Aroop Bhattacharjee : A Chartered Professional Engineer with over 30 years’ experience, working at various global locations, currently based in The Netherlands. I help organizations on : • Reducing costs • Reducing time to market • Increasing margins, and • Increasing employee engagement I take a holistic approach to create healthy organizations. The outcomes include not only physical, infrastructural, and procedural changes to existing manufacturing or business systems but also facilitate a necessary mindset shift at key organizational levels for successful implementation. Currently studying “AI and Applications” at Technical University Eindhoven.

Hamed Taherdoost is an award-winning leader and R&D professional. He is the Founder of Hamta Group | Hamta Business Corporation and Associate Professor at University Canada West. He holds PhD of Computer Science from Universiti Teknologi Malaysia, Master of Computer Science (Information Security), and Bachelor degree in the field of Science of Power Electricity. With over 20 years of experience in both industry and academic sectors, Dr Hamed has established himself as an industry leader in the field of Management and Information Systems. Dr. Hamed has been a part of many important breakthroughs in IT and industrial projects. He specializes in Information Technology Management, and Research & Development. He started his career in 2001 as an electrical designer and programmer in Tablokar, a leading switchgear panel manufacturer in the Middle East. He has subsequently worked at international companies from Cyprus, the UK, Belize, Iran, Malaysia, and Canada and has held positions as varied as Project Manager, R&D Manager, CTO and CEO.

Xiaoyan Dai has received her Ph.D in image processing from Muroran Institute of Technology in Japan in 2004 where she specialized in computer vision and machine learning. Since then, she has focused on image processing and document processing in Canon Inc. From 2019, she joined Research Center of Kyocera Corporation as an Expert. Her current interests include deep learning and computer vision for objects detection, fraud detection, object recognition and object tracing. She serves as committee member for multiple international conferences

Dr. Overmyer is currently Instructional Assistant Professor at Illinois State University in Bloomington/Normal, Illinois, where he teaches programming languages, software engineering, and computer architecture. Dr. Overmyer started his career in the software field with TRW, Inc. as a user interface designer and requirements engineer on the Space Defense Operations Center project in Colorado’s Cheyenne Mountain NORAD Complex. After completing several government projects with TRW, he was appointed manager of TRW’s Research and Technology Center in Virginia. He earned a Ph.D. in Information Technology (Information and Software Systems Engineering), while appointed as Research Instructor in George Mason University’s Center of Excellence in Command, Control, Communications and Intelligence. Dr. Overmyer has nearly 10 years of industrial experience, followed by over 30 years of academic experience. During his academic career, he was awarded two summer faculty fellowships at NASA’s Johnson Space Center and a National Science Foundation research grant. While an Associate Professor at Drexel University, he was also the Founding Director of the Pennsylvania Governor’s School for Information Technology, a five-week summer residential program for talented high school students. Internationally, he served at Subject Coordinator for both Software Engineering and Information Systems while at Massey University in New Zealand, and taught for 3 years as a founding Professor of Computer Science at Nazarbayev University in Astana, Kazakhstan.

Dr. Ali Soofastaei is a seasoned technology leader and expert in full-stack data governance, AI, and advanced analytics. With over 15 years of global experience, Ali has led high-impact projects in the mining and resource industries, utilizing data-driven solutions to drive operational excellence, sustainability, and innovation. As the Program Leader for Data and Analytics at Newmont, the world’s largest gold mining company, Ali oversaw multi-million-dollar initiatives, focusing on predictive analytics, AI applications, and digital transformation. His leadership in developing Centers of Excellence (CoE) has been instrumental in positioning organizations as leaders in data-driven decision-making and operational efficiency. Ali holds a Ph.D. in Information Technology and Mechanical Engineering from the University of Queensland and has held senior roles at Vale, BHP, and Shell. He has published over 60 journal papers, authored 25 book chapters, and holds multiple patents in AI-driven technologies. Ali is recognized as a thought leader in AI, machine learning, and business intelligence, frequently speaking at international conferences and contributing to the advancement of digital technologies across industries.

