About me

I am a research scientist in the Artificial Intelligence Technology Group at MIT Lincoln Laboratory and an Associate in Computer Science for Harvard John A. Paulson School of Engineering and Applied Sciences. My research interests are multimodal learning and cross-modal synthesis applied to remote sensing and medical image analysis.

I completed my Ph.D. in Computer Science from Harvard University under the supervision of H.T. Kung. I received the B.S. and M.S. degree in Electrical and Computer Engineering from Carnegie Mellon University. I was a recipient of a National Science Foundation Graduate Research Fellowship, a National Defense Science and Engineering Graduate Fellowship, and a Lincoln Scholars Fellowship. 

News

2025: We published a new vision-language dataset for remote sensing, fMoW-mm.

2025: Our paper titled "Measuring and Mitigating Hallucinations in Vision-Language Dataset Generation for Remote Sensing" has been accepted to AAAI Good Data 2025.

2024: I served as Co-Chair for the Multi-Modal AI session at the RAAINS workshop.

2024: I co-taught A Practical Guide to Applied Generative AI course at the RAAINS workshop.

2023: I chaired and organized the Multimodal Learning for Earth and Environment (MultiEarth) workshop at CVPR 2023 with Phillip Isola, Taylor Perron, and Bill Freeman, among others. 2023 White Paper. 

2022: RadTex: Learning Efficient Radiograph Representations from Text Reports won the best paper award at MICCAI REMIA 2022.

2022: I chaired and organized the Multimodal Learning for Earth and Environment (MultiEarth) workshop at CVPR 2022 with Phillip Isola, Taylor Perron, and Bill Freeman, among others. 2022 White Paper. 

Selected Publications (All papers)

Datasets, Models, Challenges

fMoW-mm Dataset

MultiEarth 2023 Dataset

MultiEarth 2022 Dataset

MultiEarth Starter Code

MultiEarth 2023 Sub-Challenges

MultiEarth 2022 Sub-Challenges