Columbia University · New York, NY 10027 · (206) 915-1164 · doerlbh@gmail.com

I am a Ph.D. candidate at Center for Theoretical Neuroscience and Zuckerman Mind Brain Behavior Institute in Columbia University, co-advised by Prof. Nikolaus Kriegeskorte and Prof. Ning Qian. I am interested in a variety of stuff ranging from geometric topology and dynamical systems to Bayesian statistics and machine learning, as well as their applications to neuroscience, genomics, and cognitive sciences. Most recently, I am developing neuroscience-inspired AI systems and applying ML to understand human brains (Neuro <-> AI).

I graduated from the University of Washington, Seattle (UW), as part of the NIH-funded Computational Neuroscience Training Program, with B.S. in Applied & Computational Mathematics and B.A. in Psychology with Honors. I still maintain close collaborations and affliations with Columbia Department of Systems Biology and UW Department of Applied Mathematics.


I am very grateful to have mentors and collaborators with whom I have the honor to work on various interesting problems spanning vision neuroscience, mathematical biology, artificial intelligence, reinforcement learning, genome sciences, protein design, computational psychiatry, and human-computer interaction. I also maintain close industrial collaborations with IBM Research and Microsoft Research.

Research by areas: neuroscience (NS), machine learning (ML), computer vision (CV), geometric topology (GT), systems biology (SB), applied mathematics (AM), human-computer interaction (HCI), Bayesian statistics (BS), translational medicine (TM) .

2017nowIBM Research HQCompNeuro & AI Foundations Groups[7][8][11][15][18]NS,ML,CV,GT,TM
20172017Microsoft Research HQEPIC Group[19]NS,CV,HCI
2018nowColumbia University Visual Inference Group, Kriegeskorte Lab [9][14][17]NS,ML,CV,GT
2018now NeuroTheory Center, Qian Lab [10]NS,ML,CV,GT,BS
20182018 Dept. of Systems Biol., Rabadan Lab [13][17]SB,ML,GT
20172017 Dept. of CS, Pe’er Lab [6]SB,ML,TM
20172017University of Washington UbiComp Lab of CSE & EE, Patel Lab HCI,ML,TM
20152017 Institute for Protein Design, Baker Lab [12]SB
20162016 Dept. of Applied Math, Qian Lab AM,SB
20142016 Vision Neuroscience Group, Olavarria Lab [2][16]NS,CV
20142017BIME State Key Lab of Pathogen & Biosecurity [1][3][4][5]SB,AM,TM
20132013BGI Research HQ Personalized Genome Group SB,TM


19 total = 10 conferences + 6 journals + 3 under review / arXiv preprints  


  • [19]  under review.  
    Mar Gonzalez-Franco, Baihan Lin, Christopher Berger, and Jaron Lanier.

    Enhanced arm reach after a Pinocchio illusion in Virtual Reality.


  • [18]  NIPS 2019 Workshop on Biological & Artificial RL (BARL).  
    Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf, Jenna Reinen, Irina Rish.

    Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders.

    [arXiv]  [bibTex

  • [17]  NIPS 2019 Workshop on Learning Meaningful Representations of Life (LMRL).  
    Baihan Lin, Raul Rabadan, Nikolaus Kriegeskorte.

    What About Higher-Order Cellular Complexity? An Inquiry with Topological Simplicial Analysis.

  • [16]  Journal of Comparative Neurology.  
    Adrian Andelin, Zane Doyle, Robyn Laing, Joseph Turecek, Baihan Lin, and Jaime Olavarria.

    Influence of Ocular Dominance Columns and Patchy Callosal Connections on Binocularity in Lateral Striate Cortex: Long Evans vs. Albino Rats.


  • [15]  TIPS 2019.  
    Baihan Lin.

    Modeling Neurological and Psychiatric Disorders with Reward Biased Reinforcement Learning Models.

    [link]  [bibTex

  • [14]  CCN 2019.  
    Baihan Lin, Marieke Mur, Tim Kietzmann, Nikolaus Kriegeskorte.

    Visualizing Representational Dynamics with Multidimensional Scaling Alignment.

    [arXiv]  [link]  [bibTex

  • [13]  ISMB 2019.  
    Baihan Lin.

    Cliques of single-cell RNA-seq profiles reveal insights into cell ecology during development and differentiation.

