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Hi, My name is Dong Won. My advisors and friends call me “Dong” or “Don”. Whatever is convenient for you! I’m a 5th-year Master's student (4 (B.S.) + 1 (M.S)) in the Machine Learning Department at Carnegie Mellon University. (Expected Grad. May 2022). I recently completed my B.S. in Statistics and Machine Learning at CMU, as well. I am fascinated about designing ML models to understand the relationship between what we see (vision) and what we hear (language) and implement these findings to interesting applications in education, human-robot-interaction, and robotics control. |
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07/2021: Our paper on Crossmodal clustered contrastive learning: Grounding of spoken language to gestures is accepted to GENEA Workshop @ ICMI 2021. 06/2021: Graduated from my undergraduate degree at CMU! 05/2021: We are organizing the First Workshop on Crossmodal Social Animation at ICCV 2021. 09/2020: Our paper on No Gestures Left Behind: Learning Relationships between Spoken Language and Freeform Gestures is accepted at Findings at EMNLP 2020. 07/2020: Our paper on Style Transfer for Co-Speech Gesture Animation is accepted at ECCV 2020 |
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During my Bachelor’s and Master’s, I’ve primarily worked in the MultiComp Lab, advised by Professor Louis-Philippe Morency. I'm currently dedicating my efforts to understand the fine-grained visual grounding between visual elements and language. Prior, I focused on developing models in generating nonverbal behavioral cues (gestures) conditioned on language to improve naturalness in human-robot interaction. I work with Dr. Hae Won Park at the MIT Media Lab to predict people’s engagement with an agent using graphical models. I am also collaborating with Professor Ruslan Salakhutinov to teach robots to learn optimal policies just with language. |
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Dong Won Lee, Pedro Colon-Hernandez, Ishaan Grover, Cynthia Breazeal, Hae Won Park CSCW, 2022, In Submission code / demo video |
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Chaitanya Ahuja, Dong Won Lee, Louis-Philippe Morency CVPR, 2022, In Submission |
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Dong Won Lee, Chaitanya Ahuja, Louis-Philippe Morency ICMI GENEA Workshop, 2021 paper / presentation video / code We propose a new crossmodal contrastive learning loss to encourage a stronger grounding between gestures and spoken language. |
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Chaitanya Ahuja, Dong Won Lee, Ryo Ishii, Louis-Philippe Morency Findings at EMNLP, 2020 paper / code We study relationships between spoken language and co-speech gestures to account for the long tail of text-gesture distribution. |
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Chaitanya Ahuja, Dong Won Lee, Yukiko I. Nakano, Louis-Philippe Morency ECCV, 2020 project page / paper / code We propose a new style transfer model to learn individual styles of speaker's gestures. |
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Chaitanya Ahuja, Dong Won Lee, Yukiko I. Nakano, Louis-Philippe Morency dataset page / download Link / code PATS was collected to study correlation of co-speech gestures with audio and text signals. The dataset consists of a diverse and large amount of aligned pose, audio and transcripts. |
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Graduate TA, Spring 2022 |
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Graduate TA, Spring 2021 |
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Undergraduate TA, Fall 2019, Spring 2020, Fall 2020 (3 Semesters) |
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Undergraduate TA, Fall 2020, Spring 2021 (2 Semesters) |
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Publication Chair |
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Reviewer |
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Reviewer |
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I spend some of my time writing about interesting things I come across while I study ML :) |
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Explaning the intuition behind the formulation of Transformer’s positional embeddings. |
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A more-detailed summary of C-Learning: Learning to Achieve Goals via Recursive Classification, which studies the problem of predicting and controlling future states distribution of an autonomous agent. |
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Previously, I had the incredible opportunity to be a member of a South Korean special operations unit deployed to Abu Dhabi (AKH14). Please excuse the hideous sunglasses in this photo. |
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1/15/2022: CSCW 2022 1/28/2022: ICML 2022 3/07/2022: ECCV 2022 |
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I have been blessed to meet amazing mentors who have guided me to become a better researcher (and more importantly, a good person). I believe that I can only repay what they've done for me by assisting others in their journey in any way I can. Please don't hesitate to reach out! Mentors and Advisors: (in Alphabetical Order)
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Source: source code |