Prof. Andrew E.
Chinese Academy of Sciences, China
Andrew Teschendorff studied Mathematical Physics at the University of Edinburgh (1990-1995) under the supervision of Physics Nobel Laureate Peter Higgs. In 2000 he obtained a PhD in Theoretical Physics from Cambridge University. In 2003 he became a Senior Research Fellow in Statistical Cancer Genomics at the University of Cambridge. In 2008 he moved to the University College London (UCL) Cancer Institute to work in Statistical Cancer Epigenomics and where he was awarded the Heller Research Fellowship. He currently holds an appointment as a PI at the CAS Shanghai Institute for Nutrition and Health, formerly a joint CAS-Max-Planck Partner Institute for Computational Biology, and remains an Honorary Research Fellow at the UCL Cancer Institute. Besides Statistical Cancer Epigenomics, his other research interests include Cancer System-omics & Systems Biology and Network Physics. He is an Associate Editor for various journals, notably Genome Biology, and a reviewer and statistical advisor for journals including Nature, NEJM and Science. He is the recipient of the Tait Medal and Robert Schlapp Prize in Physics, the Jennings Prize, Cambridge-MIT Initiative and Isaac Newton Trust Awards, a Wellcome Trust VIP Award, a CAS Visiting Professorship and a CAS-Royal Society Newton Advanced Fellowship. He holds various patents on algorithms for cancer risk prediction and cell-type deconvolution.
Speech Title: "Computational Dissection of Cell-Type Heterogeneity in Single-Cell and Bulk-Tissue Populations"
Abstract: In this talk, I will describe two computational methods we have developed to tackle challenges posed by cell-type heterogeneity, one at the bulk-tissue level and the other at single-cell resolution. Due to cost reasons, almost all biomedical DNA methylation data is generated at the bulk-tissue level. Thus, to draw inferences of DNA methylation changes at cell-type resolution requires cell-type deconvolution algorithms. I will describe our efforts to build a DNA methylation-atlas for arbitrary tissue-types, which, in conjunction with specific statistical algorithms, allows detection of cell-type specific DNA methylation signals in large-scale epigenome studies. I will highlight how we have used this DNA methylation atlas to reveal cell-of-origin and novel prognostic classes of various cancer-types. In the second part of the talk, I will describe a network-entropy based modelling approach for estimating stemness and differentiation potential from single-cell RNA-Seq data, and how it can be used to identify precancerous cells of high stemness and cancer-risk in the context of esophageal cancer development.
Prof. Tae-Seong Kim
Kyung Hee University, Republic of Korea
Tae-Seong Kim received the B.S. degree in Biomedical Engineering from the University of Southern California (USC) in 1991, M.S. degrees in Biomedical and Electrical Engineering from USC in 1993 and 1998 respectively, and Ph.D. in Biomedical Engineering from USC in 1999. After his postdoctoral work in Cognitive Sciences at the University of California at Irvine in 2000, he joined the Alfred E. Mann Institute for Biomedical Engineering and Dept. of Biomedical Engineering at USC as Research Scientist and Research Assistant Professor. In 2004, he moved to Kyung Hee University in Republic of Korea where he is currently Professor in the Department of Biomedical Engineering. His research interests have spanned various areas of biomedical imaging, bioelectromagnetism, neural engineering, and assistive lifecare technologies. Dr. Kim has been developing novel methodologies in the fields of signal and image processing, machine learning, pattern classification, and artificial intelligence. Lately Dr. Kim has started novel projects in the developments of smart robotics and machine vision with deep learning methodologies. Dr. Kim has published more than 380 papers and twelve international book chapters. He holds ten international and domestic patents and has received numerous best paper awards.
Speech Title: "Deep Learning Methodologies in Robot Intelligence for Natural Object Manipulation"
Abstract: In the era of artificial intelligence (AI), robot intelligence is an exciting interdisciplinary field that includes robotics, machine learning, pattern recognition, and visuomotor/sensorimotor controls. The aim of robot intelligence for grasping and manipulating objects is to achieve the dexterity of grasping and manipulation in humans. Recently, advancements in machine learning methods, particularly deep learning, have accelerated the growth of this new discipline, such that robots can learn to grasp and manipulate various objects autonomously, similarly to humans. In this talk, various deep learning and deep reinforcement learning methodologies for natural object manipulation with an anthropomorphic robot hand will be presented.