Christopher J. Tralie is a data science researcher working in applied geometry/topology and geometric signal processing. His work spans shape-based music structure analysis and cover song identification, video analysis, multimodal time series analysis, and geometry-aided data visualization.
He received a B.S.E. from Princeton University 2011, a master’s at Duke University in 2013, and a Ph.D. in at Duke University in 2017, all in Electrical Engineering. His Ph.D. was primarily supported by an NSF Graduate Fellowship, and his dissertation is entitled “Geometric Multimedia Time Series.” He did a postdoc at Duke University in Mathematics and a postdoc at Johns Hopkins University in Complex Systems. He was awarded a Bass Instructional Teaching fellowship at Duke University, and he maintains an active interest in pedagogy and outreach, including longitudinal mentoring of underprivileged youths in STEAM (STEM + arts) education.
For more information, please visit http://www.ctralie.com.
Christopher plays the violin, and he met his wife Celia, a clarinet player, in the Duke symphony orchestra back in 2013. They played a violin/clarinet duo arrangement of popular movie scores during their wedding at the Baltimore Zoo in June 2018.
- B.S.E. Electrical Engineering Princeton University 2011
- Master’s in Electrical Engineering, Duke University 2013
- Ph.D.n Electrical Engineering, Duke University 2017
- CS 371: Data Structures & Algorithms
- IDS 301: Problem Solving And Analysis with Python
- CS 472A: Analyzing And Transforming Digital Music with A Computer
- Geometric Signal Processing
- Topological Data Analysis
- Music Information Retrieval
- Computer Graphics
Selected Recent Publications:
Caroline , Moosmüller, Christopher. J Tralie, Mahdi Kooshkbaghi, Zehor Belkhatir, Maryam Pouryahya, Jose Reyes, Joseph O Deasy, Allen R Tannenbaum, and Ioannis G Kevrekidis. Periodicity scoring of time series encodes dynamical behavior of the tumor suppressor p53. In Proceedings of The 24th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2020), 2021 (To Appear).
Christopher J. Tralie and Elizabeth Dempsey. Exact, parallelizable dynamic time warping alignment with linear memory. In Proceedings of the 21st Conference of the International Society for Music Information Retrieval (ISMIR 2020).
Furkan Yesiler, Chris Tralie, Albin Andrew Correya, Diego F Silva, Philip Tovstogan, Emilia Gómez Gutiérrez, and Xavier Serra. Da-tacos: A dataset for cover song identification and understanding. In Proceedings of the 20th Conference of the International Society for Music Information Retrieval (ISMIR 2019): 2019 Nov 4-8; Delft, The Netherlands. International Society for Music Information Retrieval (ISMIR), 2019 .
Boyan Xu, Christopher J. Tralie, Alice Antia, Michael Lin, and Jose A. Perea. Twisty takens: A geometric characterization of good observations on dense trajectories. Springer Journal of Applied And Computational Topology, 2019.
Tralie, Christopher J., and Brian McFee. “Enhanced Hierarchical Music Structure Annotations via Feature Level Similarity Fusion.” ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019.
Christopher J Tralie, Paul Bendich, and John Harer. Multi-scale geometric summaries for similarity-based sensor fusion. In The 40th IEEE Aerospace Conference, Big Sky, Montana, 2019.
Christopher Tralie, Nathaniel Saul, and Rann Bar-On. Ripser.py: A lean persistent homology library for python.The Journal of Open Source Software (JOSS), 2018.
Christopher J Tralie. Cover song synthesis by analogy. In 19th International Society for Music Information Retrieval (ISMIR), Paris, France, 2018.
Christopher J Tralie and Matthew Berger. Topological eulerian synthesis of slow motion periodic videos. In IEEE International Conference on Image Processing, Athens, Greece, 2018.
Christopher J. Tralie and Jose A. Perea. (quasi)periodicity quantification in video data, using topology. SIAM Journal on Imaging Sciences, 11(2):1049–1077, 2018.
Christopher J Tralie. Early mfcc and hpcp fusion for robust cover song identification. In 18th International Society for Music Information Retrieval (ISMIR), Suzhou, China, 2017.