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Min Oh

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
  1. DeepGeni: Deep generalized interpretable autoencoder elucidates gut microbiota for better cancer immunotherapy
    Min Oh and Liqing Zhang
    Biorxiv
      2. Peer-Reviewed Journals
    1. Generalizing predictions to unseen sequencing profiles via deep generative models
      Min Oh and Liqing Zhang
      Scientific Reports 12.1 (2022): 7151
    2. DeepMicro: Deep Representation Learning for Disease Prediction Based on Microbiome Data
      Min Oh and Liqing Zhang
      Scientific Reports 10.1 (2020): 6026
    3. 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)
    4. 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
    5. 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
    6. 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
    7. 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

    Talks