Prof. Dr. Dmitry A. Zaitsev is a distinguished academic and researcher affiliated with the University of Derby, UK. He is a Senior Member of ACM and IEEE and has an extensive background in applied mathematics, computer science, and telecommunication systems. His academic credentials include an MS in Applied Mathematics from Donetsk Polytechnic Institute (1986), a Ph.D. in Automated Control from the Kiev Cybernetics Institute (1991), and a Dr.Sci. (HDR) in Telecommunication Systems and Networks from Odessa National Academy of Telecommunications (2006). Throughout his career, Prof. Zaitsev has held numerous academic and research positions. He has served as an assistant and associate professor at Donetsk Polytechnic Institute (1986–1996), an associate professor at Surgut State University (1997–1998), and a lead engineer at the Information and Computer Centre of Odessa Railway (1999–2003). He later became a professor at Odessa National Academy of Telecommunications (2006–2009), International Humanitarian University (2009–2019), and Vistula University, Warsaw (2014–2019). Since 2019, he has been a professor at Odessa State Environmental University and a Senior Research Affiliate at Fundación Centro de Supercomputación de Castilla y León (2020–present). He also holds a professorship at the University of Information Technology and Management in Rzeszów, Poland, since 2021.

Dr. Luc Rolland has joined the University of the West of Scotland as a lecturer on Robotics,Automation and Controls in the School of Computing, Engineering and Physical Sciences. With industrial partners, he has been working on robotics and automation projects which are now nominated for innovation awards in Scotland and the UK. He is now being invited to participatein the first surgical robotics designs in countries like France, Italy and Switzerland. He graduated in Mechanical Engineering from Montreal Polytechnic (Engineering University) in December 1984 and then obtained his Master degree in Robotics Workcell Simulation where the research was partly funded by General Electric Canada proposing the first digital twins for animated simulation. From 1988 until 1996, he practised engineering in instrumentation, automation, controls and robotics on large scale factory projects for three different companies: SNC-Lavalin, BPCO and Met-chem (USX Consultants). From 1996 until 1998, as a research assistant at the Lausanne Federal Polytechnic (EPFL), he has designed the Manta and Kanuk robots, the first two 4 DOF parallel robots for Schoenflies motion, the resulting paper has been cited more than 100 times. Then, in 2003, he received his PhD diploma in computer science from the University Henri Poincare in Nancy (France), benefiting from three bursaries: the INRIA, Lorraine Region and CMW-Marioni, a company that manufactures high speed milling machines. He contributed to the first parallel robot simulator aimed at milling task certification, verification and fine-tuning. He was awarded two French Government prizes for outstanding research and technology transfer to industry. After his PhD thesis, he has been involved in various robotics and automation projects often involving partnerships with the industry. Recently, at UWS, for a local industrial partner, he designed and made a robotic work cell capable of high precision over large workspaces. He prepared the first automated industrial garment cleaning system based on ozone leaving no pollution. Among others, he has designed and controlled efficient and affordable solar trackers, rapid pointers, five-bar robot for test-tube filling, reconfigurable planar tripod, the fastest linear actuators, rigid revolute joint family including universal joints, hybrid drone. Hoe worked on sense-an-avoid algorithms for drones and the first certification framework for high speed milling and autonomous vehicles. He has worked on the automatic reprogramming of painting robots for Leyland Trucks and also contributed to the dynamics assembly plants by studying integration of articulated robots on mobile vehicles. He has experience in working with the oil and gas industry as he has designed the first redundant Fire and Gas System for the Hibernia offshore platform. He continues the design and controls of parallel robots for high performance with various academic partners in UK, France, Canada, Finland and Germany.

Dr. Surajit Ray is a Professor of Statistics at the University of Glasgow, with a research focus on uncertainty quantification in AI algorithms, particularly for medical image analysis. His expertise spans the theoretical and geometric aspects of deep learning algorithms, mixture models and functional data analysis, with a special focus on handling the challenges posed by large-scale, high-dimensional data. Dr. Ray's methodological interests include multivariate mixture models, structural equation models, high-dimensional clustering, and functional clustering. These approaches are central to his work in developing innovative statistical models to analyze and interpret complex datasets effectively. In addition to his academic research, Dr. Ray is actively engaged in collaborative projects with small, medium, and large-scale industries, particularly in the realm of medical imaging. These partnerships enable him to translate his research into real-world applications, helping advance technologies in healthcare. His collaborative work extends to medical image segmentation, immunology, and climate-ecosystem modeling, where he contributes valuable statistical insights to interdisciplinary projects. Dr. Ray is also a member of the Gateway Advisory Board for the Newton Gateway to Mathematics at the Isaac Newton Institute for Mathematical Sciences, where he plays a role in shaping strategic directions for applied mathematical research. Through his work in academia and industry, he contributes significantly to advancing both the theoretical foundations and practical applications of statistics in various fields.

