Keynote Speakers


Prof. Bijoy K. Ghosh                                                                                                             

Texas Tech University, USA


Bijoy received the Ph.D. degree in Engineering Sciences from Harvard University, Cambridge, MA, in 1983. From 1983 to 2007 Bijoy was with the Department of Electrical and Systems Engineering, Washington University, St. Louis, MO, USA, where he was a Professor and Director of the Center for BioCybernetics and Intelligent Systems. Currently he is the Dick and Martha Brooks Regents Professor of Mathematics and Statistics at Texas Tech University, Lubbock, TX, USA. He received the Donald P. Eckmann award in 1988 from the American Automatic Control Council, the Japan Society for the Promotion of Sciences Invitation Fellowship in 1997. He became a Fellow of the IEEE in 2000, and a Fellow of the International Federation on Automatic Control in 2014. He was the IEEE Control Systems Society Representative to the IEEE-USA's Medical Technology Policy Committee and currently a member of the IEEE Fellow committee. Bijoy had held visiting positions at Tokyo Institute of Technology, Osaka University and Tokyo Denki University, Japan, University of Padova in Italy, Royal Institute of Technology and Institut Mittag-Leffler, Stockholm, Sweden, Yale University, USA, Technical University of Munich, Germany, Chinese Academy of Sciences, China and Indian Institute of Technology, Kharagpur, India. Bijoy's current research interest is in BioMechanics, Cyberphysical Systems and Control Problems in Rehabilitation Engineering.


Speech Title: "Bio-Mimetic Sensing with Multiple Sensors"


Abstract: We revisit the visual sensor pointing control problem as a constrained dynamics on SO(3) from the point of view of a nonlinear multi input multi output (MIMO) system. The attitude of every sensor is assumed to satisfy a constraint, such as the ones proposed by Donders and Listing for the monocular and binocular eyes and the head rotation problems. While studying the problem of controlling the pointing direction of human head, the constraint, proposed by Donders, is that for every human head rotating away from its primary pointing direction, the rotational vectors are restricted to lie on a surface called the Donders' surface. In this talk we assume the existence of Donders' surfaces for an array of visual sensors in a flock, tasked with the goal of tracking a point target in the visual space. We assume that the Donders' surfaces are described by a quadratic equation on the coordinates of the rotation vector. The inputs to the MIMO system are three external torque triplet, one for each visual sensor. The three output signals from each sensor are chosen as follows. Two of the signals are coordinates of the frontal pointing direction. The third signal measures deviation of the state vector from the Donders' surface. Thus we have a square system and recent results have shown that this system is feedback linearizable on a suitable neighborhood of the state space. We estimate a lower bound on the size of this neighborhood, by computing distance between the Donders' and the associated Singularity surface. Our results are discussed for the monocular, binocular and the trinocular cases and a comparison is made from the point of view of the observed singularities. Analysis of the feedback linearizing control problem, from the point of view of `three eyed visual sensing', is new.


Prof. Ming Chen                                                                                                                   

Zhejiang University, China


Ming Chen received his PhD in Bioinformatics from Bielefeld University, Germany, in 2004. Currently he is working as a full Professor in Bioinformatics at College of Life Sciences, Zhejiang University. His group research work mainly focuses on the systems biology, computational and functional analysis of non-coding RNAs, and bioinformatics research and application for life sciences. Prof. Chen is serving as an academic leader in Bioinformatics at Zhejiang University. He chairs the Bioinformatics society of Zhejiang Province, China. He is a committee member of Chinese societies for "Modeling and Simulation of Biological Systems", "Computational Systems Biology", "Functional Genomics & Systems Biology" and "Biomedical Information Technology".


Speech Title: "Bioinformatics Studies on Non-Coding RNAs and their Versatile Interactions"


Abstract: Advances in transctiptome technologies and computational methodologies have provided a huge impetus to non-coding RNA (ncRNA) study. We investigated transcriptome to characterize non-coding RNAs including microRNAs, siRNAs, lncRNAs, ceRNAs and cirRNAs. We developed several bioinformatics databases and pipelines to facilitate better understanding of the regulation of non-coding RNAs. In this talk, we provide an overview of ncRNA repertoire and highlight recent discoveries of their versatile interactions. Several ncRNA regulatory network studies are introduced. we describe a comprehensive workflow for in silico ncRNA analysis, providing up-to-date platforms, databases and tools dedicated to ncRNA identification and functional annotation. A recent work on cirRNAs (CircFunBase) is introduced.


