John P. Lehoczky
Thomas Lord University Professor of Statistics and Mathematical Sciences received his Ph.D. in statistics from Stanford University in 1969. Dr. Lehoczky's main teaching and research interests involve the theory and application of stochastic processes to model the behavior of real applications. Over the last five years, Dr. Lehoczky has focused on two broad application areas: financial markets and real-time computer systems. In finance, he has been involved in the development of new simulation methodologies to price and hedge complex securities. More recently, Dr. Lehoczky has been focusing on the estimation of parameters of stochastic differential equations and its application to term structure or asset price process models. His research in real-time computer systems involves collaboration with researchers at the CMU School of Computer Science, Software Engineering Institute, Electrical and Computer Engineering Department and the Department of Mathematical Sciences. Dr. Lehoczky is developing, jointly with Professor Steve Shreve, a new analytic methodology called real-time queuing theory, which predicts the ability of a queuing system to satisfy the timing requirements of the tasks, which use it. The theory is being implemented and tested on several pilot systems at CMU. He has been published extensively in a variety of journals including Annals of Applied Probability, Management Science, and Real-Time Systems and Dr. Lehoczky has served on the editorial staff of Management Science, IEEE Transactions on Computers, and Real Time Systems
Associate Teaching Professor of Statistics, received her PhD in Statistics from the University of Washington in Seattle in 2006. Her primary research interests involve exploring and determining the structure of high-dimensional clustering and classification problems with specific applications in record linkage and text disambiguation. Most of her projects also include the development of graphics and visualization methodology, a necessary tool for high-dimensional problems. One of her current primary collaborations is with researchers in the Department of Engineering and Public Policy and focuses on modeling changes in technology, innovation, and entrepreneurship using patents as proxies for intellectual innovation and productivity. She publishes primarily in statistical computing journals such as Journal of Computational and Graphical Statistics and Advances in Data Analysis and Classification. She is also very active in developing both graduate and undergraduate curriculum, including research programs that train undergraduates to join the modern statistics workforce. She teaches a wide variety of classes, recently in regression, clustering, classification, graphics, probability, statistical inference, and networks. In 2013, she received Dietrich College's Elliott Dunlap Smith Award for Distinguished Teaching and Educational Service.
Associate Professor of Statistics, received his Ph.D. in statistics from the University of California, Berkeley in 2004. His primary research interests focus on addressing inference problems in the physical sciences using novel, often computationally- intensive, statistical methods. He is a part of the McWilliams Center for Cosmology at CMU and actively collaborates with researchers in astronomy, particle physics, and risk assessment. He has published in the Journal of the American Statistical Association and the Astrophysical Journal, among others.
Mark J. Schervish
Professor of Statistics, received his Ph.D. in statistics from the University of Illinois in 1979. Dr. Schervish's main research interests involve the foundations of inference, the theory and application of Gaussian processes, and modeling of financial data. Dr. Schervish has collaborated with researchers in Civil, Mechanical, and Electrical Engineering on statistical modeling of real-world processes. Dr. Schervish has published a number of books and many articles in journals such as /Journal of the American Statistical Association/, /The Annals of Statistics, Biometrika,/ and others.