    [slides]  [poster]  [link]  [bibTex

  • [12]  Journal of the American Chemical Society.  
    Zibo Chen, Matthew Johnson, Jiajun Chen, Matthew Bick, Scott Boyken, Baihan Lin, James DeYoreo, Justin Kollman, David Baker, and Frank DiMaio.

    Self-assembling 2d arrays with de novo protein building blocks.

    [pdf]  [JACS cover story]  [bibTex

  • [11]  IJCAI 2019.  
    Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi.

    Split Q Learning: Reinforcement Learning with Two-Stream Rewards.

    [arXiv]  [slides]  [poster]  [bibTex

  • [10]  IJCAI 2019 Workshop on Human Brain and Artificial Intelligence (HBAI).  
    Baihan Lin.

    Neural Networks as Model Selection with Incremental MDL Normalization.

    [arXiv]  [slides]  [poster]  [code]  [link]  [bibTex


  • [9]  arXiv.  
    Baihan Lin, Nikolaus Kriegeskorte.

    Adaptive Independence Tests with Geo-Topological Transformation.

    [arXiv]  [bibTex

  • [8]  arXiv.  
    Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf, Irina Rish.

    Adaptive Representation Selection in Contextual Bandit with Unlabeled History.

    [arXiv]  [bibTex

  • [7]  IEEE ICDM 2018 Workshop on Data Science and Big Data Analytics (DSBDA).  
    Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf, Irina Rish.

    Contextual Bandit with Adaptive Feature Extraction.

    [pdf]  [bibTex


  • [6]   RECOMB/ISCB 2017 Conference on Regulatory & Systems Genomics.  
    Avinash Bukkittu, Baihan Lin, Trung Vu, and Itsik Pe’er.

    Parkinson’s disease digital biomarker discovery with optimized transitions and inferred markov emissions.

    [arXiv]  [slides]  [bibTex

  • [5]   PLoS One.  
    Yue Teng, Dehua Bi, Guigang Xie, Yuan Jin, Yong Huang, Baihan Lin, Xiaoping An, Dan Feng, and Yigang Tong.

    Dynamic Forecasting of Zika Epidemics Using Google Trends.

    [pdf]  [bibTex

  • [4]   Journal of Infection.  
    Yue Teng, Dehua Bi, Guigang Xie, Yuan Jin, Yong Huang, Baihan Lin, Xiaoping An, Yigang Tong, and Dan Feng.

    Model-informed risk assessment for Zika virus outbreaks in the Asia-Pacific regions.

    [pdf]  [bibTex

  • [3]   Frontiers in cellular and infection microbiology.  
    Yue Teng, Shufeng Liu, Xiaocan Guo, Shuxia Liu, Yuan Jin, Tongtong He, Dehua Bi, Pei Zhang, Baihan Lin, Xiaoping An, Dan Feng, Zhiqiang Mi and Yigang Tong.

    An Integrative Analysis Reveals a Central Role of P53 Activation via MDM2 in Zika Virus Infection Induced Cell Death.

    [pdf]  [bibTex


  • [2]   NeuroFutures Conference 2016.  
    Baihan Lin, Adrian K Andelin, Jaime F Olavarria.

    Ocular Dominance Columns in Rat Visual Cortex: a Quantitative Model to Analyze Deprivation-Induced Cortical Plasticity.

    [pdf]  [link]  [bibTex


  • [1]   Scientific Reports.  
    Yue Teng, Yuzhuo Wang, Xianglilan Zhang, Wenli Liu, Hang Fan, Hongwu Yao, Baihan Lin, Ping Zhu, Wenjun Yuan, Yigang Tong and Wuchun Cao.

    Systematic Genome-wide Screening and Prediction of microRNAs in EBOV During the 2014 Ebolavirus Outbreak.

    [pdf]  [bibTex


I am always curious about the world and I like exploring new things. Although New York Manhattan is a much more crowded and monochromatic place than the scenic Seattle Washington with all the natural beauty, I try to maintain my hobbies in skateboarding and longbaording (well, kayaking is obviously not gonna happen here easily). Beyond my footsteps, I enjoy traveling to different places (so far only 3 continents reached) - I am planning a roadtrip across Northern Europe some time soon.

Despite doing many theoretical research, I like making tangible things: woodworking, DIY, electronics and interior design. A recent woodwork project of mine is a modern sofa (process). As another project under development, I am creating tangible representations of high-dimensional interaction data (more details yet to come).

Other than these life bits, I play flute and am currently learning guitar, the Jazz-style, (at least while my neighbors and my cats can still bear it). ;)