ChengXiang Zhai, Ph.D.

Professor and Willett Faculty Scholar, Department of Computer Science, University of Illinois, “Statistical Approaches to Analysis of Traditional Chinese Medicine Practice Records” at 11:00 am on Thursday, May 18, 2017, in Rooms 407A/B BAUM, 5607 Baum Blvd., The Offices at Baum.

Abstract: Traditional Chinese Medicine (TCM) can provide important complementary medical care to modern medicine, and is widely practiced in China and many other countries.  Recently, TCM patient records have been digitalized, leading to a large number of online patient records. The data contains potentially valuable knowledge about diagnosis and treatment of various diseases using the TCM methodology and thus creates an interesting opportunity to apply data mining techniques to extract such knowledge. In this talk, I will present some of our recent work on using statistical approaches to analyze TCM patient records for disease profiling, disease subcategorization, and survival analysis. In disease profiling, we propose a new probabilistic model for the joint analysis of symptoms, diagnoses, and herbs in patient records to discover the typical symptoms and typical herbs associated with different diseases. In disease subcategorization, we study how to cluster patient records to discover subcategories of diseases and show that we can use machine learning to leverage the knowledge in a TCM dictionary of herb functions for improving the accuracy of subcategorization. In survival analysis, we cluster lung cancer patients and compare the survival time of different clusters of patients and show that integration of medical records with molecular interaction networks and TCM knowledge graph is effective for addressing the problem of missing data in the medical records. The experimental results on multiple TCM patient data sets show the benefit of integrating medical records with other biomedical knowledge bases and the promise of leveraging TCM patient records for improving precision medicine.

Biography: ChengXiang Zhai is a Professor of Computer Science and a Willett Faculty Scholar at the University of Illinois at Urbana-Champaign (UIUC), where he is also affiliated with the Institute for Genomic Biology, Department of Statistics, and School of Information Sciences. He received a Ph.D. in Computer Science from Nanjing University in 1990, and a Ph.D. in Language and Information Technologies from Carnegie Mellon University in 2002. He worked at Clairvoyance Corp. as a Research Scientist and a Senior Research Scientist from 1997 to 2000. His research interests are in the general area of intelligent information systems, including specifically information retrieval, data mining, and their applications in biomedical and health informatics, and intelligent education systems.  He has published over 200 papers in these areas with high citations, and a textbook on text data management and analysis. He is an Editor-in-Chief of Springer’s Information Retrieval Book Series and an Associate Editor of BMC Medical Informatics and Decision Making, and previously served as an Associate Editor of ACM Transactions on Information Systems, Associate Editor of Elsevier’s Information Processing and Management. He is an ACM Distinguished Scientist, and received a number of awards, including Association for Computing Machinery SIGIR Test of Time Paper Award (three times), the 2004 Presidential Early Career Award for Scientists and Engineers (PECASE), an Alfred P. Sloan Research Fellowship, IBM Faculty Award, HP Innovation Research Award, and UIUC Campus Award for Excellence in Graduate Student Mentoring.

Thomas Richardson, Ph.D.

Professor and Chair, Department of Statistics, University of Washington, Nested Markov Models” at 11:00 am on Thursday, April 20, 2017, in Rooms 407A/B BAUM, 5607 Baum Blvd., The Offices at Baum.

Abstract: Directed acyclic graph (DAG) models may be characterized in several different ways: via a factorization, via d-separation or a local Markov property. It has been known for a long time that marginals of DAG models also imply equality constraints that are not conditional independences. The well-known ‘Verma constraint’ is an example.

In this talk, we will show that equality constraints of this type can be viewed as conditional independences in kernel objects obtained from joint distributions via a fixing operation that generalizes conditioning and marginalization. We use these constraints to define, a graphical model, called the “nested Markov model”, that is associated with acyclic directed mixed graphs (ADMGs).

Naturally associated with a DAG with latent variables, is an ADMG known as the “latent projection”. The nested Markov model associated with an ADMG is a (smooth) supermodel of the model associated with the original latent variable model. Nested Markov models thus constitute a natural class in which to perform causal model search.

This is joint work with Robin Evans (Oxford), James Robins (Harvard) and Ilya Shpitser (Johns Hopkins).

Biography:  Dr.  Richardson is Professor and Chair of the Department of Statistics. He is also an Adjunct Professor in the Departments of Economics and Electrical Engineering and a member of the eScience Steering Committee. He received his BA in Mathematics & Philosophy from the University of Oxford and his MS and PhD in Logic, Computation & Methodology from Carnegie Mellon University. He is a Fellow of the Center for Advanced Studies in the Behavioral Sciences at Stanford University. His research interests include Graphical Models and Causality.

 


Computational Causal Discovery - Feb 2015

Clark Glymour , PhD, Alumni University Professor of Philosophy, Carnegie Mellon University“

Machine Learning for Predictive Phenotyping from EHR Data

C. David Page, PhD , Professor of Biostatistics & Medical Informatics, University of Wisconsin Madison

Clustering Heterogeneous Samples During Model Selection - April 2015

Kathleen Gates, PhD, Assistant Professor of Quantitative Psychology, University of North Carolina, Chapel Hill

In Search of a Common Scale for Causal Fusion in Neuroimaging - May 2015
Very High Dimensional Causal Structure and Markov Boundary Discovery: Key Algorithmic Developments and the Insights Gained about the R&D Process - Sept 2015

Constantin Aliferis, MD, PhD, FACMI, Director of the Institute for Health Informatics at the University of Minnesota

Integrative Causality Tests for Complex Diseases - Oct 2015
Algorithmic Methods in Computational and Systems Biology - Dec 2015

Teresa M. Przytycka, PhD, Senior Investigator, National Center for Biotechnology Information, National  Institutes of Health

Learning in Signaling Networks - Dec 2015

Karen Sachs, PhD , Research Scientist in the School of Medicine, Stanford University

General Purpose Satisfiability Solvers for Causal Discovery - Jan 2016

Frederick Eberhardt, PhD, Professor of Philosophy, Division of the Humanities and Social Sciences, Caltech

A Single Cell Approach to Network Rewiring - Feb 2016

Dana Pe’er, Ph.D., Associate Professor of Biological Sciences and Computer Science, Department of Systems Biology, Columbia University

Logic-Based Causal Discovery for Heterogeneous Datasets - Aug 2016

Ioannis Tsamardinos, PhD, Associate Professor, Department of Computer Science, University of Crete, Greece.

Learning Causal Effects: Bridging Instruments and Backdoors - Sept 2016

Ricardo Silva, Ph.D., Lecturer, Department of Statistical Science and Centre for Computational Statistics and Machine Learning, University College London.

High-Dimensional Consistency in Score-Based and Hybrid Structure Learning

Marloes Maathuis, Ph.D., Professor, Department of Statistics, ETH Zurich, Switzerland

Unmasking All Forms of Cancer: Toward integrated maps of all tumor subtypes

Josh Stuart, Ph.D., Professor, Biomolecular Engineering Department, University of California Santa Cruz