Jessie Huang

Postdoctoral Researcher

Yale University

jiexi dot huang at yale dot edu

About Me

I'm a postdoc in the department of computer science at Yale University. I'm working in the lab of Prof. Smita Krishnaswamy. My current research interest is generally in machine learning and its applications in biological and medical applications.

Between 2017 and 2018, I worked as a postdoc under the supervision of Prof. Doina Precup in the Reasoning and Learning Lab at McGill University in Montreal, Canada, between 2017 and 2018. I focused on reinforcement learning and understanding how to set goals and rewards for RL agents. If we would like the agent to behave safely, how should we design the learning process? How can we train agents to perform well on multiple tasks? Please see my NeurIPS 2018 paper for details.

Before machine learning, I lived in San Francisco and worked as an engineering consultant at Exponent. I received my PhD in Mechanical Engineering from University of Michighan, Ann Arbor, supervised by Prof. Michael Thouless. If you are interested in my work about fractures, cracks, and how to utilize cracking and healing of polymeric materials for DNA stretching and drug release, or how things break and fail, I'm always happy to talk. I hope that one day I can use machine learning to help tackle those engineering problems.

Publications

Machine Learning

  1. Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease [PDF], Manik Kuchroo*, Jessie Huang*, Partrick Wong*, et al. Nature Biotechnology, 1-11, 2022. (* First author, equal contribution)

  2. Data-driven approaches for genetic characterization of SARS-CoV-2 lineages, Mostefai, Fatima, Isabel Gamache, Jessie Huang, Arnaud N’Guessan, Justin Pelletier et al. Frontiers in Medicine, 9, 2022 .

  3. Neural Network Predicts Need for Red Blood Cell Transfusion for Patients with Acute Gastrointestinal Bleeding Admitted to the Intensive Care Unit [PDF]. Dennis Shung, Jessie Huang, et al. Nature Scientific Reports. 2021.

  4. Learning shared neural manifolds from multi-subject fMRI data, Jessie Huang*, Erica Busch*, Tom Wallenstein*, et al. (Preprint)

  5. Topological analysis of single-cell hierarchy reveals inflammatory glial landscape of macular degeneration [PDF], Under Review. 2021.

  6. Visualizing high-dimensional trajectories on the loss-landscape of ANNs [PDF]. Stefan Horoi, Jessie Huang, Guy Wolf and Smita Krishnaswamy. Symposium on Intelligent Data Analysis (IDA). 2022.

  7. Trajectory Net: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics [PDF], Alex Tong, Jessie Huang, Guy Wolf, David Van Dijk, and Smita Krishnaswamy. International Conference on Machine Learning (ICML). 2020.

  8. Learning Safe Policies with Expert Guidance [PDF], Jessie Huang, Fa Wu, Doina Precup, and Yang Cai. Advances in Neural Information Processing Systems (NeurIPS). 2018.

Other

  1. Self-healing of Pores in PLGAs [HTML, PDF] J Huang, JM Mazzara, SP Schwendeman, MD Thouless, Journal of Controlled Release 206, 20-29, 2015

  2. The Collapse and Expansion of Liquid-Filled Elastic Channels and Cracks [HTML] F Meng, J Huang, MD Thouless, Journal of Applied Mechanics 82.10, 2015

  3. The Control of Crack Arrays in Thin Films [HTML, PDF] J Huang, BC Kim, S Takayama, MD Thouless, Journal of Materials Science 49.1, 255-268, 2014

  4. Fracture-Based Fabrication of Normally Closed, Adjustable, and Fully Reversible Microscale Fluidic Channels [HTML] BC Kim, C Moraes, J Huang, T Matsuoka, MD Thouless, S Takayama, Small 10 (19), 4020-2029, 2014

  5. Fracture-Based Micro and Nanofabrication for Biological Applications [HTML] BC Kim, C Moraes, J Huang, MD Thouless, S Takayama, Biomaterials Science 2(3), 288-296, 2014

  6. Guided Fracture of Films on Soft Substrates to Create Micro/Nano-Feature Arrays with Controlled Periodicity [HTML] BC Kim, T Matsuoka, C Moraes, J Huang, MD Thouless, S Takayama, Scientific Reports, 3, 3027, 2013

  7. Nanoscale Squeezing in Elastomeric Nanochannels for Single Chromatin Linearization [HTML] T Matsuoka, BC Kim, J Huang, NJ Douville, MD Thouless, S Takayama, Nano Letters 12 (12), 6480-6484, 2012

Workshop and Presentations

  1. Multiscale PHATE Exploration of Covid-19 Data. Jessie Huang*, Manik Kuchroo*, Jean-Christophe Grenier, Julie Hussin, Guy Wolf, Smita Krishnaswamy. Women in Machine Learning 2020 Workshop.

  2. Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease. Manik Kuchroo*, Jessie Huang*, Partrick Wong*, et al. NeurIPS 2020 Learning Meaningful Representations of Life Workshop.

  3. Visualizing high-dimensional trajectories on the loss-landscape of ANNs. Stefan Horoi, Jessie Huang, Guy Wolf and Smita Krishnaswamy. NeurIPS 2020 Deep Learning through Information Geometry Workshop.

  4. Neural Network Predicts Need for Red Blood Cell Transfusion for Patients with Acute Gastrointestinal Bleeding Admitted to the Intensive Care Unit. Jessie Huang, Dennis Shung, et al. Yale Postdoc Symposium.

  5. Trajectory Net: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics. Alex Tong, Jessie Huang, Guy Wolf, David Van Dijk, Smita Krishnaswamy. Neurips 2019 Learning Meaningful Representations of Life Workshop.

  6. Learning Safe Policies with Expert Guidance [PDF]. Jessie Huang, Fa Wu, Doina Precup, Yang Cai. appear in Adaptive Learning Agents (ALA) Workshop at the Federated AI Meeting 2018 collocated with ICML 2018 in Stockholm.