Speakers
AI for Health - Augmenting Clinical Care
Today, we are collecting an immense amount of health data both inside and outside of the hospital. While clinicians are studying ever more data about their patients, they are still ignoring the vast majority of it. Transforming these observational data
into actionable knowledge is challenging due to a number of reasons including the presence of confounders, missing context, and complex longitudinal relationships. At the same time, due to the high-stakes nature of healthcare,
the field requires tools that are not only accurate, but also interpretable and robust. In this talk, I will present ongoing work focused on developing solutions to these challenges. In particular, I will show how clinical domain
expertise can be used to help guide model training and selection.
Bio
Jenna Wiens is a Morris Wellman Assistant Professor of Computer Science and Engineering (CSE) at the University of Michigan in Ann Arbor. Her primary research interests lie at the intersection of machine learning,
data mining, and healthcare. She is particularly interested in time-series analysis. The overarching goal of her research agenda is to develop the computational methods needed to help organize, process, and transform patient data
into actionable knowledge. Jenna received her PhD from MIT in 2014. In 2015 she was named Forbes 30 under 30 in Science and Healthcare; she received an NSF CAREER Award in 2016; and recently she was named to the MIT Tech Review's
list of Innovators Under 35.
Identifying Shifts in Evolutionary Semantic Spaces
Bio
Qiaozhu Mei is an associate professor at the University of Michigan School of Information. He received his PhD degree from the Department of Computer Science at the University of Illinois at Urbana-Champaign and
Bachelor's degree from Peking University. His interested in analyzing large scale text data, social and information networks, and user behavior data. His research is broadly applied to Web search and mining, social computing, scientific
literature mining, and health informatics. He also broadly interested in natural language processing, machine learning, and social network analysis.
Generation in Neural Machine Translation
Bio
Kyunghyun Cho is an assistant professor of computer science and data science at New York University and a research scientist at Facebook AI Research. He was a postdoctoral fellow at University of Montreal until summer
2015 under the supervision of Prof. Yoshua Bengio, and received PhD and MSc degrees from Aalto University early 2014 under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He tries his best to find
a balance among machine learning, natural language processing, and life, but almost always fails to do so.
Learning from Time Series Sensor Data (Cancelled)
Many applications, ranging from automobiles to financial markets and wearable sensors, generate large amounts of time series data. In most cases, this data is multivariate and heterogeneous, where the readings come from various types of entities, or sensors.
These time series datasets are often sparse, unlabeled, dynamic, and difficult to interpret. Therefore, there is a need for methods that learn interpretable structure from such data, especially for methods that can apply across
many different domains. In this talk, I will discuss several approaches for analyzing time series data, as well as future directions of research in this field, incorporating different research areas ranging from distributed convex
optimization to deep learning.
Bio
Jure Leskovec is Associate Professor of Computer Science at Stanford University, Chief Scientist at Pinterest, and investigator at Chan Zuckerberg Biohub. His research focuses on machine learning and data mining
applied to social, information and biological networks, their evolution, and the diffusion of information and influence over them. Computation over massive data is at the heart of his research and has applications in computer science,
social sciences, economics, marketing, and healthcare. This research has won several awards including a Lagrange Prize, Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship, and numerous best paper awards. Leskovec
received his bachelor's degree in computer science from University of Ljubljana, Slovenia, and his PhD in in machine learning from the Carnegie Mellon University and postdoctoral training at Cornell University.