Kshirasagar (Sagar) Naik is a Full Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His research interests focus on wireless networks and wireless devices, sensor networks, vehicular networks, autonomous vehicles, cyber-physical systems, and forest fires. The properties of the systems studied in his research are anomaly, security, reliability, performance, security, and energy efficiency. Machine learning, deep learning, and computer vision are extensively used in his research methodologies. He served as a regular or guest editor of several international journals, namely, Journal of Circuits, Systems, and Computers, Peer-to-Peer Network and Applications, International Journal of Distributed Systems, IEEE Transactions on Parallel and Distributed Systems, IEEE Journal on Selected Areas in Communications, and IEEE Transactions on Cloud Computing.He is a co-author of two textbooks, namely, Software Testing and Quality Assurance (Wiley, 2008) and Software Evolution and Maintenance (Wiley, 2014).

Omar Aljawfi, PhD is an Assistant Professor of Medicine (Adjunct) at Georgetown University and an Informatics Analyst & Data Scientist at the Center for Biostatistics, Informatics, and Data Science (CBIDS) at MedStar Health Research Institute. He is also a member of the Data Science Training Core (DSTC) at AIM-AHEAD. Dr. Aljawfi specializes in AI applications in healthcare, focusing on machine learning, clinical decision support, and health equity. His work spans synthetic data generation, privacy-preserving data sharing, and AI-driven disease diagnosis. He also teaches courses on AI in Systems Medicine and equity-driven healthcare data science. His research interests include deep learning for precision medicine and ethical AI in healthcare. Beyond academia, Dr. Aljawfi actively advocates for inclusive AI education and equitable healthcare innovations.

Prof. Ivan Ganchev is а Senior Member of the URSI1 and a SeniorMember of the IEEE2; the IEEE Communications Society (Essential); the IEEE Computer Society Special Technical Communities on: (i) Smart and Circular Cities, (ii) Wearable and Ubiquitous Computing, and (iii) Reliable, Safe, Secure and Time Deterministic Intelligent Systems; the IEEE Computer Society Technical Communities on: (i) Intelligent Informatics, (ii) Big Data, and (iii) Internet of Everything; the IEEE Computer Society Technical Committees on: (i) Internet, (ii) Computer Communications, and (iii) Cloud Computing; the IEEE Communities on: (i) Cloud Computing, (ii) Big Data, (iii) Future Networks, (iv) Internet of Things, and (v) IEEE Smart Cities; and the IEEE Consultants Network. Prof. Ganchev received his doctoral and engineering (summa cum laude) degrees from the Saint-Petersburg University of Telecommunications in 1995 and 1989, respectively. He is a Deputy Director of the Telecommunications Research Centre (TRC), University of Limerick (Ireland), an ITU-T 3 Invited Expert, and an IET4 Invited Lecturer. In addition, he also holds a Full Professorship at the University of Plovdiv “Paisii Hilendarski” and the Institute of Mathematics and Informatics of the Bulgarian Academy of Sciences. Prof. Ganchev was involved in 40+ international and national research projects. Previously he served as a member of the EU FP5 Academic Network for Wireless Internet Research in Europe (ANWIRE) and 8 European ‘COoperation in Science and Technology’ (COST) Actions: COST 285 “Modelling and Simulation Tools for Research in Emerging Multi-service Telecommunications”, COST 290 “Traffic and QoS Management in Wireless Multimedia Networks”, IC0906 “Wireless Networking for Moving Objects (WiNeMO)”, IC0905 “Techno-Economic Regulatory Framework for Radio Spectrum Access for Cognitive Radio/Software Defined Radio (TERRA)”, IC1304 “Autonomous Control for a Reliable Internet of Services (ACROSS)”, IC1303 “Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE)”, CA15127 “Resilient Communication Services Protecting End-User Applications from Disaster-based Failures (RECODIS)”, and CA16226 “Indoor Living Space Improvement: Smart Habitat for the Elderly (SHELD-ON)”. Prof. Ganchev’s research interests include: Machine Learning (ML) and Artificial Intelligence (AI) for smart communications and smart health; novel telecommunication and information paradigms; modelling, simulation, and emulation of complex telecommunication and information systems, networks, and services; information modelling; smart networks and services; smart ubiquitous networking; Internet of Things (IoT); Internet of Services (IoS); Internet tomography; CPS systems; Ambient Assisted Living (AAL); Enhanced Living Environments (ELE). Prof. Ganchev has served on the Technical Program Committee of 40+ prestigious international conferences, symposia, and workshops. He has authored/co-authored 1 monographic book, 3 textbooks, 4 edited books, and 300+ research papers in refereed international journals, books, and conference proceedings. He was a recipient of IEEE IAEAC 2017 Best Paper Award and IEEE IS 2012 Best Paper Award, and was nominated for ITU-T Kaleidoscope 2008 Best Paper Award. He has presented over 20 plenary/keynote/invited talks at renown international scientific forums.

