About me

Hi! I’m Changyeon Lee (Nick).

I’m an incoming Master’s student in Computer Science at University of Maryland, College Park, and a founding machine learning engineer at Miraflow AI, working with Aerin Kim.

I recently earned my B.S. in Computer Science from Yonsei University, graduating Summa Cum Laude (Top 1% of the College of Computing). I was honored as the Bachelor’s Degree Graduation Representative for the university, receiving my diploma and Summa Cum Laude award from the president. I also had the privilege of delivering the commencement speech for the College of Computing.

Previously, I was a visiting research intern at Purdue University, where I conducted research under the guidance of Prof. Eric Matson. This research was fully funded by IITP, an organization affiliated with the Ministry of Science and ICT of the Republic of Korea.

I also worked as a research intern at the Computational Intelligence & Photography Lab at Yonsei University, advised by Prof. Seon Joo Kim.

My primary interests lie at the intersection of Image/Video Understanding and Generation, Multimodal Representation Learning, Efficient & Trustworthy ML. My goal is to develop image and video models that are scalable, efficient, and reliable while effectively leveraging multimodal information.

For more details, please refer to my CV.

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Publications

(* denotes equal contribution)

• Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024
  Nuria Alina Chandra, Ryan Murtfeldt, Lin Qiu, Arnab Karmakar, Hannah Lee, Emmanuel Tanumihardja, Kevin Farhat, Ben Caffee, Sejin Paik, Changyeon Lee, Jongwook Choi, Aerin Kim, Oren Etzioni
  Under Review
  [Paper]

• Cost-Efficient and Effective Counter Unmanned Aerial System via Visual-Acoustic Sensing
  Changyeon Lee*, Dongju Yu*, Soyeon Cho*, Dane W. Hindsley, Halaevalu F. Patterson, Megan A. Clecak, Eric T. Matson
  IEEE International Conference on Robotic Computing (IRC) 2024
  [Paper]

• DistilDIRE: A Small, Fast, Cheap and Light Diffusion Synthesized Deepfake Detection
  Yewon Lim*, Changyeon Lee*, Aerin Kim, Oren Etzioni
  ICML 2024 Workshop on Foundation Models in the Wild
  [Paper] [Code]

• The Tug-Of-War Between Deepfake Generation and Detection
  Hannah Lee, Changyeon Lee, Kevin Farhat, Lin Qiu, Steve Geluso, Aerin Kim, Oren Etzioni
  ICML 2024 Workshop on Data-centric Machine Learning Research
  [Paper]

• Towards Interpretable Controllability in Object-Centric Learning
  Jinwoo Kim*, Janghyuk Choi*, Jaehyun Kang, Changyeon Lee, Ho-Jin Choi, Seon Joo Kim
  CVPR 2024 Workshop on Causal and Object-Centric Representations for Robotics
  [Paper]