Dr. Ting Hu
Dr. Ting Hu received her PhD in computer science from Memorial University in 2010. Afterward, she completed her postdoctoral training in computational genetics from Dartmouth College. Dr. Hu has been nominated Best Paper Awards repeatedly from top international conferences in the field of evolutionary computation, including ACM GECCO and EuroGP. She has also served as program chairs for these conferences.
As a faculty member in the School of Computing, Dr. Ting Hu’s expertise will contribute to the broadening of fundamental AI research. By developing AI algorithms that are more transparent and interpretable, Dr. Hu will continue to enhance the applications of AI and machine learning techniques in biomedicine.
Evolutionary computation, Machine learning, Complex networks, Computational genetics, Complex diseases
Dr. Christian Muise
Dr. Christian Muise completed his PhD at the University of Toronto in 2014 with the Knowledge Representation and Reasoning Group in the area of Automated Planning. After graduating, he was a postdoctoral fellow at the University of Melbourne’s Agentlab studying techniques for multi-agent planning and human-agent collaboration. Subsequently, he was a research fellow with the MERS group at MIT’s Computer Science and Artificial Intelligence Laboratory studying decision making under uncertainty. Most recently, Dr. Muise was a Research Scientist at the MIT-IBM Watson AI Lab, where he researched data-driven techniques for inducing behavioral insight and lead a project devising next-generation dialogue agents.
As a faculty member in the School of Computing, Dr. Muise’s lab will explore the ways we can either specify or learn models of the world, enabling the efficient creation and analysis of autonomous systems. By bridging the fields of symbolic reasoning and machine learning, the lab will explore the frontier of what is possible with modern AI systems.
Artificial intelligence, Automated planning, Model understanding, learning, and acquisition, Goal-oriented dialogue systems