I'm a first year Computer Science PhD student in the GRASP Lab at the University of Pennsylvania
advised by Prof. Jianbo Shi and Prof. James Gee.
My research interests in computer vision and machine learning include image segmentation, generative models, and 3D scene understanding.
I also enjoy leveraging AI in the medical, climate, and robotics domains.
Previously, I spent 4.5 wonderful years at MIT, where I completed my bachelor's degree in Computer Science with a minor in Mathematics
and my master's degree in Computer Science with a concentration in AI.
I am grateful for the opportunities to conduct research at MIT and intern twice at Meta, including Meta AI (FAIR).
Modeling Extreme Heat Risk in Urban Areas Using Computer Vision and Data Analysis
MIT MEng Thesis, 2023
Writing a paper to submit to a workshop or journal
I developed a model that estimates the extreme heat risk of an urban area at the census tract level. To construct
this model, I fine-tuned a vision transformer to segment risk factors from aerial images. I also incorporated
heat hazard and vulnerability factors from land surface temperature, building, and socioeconomic datasets. This
research focuses on developing a heat risk model for Boston, which experiences intense urban heat islands.
Chemistry Insights for Large Pretrained GNNs
NeurIPS AI for Science Workshop, 2022
During my internship at Meta AI, I worked with the Open Catalyst team, which uses AI to discover catalysts for
renewable energy storage. Large graph neural networks (GNNs) have shown good progress on the Open Catalyst 2020 (OC20)
dataset to predict the forces and energies of atoms and systems, but we have little understanding of how or why these
models work. Hence, we present perturbation analyses of GNN predictions on OC20, and we observed evidence that
aligns with chemical intuition.
A Novel Digital Algorithm for Identifying Liver Steatosis Using Smartphone-Captured Images
Sofia G. Baptista,
Nicola M. Parry,
Stuti G. Shroff,
Leigh Anne Dageforde (* equal contribution)
Transplantation Direct, 2022
Linking Threat Tactics, Techniques, and Patterns with Defensive Weaknesses, Vulnerabilities and Affected Platform Configurations for Cyber Hunting
3D Subway Surfers: A Live Action Version of the Mobile Game
MIT 6.835 Intelligent Multimodal User Interfaces, Spring 2022
My team implemented a live action implementation of the Subway Surfers mobile game that enables game play using
full-body movements, gestures, and speech commands. We wrote Python code to detect the player's pose using OpenCV and
MediaPipe and recognize speech commands using the SpeechRecognition library and Google Cloud Speech API. Overall, our
4 volunteers rated our game highly with a mean score of 4.5/5.0. We received positive feedback that the commands are
intuitive, and the system is responsive to the player's movements.
Creating a Chatbot for Three-Way Conversations
MIT 6.S898 Deep Learning, Fall 2021
Natural language processing research often features dialogue systems and chatbots that synthesize 2-way conversations,
but few systems involve more participants. We trained a Seq2Seq model on the Persona-Chat dataset and adapted it to
train our chatbot for 3-way conversations. We improved our Seq2Seq model performance by adding attention and adjusting
the embedding size and number of layers. Our work demonstrates that we can create a chatbot that generates engaging,
consistent, rational, and creative responses for 3-way conversations by training a Seq2Seq model on Persona-Chat.