Alex Wong is an Assistant Professor in the department of Computer Science and the director of the Vision Laboratory at Yale University. He also serves as the Director of AI (consulting capacity) for Horizon Surgical Systems. Prior to joining Yale, he was an Adjunct Professor at Loyola Marymount University (LMU) from 2018 to 2020. He received his Ph.D. in Computer Science from the University of California, Los Angeles (UCLA) in 2019 and was co-advised by Stefano Soatto and Alan Yuille. He was previously a post-doctoral research scholar at UCLA under the guidance of Soatto. His research lies in the intersection of machine learning, computer vision, and robotics and largely focuses on multimodal 3D reconstruction, robust vision under adverse conditions, and unsupervised learning. Wong is one of the early pioneers in unsupervised learning for monocular depth estimation and completion from multi-sensor fusion. He and Soatto are also the first to demonstrate adversarial and (stereoscopic) universal perturbations for learning-based stereo depth estimation. His work has received the outstanding student paper award at the Conference on Neural Information Processing Systems (NeurIPS) 2011 and the best paper award in robot vision at the International Conference on Robotics and Automation (ICRA) 2019.
Keywords: Multimodal Learning, Sensor Fusion, 3D Vision, Unsupervised Learning, Vision under Adverse Conditions, Adversarial Robustness, Medical Image Understanding
Teaching
| Semester | Course ID | Course Title |
|---|---|---|
| Spring 2024 | CPSC 381/581 | Machine Learning |
| Fall 2023 | CPSC 480/580 | Computer Vision |
| Spring 2024 | CPSC 480/580 | Computer Vision |
| Fall 2022 | CPSC 676 | Advanced Computer Vision Seminar |
| Fall 2020 | CMSI 535 | Machine Learning |
| Spring 2020 | CMSI 371 | Computer Graphics |
| Fall 2020 | CMSI 533 | Data Science and Machine Learning |
| Spring 2019 | CMSI 371 | Computer Graphics |
| Fall 2019 | CMSI 281 | Data Structures |
| Spring 2018 | CMSI 371 | Computer Graphics |
Teaching is a passion of mine. I have served as a teaching assistant/fellow throughout my graduate career at UCLA and during my time as an adjunct professor at Loyola Marymount University (LMU). Note: CPSC denotes courses at Yale, and CMSI course at LMU. Here are some excerpts from recent teaching evaluations on my courses:
“Prof. Wong is one of the best professors in the CS department… emphasized and explained the intuition and purpose behind each concept… lectures can get technical and are quite thorough, he makes sure you don’t get lost…” (Computer Vision, Spring 2023).
“Dr. Wong’s lecture is awesome. Machine learning involves lots of math but he made it easy by explaining basic maths and then applies them to algorithm. I was new to ML, but he made me love the subject.” (Machine Learning, Fall 2020).
“Besides Professor Wong ‘s strong teaching abilities and well organized lectures. His understanding and care for his students really helped me succeed in his class and feel motivated.” (Computer Graphics, Spring 2020).
“I think he is a great teacher who is very knowledgeable and furthermore genuinely is invested in his students.” (Computer Graphics, Spring 2020).
Recent Talks
| Date | Venue | Title |
|---|---|---|
| August 8, 2024 | Korea Institute of Industrial Technology (KITECH), National Collaboration Center (NCC) | The Know-Hows of Multimodal Depth Perception |
| May 4, 2024 | Embracing Challenges and Opportunities: Perspective of Asian American Scholars | Perception from Vision and More |
| February 3, 2024 | George Mason University | Unsupervised Learning of Depth Perception and Beyond |
| November 04, 2023 | Northeast Robotics Colloquium (NERC) 2023 | Unsupervised Learning of Depth Perception and Beyond |
| October 27, 2023 | Theory and Practice of Foundation Models Workshop | Unsupervised Learning of Depth Perception and Beyond |
| September 25, 2023 | University of Delaware AI Symposium | Unsupervised Learning of Depth Perception and Beyond |
| October 7, 2022 | Grundfest Memorial Lecture Series | Rethinking Supervision for Some Vision Tasks |
| September 22, 2022 | Institute for Foundations of Data Science | Panelist for Advances in Artificial Intelligence |
Awards and Honors
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Best Paper in Robot Vision. International Conference on Robotics and Automation (ICRA), 2019. IEEE Robotics and Automation Society.
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Outstanding Student Paper. Neural Information Processing Systems (NeurIPS), 2011. Neural Information Processing Systems Foundation.
Selected Publications
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Mingqi Han, Eric A. Bushong, Dane M. Wolf, Alexandre Tiard, Alex Wong, Morgan R. Brady, Milica Momcilovic, Mayuko Segawa, Ralph Zhang, Anton Petcherski, Matthew Madany, Shili Xu, Jason T. Lee, Masha V. Poyurovsky, Kellen Olszewski, Travis Holloway, Adrian Gomez, Maie St. John, Aaron Lisberg, Steven M. Dubinett, Carla M. Koehler, Orian S. Shirihai, Linsey Stiles, Stefano Soatto, Saman Sadeghi, Mark H. Ellisman, and David B. Shackelford. Spatial Mapping of Mitochondrial Networks and Bioenergetics in Lung Cancer. Nature 2023.
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Tian Yu Liu, Parth Agrawal, Allison Chen, Byung-Woo Hong, and Alex Wong. Monitored Distillation for Positive Congruent Depth Completion. In the Proceedings of European Conference on Computer Vision (ECCV) 2022.
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Zachary Berger, Parth Agrawal, Tian Yu Liu, Stefano Soatto, and Alex Wong. Stereoscopic Universal Perturbations across Different Architectures and Datasets. In the Proceedings of Computer Vision and Pattern Recognition (CVPR) 2022.
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Alex Wong, and Stefano Soatto. Unsupervised Depth Completion with Calibrated Backprojection Layers. In the Proceedings of International Conference on Computer Vision (ICCV) 2021. Oral.
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Alex Wong, Xiaohan Fei, Stephanie Tsuei and Stefano Soatto. Unsupervised Depth Completion from Visual Inertial Odometry. In the Robotics and Automation Letters (RA-L) 2020 and Proceedings of International Conference on Robotics and Autonomation (ICRA) 2020.
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Xiaohan Fei, Alex Wong, and Stefano Soatto. Geo-Supervised Visual Depth Prediction. In the Robotics and Automation Letters (RA-L) 2019 and Proceedings of International Conference on Robotics and Autonomation (ICRA) 2019. Oral. Best Paper Award in Robot Vision (ICRA).
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Michael Shindler, Alex Wong, and Adam Meyerson. Fast and Accurate k-Means for Large Datasets. In the Proceedings of Neural Information Processing Systems (NeurIPS) 2011. Oral. Outstanding Student Paper Award<.