师资队伍

基本信息

姓名:吴昊

部门:计算数学

职称:教授

E-mail:hwu@tongji_dot_edu.cn

办公室:致远楼 313

研究方向:

My basic interest is the modeling and analysis of complex systems in science and engineering. Some recent research directions include:

  1. Advanced Markov and nonMarkov modeling techniques
  2. Spectral analysis and mode detection of stochastic systems
  3. Machine learning approaches to time series analysis
  4. Applications in biophysics
教育背景:

2002/09-2007/07    Ph.D.               Tsinghua University

1998/09-2002/07    Bachelor         Tsinghua University

工作经历:

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

学术访问:

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.

论文与出版物:

Representative works in recent years:

Journals:

  • 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%)
科研项目:

2018-                   国家人才项目

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.

欢迎有志于从事计算数学与生命科学、机器学习交叉研究的同学联系我。

联系我们

    电话:86-21-65981384

    地址:上海市四平路1239号 致远楼

Copyright © 2018  同济大学数学科学学院 版权所有.