Yingying Xu
許 (キョ )インイン , 许滢滢E-Mail: yingying.xu@riken.jp

Current status
Special Postdoctoral researcher of Basic Science (SPDR) in RIKEN:

2020 April-

RIKEN Interdisciplinary Theoretical and Mathematical Sciences(iTHEMS) Program

2021 April-Center for Advanced Intelligence Project (AIP), RIKEN, Japan
Mathematical Statistics team, Hidetoshi Shimodaira group2020 April – 2021 March

Past research positions
Postdoctoral researcher, Tokyo Institute of Technology
2019 May - 2020 March
Kabashima Yoshiyuki Lab.
Department of Mathematical and Computing Science,
School of Computing,
Focus: Developing methodologies for theoretical analysis
through statistical physics for mathematical and information
processing problems such like optimization problem of a discrete system.
Postdoctoral researcher, Aalto university & Helsinki University

2016 April – 2019 April

Aalto University, School of Science, Department of Computer Science, Finland
The Finnish Centre of Excellence in Computational Inference Research (COIN);
Helsinki University, Computer science department, Finland
Focus: genome-wide epistasis analysis by direct-coupling analysis model

Technical advisor for companies
Sigma-i2019 July---2020 March
Focus: Quantum computer, quantum annealing, user company consulting
Website: https://sigmailab.com
MI-6Focus: Material information
Website: https://mi-6.co.jp

2019 September—2019 December

Research Collaboration visit
Lenka Zdeborova,
Institut de Physique Theorique, CEA, Sacaly, France

2014-2015

Ayaka Sakata
The Institute of Statistical Mathematics, Tokyo, Japan2017 June. – Jul.

Haijun Zhou
Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, China

2016

Education
PhD&Master2011 APR – 2016 March

Department of Computational Intelligence and Systems Science,
Tokyo Institute of Technology
Kabashima Yoshiyuki lab
Focus: Statistical mechanics physics--Replica Method, Belief Propagation Algorithm
Project: Statistical mechanics approach to 1-bit Compressive SensingBachelor

2007 APR –2011 MAR

Department of Physics,
Tokyo Institute of Technology,
Nishimori Hidetoshi lab (2010-2011)
Focus: Statistical mechanics physics--Spin Glass theory
Graduation thesis: "The possibility of Spin-Glass Phase in Random Fields"
High school

2002 SEPT –2005 JUN

Wuhan foreign Language School, China

Scholarships&Fundings
Japan Society for the Promotion of Science (JSPS) research felloship DC2, 2014-2016
Rotary Yoneyama Memorial Foundation, 2012-2013
Foundation of Seiho scholarship, 2010-20112

Japan Student Services Organization, 2007-2008

Award
Best poster award, at the master’s poster session 2013,
Department of computational intelligence and systems science, Tokyo Institute of Technology

Activities
Internship2011 Jun. – Aug.

Department of Applied Physics and Photonics, Vrije Universiteit Brussel, Belgium
Project: 3-D display modeling
Organized by IAESTE
Exchange study

2012 Sep. – 2012 Dec.

Institute of Neuroinformatics and Physics
University and Swiss Federal Institute of Technology Zurich, Switzerland
Ruedi Stoop group
Focus: Dynamical systems in biology

Language Skills
Chinese:Japanese:
English:nativefluent, studied Japanese over ten years, Passed level 1 in 2004
fluent3

Main Publications
1. Yingying Xu and Yoshiyuki Kabashima,“Statistical mechanics approach to 1-bit compressed sensing”,
Journal of Statistical Mechanics: Theory and Experiment, P02041(1-22), February, 2013.
2. Yingying Xu, Yoshiyuki Kabashima, Lenka Zdeborova,“Bayesian signal reconstruction for 1-bit compressed sensing”,
Journal of Statistical Mechanics: Theory and Experiment, P11015(1-23), November, 2014.
3. Yingying Xu and Yoshiyuki Kabashima,
Statistical mechanics analysis of thresholding 1-bit compressed sensing
Journal of Statistical Mechanics: Theory and Experiment, P083405(1-16), August, 2016.
4. Marcin J. Skwark, Nicholas J Croucher, Santeri Puranen, Claire Chewapreecha, Maiju
Pesonen, Yingying Xu, Paul Turner, Simon R. Harris, Julian Parkhill, Stephen D. Bentley, ErikAurell, Jukka Corander,“Interacting networks of resistance, virulence and core machinery genes identified bygenome-wide epistasis analysis”,
PLOS Genetics,13(2): e1006508. doi:10.1371/journal.pgen 1006508, February 16, 2017.
5. Yingying Xu, Erik Aurell, Jukka Corander and Yoshiyuki Kabashima“Statistical properties of interaction parameter estimates in direct couplings analysis”
arXiv:1704.01459.
6. Santeri Puranen, Maiju Pesonen, Johan Pensar, Yingying Xu, John A. Lees, Stephen
D.Bentley, Nicholas Croucher, Jukka Corander, Erik Aurell,“SuperDCA for genome-wide epistasis analysis”,
Microbial Genomics, 2018 4, doi:10.1099/mgen.0.000184.
7. Ayaka Sakata and Yingying Xu“Approximate message passing for nonconvex sparse regularization with stability and asymptoticanalysis”,
Journal of Statistical Mechanics: Theory and Experiment, Vol. 2018, No. 3, pp. P033404(1-36), February,
2018
8. Yingying Xu, Santeri Puranen, Jukka Corander and Yoshiyuki Kabashima“Inverse finite-size scaling for high-dimensional significance analysis”,
Physical Review. E 97, 062112, Published 6 June 2018
9. Johan Pensar, Santeri Puranen, Brian Arnold, Neil MacAlasdair, Juri Kuronen, Gerry Tonkin-Hill, Maiju
Pesonen, Yingying Xu, Aleksi Sipola, Leonor Sánchez-Busó, John A Lees, Claire Chewapreecha, Stephen
D Bentley, Simon R Harris, Julian Parkhill, Nicholas J Croucher, Jukka Corander,“Genome-wide epistasis and co-selection study using mutual information”,
Nucleic acids research, Oxford University Press, 47, 18, e112-e112, Oct 10 201910. Johan Pensar, Yingying Xu, Santeri Puranen, Maiju Pesonen, Yoshiyuki Kabashima, Jukka Corander,“High-dimensional structure learning of binary pairwise Markov networks: a comparative numericalstudy”,
Computational Statistics & Data Analysis, North-Holland,141, 62-76, Jan 1 2020
11. Alia Abbara, Yoshiyuki Kabashima, Tomoyuki Obuchi, Yingying Xu,“Learning performance in inverse Ising problems with sparse teacher couplings”,
Journal of Statistical Mechanics: Theory and Experiment, (2020) 073402, July 3 2020
12. Kimmo Suotsalo, Yingying Xu, Jukka Corander, Johan Pensar,“High-dimensional structure learning of sparse vector autoregressive models using fractional marginal
pseudo-likelihood”,
arXiv:2011.01484v1, Nov 3 20204