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Bert Huang, Ph.D. Research Interests Education Employment History Bert Huang, Ph.D. Assistant Professor, Department of Computer Science Virginia Tech, Blacksburg, VA 24060 [email protected], (646) 875-8838 http://berthuang.com Research Interests Machine learning, structured prediction, multi-relational learning, large-scale machine learning, probabilistic inference, belief propagation, network analysis, graph mining, combinatorial optimization, computational learning theory, social media analysis, data science, big data, computational social science. Education Doctor of Philosophy, Computer Science Columbia University, 2011 Thesis: Learning with Degree-Based Subgraph Estimation Advised by T. Jebara and A. Salleb-Aouissi Master of Science, Computer Science Columbia University, 2006 Bachelor of Science, Computer Science, Philosophy Brandeis University, 2004 Employment History Assistant Professor. Virginia Tech Department of Computer Science. Spring 2015–Present Postdoctoral Research Associate. University of Maryland Dept. of Computer Science. Fall 2011–Fall 2014 Graduate Research Assistant. Columbia University Dept. of Computer Science. Fall 2006–Summer 2011 Research Intern. IBM Research, Thomas J. Watson Research Center. Summer 2010 Lecturer. Columbia University Department of Computer Science. Fall 2008–Spring 2010 Teaching Virginia Tech Department of Computer Science Instructor, Introduction to Artificial Intelligence. Spring 2015. University of Maryland Department of Computer Science Co-Instructor, Link Mining (joint with L. Getoor). Spring 2012. Columbia University Department of Computer Science Instructor, Object Oriented Programming and Design in Java. Spring 2010. Instructor, Data Structures in Java. Fall 2009. Instructor, Data Structures and Algorithms. Spring 2009. Instructor, Introduction to Computer Science and Programming in C. Fall 2008. Teaching Assistant, Machine Learning. Spring 2007. Teaching Assistant, Introduction to Computer Science and Programming in C. Spring 2006. Awards Andrew P. Kosoresow Memorial Award for Excellence in Teaching and Service. Columbia University Department of Computer Science, 2010. Service Award. Columbia University Department of Computer Science, 2009. Key Collaborators Stephen Bach (UMD), Jordan Boyd-Graber (UMD), Hal Daumé III (UMD), Shobeir Fakhraei (UMD), Lise Getoor (UMD), Jennifer Golbeck (UMD), Dan Goldwasser (UMD), Phil Gross (Google Research), Tony Jebara (Columbia), Angelika Kimmig (Leuven), Ben London (UMD), Michele Merler (IBM Research), Hui Miao (UMD), Jay Pujara (UMD), Naren Ramakrishnan (VT), Arti Ramesh (UMD), Cynthia Rudin (MIT), Ansaf Salleb-Aouissi (Columbia), Blake Shaw (Foursquare), Ben Taskar (University of Washington), David Waltz (Columbia), Lexing Xie (Australia National University/NICTA). Publications Refereed Journal Papers Network-Based Drug-Target Interaction Prediction with Probabilistic Soft Logic S. Fakhraei, B. Huang, L. Raschid, L. Getoor. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Vol. 11, No. 5, October 2014. Semantic Model Vectors for Complex Video Event Recognition M. Merler, B. Huang, L. Xie, G. Hua, A. Natsev. IEEE Transactions on Multimedia. Vol. 14, No. 1, February 2012. Machine Learning for the New York City Power Grid C. Rudin, D. Waltz, R. Anderson, A. Boulanger, A. Salleb-Aouissi, M. Chow, H. Dutta, P. Gross, B. Huang, S. Ierome, D. Isaac, A. Kressner, R. Passonneau, A. Radeva, and L. Wu. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 34, No. 2, February 2012. Refereed Full Conference Papers Unifying Local Consistency and MAX SAT Relaxations for Scalable Inference with Rounding Guarantees S. Bach, B. Huang, L. Getoor. International Conference on Artificial Intelligence and Statistics (AISTATS) 2015. Oral presentation. Forecasting Protests by Detecting Future Time Mentions in News and Social Media S. Muthiah, B. Huang, J. Arredondo, D. Mares, L. Getoor, G. Katz, N. Ramakrishnan. Conference on Innovated Applications of Artificial Intelligence (IAAI) 2015. Deployed Application Award. Discovering Evolving Political Vocabulary in Social Media A. Mahendiran, W. Wang, J. Arredondo, B. Huang, L. Getoor, D. Mares, N. Ramakrishnan. International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC) 2014. ‘Beating the News’ With EMBERS: Forecasting Civil Unrest Using Open Source Indicators N. Ramakrishnan, P. Butler, N. Self, R. Khandpur, P. Saraf, W. Wang, J. Cadena, A. Vullikanti, G. Korkmaz, C. Kuhlman, A. Marathe, L. Zhao, H. Ting, B. Huang, A. Srinivasan, K. Trinh, L. Getoor, G. Katz, A. Doyle, C. Ackermann, I. Zavorin, J. Ford, K. Summers, Y. Fayed, J. Arredondo, D. Gupta, D. Mares. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2014. Learning Latent Engagement Patterns of Students in Online Courses A. Ramesh, D. Goldwasser, B. Huang, H. Daumé III, L. Getoor. AAAI Conference on Artificial Intelligence 2014. Oral presentation. PAC-Bayesian Collective Stability B. London, B. Huang, B. Taskar, L. Getoor. International Conference on Artificial Intelligence and Statistics (AISTATS) 2014. A Hypergraph-Partitioned Vertex Programming Approach for Large-Scale Consensus Optimization H. Miao, X. Liu, B. Huang, L. Getoor. IEEE International Conference on Big Data 2013. Hinge-Loss Markov Random Fields: Convex Inference for Structured Prediction S. Bach, B. Huang, B. London, L. Getoor. Conference on Uncertainty in Artificial Intelligence (UAI) 2013. Collective Stability in Structured Prediction: Generalization from One Example B. London, B. Huang, B. Taskar, L. Getoor. International Conference on Machine Learning (ICML) 2013. Oral presentation. A Flexible Framework for Probabilistic Models of Social Trust B. Huang, A. Kimmig, L. Getoor, J. Golbeck. International Conference on Social Computing, Behavioral- Cultural Modeling, and Prediction (SBP) 2013. Oral presentation. Learning a Distance Metric from a Network B. Shaw, B. Huang, T. Jebara. Neural Information Processing Systems (NIPS) 2011. Fast b-Matching via Sufficient Selection Belief Propagation. B. Huang, T. Jebara. International Conference on Artificial Intelligence and Statistics (AISTATS) 2011. Collaborative Filtering via Rating Concentration B. Huang, T. Jebara. International Conference on Artificial Intelligence and Statistics (AISTATS) 2010. Exact Graph Structure Estimation with Degree Priors B. Huang, T. Jebara. International Conference on Machine Learning and Applications (ICMLA) 2009. Oral presentation. Alive on Back-Feed Culprit Identification via Machine Learning B. Huang, A. Salleb-Aouissi, P. Gross. International Conference on Machine Learning and Applications (ICMLA) 2009. Special Session on Machine Learning in Energy Applications. Discovering Characterization Rules from Rankings A. Salleb-Aouissi, B. Huang, D. Waltz. International Conference on Machine Learning and Applications (ICMLA) 2009. Oral presentation. Maximum Entropy Density Estimation with Incomplete Presence-Only Data B. Huang, A. Salleb-Aouissi. International Conference on Artificial Intelligence and Statistics (AISTATS) 2009. Vers des Machines Vecteurs Support “Actionnables”: Une Approche Fonde sur le Classement A. Salleb-Aouissi, B. Huang, D. Waltz. Extraction et Gestion des Connaissances (EGC) 2008. Oral presentation. Best paper award. Loopy Belief Propagation for Bipartite Maximum Weight b-Matching B. Huang, T. Jebara. International Conference on Artificial Intelligence and Statistics (AISTATS) 2007. Oral presentation. Refereed Short Conference Papers, Workshop Papers, and Abstracts Rounding Guarantees for Message-Passing MAP Inference with Logical Dependencies S. Bach, B. Huang, L. Getoor. NIPS Workshop on Discrete and Combinatorial Problems in Machine Learning (DISCML) 2014. On the Strong Convexity of Variational Inference B. London, B. Huang, L. Getoor. NIPS Workshop on Advances in Variational Inference 2014. Collective Classification of Stance and Disagreement in Online Debate Forums D. Sridhar, J. Foulds, B. Huang, M. Walker, L. Getoor. Bay Area Machine Learning Symposium 2014. Probabilistic Soft Logic for Social Good S. Bach, B. Huang, L. Getoor. KDD Workshop on Data Science for Social Good 2014. Understanding MOOC Discussion Forums using Seeded LDA A. Ramesh, D. Goldwasser, B. Huang, H. Daumé III, L. Getoor. ACL Workshop on Innovated Use of NLP for Building Educational Applications 2014. Uncovering Hidden Engagement Patterns for Predicting Learner Performance in MOOCs A. Ramesh, D. Goldwasser, B. Huang, H. Daume III, L. Getoor. ACM Conference on Learning at Scale. Work-in-Progress paper. PAC-Bayes Generalization Bounds for Randomized Structured Prediction B. London, B. Huang, B. Taskar, L. Getoor. NIPS 2013 Workshop on Perturbations, Optimization, and Statistics. Oral presentation. Large-Margin Structured Learning for Link Ranking S. Bach, B. Huang, L. Getoor. NIPS 2013 Workshop on Frontiers of Network Analysis. Best paper award. Collective Inference and Multi-Relational Learning for Drug-Target Interaction Prediction S. Fakhraei, B. Huang, L. Getoor. NIPS 2013 Workshop on Machine Learning in Computational Biology. Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic A. Ramesh, D. Goldwasser, B. Huang, H. Daumé III, L. Getoor. NIPS 2013 Workshop on Data Driven Education. Collective Activity Detection Using Hinge-Loss Markov Random Fields B. London, S. Khamis, S. Bach, B. Huang, L. Getoor, L. Davis. CVPR 2013 Workshop on Structured Prediction. Oral presentation. Fairness in Assignment Markets with Dual Decomposition B. Huang. ICML 2013 Workshop on Peer Reviewing and Publication Models. Oral presentation.
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