I am a fourth year PhD student at Carnegie Mellon University in the Computer Science Department where I am fortunate to be advised by Nina Balcan. Before that, I completed my MSc at the University of Alberta, advised by Richard Sutton and András György.
I am interested in the theoretical foundations of Machine Learning, and especially questions about the impact of Machine Learning on society. Given the complexity of learning systems, I think one of the most promising approaches to ensuring a positive impact is to explicitly incorporate social values, like privacy, fairness, and interpretability, into our algorithms. I have ongoing projects exploring fairness in machine learning and differentially private clustering, algorithm parameter tuning, auction design, and pricing problems. I have also worked on actor-critic methods for Reinforcement Learning, Online Learning, label efficient multi-class learning, and distributed learning.
Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia. Envy-free Classification.
Maria-Florina Balcan, Travis Dick, Colin White. Data-Driven Clustering via Parameterized Lloyd's Families. NIPS 2018.
Maria-Florina Balcan, Travis Dick, Ellen Vitercik. Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization. FOCS 2018.
Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik. Learning to Branch. ICML 2018.
Maria-Florina Balcan, Travis Dick, Yingyu Liang, Wenlong Mou, Hongyang Zhang. Differentially Private Clustering in High-Dimensional Euclidean Spaces. ICML 2017.
Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria-Florina Balcan, Alex Smola. Data Driven Resource Allocation for Distributed Learning. AIStats 2017.
Maria-Florina Balcan, Travis Dick, Yishay Mansour. Label Efficient Learning by Exploiting Multi-class Output Codes. AAAI 2017.
Travis Dick, András György, Csaba Szepesvári. Online Learning in Markov Decision Processes with Changing Cost Sequences. ICML 2014.
Roshan Shariff, Travis Dick. Lunar Lander: A Continuous-Action Case Study for Policy Gradient Actor Critic Algorithms. RLDM 2013 (poster).
Patrick Pilarski, Travis Dick, Richard Sutton. Real-time Prediction Learning for the Simultaneous Actuation of Multiple Prosthetic Joints. ICORR 2013.
Travis Dick, Camilo Perez, Azad Shademan, Martin Jagersand. Realtime Registration-based Tracking via Approximate Nearest Neighbour Search. RSS 2013.
Maria-Florina Balcan, Travis Dick, Ellen Vitercik. Dispersion for Private Optimization of Piecewise Lipschitz Functions. ICML Privacy in Machine Learning and AI Workshop 2018.
Maria-Florina Balcan, Travis Dick, Ellen Vitercik. Differentially Private Algorithm Configuration. ICML Private and Secure Machine Learning Workshop 2017.
Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria-Florina Balcan, Alex Smola. Data Driven Resource Allocation for Distributed Learning. AAAI Distributed Machine Learning Workshop 2017.
Maria-Florina Balcan, Travis Dick, Yishay Mansour. On the Geometry of Output-code Multi-class Learning. ICML Data Efficient Machine Learning Workshop 2016.
In the Spring semesters of 2015 and 2016, I was a TA for Introduction to Machine Learning (10-601). In 2015 the course was taught by Nina Balcan and Tom Mitchell, and in 2016 it was taught by Nina Balcan and William Cohen. I received a TA Award from the Machine Learning Department for TAing 10-601 in Spring 2016.