I received my PhD from Cornell University under my advisor, Thorsten Joachims. As a graduate student my area of research was learning functions with structured outputs in domains requiring approximation inference algorithms, in particular structural SVMs. When using approximations many of the theoretical guarantees of structured learning algorithms no longer hold and in practice the learned models are of inconsistent quality, and my thesis sought to provide new theory and empirical understanding to this area.

In addition to my thesis topic, my graduate work has included supervised clustering, learning ranking, loopy MRF learning, approximate structural learning, data mining of the survey data from Project FeederWatch, and working on the technical aspects of an interactive art/music exhibition.

While I was an undergrad at Duke, I worked with Dr. Susan Rodger on computer science education visualization tools. This included in 2001 the JAWAA editor, and in 2002 onward the totally new JFLAP 4.0.

Descriptions and links to some of the software I wrote during my academic career, both undergraduate and doctorate, can be viewed at at my page of projects.

In addition to browsing my ACM publication profile which is more or less complete, we can see my published documents in reverse chronological order:

Z. Ahmed, S. Amizadeh, M. Bilenko, R. Carr, W.S. Chin, Y. Dekel, X. Dupre, V. Eksarevskiy, S. Filipi, T. Finley, A. Goswami, M. Hoover, S. Inglis, M. Interlandi, N. Kazmi, G. Krivosheev, P. Luferenko, I. Matantsev, S. Matusevych, S. Moradi, G. Nazirov, J. Ormont, G. Oshri, A. Pagnoni, J. Parmar, P. Roy, M. Z. Siddiqui, M. Weimer, S. Zahirazami, Y. Zhu, Machine Learning at Microsoft with ML.NET, KDD ‘19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019: pp 2448-2458. [ACM]

G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, T.Y. Liu, LightGBM: a highly efficient gradient boosting decision tree, NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017: pp 3149-3157. [ACM]

K. Tran, S. Hosseini, L. Xiao, T. Finley, M. Bilenko, Scaling Up Stochastic Dual Coordinate Ascent, KDD ‘15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015: pp 1185-1194. [ACM]

T. Joachims, T. Finley, and Chun-Nam J. Yu, Cutting-plane training of structural SVMs, Machine Learning Journal, 77(1):27-59, 2009. [PDF]

A. L. Gonzales, T. Finley, S. P. Dunan, (Perceived) interactivity: does interactivity increase enjoyment and creative identity in artistic spaces?, CHI 2009: 415-418. [ACM]

T. Finley, Supervised Clustering with Structural SVMs, PhD Thesis, Cornell University, Department of Computer Science, 2008. [Download]

T. Finley, T. Joachims, Supervised k-Means Clustering, Cornell Computing and Information Science Technical Report, 1813-11621, 2008. [Download]

T. Finley, T. Joachims, Training Structural SVMs when Exact Inference is Intractable, ICML, 2008. [PDF]

T. Finley, T. Joachims, Parameter Learning for Loopy Markov Random Fields with Structural Support Vector Machines, ICML Workshop on Constrained Optimization and Structured Output Spaces, 2007. [PDF]

Yisong Yue, T. Finley, F. Radlinski, T. Joachims, A Support Vector Method for Optimizing Average Precision, Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR), 2007. [PDF]

Susan Rodger and Thomas Finley, JFLAP - An Interactive Formal Languages and Automata Package, ISBN 0763738344, Jones and Bartlett, published 2/27/06, 2006. [Info]

Susan H. Rodger, Bart Bressler, Thomas Finley, and Stephen Reading, Turning Automata Theory into a Hands-on Course, Thirty-seventh SIGCSE Technical Symposium on Computer Science Education, 2006.

T. Finley and T. Joachims, Supervised Clustering with Support Vector Machines, Proceedings of the International Conference on Machine Learning (ICML), 2005. [PDF]

Ryan Cavalcante, Thomas Finley and Susan H. Rodger, A Visual and Interactive Automata Theory Course with JFLAP 4.0, Thirty-fifth SIGCSE Technical Symposium on Computer Science Education, 2004 (p.140-144). [PDF]

Ayonike Akingbade, Thomas Finley, Diana Jackson, Pretesh Patel and Susan H. Rodger, JAWAA: Easy Web-Based Animation from CS 0 to Advanced CS Courses, Thirty-fourth SIGCSE Technical Symposium on Computer Science Education, p. 162-166, 2003. [PDF]