Jack Lanchantin

I'm a 5th year PhD student in computer science at the University of Virginia, advised by Dr. Yanjun Qi. My research interests are in attention methods for deep learning, as well as interpretable machine learning models for biomedical data. I was previously a research intern at Microsoft AI and Research in the Computer Vision Group.


See Google Scholar for complete list.

Neural Message Passing for Multi-Label Classification
Jack Lanchantin, Arshdeep Sekhon, Yanjun Qi
European Conference on Machine Learning (ECML-PKDD) 2019 - W├╝rzburg, Germany
[PDF] [arXiv] [Code]

Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
Ji Gao, Jack Lanchantin, Mary Lou Soffa, Yanjun Qi
Deep Learning and Security Workshop (DLS) 2018 - San Francisco, CA
[PDF] [Slides] [arXiv] [Code]

Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin
Ritambhara Singh, Jack Lanchantin, Arshdeep Sekhon, Yanjun Qi
Advances in Neural Information Processing Systems (NIPS) 2017 - Long Beach, CA
[PDF] [arXiv] [Slides] [Code] [Kipoi] [Poster]

Opportunities and Obstacles for Deep Learning in Biology and Medicine
Travers Ching, Daniel S Himmelstein, Brett K Beaulieu-Jones, Alexandr A Kalinin, Brian T Do, Gregory P Way, Enrico Ferrero, Paul-Michael Agapow, Wei Xie, Gail L Rosen, Benjamin J Lengerich, Johnny Israeli, Jack Lanchantin, Stephen Woloszynek, Anne E Carpenter, Avanti Shrikumar, Jinbo Xu, Evan M Cofer, David J Harris, Dave DeCaprio, Yanjun Qi, Anshul Kundaje, Yifan Peng, Laura K Wiley, Marwin HS Segler, Anthony Gitter, Casey S Greene
Journal of the Royal Society Interface 2018
[PDF] [JRSI] [Nature Tech Blog]

Memory Matching Networks for Genomic Sequence Classification
Jack Lanchantin, Ritambhara Singh, Yanjun Qi
International Conference on Learning Representations (ICLR) Workshops 2017 - Toulon, France
[PDF] [arXiv] [Poster]

Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks
Jack Lanchantin, Ritambhara Singh, Beilun Wang, Yanjun Qi
Pacific Symposium on Biocomputing (PSB) 2017 - Kohala Coast, HI
[PDF] [arXiv] [Slides] [Code] [Poster]

Deep Motif: Visualizing Genomic Sequence Classifications
Jack Lanchantin, Ritambhara Singh, Zeming Lin, Yanjun Qi
International Conference on Learning Representations (ICLR) Workshops 2016 - San Juan, PR
[PDF] [arXiv] [Code] [Poster]

DeepChrome: Deep Learning for Predicting Gene Expression from Histone Modifications
Ritambhara Singh, Jack Lanchantin, Gabriel Robins, Yanjun Qi
European Conference on Computational Biology (ECCB) 2016 - The Hague, Netherlands
[PDF] [arXiv] [Slides] [Code]

Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction
Ritambhara Singh, Jack Lanchantin, Gabriel Robins, Yanjun Qi
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) 2016
[PDF] [arXiv] [Slides] [Code]

MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction
Zeming Lin, Jack Lanchantin, Yanjun Qi
The 30th AAAI Conference on Artificial Intelligence (AAAI) 2016 - Phoenix, AZ
[PDF] [arXiv] [Slides] [Code]


Scene Labeling with Convolutional Neural Nets
Zeming Lin, Jack Lanchantin 2015
[Slides] [Code]

Exploring the Naturalness of Code with Recurrent Neural Nets
Jack Lanchantin, Ji Gao 2016
[PDF] [arXiv] [Slides] [Code]

Grants and Awards

UVA Presidential Fellow in Data Science 2019
ARCS Scholar 2019
Olsson Endowed Graduate Engineering Fellow 2018
NIPS TIML Workshop Best Paper Award (Deep Motif Dashboard) 2017
NIPS Travel Award 2017
UVA Computer Science Outstanding Teaching Award 2017
PSB Travel Award 2017
NIH Biomedical Data Sciences Training Grant 2016
Lockheed Martin Honors Fellow 2013


Deep Learning for Genomics [Slides] UVA AIML Seminar 2019
Deep Motif Dashboard [Slides] NIPS Workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments - Long Beach, CA 2017
Deep Motif [Slides] ICML Workshop on Computational Biology - New York, NY 2016
Deep Learning Intro [Slides] UVA CS 6316 Fall 2016


Graduate Machine Learning TA Fall 2016
Algorithms TA Spring 2015
Intro to Programming TA Spring 2015