Minji Yoon

About

I am currently a PhD student at the Computer Science Department at Carnegie Mellon University, where I am advised by Prof. Christos Faloutsos. My research interests are in the area of Machine Learning and Graph Mining.

More specifically, I’m interested in Automation and Democratization of Graph Mining using various Machine Learning techniques. My recent work includes automation in 1) polishing/generation of graph structures, 2) generation of node representations, and 3) solution generation in the application level. Ultimately, I’d like to empower all users to benefit from Graph Mining, regardless of their level of expertise in the field.

I was a research intern at Amazon.com and LinkedIn. Before joining CMU, I received my bachelor’s and master’s degrees from Seoul National University, South Korea.

Publication

• Autonomous Graph Mining Algorithm Search with Best Speed/Accuracy Trade-off
  Minji Yoon, Theophile Gervet, Bryan Hooi, and Christos Faloutsos
  20th IEEE International Conference on Data Mining (ICDM) 2020
  (Acceptance Ratio: 9.8%)
  [PDF] [Code] [Slide]
• Provably Robust Node Classification via Low-Pass Message Passing
  Yiwei Wang, Shenghua Liu, Minji Yoon, Hemank Lamba, Wei Wang, Christos Faloutsos, and Bryan Hooi
  20th IEEE International Conference on Data Mining (ICDM) 2020
  (Acceptance Ratio: 9.8%)
  [PDF]
• MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams
  Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, and Christos Faloutsos
  34th AAAI Conference on Artificial Intelligence (AAAI) 2020
  [PDF]
• Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach
  Minji Yoon, Bryan Hooi, Kijung Shin, and Christos Faloutsos
  SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2019, Alaska, USA
  [PDF(old)] [PDF(updated)] [Code] [Poster]
• Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees
  Minji Yoon, Woojeong Jin, and U Kang
  The Web Conference (WWW) 2018, Lyon, France
  (Acceptance Ratio: 14.8%)
  [PDF] [Code] [Slide]
• TPA: Fast, Scalable and Accurate Method for Approximate Random Walk with Restart on Billion Scale Graphs
  Minji Yoon, Jinhong Jung, and U Kang
  IEEE International Conference on Data Engineering (ICDE) 2018, Paris, France
  [PDF] [Code] [Slide]

Work Experiences

• Standardization team, LinkedIn
  Machine Learning Engineer Intern (May. 2020 - Aug. 2020)
  Developed an algorithm to optimize computation graphs in Graph Neural Networks (GNNs)
• CTPS Machine Learning Accelation team, Amazon.com
  Applied Scientist Intern (May. 2019 - Aug. 2019)
  Developed a fast and scalable algorithm for fraud detection in Amazon.com
• Data Mining Lab, Seoul National University
  Research Intern (Apr. 2017 - Jun. 2018)
  Developed fast, accurate and scalable algorithms for Random Walk with Restart (RWR)
• Session team, SAP Labs Korea
  Software Developer (Sep. 2014 - Mar. 2017)
  Developed an in-memory database SAP HANA

AWARDS & HONORS

• Kwanjeong Educational Foundation Scholarship, Kwanjeong Foundation (Sep. 2018 - Aug. 2022)
  4 years for Doctor’s Degree.
• National Science & Technology Scholarship, KOSAF (Mar. 2008 - Feb. 2012)
  Full tuition exemptions for 8 semesters.
• Cum Laude Graduation Honors, Seoul National University (Feb. 2012)