Professor Boffi's work centers on mathematical and scientific applications of machine learning. His group develops algorithms for problems in computational mathematics that cannot be solved with more traditional numerical techniques, but which can be solved with machine learning. Recent interests include high-dimensional partial differential equations and sampling from complex probability distributions with generative models such as diffusions and stochastic interpolants.

Dr. Chen is an assistant professor of information systems at Heinz College and an affiliated faculty member of the Machine Learning Department. He works on machine learning for healthcare and for information systems in developing countries. In these applications, his work revolves around forecasting, such as predicting how long a patient will stay in a hospital, or when and where farmers in rural India should sell their crops. To produce forecasts, George typically uses nonparametric methods that, instead of specifying a model for the data in advance, let the data decide on what model to use, essentially through an election-like process where each data point casts a vote. Since these methods inform interventions that can be costly and affect people’s well-being, ensuring that predictions are reliable and interpretable is essential. To this end, in addition to developing nonparametric predictors, George also produces theory for when and why they work, and identifies forecast evidence that would be helpful to practitioners for decision making.

Dr. Morency is Assistant Professor in the Language Technology Institute at Carnegie Mellon University where he leads the Multimodal Communication and Machine Learning Laboratory (MultiComp Lab). He was formerly a research assistant professor in the Computer Sciences Department at University of Southern California and research scientist at USC Institute for Creative Technologies. Dr. Morency received his Ph.D. and Master degrees from MIT Computer Science and Artificial Intelligence Laboratory. His research focuses on building the computational foundations to enable computers with the abilities to analyze, recognize and predict subtle human communicative behaviors during social interactions. In particular, Dr. Morency was lead co-investigator for the multi-institution effort that created SimSensei and MultiSense, two technologies to automatically assess nonverbal behavior indicators of psychological distress. He is currently chair of the advisory committee for ACM International Conference on Multimodal Interaction and associate editor at IEEE Transactions on Affective Computing.

RESEARCH INTERESTS:

• Human Communication Dynamics
  - Analyze, recognize and predict subtle human communicative behaviors during social interactions.
• Multimodal Machine Learning
  - Probabilistic modeling of acoustic, visual and verbal modalities
  - Learning the temporal contingency between modalities
• Health Behavior Informatics
  - Technologies to support clinical practice during diagnosis and treatment of mental health disorders

Dr. Neubig's research studies machine learning methods for natural language processing, with a focus on machine translation and semantics. This includes development of new algorithms for structured prediction, or unsupervised and semi-supervised learning of structure from unlabeled data. His models particularly focus on neural networks and deep learning, as well as Bayesian methods. He is also interested in methods to improve the efficiency of training and inference, and is a main developer of the DyNet neural network toolkit, which is designed to make it possible to easily and efficiently implement the sorts of complicated models that are used in these tasks.

Dr. Sandholm's research interests are in active learning, stochastic optimization, electronic commerce; game theory; mechanism design; artificial intelligence; multiagent systems; auctions and exchanges; automated negotiation and contracting; voting; coalition formation; safe exchange; search, integer programming and combinatorial optimization; preference elicitation; normative models of bounded rationality; resource-bounded reasoning; privacy; multiagent reinforcement learning; game solving; equilibrium finding, kidney exchange; poker algorithms. 

Dr. Wu works on algorithms and machine learning. His recent work focuses on (1) how to make machine learning better aligned with societal values, especially privacy and fairness, and (2) how social and economic interactions influence machine learning. His studies these questions using methods and models from machine learning, statistics, optimization, differential privacy, game theory, and mechanism design.