News

News update please refer to here.

[Feb 2023] I am honored to be selected as a 2023 Sloan Fellow in Neuroscience!

[Oct 2022] Invited talk at the Friday Morning Seminar (FMS) at Georgia Technology Research Institute.

[Sep 2022] Invited talk at the Frontiers in Neuroscience Seminar Series at Emory University.

[June 2022] Invited talk at The Fourth Chinese Computational & Cognitive Neuroscience Conference.

[May 2022] Selected as a DARPA Riser.

[May 2022] Serve as an Area Chair for International Conference on Machine Learning (ICML) 2022 and Neural Information Processing Systems (NeurIPS) 2022.

[Apr 2022] Invited talk at the NSF Institute A3D3: Accelerated Artificial Intelligence Algorithms for Data-Driven Discovery.

[Mar 2022] Two posters accepted by Cosyne2022.

[Jan 2022] Invited talk at GT Neuro Seminar Series.

[Jan 2022] Invited talk at Wu Tsai Neurosciences Institute and Stanford Data Science.

[Jan 2022] I started as an Assistant Professor at CSE, Gatech!

[Nov 2021] Presented at SFN Minisymposium: Manifold Learning and Investigation of Neuronal Population Dynamics.

[Oct 2021] New preprint "Domain Generalization via Domain-based Covariance Minimization" is out on arXiv. Please check out!

[Sep 2021] I gave an invited seminar talk on "Understand The Brain Using Interpretable Machine Learning Models" at the Department of Biostatistics & Bioinformatics at Duke.

[Sep 2021] Our paper "Brain kernel: a new spatial covariance function for fMRI data" has been accepted by NeuroImage! Please check out here.

[Sep 2021] New preprint with Chethan Pandarinath: "Neural Latents Benchmark ‘21: Evaluating latent variable models of neural population activity" is out on arXiv. This is an exciting benchmark for Neural Latents aiming at evaluating latent variable models of neural population activity. It's accepted to the NeurIPS 2021 Datasets and Benchmarks Track! 

[Aug 2021] The code for my NeurIPS2018 paper (olfactory or multi-trial GPLVM) is out. Please check out here.

[July 2021] Teaching assistant (remote) for the 10th Computational & Cognitive Neuroscience (CCN) Summer School, July 17-August 8 2021, in Suzhou, China.

[July 2021] Invited to speak at Professional Development for NMA2021 - academia panel.

[June 2021] I co-presented "Semi-supervised Learning for Animal Behavior Analysis and Understanding" at Gatsby Tri-Center (ColumbiaU, UCL, HebrewU) Meeting 2021.

[May 2021] I was invited to present "New methods for identifying latent manifold structure from neural data" at NeuroChat 2021, Chinese Association for Psychological & Brain Science. Here is the link to the talk.

[Apr 2021] Invited seminar talk on "Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking" at the University of Washington’s NeuroAI Journal Club. Here is the talk.

[Mar 2021] Our paper "Brain kernel: a new spatial covariance function for fMRI data" is out on bioRxiv. Please check out!

[Feb 2021] Our paper "Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders" is out on bioRxiv. Please check out!

[Feb 2021] I gave an invited seminar talk on "Understand The Brain Using Interpretable Machine Learning Models" at the Department of Physiology & Biophysics, University of Washington.

[Jan 2021] Our abstract "Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking" is accepted by Cosyne2021. See you online in February! 

[Jan 2021] Deep Graph Pose (DGP) is now on NeuroCAAS with the link. This is supported by Ryan Glassman and Taiga Abe from Columbia U.

[Jan 2021] I am invited as a guest speaker/panelist at SfN Global Connectome social event: Junior Scientist Club in Neuroscience + AI. 

[Dec 2020] I am invited to present our work on "Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking" at the 2020 Machine Learning in Science and Engineering Virtual Conference.

[Dec 2020] I gave an invited seminar talk on "Understand The Brain Using Interpretable Machine Learning Models" at the School of Computational Science and Engineering, Georgia Institute of Technology.

[Nov 2020] I am invited to present our work on "Exploiting unlabeled frames to build better models for behavioral video analysis" at Brain Initiative Team-Research Circuit Program (U19): Motor Control at Columbia University.

[Oct 2020] I co-presented our work on "Exploiting unlabeled frames to build better models for behavioral video analysis" with my colleagues at Neuromatch 3.0 Virtual Conference. I also hosted 5 sessions in Theme D: Cognition, Motivation and Emotion and Theme E: Computational modelling and Techniques at Neuromatch 3.0.

[Sep 2020] Our paper "Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking" is out on bioRxiv. It's also accepted to NeurIPS2020 (acceptance rate 20%). See you online in December! 

[July 2020] I gave 2 days' tutorial lectures at Neuromatch Academy 2020. Here is the link to the tutorial on D3.

[July 2020] I was invited to give a talk at the 2020 International Conference on Mathematical Neuroscience (ICMNS). Here is the link to the talk.

[May 2020] I was invited to give a talk at ML Explained - A.I. Socratic Circles - AISC. Here is the link to the talk.

