Keynote Speakers

Bhargab B. Bhattacharya

prof. Bhargab B. Bhattacharya

Bhargab B. Bhattacharya had been on the standing faculty of Indian Statistical Institute, Kolkata, for more than 35 years before retiring in 2018. He then served as Distinguished Visiting Professor of Computer Science & Engineering at Indian Institute of Technology Kharagpur, for three and half years (2019 – 2022). He received the B.Sc. degree in Physics, B.Tech. and M.Tech. degrees in Radiophysics and Electronics, and the PhD degree in Computer Science, all from the University of Calcutta. He served as Visiting Professor at the University of Nebraska-Lincoln, and Duke University, USA, University of Potsdam, Germany, Tsinghua University, Beijing, China, and at Kyushu Institute of Technology, Iizuka, Japan. His research area includes digital geometry, image analysis, and electronic design automation for integrated circuits and microfluidic biochips. He has published more than 400 technical articles and he holds ten US patents. Dr. Bhattacharya is a Fellow of the Indian National Academy of Engineering, a Fellow of the National Academy of Sciences (India), and a Fellow of the IEEE.

He will give the lecture entitled:
Digital Geometry in Medical Diagnostics and Biochemistry

Abstract: Digital Geometry (DG) has found profound applications in combinatorial image analysis, computer graphics, as well as in several medical diagnostic tools including computerized tomography. In this talk, we will demonstrate two novel applications of DG. First, we discuss how the basic concepts such as digital straightness, discrete curvature, and concavity index can be utilized to develop automated and powerful tools for detecting and classifying various bone-fractures with high accuracy from a class of orthopaedic X-ray images. In the second part of the talk, we will focus on a completely new discipline of biochemistry where DG finds its presence: automated production of fluid samples over a range of concentration factors (called gradients), using a tiny microfluidic device called Lab-on-Chip (LoC). The optimization of reagent-cost and sample-preparation time during gradient generation using droplet-based LoCs is a major challenge to handle. We show that complex-shaped gradients on a discrete grid can be nicely approximated as a sequence of linear gradients. The optimization problem thus reduces to approximating a digital curve on a square grid with the minimum number of digital straight-line segments (DSS) - which is well studied in the literature. Experimental results on various benchmark gradient-profiles establish the efficiency of the method.

Benedek Nagy

prof. Benedek Nagy

Benedek Nagy is a full professor at Department of Mathematics in Eastern Mediterranean University (EMU), Famagusta. His research interests include digital geometry, formal languages and automata, computing paradigms and combinatorics. He got BSc+MSc in Physics, BSc in Programming Mathematics, BA+MA in Philosophy and Logic, MSc in Software Engineering Mathematics and BA+MA in General and Applied Linguistics from University of Debrecen (UD), in Hungary, in 1996, 1997, 1998, 1999 and 2000, respectively. He received PhD degree in mathematics and computer science from UD in 2004, his theses was about digital geometry (digital distances) on various grids. He got habilitation degree in 2007. He has received Prize of Foundation László Patai in 2004 and János Kemény Prize in 2006. He was a visiting researcher for a semester at Indiana University (USA) in 2002. He also worked as a researcher at the Research Group of Mathematical Linguistics in the Rovira i Virgili University, Tarragona (Spain) from 2003 till 2008. He was a postdoc at Bremen University (Germany) and at Uppsala University (Sweden). He also visited Kyoto-Sangyo University (Japan) for 2 months. He also served the Faculty of Informatics at UD as a vice-dean between 2010 and 2012. From 2013 he is working at EMU in Cyprus. He has around 250 scientific publications and he gave over 100 scientific talks in various conferences and workshops. He has been co-chair at conferences MCU’15 and NCMA’16. He is participating regularly at IWCIA from 2004.

He will give the lecture entitled:
Non-traditional 2D grids in Combinatorial Imaging – Advances and Challenges

Abstract: On the one hand, the digital image processing and many other digital applications are mostly based on the square grid. On the other hand, there are two other regular grids, the hexagonal and the triangular grids. Moreover, there are eight semi-regular grids based on more than one type of tiles. These non-traditional grids and their dual grids have various advantages over the square grid, e.g., on some of them no topological paradox occur. Most of them have more symmetries, i.e., more directions of symmetry axes and also a smaller angle rotation may transform most of these grids into themselves. However, since most of these grids are not point lattices we need to face some challenges to work with them; they may define various digital geometries. We show how a good coordinate system can be characterized, what type of digital distances are studied, tomography and distance transform. Other grid transformations, including translations and rotations with some of them interesting properties are mentioned. Mathematical morphology and cell complexes are also shown. The advantages and challenges are overviewed by various examples on the triangular grid, as a characteristic example for a non-traditional grid.