Prof. Hesham H. Ali                                                                                                               

University of Nebraska at Omaha, USA


Hesham H. Ali is a Professor of Computer Science and Lee and Wilma Seemann Distinguished Dean of the College of Information Science and Technology at the University of Nebraska at Omaha (UNO). He also serves as the director of the UNO Bioinformatics Core Facility that supports a large number of biomedical research projects in Nebraska. He has published numerous articles in various IT areas including scheduling, distributed systems, data analytics, wireless networks, and Bioinformatics. He has also published two books in scheduling and graph algorithms, and several book chapters in Bioinformatics. He has been serving as the PI or Co-PI of several projects funded by NSF, NIH and Nebraska Research Initiative in the areas of data analytics, wireless networks and Bioinformatics. He has also been leading a Research Group that focuses on developing innovative computational approaches to model complex biomedical systems and analyze big bioinformatics data. The research group is currently developing several next generation big data analytics tools for mining various types of large-scale biological and medical data. This includes the development of new graph theoretic models for assembling short reads obtained from high throughput instruments, as well as employing a novel correlation networks approach for analyzing large heterogeneous biological and health data associated with various biomedical research areas, particularly projects associated with infectious diseases, microbiome studies and aging research. He has also been leading two projects for developing secure and energy-aware wireless infrastructure to address tracking and monitoring problems in medical environments, particularly to study mobility profiling for advancing personalized healthcare.


Speech Title: "Innovative Graph-Based Tools for Big Data Analytics in Bioinformatics"


Abstract: With continuous advancements of biomedical instruments and the associated ability to collect diverse types of valuable biological data, numerous research studies have been recently focused on how to best extract useful information from the ‘Big Data’ currently available. The currently available data is not only massive in size, but it also exhibits all the features of complex big data systems with a high degree of variability, veracity and velocity. How to leverage this raw data to advance biomedical research, particularly in dealing with outbreaks and infectious diseases, and improve health care, through personalized and targeted medicine, can be considered the most exciting scientific challenge of our generation. Although many analytical tools have been developed recently to take advantage of this massive raw data, researchers are still scratching the surface regarding what can be mined and utilized to advance biomedical research in general and healthcare in particular. In this talk, we present new big data analytics tools using graph modeling and network analysis along with how to effectively utilize High Performance Computing in implementing such tools. We demonstrate how the proposed tools can be applied to analyze complex data and reveal new useful relationships in various case studies. We illustrate how the graph-based tools led to new biological discoveries by efficiently integrating heterogenous data associated with infectious diseases, aging research and microbiome studies.


Prof. Ashoka Polpitiya                                                                                                         

Sri Lanka Technological Campus, Sri Lanka


Ashoka Polpitiya, DSc, is a Professor in Electrical Engineering at Sri Lanka Technological Campus since 2016. Prior to this, he was the Director of Bioinformatics and Biostatistics at Sera Prognostics Inc., in Salt Lake City, Utah where he still works as a consultant. He has also worked in the past as the Lead Bioinformatician for Proteomics at the Translational Genomics Research Institute in Phoenix, Arizona and as a Senior Scientist at the Pacific Northwest National Laboratory (PNNL). He has published articles and developed software tools to address various analytics issues in Genomics and Proteomics experiments. Dr. Polpitiya received his BS in Electrical Engineering from University of Peradeniya, Sri Lanka, an MS and a PhD both from the Washington University in St. Louis in 2000 and in 2004, respectively, in Systems Science and Mathematics. He spends his time in both Sri Lanka and US, working for SLTC and Sera Prognostics.


Speech Title: "Biomarker Discovery in Diagnostics: A Case Study on Premature Delivery"


Abstract: Premature delivery or Preterm birth is a major concern across the developing and developed world. It remains as the leading cause of perinatal mortality with a significant strain on healthcare costs. Risk factors and biomarkers used so far have been ineffective in identifying the majority of preterm deliveries. This study focuses on developing and validating a mass spectrometry based protein biomarker test to predict spontaneous preterm delivery in asymptomatic pregnant women. Maternal serum was processed by a proteomic workflow, and proteins were quantified by multiple reaction monitoring mass spectrometry. We evaluated a predictor composed of insulin-like growth factor binding protein 4 (IBP4) and sex hormone binding globulin (SHBG) in a clinical validation study to classify spontaneous preterm delivery cases (<370/7 weeks gestational age). The predictor had an area under the receiver operating characteristic curve value of 0.75. This early detection would guide enhanced levels of care and accelerate development of clinical strategies to prevent preterm delivery.