[April 2020] Mingbo Cai presented our paper "Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis" at NeuroChat 2020. Here is the link to the talk.

[Mar 2020] I was invited to give a talk on "New methods for identifying latent manifold structure from neural data" at workshop New Mathematical Methods for Neuroscience in the Fields Institute at Toronto. Here is the link to the talk.

[Mar 2020] I was invited to give a talk on "Extracting structure from high‐dimensional neural recordings with Bayesian latent variable models" at Cosyne 2020 workshop Interpretable computational neuroscience: What are we modeling and what does it have to do with the brain? Day 1 at Denver. I also co-organized the workshop on Day 2. Here is the link to the talk. 

[Dec 2019] I was invited to give a talk on "Extracting structure from high‐dimensional neural recordings with Bayesian latent variable models" at University of Tübingen in Germany.

[Dec 2019] I was invited to give a talk on "Gaussian process based priors for brain decoding" at Ecole Normale Superieure in Paris.

[July 2019] I successfully defended and graduated! Here is my thesis. 

[July 2019] The code for my NeurIPS2017 paper (P-GPLVM or LMT) is out. Please check out here.

[May 2019] Our paper "Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks " has been accepted to UAI2019 as an oral (top 6.8%). This is my first last-author paper and is a great work in collaboration with Roger She. See you at Israel in July! Here is the link to the talk. 

[May 2019] Our paper "Dependent relevance determination for smooth and structured sparse regression" has been accepted to JMLR. This is my first PhD work in Pillow lab. Coming soon! (It's out!)

[Jan 2019] Our abstract "Learning a latent manifold of odor representations in piriform cortex" has been accepted to COSYNE2019 as an oral presentation (top 3%). See you at Lisbon in March! Here is the link to the talk. 

[Dec 2018] Our paper "Deterministic Variational Inference for Robust Bayesian Neural Networks" has been accepted to ICLR2019 as an oral presentation (top 1.5%). See you at New Orleans in May! Here is the link to the talk. 

[Nov 2018] I gave a seminar talk on "Bayesian latent variable model and structure learning for high-dimensional neural recordings" at Center for Theoretical Neuroscience at Columbia University.

[Oct 2018] I am selected to present at the upcoming EECS Rising Stars Workshop at MIT.

[Sep 2018] Our new paper "Learning an olfactory topography from neural activity in piriform cortex " is accepted to NIPS 2018 @ Montreal, coming soon!

[May 2018] Oral presentation "A latent variable model for identifying nonlinear manifolds from spike train data " in PNI Retreat 2018.

[Mar 2018] Poster presentation "Extracting nonlinear manifolds from spike train data with Gaussian process latent variables" in Cosyne 2018 @ Denver.

[Dec 2017] The codes for my NIPS2014 paper and NIPS2015 paper are both released on github. The links can be found in the publication.

[Dec 2017] Poster presentation "Gaussian process based nonlinear latent structure discovery in multivariate spike train data" in NIPS 2017 @ Long Beach.

[Nov 2017] Oral presentation in the second annual research computing day in Princeton University.

[Oct 2017] I gave a talk about my brain kernel work in PNI-Intel collaboration meeting.

[Oct 2017] I passed my general exam! My Ph.D. proposal is "Bayesian latent structure discovery for large-scale neural activity recordings".

[Sep 2017] I attended the first Google student research summit @ San Bruno, and gave a poster presentation.

[Sep 2017] Our new paper "Gaussian process based nonlinear latent structure discovery in multivariate spike train data" is accepted to NIPS 2017 @ Long Beach, coming soon!

[Jun 2017] Oral presentation "Brain kernel: a covariance function for fMRI data via a continuous nonlinear latent embedding" in 11th conference on Bayesian Nonparametrics @ Paris.

[Mar 2017] Our paper "Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature" was submitted to arXiv. 

[Dec 2016] Prof. Pillow was invited to present my work "Finding interpretable sparse structure in fMRI data with dependent relevance determination priors" in Interpretable Machine Learning for Complex Systems workshop in NIPS 2016 @ Barcelona.

[Oct 2016] Poster presentation in the first annual research computing day in Princeton University.

[Jun 2016] Oral presentation about my Bayesian structured sparsity work in PNI-Intel collaboration meeting.

[Feb 2016] Poster presentation "A Bayesian approach to structured sparsity for fMRI decoding" in Cosyne 2016 @ Salt Lake City. 

[Dec 2015] Poster presentation "Convolutional spike-triggered covariance analysis for neural subunit models" in NIPS 2015 @ Montreal. 

[Feb 2015] Poster presentation "Convolutional spike-triggered covariance analysis for estimating subunit models" in Cosyne 2015 @ Salt Lake City.

[Dec 2014] Poster presentation "Sparse Bayesian structure learning with dependent relevance determination prior" in NIPS 2014 @ Montreal.

[Dec 2013] Oral presentation "Weighted Task Regularization for Multitask Learning" in IEEE 13th International Conference on Data Mining Workshops (ICDMW) 2013 @ Dallas.