Multimodal research · Data infrastructure · Computer vision
I build the data foundations behind image generation.
Research scientist at Reve AI, leading data infrastructure and curation for billion-scale multimodal datasets used in image generation and editing. PhD in Computer Science from Stanford University.
At Reve AI
My work connects large-scale data infrastructure, model capabilities, and the creative needs of real products.
My team builds billion-scale multimodal datasets and infrastructure for pre-training state-of-the-art image generation and editing models. We curate high-quality data around aesthetics, style, control, quality, and safety, and fine-tune frontier-scale MLLMs for creative applications.
Prior research
During my PhD, I designed systems and learning methods for understanding large, unstructured collections of images and video, with an emphasis on weak supervision.
AAAI Conference on Artificial Intelligence (AAAI) 2024
TL;DR: We turn a stock photo database into a weakly labeled dataset for learning what makes an aesthetically pleasing composition. Despite being only weakly supervised, our system outperforms supervised methods trained on large, crowd-annotated datasets.
European Conference on Computer Vision (ECCV) 2022
TL;DR: We propose an efficient neural network for processing every frame of a video to detect very temporally fine-grained events at the granularity of a single frame.
International Conference on Computer Vision (ICCV) 2021
TL;DR: We generate large amounts of dense weak-supervision on unlabeled and untrimmed sports video in order to learn more robust features for recognizing fine-grained actions.
Conference on Knowledge Discovery and Data Mining (KDD) 2021
TL;DR: We conduct an analysis of the visual content in 300,000 hours of US cable TV news. I built infrastructure to process the video and the TV news analyzer, a public interface and query engine for interactive search and visualization of all of the data.
Symposium on Networked Systems Design and Implementation (NSDI) 2020
Awards: USENIX NSDI Community Award, IRTF Applied Networking Research Prize
TL;DR: A public research platform for conducting video streaming experimentation. I designed the JS/HTML web client, which streams video and audio chunks over a WebSocket connection.
Conference on Internet of Things Design and Implementation (IoTDI) 2018
Conference on Internet of Things Design and Implementation (IoTDI) 2018
Conference on Mobile Systems, Applications, and Services (MobiSys) 2016
IoT-App Workshop @ SenSys 2015
Experience & education
2017 - 2024
Computer Science, Graphics Lab
2020
Creative Intelligence Lab
2016, 2017
Security team
2015
Data Analytics Infrastructure
2014
Experimentation Platform
Travel and Hobbies
Photographs from travel, landscapes, architecture, and wildlife.