Research/Areas of Interest: Machine learning, structured output learning, algorithmic fairness, weak supervision, probabilistic inference, graph mining, social media analysis, data science, big data, computational social science.

Education

  • Doctor of Philosophy, Columbia University, USA, 2011
  • Master of Science, Columbia University, USA, 2006
  • Bachelor of Science, Brandeis University, USA, 2004

Biography

Bert Huang is an assistant professor in the Department of Computer Science and the Data-Intensive Studies Center at Tufts University. He earned his Ph.D. from Columbia University in 2011, was a postdoc at the University of Maryland, and previously was an assistant professor at Virginia Tech. His research addresses topics surrounding machine learning. He focuses on modeling complex phenomena, learning in settings with low-quality data, and making algorithms fairer. His papers appear at conferences including NeurIPS, ICML, UAI, and AISTATS. He is also an action editor for the Journal of Machine Learning Research.