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Basics

Name Dongjae Shin
Label Chemical Engineer
Email djayshin_at_stanford.edu

Work

  • 2024.02 - Present

    Menlo Park, CA, USA

    Postdoctoral Scholar
    SUNCAT Center for Interface Science and Catalysis, Stanford University
    Developing AI-driven navigation algorithm for catalytic experiments based on uncertainty quantification, and comparability assessment tools for catalytic performance results from multiple sources.
    • Advisors: Dr. Kirsten T. Winther and Dr. Christopher J. Tassone
  • 2023.09 - 2024.01

    Seoul, South Korea

    Senior Researcher
    Research Institute of Advanced Materials (RIAM), Seoul National University
    Developing Bayesian optimization framework to optimize synthesis condition maximizing performances for a catalyst.
    • Advisor: Prof. Jeong Woo Han
  • 2012.03 - 2013.12

    Seoul, South Korea

    Sergeant
    Capital Defense Command (CDC), Republic of Korea Army
    Mandatory military service of 21 months in a chemical, biological, and radiological (CBR) unit

Education

Awards

  • 2023.02.01
    Graduate Catalyst Research Award
    KIChE Catalysis Division
    Awarded to only two doctoral students in that year in recognition of their outstanding research achievements in the field of catalysis.
  • 2022.06.01
    NRF Ph.D. Fellowship
    National Research Foundation of Korea (NRF)
    ~32,000 USD of research fund was provided for two years to support my Ph.D. research on AI-aided catalyst design.
  • 2022.04.22
    Hoimyung Graduate Research Award
    KIChE Catalysis Division
    Awarded to only one graduate student in catalysis division at a semi-annual KIChE conference in recognition of outstanding research achievement in the field of catalysis.

Languages

Korean
Native speaker
English
Professional working proficiency

Interests

Computational Heterogeneous Catalysis
Density Functional Theory (DFT)
High-throughput Screening
Ab-initio Thermodynamics
Reaction Mechanism
Proactive Catalyst Design
Artificial Intelligence
Machine Learning (ML)
Deep Learning (DL)
Bayesian Optimization for Real Experimental Design
Uncertainty Quantification
AI-ready Data Curation