Yang, Qian

Qian Yang

Assistant Professor, School of Computing

Email qyang@uconn.edu
Phone (860) 486-5472
Mailing Address University of Connecticut 371 Fairfield Way, Unit 4155 Storrs, CT 06269-4155
Campus Storrs
Link Affiliation Website
Google Scholar Link

Brief Bio

Dr. Wang's research lies at the intersection of computational science and the physical sciences, with an emphasis on machine learning for materials, physics, and chemistry applications. She completed her Ph.D. from the Institute for Computational and Mathematical Engineering at Stanford University, and holds a B.A. in applied mathematics/computer science from Harvard College. Before joining UConn, she was a postdoctoral scholar in the Materials Computation and Theory group at Stanford University.

  • Machine Learning,
  • Algorithms
  • Learning Predictive Models of Chemistry from Molecular Dynamics Data
  • Data-Driven Model Reduction of Nonlinear Dynamical Systems
  • Machine Learning with Scientific Data
  • CSE 3666: Introduction to Computer Architecture (Fall 2018)
Holistic computational structure screening of more than 12000 candidates for solid lithium-ion conductor materials
AD Sendek, Q Yang, ED Cubuk, KAN Duerloo, Y Cui, EJ Reed
Energy & Environmental Science 10 (1), 306-320
Methods and systems for sensing equilibrium
E Lieberman, KE Forth, R Piedrahita, Q Yang
US Patent App. 12/236,433
DefectTrack: a deep learning-based multi-object tracking algorithm for quantitative defect analysis of in-situ TEM videos in real-time
R Sainju, WY Chen, S Schaefer, Q Yang, C Ding, M Li, Y Zhu
Scientific Reports 12, 15705
Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics
Q Yang, CA Sing-Long, EJ Reed
Chemical science 8 (8), 5781-5796
The middle science: Traversing scale in complex many-body systems
AE Clark, H Adams, R Hernandez, AI Krylov, AMN Niklasson, S Sarupria, ...
ACS Central Science 7 (8), 1271-1287
Transferable Kinetic Monte Carlo Models with Thousands of Reactions Learned from Molecular Dynamics Simulations
E Chen, Q Yang, V Dufour-Décieux, CA Sing-Long, R Freitas, EJ Reed
The Journal of Physical Chemistry A 123 (9), 1874-1881
Practical Active Learning with Model Selection for Small Data
M Pardakhti, N Mandal, A Ma, Q Yang
ICMLA Special Session: Machine Learning Surrogate Models in Science and …
Rapid data-driven model reduction of nonlinear dynamical systems including chemical reaction networks using L1-regularization
Q Yang, CA Sing-Long, EJ Reed
Chaos 30, 053122
L1 regularization-based model reduction of complex chemistry molecular dynamics for statistical learning of kinetic Monte Carlo models
Q Yang, CA Sing-Long, EJ Reed
MRS Advances 1, 1767-1772
Data-driven search for promising intercalating ions and layered materials for metal-ion batteries
S Parida, A Mishra, Q Yang, A Dobley, CB Carter, AM Dongare
Journal of Materials Science 59 (3), 932-949
Applying a Competency-Based Education Approach for Designing a Unique Interdisciplinary Graduate Program: A Case Study for a Systems Engineering Program
A Thompson, MD Stuber, S Han, A Dutta, H Xu, S Zhou, Q Yang, F Miao, ...
ASEE Annual Conference & Exposition (ASEE)
Efficient Creation of Jettability Diagrams Using Active Machine Learning
M Pardakhti, SY Chang, Q Yang, AWK Ma
3D Printing and Additive Manufacturing
Data-Driven Methods for Building Reduced Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics Simulations
Q Yang, CA Sing-Long, E Chen, EJ Reed
Computational Approaches for Chemistry Under Extreme Conditions, 209-227
Automated Structural Analysis of Small Angle Scattering Data from Common Nanoparticles via Machine Learning
G Roberts, MP Nieh, AWK Ma, Q Yang
Digital Discovery
Uncertainty quantification for equations of state: copper as an example
ES Krakovsky, CW Greeff, T Sjostrom, CE Starrett, Q Yang, S Crockett, ...
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Semi-Siamese Network for Robust Change Detection Across Different Domains with Applications to 3D Printing
Y Niu, E Chadwick, AWK Ma, Q Yang
International Conference on Computer Vision Systems, 183-196
Deep Learning for Automated Quantification of Irradiation Defects in TEM Data: Relating Pixel-Level Errors to Defect Properties
R Sainju, G Roberts, WY Chen, B Hutchinson, Q Yang, C Ding, ...
Microscopy and Microanalysis 29 (Supplement_1), 1559-1560
Real-time Multi-Object Tracking of Ion-irradiation Induced Defects in in situ TEM Videos
R Sainju, WY Chen, S Schaefer, Q Yang, C Ding, M Li, Y Zhu
Microscopy and Microanalysis 28 (S1), 2058-2059
MultiTaskDeltaNet: Change Detection-based Image Segmentation for operando ETEM with Application to Carbon Gasification Kinetics
Y Niu, T Li, Y Zhu, Q Yang
arXiv preprint arXiv:2507.16803
Charting the Chemical Space of Zintl Phases with Graph Neural Networks and Bonding Insights
R Chaliha, MK Kothakonda, CW Lee, JN Law, Q Yang, S Bobev, P Gorai