Jessica Zhang

prof. Jessica Zhang

Jessica Zhang is the George Tallman Ladd and Florence Barrett Ladd Professor of Mechanical Engineering at Carnegie Mellon University with a courtesy appointment in Biomedical Engineering. She received her B.Eng. in Automotive Engineering, and M.Eng. in Engineering Mechanics from Tsinghua University, China; and M.Eng. in Aerospace Engineering and Engineering Mechanics and Ph.D. in Computational Engineering and Sciences from Institute for Computational Engineering and Sciences (now Oden Institute), The University of Texas at Austin. Her research interests include computational geometry, isogeometric analysis, finite element method, data-driven simulation, image processing, and their applications in computational biomedicine, materials science and engineering. Zhang has co-authored over 200 publications in peer-reviewed journals and conference proceedings and received several Best Paper Awards. She published a book entitled “Geometric Modeling and Mesh Generation from Scanned Images” with CRC Press, Taylor & Francis Group. Zhang is the recipient of Simons Visiting Professorship from Mathematisches Forschungsinstitut Oberwolfach of Germany, US Presidential Early Career Award for Scientists and Engineers, NSF CAREER Award, Office of Naval Research Young Investigator Award, and USACM Gallagher Young Investigator Award. At CMU, she received David P. Casasent Outstanding Research Award, George Tallman Ladd and Florence Barrett Ladd Professorship, Clarence H. Adamson Career Faculty Fellow in Mechanical Engineering, Donald L. & Rhonda Struminger Faculty Fellow, and George Tallman Ladd Research Award. She is a Fellow of AIMBE, ASME, SMA, USACM and ELATES at Drexel. She is the Editor-in-Chief of Engineering with Computers.

She will give the lecture entitled:
Machine Learning Enhanced Simulation and PDE-Constrained Optimization for Material Transport Control in Neurons

Abstract: The intracellular transport process plays an important role in delivering essential materials throughout branched geometries of neurons for their survival and function. Many neurodegenerative diseases have been associated with the disruption of transport. Therefore, it is essential to study how neurons control the transport process to localize materials to necessary locations. First, we develop an isogeometric analysis (IGA) based platform for material transport simulation in neurite networks. We model the transport process by reaction-diffusion-transport equations and represent geometry of the networks using truncated hierarchical tricubic B-splines (THB-spline3D). We solve the Navier-Stokes equations to obtain the velocity field of material transport in the networks. We then solve the transport equations using the streamline upwind/Petrov-Galerkin (SU/PG) method. Next, we develop a novel optimization model to simulate the traffic regulation mechanism of material transport in neurons. The transport is controlled to avoid traffic jam of materials by minimizing a pre-defined objective function. The optimization subjects to a set of partial differential equation (PDE) constraints that describe the material transport process based on a macroscopic molecular-motor-assisted transport model of intracellular particles. Different simulation parameters are used to introduce traffic jams and study how neurons handle the transport issue. Our model effectively simulates the material transport process in healthy neurons and explains the formation of a traffic jam caused by reduced number of microtubules (MTs) and MT swirls in abnormal neurons. To enable fast prediction of the transport process within complex neurite networks, we develop a Graph Neural Networks (GNN) based model to learn the material transport mechanism from simulation data. In this study, we build the graph representation of the neuron by decomposing the neuron geometry into two basic structures: pipe and bifurcation. Different GNN simulators are designed for these two basic structures to predict the spatiotemporal concentration distribution given input simulation parameters and boundary conditions. In particular, we add the residual term from PDEs to instruct the model to learn the physics behind the simulation data. To recover the neurite network, a GNN-based assembly model is used to combine all the pipes and bifurcations following the graph representation. The loss function of the assembly model is designed to impose consistent concentration results on the interface between pipe and bifurcation. Through machine learning, we can quickly and accurately provide a prediction of complex material transport patterns including traffic jam and MT swirls.