詹妮 About
I work on AI and materials simulation research. I did a postdoc at Princeton University, advised by Ryan P. Adams, focusing on neural network wave functions. I completed my PhD at Carnegie Mellon University, advised by John R. Kitchin, with dissertation titled "Machine learning models and uncertainty for atomic simulations". My published research includes fast, accurate models to approximate quantum chemical methods, investigation of transport phenomena and dynamics of liquid alloys, uncertainty quantification for machine learning and physical properties, and graphical and probabilistic methods for finance and macroeconomics.
While at CMU, I also received an MS in machine learning and interned at JPMorgan Chase, working on strategy at the Treasury and Chief Investment Office. Previously, I worked as a chemical engineer and graduated magna cum laude with a BS in chemical engineering from the University of Texas at Austin.
Publications
-
Expressivity of determinantal ansatzes for neural network wave functions [doi] [github]
Ni Zhan, William A. Wheeler, Gil Goldshlager, Elif Ertekin, Ryan P. Adams, Lucas K. Wagner
Journal of Chemical Theory and Computation 2025
-
AlgoTune: Can Language Models Speed Up General-Purpose Numerical Programs? [arXiv]
Ori Press, Brandon Amos, Haoyu Zhao, Yikai Wu, Samuel K. Ainsworth, Dominik Krupke, Patrick Kidger, Touqir Sajed, Bartolomeo Stellato, Jisun Park, Nathanael Bosch, Eli Meril, Albert Steppi, Arman Zharmagambetov, Fangzhao Zhang, David Perez-Pineiro, Alberto Mercurio, Ni Zhan, Talor Abramovich, Kilian Lieret, Hanlin Zhang, Shirley Huang, Matthias Bethge, Ofir Press
NeurIPS 2025
-
Space Group Equivariant Crystal Diffusion [arXiv]
Rees Chang, Angela Pak, Alex Guerra, Ni Zhan, Nick Richardson, Elif Ertekin, Ryan P. Adams
NeurIPS 2025
-
Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics [arXiv]
Menghang Wang, Jingxuan Ding, Grace Xiong, Ni Zhan, Cameron J. Owen, Albert Musaelian, Yu Xie, Nicola Molinari, Ryan P. Adams, Sossina Haile, Boris Kozinsky
Preprint 2025
-
Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrodinger Equation [arXiv] [poster]
Kevin Han Huang, Ni Zhan, Elif Ertekin, Peter Orbanz, Ryan P. Adams
ICML 2025
-
Practical Application of Machine Learning in Catalysis [doi]
Zachary W. Ulissi, Kevin Tran, Junwoong Yoon, Muhammed Shuaibi, Mingjie Liu, Ni Zhan, Kirby Broderick, John R. Kitchin
Computational Catalysis, Royal Society of Chemistry 2024
-
Model-Specific to Model-General Uncertainty for Physical Properties [doi] [github] [graphical abstract]
Ni Zhan, John R. Kitchin
Industrial & Engineering Chemistry Research 2021
- Machine Learning Models and Uncertainty for Atomic Simulations [PDF] [recording]
Ni Zhan
PhD Dissertation 2021
Thesis Committee: John Kitchin, Zachary Ulissi, Michael Widom, Aditya Khair, Erik Ydstie -
Origin of the Stokes-Einstein Deviation in Liquid Al-Si [doi] [code] [simulation preview]
Ni Zhan, John R. Kitchin
Molecular Simulation 2021
Presented talk at American Chemical Society Spring Meeting 2021
-
Uncertainty quantification in machine learning and nonlinear least squares regression models [doi] [github]
Ni Zhan, John R. Kitchin
Aiche Journal 2021
Presented two talks at Aiche Annual Meeting 2019 and Machine Learning in Science and Engineering Conference 2019 -
Where does the Stimulus go? Deep Generative Model for Commercial Banking Deposits [arXiv] [poster]
Ni Zhan
Neurips Workshop on ML for Economic Policy 2020 -
Graphical models for financial time series and portfolio selection [doi] [graphical abstract]
Ni Zhan, Yijia Sun, Aman Jakhar, He Liu
Proceedings of International Conference on AI in Finance 2020
Presented two talks at International Conference on AI in Finance, 2020 and Toronto Machine Learning Society, 2021
-
Modeling Superalloys using Machine Learned Potential [poster]
John Kitchin, Jenny Zhan, Michael Widom, Bojun Feng, Jim Lill, Chris Woodward
Published as Chapter in PhD Dissertation
Presented talk at Department of Defense High Performance Computing User Group Meeting, 2019
