddle point problems (opens in a new tab)
Where alumni went16 listed, 25% to industry
Industry 2Academia 6
Most often to
- Amazon
Also to
- Georgia Tech
- Cornell University
- UT Austin
Apply to Georgia TechFall 2027
Georgia Tech Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- optional
- English
- TOEFL/IELTS
- Fee
- See program page
Not yet confirmed for this intake.
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