Sofie Haesaert is an Assistant Professor in the Control Systems group at the Department of Electrical Engineering, Eindhoven University of Technology. Her work focuses on formal verification and control synthesis methods for cyber-physical systems, particularly through stochastic simulation relations and temporal logic specifications. Education: BSc (cum laude) and MSc (cum laude) in Mechanical Engineering and Systems & Control from Delft University of Technology; PhD from Eindhoven University of Technology (2017) Experience: Postdoctoral researcher at Caltech (2017-2018), then returned to TU/e as Assistant Professor Her research interests include: Cyber-physical systems verification Stochastic control methods Temporal logic specification Markov decision processes Formal methods in control engineering Model abstractions and simulation relations Recent publications show strong focus on: Stochastic temporal logic control Robust and risk-aware control Multi-agent system verification Formal synthesis via simulation relations AI integration in control systems Software tools for formal control Scientific achievements: Veni Grant recipient (2020) Co-developer of the SySCoRe toolset for stochastic control synthesis Contributor to formal verification benchmarks through ARCH-COMP reports She contributes to education through courses on: Control principles for engineered systems Control challenges in autonomous racing Supervisory control of cyber-physical systems Haesaert collaborates across disciplines including computer science, applied mathematics, and robotics, with over 750 citations and significant contributions to formal control theory for stochastic systems. Her work bridges theoretical developments with practical applications in autonomous systems and complex control architectures.
Adrien Depeursinge is a Professor at HES-SO Valais-Wallis - Haute Ecole de Gestion, affiliated with the School of Economics and Services and the Management Information Systems department. His research focuses on radiomics, personalized medicine via image-based analysis, and clinical workflow optimization. He leads the development of the QuantImage platform, a physician-centered web-based tool for radiomics research, and contributes to radiomics standardization efforts through initiatives like the Image Biomarker Standardization Initiative (IBSI). His work emphasizes machine learning applications in healthcare, including tumor segmentation, biomarker extraction, and improving diagnostic accuracy through computational models. Key research themes include: 1) Radiomics – developing quantitative imaging features for cancer diagnosis/prognosis; 2) Medical Imaging Analysis – advancing texture-based models, multi-modal fusion, and automated lesion detection; 3) Physician-AI Collaboration – designing user-centric tools for clinical integration. His contributions span neuro-oncology (brain metastases), head-and-neck cancer, and multiple sclerosis imaging. Publications emphasize methodological advancements (e.g., kernel optimization in CNNs, steerable detectors) and clinical validation (e.g., reproducibility of radiomics features across imaging protocols). He collaborates with institutions like the University Hospital of Lausanne (CHUV) and international teams on projects like the HECKTOR challenge for PET/CT tumor segmentation. His work bridges technical innovation with clinical impact, aiming to translate radiomics into actionable clinical tools. QuantImage v2, his flagship tool, enables no-code development of machine learning models using clinical imaging data. Research also includes phantom-based validation of radiomics features and addressing challenges in feature stability across imaging modalities. Current projects explore improving contour quality for radiomics studies and optimizing AI explainability in medical decision-making.
