I am a PhD candidate in Electrical Engineering at Northeastern University, working with the Machine Learning group @ SPIRAL supervised by Professor Jennifer Dy. My current research interests include 3D computer vision (particularly 3D reconstruction and novel-view synthesis), generative modeling, continual learning, adversarial attacks, and leveraging them for interpretability. Additionally, I have worked on unsupervised 3D reconstruction and classification, time-series forecasting and implicit neural networks.
I am concurrently a Student Researcher with Google Pixel Biometrics AI Research (BAIR), where I am working on Generative AI. Previously, I interned with Google’s Project Starline (now Google Beam) and developed a novel feed-forward framework for efficient and scalable 3D reconstruction and NVS, achieving state-of-the-art performance in a single inference pass.
Besides my academic interests, I enjoy community service and reading biographies.
Research Interests
- 3D computer vision (reconstruction, diffusion models)
- Adversarial attacks for robust and interpretable ML
- 3D object detection, representation learning and reconstruction
- 2D object detection, segmentation and tracking
Work Experience
June 2025 ~
Student Researcher, Pixel Biometrics AI Research (BAIR), Google, Seattle WA
Sept 2024 - May 2025
Student Researcher, Google Beam, Google, Cambridge MA
May - August 2024
Research Intern, Google Beam, Google, Playa Vista LA
Sept 2021 - Present
Graduate Research Assistant at Machine Learning Lab, SPIRAL
Sept 2020 - Aug 2021
External Consultant for ML and AI
Sept 2018 - Aug 2020
Graduate Research Assistant at Robotics and Computer Vision (RCV) Lab
Sept 2015 - May 2018
Research Intern at TUKL NUST R&D Centre
News
- [08/10/2025] Thrilled to share that my research internship work with Google Beam “LVT: Large-Scale Scene Reconstruction via Local View Transformers” got accepted at SIGGRAPH Asia 2025!
- [04/20/2025] I will be interning with the Pixel Biometrics AI Research team at Google this Summer!
- [01/22/2025] Our paper “STAR: Stability-Inducing Weight Perturbation for Continual Learning” got accepted at ICLR 2025.
- [01/05/2025] Our paper “ADAPT to Robustify Prompt Tuning Vision Transformers” got accepted at TMLR.
- [03/16/2024] I will be interning with the Project Starline team at Google this Summer!
- [05/01/2023] I will be serving as a workflow chair for AAAI 2024.
- [12/01/2022] Passed my PhD Qualification Exam.
- [07/18/2022] Presented my research on Sparse and Interpretable Adversarial Attacks at WiML Workshop @ ICML’22.
- [09/01/2021] Started my PhD at Northeastern University, under the supervision of Professor Jennifer Dy.
- [09/01/2020] Starting working as a Consultant for ML/AI at Endress+Hauser, Germany (remote).
- [06/28/2020] Completed my MS in Electrical Engineering at KAIST under the supervision of Professor In So Kweon.
- [06/15/2020] Successfully defended my Master’s thesis titled “Occlusion-Robust Object Re-identification”.
- [12/20/2019] Gave an invited talk about my research on adversarial attacks at KEEP-I (KAIST EE Partners - International).
- [09/06/2019] Delivered an invited talk reflecting on my academic and overall experience at KAIST at KAIST EE camp.
- [09/01/2018] Started my Master’s at KAIST supervised by Professor In So Kweon.
Teaching
- EECE7397 Advanced Machine Learning, Northeastern University: Teaching Assistant with Professor Jennifer Dy.
Academic Service
- Workflow Chair: AAAI 2024
- Managed the AAAI 2024 paper review process for 12,100 submissions, working with 7k reviewers, 765 senior program committee (SPC), and 320 area chairs (AC).
- Used topic modeling and text similarity to determine reviewer, SPC, and AC assignments.
- Conference Reviewer: NeurIPS 2025, ICLR 2025, CVPR 2024, ECCV 2024, ACCV 2024, ICCV 2023, NeurIPS 2023 (New in ML Workshop)
- Volunteer: ICML 2022