Chun-Biu Li

Recent Courses

• 2026 Fall, Reinforcement Learning, master level, 7.5hp
• 2026 Fall, Unsupervised Learning, master level, 7.5hp
• 2026 Spring, Training, Validation, Problem Diagnosis, and Troubleshooting of Using Deep Neural Networks in Life Science Applications, PhD course for Data-Driven Life Science Research School
• 2026 Spring, Statistical Theory and Applications of Deep Diffusion Models, PhD course, 7.5hp (carried out in parallel with FK8047)
• 2026 Spring, Statistical Deep Learning, master level, 7.5hp
• 2025 Fall, Statistical Information Theory, master level, 7.5hp
• 2025 Fall, Bayesian Methods, master level, 7.5hp

Current Postdocs/Graduate Students (from Oct 2016 only)

• Marina Herrera Sarrias, PhD student, “Deep generative models for the study of protein mutation dynamics”, June 2024 – present
• Nik Tavakolian, PhD student, “Understanding the genomic architechture of adaptation: Fitness landscape theory and machine learning”, Aug 2021 – present
• Li Wu, master student, “Advanced machine learning methods for anomaly detections”, Jan 2026 – present
• Fanny Walldén, master student, “On fine-tuning language models for text summarization: RLHF vs DPO”, Jan 2026 – present
• Jesper Hazin-Pipping, master student, “Comparing the inner representations of convolutional neural networks and vision transformers”, Jan 2026 – present
• Qixin Yang, master student, “Understanding the representation dynamics in diffusion models”, Jan 2026 – present
• Isaac Zúñiga, master student, “Formulating graph-based representation similarity to understand the evolutions of deep neural network representations”, Jan 2026 – present
• Avinash Singh, master student, “Uncertainty quantification in deep learning by conformal prediction: A case study on self-driving models”, Jan 2026 – present
• Gustaf Randén, master student (co-supervise with Helga Westerlind & Marina Dehara, Karolinska Inst.), “On the statistical modeling of Rheumatoid Arthritis”, Jan 2025 – present

Former Postdocs/ Graduate Students (from Oct 2016 only)

• Joakim Andersson Svendsen, master student, “Bias-variance tradeoff in diffusion models”, Jan 2025 – Jan 2026
• Stefan Miletic, master student, “Geometric insights into double descent in deep learning: A binary classification study”, Jan 2025 – Jan 2026
• Abir Myllymäki, master student, “An interpretable and comprehensive machine learning study of ADHD symptom severity from cognitive tasks and chronotype”, Jan 2025 – June 2025
• Michael Ståhle, master student, “Detecting tactical patterns in football with fast search and density peak clustering”, Feb 2017 – Feb 2025
• Chinmaya Mathur, master student, “Enhancing image classification with a hybrid CNN-Transformer model: A comparative study of ResNet-18 and a modified architecture”, Jan 2024 – Feb 2025
• Martin Björklund, master student, “Toward decoding the abstract image representations in neural networks”, Jan 2024 – June 2024
• Bipasha Pal, postdoc, “Nonequilibrium dynamics of motor proteins”, Dec 2022 – Dec 2024
• Busra Tas Kiper, PhD student, “Contemporary developments and applications of unsupervised methods for explainable machine learning”, 2019 – 2024
• Tobias Wängberg, Licentiate (co-supervise with Prof. Joanna Tyrcha, Stockhom Univ.), “Spatial statistical models and analysis of genomic expression data”, 2020 – 2023
• Sheila Farrahi, master student, “Anomaly detection in bank transactions”, Jan 2023 – Feb 2024
• Adam Goran, master student, “Beyond traditional boundaries: Harnessing the power of deep learning for enhanced survival analysis and interpretability”, Jan 2023 – Jun 2023
• Karin Pagels, master student, “A study of generative adversarial networks with application to paperboard surfaces”, Jan 2023 – Jun 2023
• Mikael Rizvanovic, master student, “Coarse graining and out-of-sample approximation for the spectral theory of complex networks”, Jan 2023 – Jun 2023
• Jakob Torgander, master student, “Straight to the heart: Classification of multi-channel ECG-signals using residual neural networks”, Sept 2022 – Jun 2023
• Daniella Zhou, master student, “The statistical study of clique-based community detection for empirical networks”, Jan 2022 – Sep 2022
• Mathias Carlsson, master student, “Discovering characteristics of distinct profitable customers using unsupervised learning”, Jan 2022 – Jun 2022
• Hilding Köhler, master student, “Unveiling the inner statistical properties of deep convolutional neural networks through the lens of unsupervised learning”, Jan 2022 – Jun 2022
• Anton Holm Klang, master student, “Adaptive density-based method for clustering in situ transcriptomic data”, Jan 2022 – Jun 2022
• Laimei Yip Lundström, master student, “Statistical modeling and inference of single cell gene expression profiles”, Jun 2021 – Jun 2022
• 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