I am an assistant professor in the Virginia Tech Department of Computer Science. I investigate machine learning with a special focus on models and data with structure stemming from natural networks. Within this focus, my work addresses open questions on theory, algorithms, and applications.
I direct the Machine Learning Laboratory. We investigate the problem of machine learning for complex phenomena. We focus on rich settings where data is best modeled as multi-relational, multi-modal networks. We emphasize the joint exploration of theoretical, algorithmic, and applied research and aim to exploit connections within these views to strengthen understanding of each.
Cyberbullying Detection with Weakly Supervised Machine Learning
E. Raisi, B. Huang. IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM)
Preprint. Best Paper Prize.
New Fairness Metrics for Recommendation that Embrace Differences
S. Yao, B. Huang. Workshop on Fairness, Accountabilty, and Transparency in Machine Learning (FATML), KDD 2017 Workshop.
Beyond Parity: Fairness Objectives for Collaborative Filtering
S. Yao, B. Huang.
Online Edge Grafting for Efficient MRF Structure Learning
W. Chaabene, B. Huang.
Recurrent Collective Classification
S. Fan, B. Huang. International Conference on Network Science (NetSci) and Satellite Symposium on Machine Learning in Network Science 2017.
Detecting Cyber Bullying: But Can it Be Stopped?
Robbie Harris, WVTF Public Radio Interview.
Virginia Tech professor builds algorithm to detect traces of cyberbullying
Izzy Rossi, News Editor, Collegiate Times.
Dispelling five common myths about cyberbullying
Ceci Leonard, Media Relations, Virginia Tech.
A Weakly Supervised Approach for Adaptive Detection of Cyberbullying Roles
B. Huang. Workshop on Computational Methods for CyberSafety. Invited talk.
Training Iterative Collective Classifiers with Back-Propagation
S. Fan, B. Huang. Workshop on Mining and Learning with Graphs.
Cyberbullying Identification Using Participant-Vocabulary Consistency
E. Raisi, B. Huang. #Data4Good: Machine Learning in Social Good Applications. ICML 2016 workshop.
Stability and Generalization in Structured Prediction
B. London, B. Huang, L. Getoor. Journal of Machine Learning Research. Volume 17.
Machine Learning for Detecting Detrimental Online Social Behavior
B. Huang, E. Raisi. Computing Community Consortium (CCC) Symposium on Computing Research: Addressing National Priorities and Societal Needs.
Video, Poster, CCCBlog post
Capturing Planned Protests from Open Source Indicators
S. Muthiah, B. Huang, J. Arredondo, D. Mares, L. Getoor, G. Katz, N. Ramakrishnan. Artificial Intelligence Magazine.
Paired-Dual Learning for Circumventing the Inference Bottleneck
B. Huang. New Perspectives on Relational Learning, Banff International Research Station for Mathematical Innovation and Discovery
Joint Models of Disagreement and Stance in Online Debate
D. Sridhar, J. Foulds, M. Walker, B. Huang, L. Getoor. Annual Meeting of the Association for Computational Linguistics (ACL) 2015
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
Planned Protest Modeling in News and Social Media
S. Muthiah, B. Huang, J. Arredondo, D. Mares, L. Getoor, G. Katz, N. Ramakrishnan. Conference on Innovative Applications of Artificial Intelligence (IAAI) 2015. Deployed Application Award.
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.
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.
PAC-Bayesian Collective Stability
B. London, B. Huang, and L. Getoor. International Conference on Artificial Intelligence and Statistics (AISTATS) 2014.
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 Innovative 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. Daumé III, L. Getoor. ACM Conference on Learning at Scale, Works-in-Progress Track. 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.
Large-Margin Structured Learning for Link Ranking
S. Bach, B. Huang, L. Getoor. NIPS Workshop on Frontiers of Network Analysis 2013. Best Student Paper Award.
Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic
A. Ramesh, D. Goldwasser, B. Huang, H. Daumé III, L. Getoor. NIPS Workshop on Data Driven Education 2013.
PAC-Bayes Generalization Bounds for Randomized Structured Prediction
B. London, B. Huang, B. Taskar, L. Getoor. NIPS Workshop on Perturbation, Optimization, and Statistics 2013.
Collective Inference and Multi-Relational Learning for Drug-Target Interaction Prediction
S. Fakhraei, B. Huang, L. Getoor. NIPS Workshop on Machine Learning in Computational Biology 2013
Fairness in Assignment Markets with Dual Decomposition
B. Huang. ICML Workshop on Peer Review and Publishing Models 2013.
Empirical Analysis of Collective Stability
B. Huang, B. London, B. Taskar, L. Getoor. ICML Workshop on Structured Learning 2013
Collective Activity Detection using Hinge-Loss Markov Random Fields
B. London, S. Khamis, S. Bach, B. Huang, L. Getoor, L. Davis. CVPR Workshop on Structured Prediction 2013
Learning Latent Groups with Hinge-Loss Markov Random Fields
S. Bach, B. Huang, L. Getoor. ICML Workshop on Interactions between Inference and Learning (Inferning) 2013
Graph-Based Generalization Bounds for Learning Binary Relations
B. London, B. Huang, and L. Getoor.
Multi-Relational Learning Using Weighted Tensor Decomposition with Modular Loss
B. London, T. Rekatsinas, B. Huang, and L. Getoor.
Social Group Modeling with Probabilistic Soft Logic
B. Huang, S. Bach, E. Norris, J. Pujara, and L. Getoor. NIPS 2012 Workshop on Social Network and Social Media Analysis: Methods, Models, and Applications.
