办公室：Room 313, Zhiyuan Building
My basic interest is the modeling and analysis of complex systems in science and engineering. Some recent research directions include:
Advanced Markov and nonMarkov modeling techniques
Spectral analysis and mode detection of stochastic systems
Machine learning approaches to time series analysis
Applications in biophysics
2002/09-2007/07 Ph.D. Tsinghua University
1998/09-2002/07 Bachelor Tsinghua University
2018/07- School of Mathematics, Tongji University Professor
2017/06-2018/06 Zuse Institute Berlin, Germany Head of Research Group
2007/09-2018/06 Institute of Mathematics, Freie Universität Berlin, Germany Postdoctroal fellow
2019/01 Peking University, China.
2017/09-2017/10 University of California, Los Angeles, USA. (IPAM Long Program)
2017/05 Rice University, USA.
2016/12 Hong Kong University of Science and Technology, HK, China.
2016/11 Oberwolfach Research Institute for Mathematics, Germany.
2015/06 University of Birmingham, UK.
2015/04 Jacobs University, Germany.
2014/03 University of Cambridge, UK.
Representative works in recent years:
F. Noe, S. Olsson, J. Kohler and H. Wu, “Boltzmann generators-sampling equilibrium states of many-body systems with deep learning,” Science, (Accepted)
H. Wu and F. Noe, “Variational approach for learning markov processes from time series data,” Journal of Nonlinear Science, 2019. (Accepted)
H. Wu, F. Nuske, S. Klus, P. Koltai and F. Noe, “Variational Koopman models: Slow collective variables and molecular kinetics from short off-equilibrium simulations,” Journal of Chemical Physics, 2017, 146(15): 154104.
A. Mardt, L. Pasquali, H. Wu, F. Noe, “VAMPnets for deep learning of molecular kinetics,” Nature Communications, 2018, 9(1): 5.
S. Klus, F. Nuske, P. Koltai, H. Wu, I. Kevrekidis, C. Schutte, F. Noe, “Data-driven model reduction and transfer operator approximation,” Journal of Nonlinear Science, 2018.
F. Litzinger, L. Boninsegna, H. Wu, F. Nüske, R. Patel, R. Baraniuk, F. Noe, C. Clementi, “Rapid Calculation of Molecular Kinetics Using Compressed Sensing,” Journal of chemical theory and computation, 14(5): 2771-2783, 2018.
F. Paul, C. Wehmeyer, E. Abualrous, H. Wu, M. Crabtree, J. Schöneberg, J. Clarke, C. Freund, T. Weikl, and F. Noe, “Protein-peptide association kinetics beyond the seconds timescale from atomistic simulations,” Nature Communications, 2017, 8(1): 1095.
S. Olsson, H. Wu, F. Paul, C. Clementi and F. Noe, “Combining experimental and simulation data of molecular processes via augmented Markov models,” Proceedings of the National Academy of Sciences (PNAS), 2017, 114(31): 8265-8270.
F. Nuske, H. Wu, J.-H. Prinz, C. Clementi and F. Noe “Markov state models from short non-equilibrium simulations—Analysis and correction of estimation bias,” Journal of Chemical Physics, 2017, 146(9): 094104.
H. Wu, F. Paul, C. Wehmeyer and F. Noe, “Multiensemble Markov models of molecular thermodynamics and kinetics,” Proceedings of the National Academy of Sciences (PNAS), 2016, 113(23): E3221-E3230.
B. Trendelkamp-Schroer, H. Wu(co-first author), F. Paul and F. Noe, “Estimation and uncertainty of reversible Markov models,” Journal of Chemical Physics, 2015, 143(17): 174101.
H. Wu, J. -H. Prinz and F. Noe, “Projected metastable Markov processes and their estimation with observable operator models,” Journal of Chemical Physics, 2015, 143(14): 144101.
H. Wu, “Maximum margin clustering for state decomposition of metastable systems,” Neurocomputing 2015, 164(21): 5-22.
H. Wu and F. Noe, “Gaussian Markov transition models of molecular kinetics,” Journal of Chemical Physics, 2015, 142(8): 084104.
H. Wu, A. Mey, E. Rosta and F. Noe, “Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states,” Journal of Chemical Physics, 2014, 141(21): 214106.
H. Wu and F. Noe, “Optimal estimation of free energies and stationary densities from multiple biased simulations,” Multiscale Modeling and Simulation: A SIAM Interdisciplinary Journal, 2014, 12(1): 25-54
A. Mey, H. Wu and F. Noe, “xTRAM: Estimating equilibrium expectations from time-correlated simulation data at multiple thermodynamic states,” Physical Review X, 2014, 4(4): 041018.
F. Noe, H. Wu, J.-H. Prinz and N. Plattner, “Projected and Hidden Markov Models for calculating kinetics and metastable states of complex molecules,” Journal of Chemical Physics, 2013, 139(18): 184114.
H. Wu, F. Noe, “Bayesian framework for modeling diffusion processes with nonlinear drift based on nonlinear and incomplete observations,” Physical Review E, 2011, 83(3): 036705.
J.-H. Prinz, H. Wu, M. Sarich, etc. “Markov models of molecular kinetics: Generation and Validation,” Journal of Chemical Physics, 2011, 134(17): 174105. (Times Cited: 523)
H. Wu, F. Noe, “Probability distance based compression of hidden Markov models,” Multiscale Modeling and Simulation: A SIAM Interdisciplinary Journal, 2010, 8(5): 1838-1861.
H. Wu, F. C. Sun and H. P. Liu, “Fuzzy particle filtering for uncertain systems,” IEEE Transactions on Fuzzy Systems, 2008, 16(5): 1114-1129.
Peer (double-blind) review conference proceedings:
H. Wu,A. Mardt, L. Pasquali, F. Noe, “Deep generative Markov state models,” in Proceedings of the conference on Neural Information Processing Systems (NIPS), Montréal, Canada, 2018. (Acceptance Rate: 21%)
H. Wu and F. Noe, “Spectral learning of dynamic systems from nonequilibrium data,” in Proceedings of the conference on Neural Information Processing Systems (NIPS), Barcelona, Spain, 2016, pp. 4179-4187. (Acceptance Rate: 22%)
H. Wu, “A Bayesian nonparametric model for spectral estimation of metastable systems,” in Proceedings of the conference on Uncertainty in artificial intelligence (UAI), Quebec City, Canada, 2014, pp. 878-887. (Acceptance Rate: 32%)
2017-2018 Einstein Stiftung Berlin (柏林爱因斯坦基金会), Grant No. CH 19. (co-PI)
2012-2014 Deutsche Forschungsgemeinschaft (德国科学基金会), Grant No. WU 744/1.