Plenary Speakers


Prof. Ralf Hofestädt                                                                                                             

Bielefeld University, Germany


Prof. Ralf Hofestädt studied Computer Science and Bioinformatics at the University of Bonn. He finished his PhD 1990 (University Bonn) and his Habilitation (Applied Computer Science and Bioinformatics) 1995 at the University of Koblenz. From 1996 to 2001, he was Professor for Applied Computer Science at the University of Magdeburg. Since 2001, he is Professor for Bioinformatics and Medical Informatics at the University Bielefeld. The research topics of the department concentrate on biomedical data management, modeling and simulation of metabolic processes, parallel computing and multimedia implementation of virtual scenarios.


Speech Title: "GenCoNet: a Graph Database for the Analysis of Comorbidities by Gene Networks"


Abstract: Based on the medical data and knowledge of the project partner from Tomsk University we could start to identify relevant genes and drugs for asthma and hypertension. Based on lists of genes associated with asthma and hypertension obtained using the HuGENavigator resource and patient drug lists, Bielefeld and Novosibirsk computed and analyzed first relevant metabolic networks. Furthermore, based on the clinical data and semi-automatic data mining approaches a new database was developed and implemented, which presents the positive and negative drug list for asthma and hypertension. A web based implementation of this data base allows the access to this information via internet (



Invited Speakers


Prof. Zhiwei Qiao                                                                                                                 

ShanXi University, China


Zhiwei Qiao received his PhD degree in transportation information engineering and control from Beijing Jiaotong University in 2011. He was a Postdoctoral Scholar and Visiting Professor with Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL, USA, from August 2012 to August 2014 and from January 2017 to August 2017, respectively. He is currently a professor with School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, China. His research interests include electron paramagnetic resonance imaging (EPRI), computed tomography (CT) and magnetic resonance imaging (MRI) etc. He mainly focuses on image reconstruction algorithm, signal processing and high performance computing. He has published a series of papers on CT and EPRI image reconstruction, especially three papers on Journal of Magnetic Resonance. Now, he is constructing the China-USA united lab for medical imaging, supported by Shanxi University and The University of Chicago.


Speech Title: "Optimization-Based Image Reconstruction from Fast-Scanned, Noisy Projections in EPR Imaging"


Abstract: Tumor oxygen concentration image is essential to oxygen-image guided, precise radiation therapy. Electron Paramagnetic Resonance Imaging (EPRI) is an advanced oxygen imaging technique. However, the scanning time is still comparatively long, leading to motion artifacts for static imaging and low time resolution for dynamic imaging. Usually, a projection signal at a specific angle is obtained by averaging thousands of repeatedly collected projections to suppress random noise and achieve a high signal to noise ratio (SNR). Reducing the repetition times of projection collected at a specific angle may effectively speed up the whole scanning process. However, the EPR images reconstructed by the conventional three dimensional filtered backprojection (FBP) algorithm from these fast-scanned, noisy projections are too noisy to be used for further image postprocessing. In the work, we investigate the capability of an optimization-based algorithm in accurate reconstruction from noisy projections. We designed a total variation constrained, data divergence minimization (TVcDM) model, derived its Chambolle-Pock (CP) solving algorithm, and then validated and evaluated the CP algorithm via mathematical and physical phantoms. Studies show that the CP algorithm may accurately reconstruct EPR images from fast-scanned, noisy projections and thus the whole scanning process may be speeded up four times compared with the full scan time demanded by the FBP algorithm.


Previous Speakers


Prof. Yinglei Lai
The George Washington University, USA
Prof. Edwin Wang
University of Calgary, Canada
Assoc. Prof. Qijun Zhao
Sichuan University, China
Prof. David Zhang

Hong Kong Polytechnic University, Hong Kong

Prof. Nozomu Hamada

Universiti Teknologi Malaysia (UTM), Malaysia

Prof. Wirachman Wisnoe

Universiti Teknologi MARA (UiTM), Malaysia