I am a first year PhD student in Robotics at Carnegie Mellon University, advised by Artur Dubrawski. My current research interests and projects are in active learning, semi-supervised clustering, and time series analysis. From 2014 to 2018 I worked on algorithms, tools, and data analysis to help fight sex trafficking using deep web and dark web data. I was one of the core developers of Traffick Jam, a tool developed at the Auton Lab at Carnegie Mellon University to combat sex trafficking online. The tool is now fielded by Marinus Analytics.
You can contact me at: boecking at -nospam- cmu.edu
Uber Presidential Fellowship, Carnegie Mellon University, 2018.
Best Paper Award at IPP 2014, Oxford Internet Institute, University of Oxford.
Líderes en vía de extinción. A data-driven journalistic investigation into killings of social leaders in Colombia, together with Maria De-Arteaga and CONNECTAS, published in El País in Colombia. Read the article here.
Boecking, B., Miller, K., Kennedy, E., & Dubrawski, A. (2018). Quantifying the Relationship between Large Public Events and Escort Advertising Behavior. Journal of Human Trafficking, 1-18.
Hundman, K., Gowda, T., Kejriwal, K., Boecking, B (2018). Always Lurking: Understanding and Mitigating Bias in Online Human Trafficking Detection. In Proceedings of AAAI/ACM Conference on AI, Ethics, and Society 2018.
Nagpal, C., Miller, K., Boecking, B., & Dubrawski, A. (2017). An Entity Resolution approach to isolate instances of Human Trafficking online. Paper presented at the 3rd Workshop on Noisy User-generated Text (W-NUT) at EMNLP 2017, Copenhagen.
Boecking, B., Hall, M., & Schneider, J. (2015). Event prediction with learning algorithms—A study of events surrounding the egyptian revolution of 2011 on the basis of micro blog data. Policy & Internet, 7(2), 159-184.
Dubrawski, A., Miller, K., Barnes, M., Boecking, B., & Kennedy, E. (2015). Leveraging publicly available data to discern patterns of human-trafficking activity. Journal of Human Trafficking, 1(1), 65-85.
Boecking, B., Hall, M., & Schneider, J. (2014). Predicting Events Surrounding the Egyptian Revolution of 2011 Using Learning Algorithms on Micro Blog Data. Paper presented at Internet, Politics, and Policy 2014: Crowdsourcing for Politics and Policy, University of Oxford (2014). Best Paper Award
Boecking, B., Chalup, S. K., Seese, D., & Wong, A. S. (2014). Support vector clustering of time series data with alignment kernels. Pattern Recognition Letters, 45, 129-135.