Sinong Geng

About

I received my Ph.D. degree from the Department of Computuer Science at Princeton University (advisor: Ronnie Sircar). My reserach interests include machine learning, stochastic optimal control and high-dimensional statistics. During my PhD study, I interned at Microsoft Research, Two Sigma, Amazon, and Los Alamos National Lab.

Before Princeton, I got my M.S. degree from the Department of Statistics at University of Wisconsin Madison (advisor: David Page). I received my B.S. degree from the School of Mathematics at Nankai University.

Selected Works

Reinforcement Learning and Stochastic Optimal Control

• Sinong Geng, Aldo Pacchiano, Andrey Kolobov and Ching-An Cheng.
  Improving Offline RL by Blending Heuristics.
  Spotlight (5%), International Conference on Learning Representations, 2024 (ICLR 2024).

• Sinong Geng, Houssam Nassif, Zhaobin Kuang, A. Max Reppen, Ronnie Sircar.
  Factor Learning Portfolio Optimization Informed by Continuous-Time Finance Models.
  ICML Frontiers4LCD, 2023

• Sinong Geng, Houssam Nassif, and Carlos A. Manzanares.
  A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Models.
  The Conference on Uncertainty in Artificial Intelligence, 2023 (UAI 2023).

• Melda Alaluf, Giulia Crippa, Sinong Geng, Zijian Jing, Nikhil Krishnan, Sanjeev Kulkarni, Wyatt Navarro, Ronnie Sircar, and Jonathan Tang.
  Reinforcement Learning Paycheck Optimization for Multivariate Financial Goals.
  Risk and Decision Analysis.

• Sinong Geng, Houssam Nassif, Carlos A. Manzanares, A. Max Reppen, and Ronnie Sircar.
  Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions.
  International Conference on Machine Learning, 2020 (ICML 2020).

Graphical Models and Healthcare

• Sinong Geng, Minhao Yan, Mladen Kolar, and Sanmi Koyejo.
  Partially Linear Additive Gaussian Graphical Models.
  International Conference on Machine Learning, 2019 (ICML 2019).

• Sinong Geng, Mladen Kolar, and Sanmi Koyejo.
  Joint Nonparametric Precision Matrix Estimation with Confounding.
  The Conference on Uncertainty in Artificial Intelligence, 2019 (UAI 2019).

• Sinong Geng, Zhaobin Kuang, Peggy Peissig, David Page, Laura Maursetter, and Karen Hansen.
  Parathyroid hormone independently predicts fracture, vascular events, and death in patients with stage 3 and 4 chronic kidney disease.
  Osteoporosis International, 2019.

• Sinong Geng *, Zhaobin Kuang*, Jie Liu, Stephen Wright, and David Page.
  Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error.
  The Conference on Uncertainty in Artificial Intelligence, 2018 (UAI 2018).

• Sinong Geng*, Zhaobin Kuang*, Peggy Peissig, and David Page.
  Temporal Poisson Square Root Graphical Models.
  International Conference on Machine Learning, 2018 (ICML 2018).

• Zhaobin Kuang, Sinong Geng, and David Page.
  A Screening Rule for L1-Regularized Ising Model Estimation.
  Neural Information Processing Systems, 2017 (NIPS 2017).

• Sinong Geng, Zhaobin Kuang, and David Page.
  An Efficient Pseudo-likelihood Method for Sparse Binary Pairwise Markov Network Estimation.
  Technical Report, Arxiv, 2017.
  

Contact Information

• Email: gengsinong@gmail.com

• LinkedIn: https://www.linkedin.com/in/gengsinong/