Improved Generalization Bounds for Large-Scale Structured Prediction
B. London, B. Huang, and L. Getoor. NIPS 2012 Workshop on Algorithmic and Statistical Approaches for Large Social Networks.
Multi-Relational Weighted Tensor Decomposition
B. London, T. Rekatsinas, B. Huang, and L. Getoor. NIPS 2012 Workshop on Spectral Learning.
A Short Introduction to Probabilistic Soft Logic
A. Kimmig, S. Bach, M. Broecheler, B. Huang, and L. Getoor. NIPS 2012 Workshop on Probabilistic Programming: Foundations and Applications. Oral presentation
Probabilistic Soft Logic for Trust Analysis in Social Networks
B. Huang, A. Kimmig, L. Getoor, and J. Golbeck. International Workshop on Statistical Relational Artificial Intelligence (StaRAI). UAI 2012 workshop.
Query-Driven Active Surveying for Collective Classification
G. Namata, B. London, L. Getoor, and B. Huang. International Conference on Machine Learning 2012 Workshop: Mining and Learning with Graphs (MLG). Oral presentation
Semantic Model Vectors for Complex Video Event Recognition
M. Merler, B. Huang, L. Xie, G. Hua, and 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. February 2012.
Learning a Distance Metric from a Network
B. Shaw, B. Huang, and T. Jebara. Neural Information Processing Systems (NIPS) 2011.
Learning a Degree-Augmented Distance Metric from a Network
B. Huang, B. Shaw, and T. Jebara. Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity. NIPS 2011 workshop. Oral presentation
Learning with Degree-Based Subgraph Estimation
Ph.D. Dissertation. Columbia University
Network Prediction with Degree Distributional Metric Learning
B. Huang, B. Shaw, and T. Jebara. Interdisciplinary Workshop on Information and Decision in Social Networks (WIDS 2011).
Fast b-Matching via Sufficient Selection Belief Propagation
B. Huang and T. Jebara. International Conference on Artificial Intelligence and Statistics (AISTATS) 2011.
Collaborative Filtering via Rating Concentration
B. Huang and T. Jebara. International Conference on Artificial Intelligence and Statistics (AISTATS) 2010.
Learning with Subgraph Estimation and Degree Priors
B. Huang and T. Jebara. New York Academy of Sciences Machine Learning Symposium, November 2009.
Exact Graph Structure Estimation with Degree Priors
B. Huang and T. Jebara. International Conference on Machine Learning and Applications (ICMLA) 2009.
Alive on Back-Feed Culprit Identification via Machine Learning
B. Huang, A. Salleb-Aouissi, and 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, and D. Waltz. International Conference on Machine Learning and Applications (ICMLA) 2009.
Maximum Entropy Density Estimation with Incomplete Presence-Only Data
B. Huang and A. Salleb-Aouissi. International Conference on Artificial Intelligence and Statistics (AISTATS) 2009.
Approximating the Permanent with Belief Propagation
B. Huang and T. Jebara. Columbia University Technical Report. 2009
Maximum Likelihood Graph Structure Estimation with Degree Distributions
B. Huang and T. Jebara. Analyzing Graphs: Theory and Applications, NIPS Workshop, December 2008.
Vers des Machines a Vecteurs de Support "Actionables": Une Approche Fondee sur le Classement. (Toward Actionable Support Vector Machines: A Ranking Based Approach)
A. Salleb-Aouissi, B. Huang, and D. Waltz. Knowledge Extraction and Management (Extraction et Gestion des Connaissances) EGC 2008, Sophia Antipolis, France. Best Paper Award.
Approximating the Permanent with Belief Propagation
B. Huang and T. Jebara. New York Academy of Sciences Machine Learning Symposium 2007.
Maximum Entropy Density Estimation with Incomplete Data
B. Huang and A. Salleb-Aouissi. New York Academy of Sciences Machine Learning Symposium 2007.
Poster and abstract.
Loopy Belief Propagation for Bipartite Maximum Weight b-Matching
B. Huang and T. Jebara. International Conference on Artificial Intelligence and Statistics (AISTATS) 2007. Oral presentation.
Loopy Belief Propagation for Bipartite Maximum Weight b-Matching
B. Huang and T. Jebara. New York Academy of Sciences Machine Learning Symposium 2006.
Poster and abstract.
spouterprod.c. A useful mex utility to compute sparse outer products in matlab, taking advantage in computation time and memory usage when you are interested in computing something
like mask.*(U*V'), where mask is a sparse binary matrix.
Some projects on github.
Please feel free to use these materials for educational purposes, but do not sell the content on these course websites for profit.
When I have free time, I try my best to be a musician. Lately, this is mostly manifested by my playing guitar to entertain my pet budgie.
Recently, I created a few recordings experimenting with the electronic instruments built into Apple GarageBand for iPad, which can be streamed from my SoundCloud page.
In my early high school years, I arranged a number of electronic pieces. Later, in high school and college, I explored combining my electronic songs with real voice and instrument recordings (here is a small subset).
During graduate school, I earned a brown belt studying with the Columbia University Tae Kwon Do Club.