NISS/Merck Meetup on Statistical and Deep Learning Methods for Biomedical Images

About the Speakers

Dr. Hongtu Zhu is a tenured professor of biostatistics at University of North Carolina at Chapel Hill and was DiDi Fellow and Chief Scientist of Statistics at DiDi Chuxing. He was Endowed Bao-Shan Jing Professorship in Diagnostic Imaging at MD Anderson Cancer Center. He chaired Departments of Statistical Cognitive Team and Feature Engineering with AI scientists and engineers on the development of innovative solutions for the world’s large ride-hailing platform at DiDi Chuxing. He is an internationally recognized expert in statistical learning, medical image analysis, precision medicine, biostatistics, artificial intelligence, and big data analytics. He has been an elected Fellow of American Statistical Association and Institute of Mathematical Statistics since 2011. He received an established investigator award from Cancer Prevention Research Institute of Texas in 2016 and received the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice in 2019. He has published more than 300 papers in top journals including Nature, Nature Genetics, PNAS, AOS, and JRSSB, as well as 45 conference papers in top conferences including NeurIPS, AAAI, KDD, ICDM, MICCAI, and IPMI. He has served/is serving an editorial board member of premier international journals including Statistica Sinica, JRSSB, Annals of Statistics, and Journal of American Statistical Association.

Dr. Andrew Janowczyk is an Assistant Research Professor at The Center of Computational Imaging and Personalized Diagnostics (CCIPD) at Case Western Reserve University, and a Senior Research Scientist in the Precision Oncology Center at the Lausanne University Hospital (CHUV), Switzerland. For over 10 years he has applied computer vision algorithms to digital pathology images. One of his areas of expertise is in leveraging deep learning to build computational models to aid pathologists in many common tasks, such as disease detection and grading. Dr Janowczyk’s 2016 Journal of Pathology Informatics Paper entitled “Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases” (over 30k views), laid out a generalized approach via open-sourced code and datasets to facilitate the development of the next generation of data scientists. In 2018, his tool “HistoQC: A quality control pipeline for digital pathology slides” received the Innovation Award at the European Congress of Digital Pathology (ECDP). He helped co-found and was elected secretary of the Swiss Digital Pathology Consortium (SDiPath). His newer research focuses on the tasks of predicting prognosis and therapy response. He maintains a research-oriented blog, andrewjanowczyk.com, which aims to provide digital pathology related insights, code, and datasets to the research community.

Dr. John Kang, is a Senior Principal Scientist at Merck. He has expertise in performing bioinformatics and statistical analyses with proteomics, micro RNA, mRNA, and SNP data, for both exploratory and clinical development purposes. He has had years of collaboration with toxicology/safety group in large pharmas. His main projects include investigations of kidney and liver toxicity. John has experiences with all stages of the clinical development processes for biomarkers; including assay quality control, drafting SAP, supporting SOP of clinical development lab, performing statistical analyses, and interacting with scientists, bioinformaticians, medical doctors, and late stage clinical statisticians. His specialties include Translational medicine, data mining, pharmacogenomics, biomarkers, next generation sequencing, clinical biomarker assay validation.

About the Moderator

Dr. Peining Tao obtained her MD from Shanghai Medical College of Fudan University and PhD in Computer Science from City University of New York. Prior to joining Merck, she worked as a contractor with Biometrics Research of Merck for statistical software implementation and methodology development. Since joining Merck in 2019 as an Associate Principal Scientist in Early Development Statistics, she provided comprehensive data analyses for complex biomarkers across diabetes, oncology, neuroscience therapeutics areas.  She has been active in methodology development, utilized advanced machine learning algorithm to identify molecular biomarker associated with disease and treatment response.  She also interested in graphical modeling and network analysis to delineate the complex interaction both within and between various biomarker layers in multi-omics studies.