Education
- Ph.D. Computer Science (with focus on artificial intelligence, machine learning, and data mining), University of California Irvine
- B.S. Chemical Engineering, Virginia Tech
Industry Experience
From 2009-2018 I worked as a software engineer at Google, working mainly on AI projects. I was the primary designer and implementer of an active learning system within Google Help that was used across most Google products including Gmail, YouTube, and Google Play.
I also played a role in the implementation, maintenance, and on call rotation for the production service that responded to millions of user queries each day. My heart was always in education though, and I spent a lot of time recruiting for Google at college campuses, giving tech talks, interviewing workshops, and resume feedback. I enjoy sharing that experience with our students, giving resume and job search feedback and mock interviews.
I was also pleased to develop and teach Grove City College’s first machine learning class where the students’ favorite assignment was creating an AI chat bot and teaching it to talk using Dr. Seuss as training data.
Research InterestsI
I am interested in a variety of machine learning and data mining projects. My thesis work was with Bayesian networks of time series data, specifically Markov-Poisson models, but I am broadly interested in AI and have experience in topics including active learning, feature mining, unsupervised clustering, Bayesian modeling of click through rate, recommender systems, and text mining.
Selected Publications
- Probabilistic Learning for Analysis of Sensor-Based Human Activity Data, Ph.D. thesis, University of California, Irvine, 2010.
- Probabilistic analysis of a large-scale urban traffic data set, Proc. of the Second International Workshop on Knowledge Discovery from Sensor Data, ACM SIGKDD Conference, 2008.
- Modeling count data from multiple sensors: A building occupancy model, Proc. of the 2nd International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (IEEE CAMSAP, 2007.
- Learning to detect events with Markov-modulated Poisson processes, ACM Transactions on Knowledge Discovery from Data, 2007.
- Adaptive event detection with time-varying Poisson processes, proceedings of the 12th ACM SIGKDD Conference, 2006.
- Prediction and ranking algorithms for event-based network data, ACM Issue on Link Mining, 2005.