Jeshwanth Challagundla is a Senior Machine Learning Software Engineer at Google, where I focus on advancing the capabilities of machine learning models and AI-driven systems. My primary work has been with the Gemini project, where I led the development and launch of the advanced data analysis feature and seamlessly integrating it with Google Workspace and other tools. I have architected scalable infrastructure for agent orchestration, dynamic prompting and multi-step planning, which enhances model adaptability and flexibility across diverse tasks. My work has also involved improving model quality using prompt engineering, fine-turning and reinforcement learning. Before Google, I worked on autonomous driving technologies at General Motors, where I helped improve perception and localization for autonomous vehicles. I have a Master's degree in Electrical Engineering from the University of Texas at Arlington, where my research focused on second-order optimization for neural network training, and I’ve published papers on topics ranging from optimization to autonomous robotics. In addition to my engineering work, I regularly contribute to the research community by reviewing papers for leading conferences like NeurIPS, ICLR, and IEEE Transactions. My interests span LLMs, deep learning, computer vision, and agentic workflows. I’m continuously seeking new ways to push the boundaries of AI and multimodal understanding to solve complex, real-world problems.

Soham is a third CSE PhD student at University of Washington in the AI for biomedical sciences (AIMS) Lab. Prior to starting his Ph.D, he spent a year as a data engineer at Microsoft in the Windows Experience team. Soham completed his master’s from Stanford in Computer Science with a depth in AI, where he was a research assistant in the Computational Neuroimage Science Lab (CNSLAB) and part of the AI for Healthcare (AIHC) bootcamp in the Stanford ML Group. Soham’s research lies at the intersection of Artificial Intelligence and Healthcare, focusing on understanding the reasoning processes of machine learning models. He is developing AI techniques to increase model interpretability in settings where features are not trivial to obtain (like Emergency Medicine), auditing AI models to make them more trustworthy by exploring causal relationships between inputs and predictions, and using multi-modal datasets (images, text, time-series, etc.) with deep learning to perform diagnoses and treatment of various ailments.

Hazrat Bilal received his MS degree in Control Science and Engineering in 2018 from Nanjing University of Science and Technology, Nanjing, China, and his PhD degree in Control Science and Engineering in 2024 from the University of Science and Technology of China, Hefei, Anhui, respectively. His research interests include robot control, fault diagnosis of robot manipulator, trajectory tracking of manipulator, Autonomous driving and Artificial Intelligence. In 2018, considering his research achievement, the Nanjing University of Science and Technology awarded him with the outstanding graduate award, while the University of Science and Technology of China award him CAS-TWAS Fellow Award. He is currently a member of IEEE, Member of IEEE Robotics and Automation Society, and Member of IEEE Control Systems Society.

Ifeoluwa Emmanuel Olagbaju is an Internal Audit Data Analytics expert, with extensive experience in leveraging data to drive risk assurance, operational efficiency, and business insights. With a background in Computer Science and an MBA in Management, Ifeoluwa has built a career in financial services, working with multinational institutions including asset managers, fund managers, and pension fund administrators. His work in internal audit has centred on data analysis, ETL development, data modeling, and reporting, empowering audit teams to make data-driven decisions. In recent years, Ifeoluwa has expanded his expertise into the field of Machine Learning (ML), focusing on how ML can bridge the gaps in anomaly detection, fraud detection, and continuous audit automation. He is exploring ways to integrate AI models into internal audit workflows, aiming to enhance risk detection and reduce audit cycle times. Passionate about knowledge sharing, he actively mentors aspiring data professionals, particularly those transitioning from non-technical backgrounds, and contributes his thoughts through writing and public speaking engagements.

Dr. Ahmad Ali completed his PhD at Shanghai Jiao Tong University. He is currently a postdoctoral researcher at Shenzhen University. Dr. Ali has reviewed over 1150 articles for well-reputed journals, including Applied Soft Computing, Information Fusion, Information Sciences, Neural Networks, Applied Intelligence, and IEEE Transactions on Industrial Informatics.