Machine Learning and Many-Body Physics (Jun. 28 - Jul. 7, 2017)

  • Published: 2017-02-20

Local Coordinators

Lei Wang         (Institute of Physics, CAS), wanglei@iphy.ac.cn

Zi Yang Meng  (Institute of Physics, CAS), zymeng@iphy.ac.cn

Zhi-Yuan Xie    (Renmin University of China), qingtaoxie@ruc.edu.cn

 

International Steering Committee

Matthias Troyer  (ETH Zurich and Microsoft Research)

Roger Melko       (Perimeter Institute)

Hong Guo           (McGill)

Xi Dai                  (Institute of Physics, CAS)

Tao Xiang           (Institute of Physics, CAS),

 

Dates and Location

June 28th-30th (School) and July 3rd-7th 2017 (Workshop) at KITS, Beijing

For the school we will arrange around 10 lectures covering the basics of machine learning, computational many-body techniques, and frontier of the newly emerging field of quantum machine learning. Each lecture is around 1.5 hours.

 

 

Registration and Information

 

Conference Brochure Download

 

 

Confirmed Lecturers

Juan Carrasquilla      (D Wave)

Giuseppe Carleo       (ETH Zurich)

Matthias Rupp          (FHI Berlin)

Miles Stoudenmire   (UC Irvine)

Haijun Zhou              (ITP, CAS)

 

 

Scheme of the Workshop

The central questions we’d like to address in this workshop are

                            “How is machine learning useful for physics/chemistry ? “

            “How can physicists/chemists help with the development of machine learning ?“

In particular, we will touch on the following topics

  • Conceptual connections of machine learning and many-body physics

  • Machine learning techniques for solving many-body physics/chemistry problems

  • Statistical and quantum physics perspectives on machine learning

  • Quantum algorithms and quantum hardwares for machine learning

 

Invited Speakers

The invited speakers include top researchers worldwide with various backgrounds (density functional theory, quantum chemistry, statistical physics, tensor networks, quantum information and computing, strongly correlated electrons etc) and keen interests on machine learning and artificial intelligence.  

 

Kieron Burke            (UC Irvine)

Juan Carrasquilla     (D Wave)

Zi Cai                        (SJTU)

Giuseppe Carleo      (ETH Zurich)

Garnet Chan            (Caltech)

Po-Chung Chen       (National Tsinghua University)

Dong-Ling Deng       (UMD)

You-Jin Deng           (USTC)

Lu-Ming Duan          (U Michigan)

Xun Gao                  (IIIS Tsinghua)

Haiping Huang         (RIKEN)

Masatoshi Imada     (Tokyo)

Ying-Jer Kao            (National Taiwan University)

Tao Li                       (Renmin University of China)

Wei Li                      (Beihang)

Xiaopeng Li             (Fudan)

Junwei Liu               (MIT)

Xiong-Jun Liu           (ICQM, Peking University)

Ye-Hua Liu               (ETH Zurich)

Zheng-Xin Liu           ((Renmin University of China)

Honggang Luo          (Lanzhou University)

Masayuki Ohzeki      (Tohoku University) 

Yang Qi                    (MIT)

Matthias Rupp          (FHI Berlin)

Maria Schuld            (Kwazulu-Natal)

Miles Stoudenmire   (UC Irvine) 

Lehan Tang              (HKBU)

Giacomo Torlai         (Perimeter Institute)

Simon Trebst            (Cologne)

Xiao Yan Xu              (IOP, CAS)

Fan Yang                   (Beijing Institute of Technology) 

Yi-Zhuang You          (Harvard)

Hui Zhai                     (Tsinghua) 

FrankYi Zhang         (Cornell)

Guang-Ming Zhang   (Tsinghua)

Pan Zhang                (ITP, CAS)

Yi Zhou                     (Zhejiang University)

 

 

 

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