Min Oh
- Ph.D. in Computer Science
- Applied Scientist at Microsoft Research
Curriculum Vitae: PDF (Last Update: 05/15/2022)
About Me
I'm an Applied Scientist at Microsoft Research. My research interests lie in machine intelligence to accelerate drug discovery and precision medicine. I received Ph.D. in Computer Science from Virginia Tech, advised by Professor Liqing Zhang. The doctoral dissertation focused on deep learning for enhancing precision medicine.
While pursuing Ph.D., I worked at Microsoft for four summers as a Research Intern (Microsoft Research, Healthcare NExT Group; 2017 and 2018) and as a Data & Applied Scientist Intern (Microsoft Azure, Networking Group; 2019 and 2020).
Before came to Virginia Tech, I was a Research Associate in the Data mining & Bioinformatics Laboratory where my advisor was Professor Youngmi Yoon (Mar 2015 - Aug 2016) and I received my bachelor's degree in Computer Engineering (Feb 2015) at Gachon University, South Korea.
News
May | 2022 | | Joined Microsoft Research as Applied Scientist (AI Architecture and Strategy) |
July | 2021 | | Joined Microsoft as Data & Applied Scientist (Azure Networking) |
May | 2021 | | Successfully defended doctoral dissertation! |
Nov. | 2020 | | Gaduate fellowship awarded from Department of Computer Science @ Virginia Tech |
June | 2020 | | Joined Microsoft as Data & Applied Scientist Intern (Azure Networking |
June | 2019 | | Joined Microsoft as Data & Applied Scientist Intern (Azure Networking) |
Nov. | 2018 | | Research grant awarded from Office of the Executive Vice President and Provost @ Virginia Tech |
June | 2018 | | Joined Microsoft Research as Research Intern (Healthcare NExT) |
June | 2017 | | Joined Microsoft Research as Research Intern (Healthcare NExT) |
Publications
First authorship only (see Google Scholar Profile or CV for the complete list of publications)
Preprints
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DeepGeni: Deep generalized interpretable autoencoder elucidates gut microbiota for better cancer immunotherapy
Min Oh and Liqing Zhang
Biorxiv
Peer-Reviewed Journals
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Generalizing predictions to unseen sequencing profiles via deep generative models
Min Oh and Liqing Zhang
Scientific Reports 12.1 (2022): 7151
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DeepMicro: Deep Representation Learning for Disease Prediction Based on Microbiome Data
Min Oh and Liqing Zhang
Scientific Reports 10.1 (2020): 6026
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Effect of Antibiotic Use and Composting on Antibiotic Resistance Gene Abundance and Resistome Risks of Soils Receiving Manure-derived Amendments
Chaoqi Chen*, Christine Pankow*, Min Oh*, Lenwood Heath, Liqing Zhang, Pang Du, Kang Xia, Amy Pruden
Environment International 128 (2019): 233-243
(*Equal contribution)
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MetaCompare: A Computational Pipeline for Prioritizing Environmental Resistome Risk
Min Oh, Amy Pruden, Chaoqi Chen, Lenwood Heath, Kang Xia, Liqing Zhang
FEMS Microbiology Ecology 94.7 (2018): fiy079
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Drug Voyager: A Computational Platform for Exploring Unintended Drug Action
Min Oh, Jaegyoon Ahn, Taekeon Lee, Giup Jang, Chihyun Park, Youngmi Yoon
BMC Bioinformatics 18.1 (2017): 131
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A Network-based Classification Model for Deriving Novel Drug-Disease Associations and Assessing Their Molecular Actions
Min Oh, Jaegyoon Ahn, Youngmi Yoon
Plos One 9.10 (2014): e111668
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Drug-Repositioning Based on Distance Features on the PPI Network
Min Oh and Youngmi Yoon
Journal of Korean Institute of Information Technology (JKIIT) 11.12 (2013): 205-211
Peer-Reviewed Abstract & Posters
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Unsupervised Deep Representation Learning for Genetic Variant-based Clustering of Individuals
Min Oh and Erdal Cosgun
ASHG 2019, Houston, USA, October 2019
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Exploring the Consistency of the Quality Scores with Machine Learning for Next-Generation Sequencing Experiments
Min Oh and Erdal Cosgun
ASHG 2018, San Diego, USA, October 2018
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A Novel Generation of Drug Pathway Elucidates New Indication of Existing Drugs
Min Oh and Youngmi Yoon
ISMB 2015, Dublin, Ireland, July 2015
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A Novel Method for Elucidating microRNA and Transcription Factor Co-Regulatory Networks in Prostate Cancer
Min Oh and Youngmi Yoon
ISMB 2014, Boston, USA, July 2014
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Network Based Classification Model for Deriving Novel Drug-Disease Associations
Min Oh and Youngmi Yoon
ISMB 2013, Berlin, Germany, July 2013
Talks
- Unsupervised Deep Learning for Next Generation Sequencing Data
Microsoft Research, Redmond, Washington, USA, August 2018
- Enabling Machine Learning for Variant Call Format Data Analysis
Microsoft Research, Redmond, Washington, USA, August 2017
- Computational Drug Repositioning Strategy & Its Application
Utah-Inha DDS & Advanced Therapeutics Research Center, Incheon, South Korea, March 2015
Teaching Assistantships
- Advanced Machine Learning (CS 5824) - Fall 2019
- Introductory Data Analytics and Visualization (CS 3654) - Spring 2018
- Introduction to Programming in C++ (CS 1044) - Fall 2016, Spring 2017
Copyright © Min Oh (오민)