Timothy Becker

Timothy Becker’s research involves mining, modeling and transforming data into functional instruments. He develops open-source software that incorporates machine learning, generative modeling and visualization methods.

He specializes in genomics but pursues other collaborative research in ecology, transportation and video/sound synthesis. He has many interdisciplinary collaborators, is open to new domains and partnerships and enjoys constructing interactive tutorials to encourage persistent learning. There is tremendous opportunity to make new and meaningful discoveries using an ever-expanding amount of data. Students that have interesting questions or a particular data source in mind should look at the faculty page for project ideas or inquire for more information.

Select Publications

Sean Congdon, John Guaglione, Omario Ricketts, Kyle V. Murphy, Megan Anderson, Darby Trowbridge, Yousuf Al-Abduladheem, Annabelle Phillips1, Allison Beausoleil, Alexus Stanley1, Timothy Becker, Adam C. Silver. “Prevalence and antibiotic resistance profiles of Staphylococus aureus associated with college-aged cohort: Life-style factors that contribute to nasal carriage”, Frontiers in Cellular and Infection Microbiology, 13:1195758 (2023). https://doi.org/10.3389/fcimb.2023.1195758

Timothy Becker, Dashzeveg Bayarsaihan and Dong-Guk Shin. “A framework for associating structural variants with cell-specific transcription factors and histone modifications in defect phenotypes”, 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Las Vegas, NV, December 6-8, (2022). https://doi.org/10.1109/BIBM55620.2022.9995130

Rossa Guzzo, Badam Enkhmandakh, Timothy Becker, Pujan Joshi, Paul Robson, Anushree VijayKumar, Mina Mina, Dong-Guk Shin, Dashzeveg Bayarsaihan. “Single-cell transcriptomics defines Dot1L interacting partners and downstream target genes in the mouse molar dental pulp”, International Journal of Developmental Biology, 66: 391-400, (2022). https://doi.org/10.1387/ijdb.220141db

Timothy Becker and Dong-Guk Shin. “Structural Variation Calling and Genotyping by Moment Based Deep Convolutional Neural Networks”, International Journal of Data Mining and Bioinformatics (IJDMB), 25:1/2, 37-52, (2021). https://doi.org/10.1504/IJDMB.2021.10039922

Timothy Becker and Dong-Guk Shin. “TensorSV: structural variation inference using tensors and variable topology neural networks” 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, Korea (South), December 16-19, (2020). https://doi.ieeecomputersociety.org/10.1109/BIBM49941.2020.9313154

Timothy Becker and Dong-Guk Shin. “Efficient methods for hierarchical multi-omic feature extraction and visualisation”, International Journal of Data Mining and Bioinformatics (IJDMB), 23, 4 (2020). https://doi.org/10.1504/IJDMB.2020.108699

Timothy Becker and Dong-Guk Shin. "HFM: Hierarchical Feature Moment Extraction for Multi-Omic Data Visualization," 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, November 18-21, (2019). https://doi.ieeecomputersociety.org/10.1109/BIBM47256.2019.8983015

Timothy Becker, Wan-Ping Lee, Joseph Leone, Qihui Zhu, Chengsheng Zhang, Silvia Liu, Jack Sargent, Kritika Shanker, Adam Mil-homens, Eliza Cerveira, Mallory Ryan, Jane Cha, Fabio C. P. Navarro, Timur Galeev, Mark Gerstein, Ryan E. Mills, Dong-Guk Shin, Charles Lee, Ankit Malhotra. “FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods”, Genome Biology 19, 38 (2018). https://doi.org/10.1186/s13059-018-1404-6

Mary Becker, Timothy Becker, Christopher Bellucci. “Diatom tolerance metrics to identify total phosphorus as candidate cause of aquatic life impairment in Connecticut, USA freshwater streams”, Ecological Indicators, 93, 638-646 (2018). https://doi.org/10.1016/j.ecolind.2018.05.046