Sherry Ruan (Chinese name: 阮珊珊) is a PhD student studying computer science at Stanford University. Her main research interest is the intersection of artificial intelligence and human-computer interaction. She is currently working with Professor James Landay, Professor Jacob Wobbrock, and Professor Andrew Ng on a study comparing the text entry performance of speech-based dictation versus small touch screen keyboards. She is also working with Professor Maneesh Agrawala on automatically extracting references between text and tables from academic papers.
Previously, she has worked on robust machine learning with Professor Percy Liang at the Stanford Artificial Intelligence Lab (SAIL). At McGill University, she worked on computing bisimulation metrics for Markov Decision Processes with Professor Prakash Panangaden and Professor Doina Precup and defining and justifying well-founded recursion principles over LF specifications with Professor Brigitte Pientka.
Sherry is also making early-stage investments in the tech industry.
- I’m organizing an AI Learning Series with Glynn Capital Management. More info can be found at https://thefutureofai.splashthat.com/.
- I hosted an innovation mixer featuring how to build AI startups on Feb 1st, 2017! More info can be found at our event page.
- Check out this NPR news report on our input study!
- Our study was also covered in several Chinese media outlets 🙂
- A fun video demonstrating our work: Stanford experiment shows speech recognition writes texts more quickly than thumbs
- My interview with Tech News Today
- Sherry Ruan, Jacob O. Wobbrock, Kenny Liou, Andrew Ng, James Landay. Speech Is 3x Faster than Typing for English and Mandarin Text Entry on Mobile Devices. Submitted. 2016.
- Sherry Ruan, Gheorghe Comanici, Prakash Panangaden, Doina Precup. Representation Discovery for MDPs Using Bisimulation Metrics. The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI). 2015.
- Sherry Ruan. Bisimulation Metric Computation for Markov Decision Processes. The Ninth Women in Machine Learning Workshop (WiML). 2014.