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Chun-Biu Li

About me

 

I am an associate professor (Docent) of Computational Mathematics at Department of Mathematics. My research interest is to develop and apply statistical and computational methods to understand how biophysical systems work. My focuses are on the interplays between information theory, data science, machine learning and statistical physics. I also teach and supervise in Mathematical Statistics.

 

Education:

 

Academic experience:

  • Associate Professor (2016 - present) Department of Mathematics, Stockholm Univ., Sweden
  • Associate Professor (2008 - 2016) Research Institute for Electronic Science, Hokkaido Univ.; Department of Mathematics, Hokkaido Univ., Japan
  • JST/CREST Researcher (2005 - 2008) Kobe Univ. & Research Institute for Electronic Science, Hokkaido Univ., Japan
  • Postdoc (2004 - 2005) Kobe Univ., Japan
  • Visiting Scientist (2003 - 2004) Ilya Prigogine Center for Statistical Mechanics and Complex Systems, Univ. of Texas at Austin, USA

 

Teaching

 

Recent Courses

  • 2022 Spring, Statistical Deep Learning MT 7042, master level, 7.5hp
  • 2022 Spring, Unsupervised Learning MT7039, master level, 7.5 hp
  • 2021 Fall, Statistical Learning MT7038 (with Taras Bodnar), master level, 7.5 hp
  • 2021 Fall, Bayesian Methods MT7003 (with Michael Höhle), master level, 7.5hp
  • 2021 Fall - 2022 Spring, Deep Understanding of the Information Processing of Deep Learning, PhD course, 7.5 hp

 

Current Postdocs/Students (from Oct 2016 only)

  • Daniella Zhou, master student, "The statistical study of clique-based community detection for empirical networks", Jan 2022 - present
  • Mathias Carlsson, master student, "Discovering characteristics of distinct profitable customers using unsupervised learning", Jan 2022 - present
  • Hilding Köhler, master student, "Unveiling the inner statistical properties of deep convolutional neural networks through the lens of unsupervised learning", Jan 2022 - present
  • Anton Holm Klang, master student, "Adaptive density-based method for clustering in situ transcriptomic data", Jan 2022 - present
  • Laimei Yip Lundström, master student, "Statistical modeling and inference of single cell gene expression profiles", June 2021 - present
  • Nik Tavakolian, PhD student, "Understanding the genomic architechture of adaptation: Fitness landscape theory and machine learning", Aug 2021 - present
  • Tobias Wängberg, PhD student (co-supervise with Prof. Joanna Tyrcha, Stockhom Univ.), "Spatial statistical models and analysis of genomic expression data", Sept 2020 - present
  • Bursa Tas, PhD student, "Statistical learning and stochastic modeling of molecular motor", Feb 2019 - present
  • Michael Ståhle, master student, "Data driven statistical learning for football manager assistant", Feb 2017 - present

 

Former Postdocs/Students (from Oct 2016 only)

  • Fredrika Lundahl, master student, "Targeted selection, a federated learning algorithm for personalization", Feb 2021 - June 2021
  • Ruben Ridderström, master student, "Nonlinear dimensionality reductuon from information-theoretic optimal manifold", Feb 2021 - June 2021
  • Nik Tavakolian, master student, "Clustering DNA barcode reads from high resolution evolutationary dynamics", Feb 2021 - June 2021
  • Marina Herrera Sarrias, master student, "Spectral distance between complex networks using graph Laplacians", Sept 2020 - June 2021
  • Hiam Shaba, master student, "Statistical survey of clustering using message passing", Sept 2020 - Feb 2021
  • Tobias Wängberg, master student, "Survey Stochastic Neighbor Embedding (SNE) for dimensionality reduction and data visualization", Feb 2020 - Sept 2020
  • Jie Wen, master student, "Generalization of density-based clustering method and its application to insurance data", Oct 2019 - Sept 2020
  • Carl Samuelsson, master student, "Inferential and predictive comparison of boosted decision trees to classify user retenion on music streaming service", Feb 2020 - June 2020
  • Fanny Bergstrom, master student, "Statistical investigation of spectral clustering for cluster discovery and feature selection", Feb 2020 - June 2020
  • Thi Thuy Nga Nguyen, master student, "Exploring nonlinear dimensionality reduction using diffusion maps", Feb 2020 - June 2020
  • Gonzalo Aponte Navarro, master student, "Hidden Markov Models for speech recognition", Feb 2019 - Jan 2020
  • Huixin Zhong, master student, "Multivariate change point detection based on principal component analysis", Feb 2019 - Sept 2019
  • Oliver Murquist, master student, "Machine learning for actuaries: Understanding tree based methods using insurance fraud data", Sept 2017 - Sept 2019
  • Ellinor Krona, master student, "Investigation of cohort effects in Swedish mortality rates", Feb 2019 - Jun 2019
  • Nguyen Huong Thu, postdoc, "Information flow and causality detection", Oct 2017 - Feb 2019
  • Yuji Tamiya, PhD student (affiliated to Hokkaido Univ.) , "Nonequilibrium dynamics of motor protein",  Oct 2016 - Feb 2018
  • Rickard Strandberg, master student (co-supervise with Prof. Marie Reilly, Karolinska Inst.), "Effective design and analysis of pooled ELISpot experiments", Feb 2017 - Dec 2017
  • Felix Martinsson, master student, "Machine learning and financial data analysis", Jan 2017 - Sep 2017
  • Satoru Tsugawa, postdoc, "Statistical analysis and modeling of plant morphogenesis", Oct 2016 - Jan 2017

 

Research

 

Main topics:

Data-driven nonparametric statistical analyses for biophysical applications

Keywords: Time series analysis, machine learning, statistical inference, network theory, dynamical systems, single molecule biophysics

 

Statistical theory of information flows and multivariate dependence

Keywords: Causality detection, transfer entropy, multivariate time series, information theory, information geometry, collective dynamics

 

Statistical theory of stochasticity and fluctuations and their constructive roles in biophysical systems

Keywords: Fluctuation theorems, information engines, protein motors, nonequilibrium statistical physics, spatio-temporal variability, multiscale correlations, stochastic modeling, morphogenesis