Dan Fu

Analyzing Who and What Appears in a Decade of US Cable TV News

Cable TV news reaches millions of U.S. households each day, and decisions about who appears on the news, and what stories get talked bout, can profoundly influence public opinion and discourse. We use computational techniques to analyze a data set of nearly 24/7 video, audio, and text captions from three major U.S. cable TV networks (CNN, FOX News, and MSNBC) from the last decade.
arXiv / stanford cable tv news analyzer

Epoxy: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings

We take a first step towards building models that can be iterated on at programmatically-interactive speeds (seconds instead of hours or days). We build on work in weak supervision and pre-trained embeddings to create models that can approach the quality of training deep networks, but without the cost of training to fine-tune feature representations.
arXiv / code / video

FlyingSquid: Faster and More Interactive Weak Supervision

ICML 2020

We present FlyingSquid, a new weak supervision framework that runs orders of magnitude faster than previous work. Our speedups come from a closed-form solution to latent variable estimation, which enables weakly-supervised video analysis and online learning applications.
arXiv / blog post / code / talk

Multi-Resolution Weak Supervision for Sequential Data

NeurIPS 2019

We present a framework to apply weak supervision to multi-resolution data like videos and time-series data that can handle sequential correlations among supervision sources. We experimentally validate our system over population-level video datasets and gait sensor data.
arXiv

Rekall: Specifying Video Events using Compositions of Spatiotemporal Labels

AI Systems @ SOSP 2019

We present Rekall, a data model and programming model for detecting new events in video collections by composing the outputs of pre-trained models. We demonstrate the use of Rekall in analyzing video from cable TV news broadcasts, films, static-camera vehicular video streams, and commercial autonomous vehicle logs.
arXiv / blog post / code / demo videos

Influencing Agents for Flock Formation in Low-Density Settings, 2017-2018

AAMAS 2018

We study the problem of how to control flocking behavior in low-density environments. We find that some strategies that have worked well in high-density conditions are often less effective in lower-density environments, and propose new strategies for low-density environments. In an undergraduate thesis, we also explored using genetic algorithms to evolve influencing agent behaviors.
aamas paper / undergraduate thesis / code

Automatically Scalable Computation

The ASC project seeks to create a powerful, practical automatic parallelization runtime by using machine learning to predict future states of a program and running speculative executions from those states. If the main program hits any of the predicted states, ASC can speed up execution by skipping the main program to the end of the speculative execution. Prior work has shown that this approach is promising, but did not actually manage to achieve speedup on native hardware, since it the main program on an emulator. We have been working on making this approach work on native hardware.
arXiv

Modeling Effects of Meth on Temperature

PLOS One 2015, CNS 2013 Paris, Siemens Research Competition 2013 semifinalist

We develop mathematical models to understand the complex response of body temperature to methamphetamine and use our models to separate out the individual components of the response. We further analyze the ways that this response changes when orexinergic neurotransmission is inhibited.
pdf

Dynamics of Genetic Oscillatory Networks

PLOS One 2014, Siemens Research Competition 2012 team runner-up

We analyze a family of delay differential equations used to model genetic oscillatory networks, and use our findings to develop two new mathematical methods for analyzing such models.
pdf / talk / coverage

Google Internship (Summer 2017)

In Summer 2017, I conducted a machine learning/data science project for the Google Flights team to try to predict flight arrival times using a mix of historical data and live positional data. I built a few models (including two unique ML models) and trained and evaluated them. Some of these models could outperform official airline estimates in many cases. This exploratory work was converted into production after my internship.
coverage

Ballroom Matchmaker

For the final project of an AI class I took in fall 2016, I helped build a ballroom matchmaker program to intelligently form partnerships in a rookie class. We treated the task as a local search problem with a complex cost function. We are hopeful that this tool will help make the lives of future HBDT rookie captains much easier.
pdf / code

Ballroom Runthroughs Script

When I was the Captain of the Harvard Ballroom team, I wrote a dead-simple Python script to help me run competitive rounds. The only requirement I had was that it had to run on my computer, so it has hard dependencies on afplay (to play songs from the command line), sox (to program in fadeouts in songs), and say (this is a weaker dependency, but I liked to have the script yell at me when it was time to start a new round). As a result, it is OS X only, but I thought I'd put it online in case anyone else found it useful.
code

Google Internship (Summer 2016)

I had two main projects when I was working with the Google My Business Android team at Google in 2016: a research project to extend and evaluate an image classifier to automatically categorize uploaded images, and a development project to integrate a newsfeed to drive increases in daily active users. While neither project made it to production, the work I did helped inform major product decisions (including the decision to move away from classifying images in the first place).

Tamr Internship (Summer 2015))

When I was at Tamr, I worked on two main projects - augmenting the main product to handle a new use case for a client, and working on Tamr on Google Cloud Platform Tamr on Google Cloud Platform prior to its launch. My first project eventually led to a major deal with the client, and my second project helped the product launch successfully, leading to sales leads for both Tamr and GCP.

Interactive Intelligence Internship (Summer 2014)

At Interactive Intelligence, I built the Interaction Speech Tuner, a full-stack web application to tune Interactive Intelligence's automated speech recognition system.

DyKnow Internship (Summer 2013)

When I was at DyKnow, I helped build an analytics feature in the DyKnow web app to allow teachers to track student participation and understanding over time. I also integrated the new web app with Google Drive and Dropbox to make it easier for teachers to upload lesson plans.

ScoreZone

When I was a freshman in high school, I built a sports app for students to report live game results to the whole school. In an age before schools had learned what to do with social media, it was very popular among parents who didn't want to wait to find out how their kids had played.
app