Assoc. Prof. Dr. Ayhan Gün is an Associate Professor in the Department of Electrical and Electronics Engineering at Kütahya Dumlupınar University's Faculty of Engineering. With a career spanning over two decades, he has held various academic positions including Research Assistant, Assistant Professor, and currently Associate Professor since 2024. His extensive administrative experience includes serving as Head of the Control and Command Systems Department (2007-2021) and various leadership roles in university-industry collaboration initiatives. Dr. Gün completed his Bachelor's degree at Near East University (1991-1996), Master's at Dumlupınar University (1998-2001), and PhD at Eskişehir Osmangazi University (2001-2007). His research focuses on control systems, mathematical modeling, artificial neural networks, robotics, SCADA, PLC programming, electromechanical systems, nonlinear control, fuzzy logic, optimization techniques, automation, biomechanics, and mechatronics. His recent publications demonstrate a consistent research trajectory in control engineering, with particular emphasis on optimization algorithms applied to quadrotor control, inverted pendulum systems, and electrical motor design. His work bridges theoretical control concepts with practical implementations in robotics and power systems. A significant portion of his research involves applying swarm intelligence and evolutionary algorithms to solve complex control problems. Bilim, Sanayi ve Teknoloji Bakanlığı Kurumsal Kapasitenin Arttırılması (2016) BİLİM SANAYİ VE TEKNOLOJİ BAKANLIĞI Çift Beslemeli İndüksiyon Generatörü Tasarımı ve İmalatı (2016) Dr. Gün has supervised multiple graduate students and managed numerous research projects, including the current 'Robotic Arm Design and Implementation for Patients with Hemiparetic Arms' project. His external roles include serving as an expert witness for judicial institutions, project referee for TÜBİTAK, and publication reviewer for IEEE Transactions. He has also contributed to regional development through his work with Kütahya Governorship's Planning and Development Board.
Tayfun Günel is a Professor in the Department of Electronics and Communication Engineering at Istanbul Technical University (ITU) , Faculty of Electrical and Electronics Engineering. He holds a PhD (1993), MSc (1988), and BSc (1986), all from ITU. His research spans microwave circuits, radar systems, antennas, and optimization using genetic algorithms and soft computing. His research interests include Microwave Circuits , Radar and Antennas , Optimization , and Genetic Algorithms . His work focuses on impedance matching, microstrip antennas, noise modeling, and metamaterial-based microwave components. He has taught courses such as Electromagnetic Fields, Radar Systems, and Satellite Communication Systems. The recent publications reflect a strong trend in microwave circuit design , antenna miniaturization , and the application of evolutionary algorithms (genetic algorithms, PSO) and machine learning (neural networks, SVR) in electromagnetic design and optimization. There is a consistent focus on practical microwave components like transmission lines, patches, and amplifiers, often using nanomaterials (e.g., carbon nanotubes) and metamaterials . His work bridges theoretical modeling with computational optimization for real-world RF and radar applications. Email: gunelmur@itu.edu.tr Professor Günel has supervised 2 completed PhD theses, 2 ongoing PhD theses, 23 completed master's theses, and 1 ongoing master's thesis, demonstrating a significant contribution to student mentoring. There are no specific grants or funding sources mentioned in the provided text. He is affiliated with research in microwave systems and antenna design , likely operating within the broader research ecosystem of the Electronics and Communication Engineering Department at ITU, which includes labs such as the Microwave Systems and Antennas Laboratory and the Radar and Microwave Technologies Research Laboratory.
Jack Baker is the William Alden Campbell and Martha Campbell Professor of Engineering and Associate Dean for Faculty Affairs in the Stanford Doerr School of Sustainability at Stanford University. He is a Professor of Civil & Environmental Engineering with expertise in probabilistic and statistical tools for quantifying and managing disaster risk and resilience. His work has significantly influenced building codes, performance-based engineering guidelines, and catastrophe risk models. Dr. Baker's educational background includes: Ph.D. in Civil & Environmental Engineering from Stanford University (2005) M.A. in Statistics from Stanford University (2004) M.S. in Civil & Environmental Engineering from Stanford University (2002) B.A. in Mathematics/Physics from Whitman College (2000) His research focuses on disaster risk and resilience, particularly in earthquake engineering and seismic hazard analysis. Baker uses probabilistic and statistical approaches to analyze risk in spatially distributed systems, characterize earthquake ground motions, and simulate post-disaster recovery processes. His work bridges theoretical frameworks with practical applications in building codes and risk management strategies. He has made significant contributions to understanding the relationship between ground motion characteristics and structural response, while also expanding into climate-related hazards like atmospheric rivers and their compound effects. His recent publications demonstrate a growing focus on interdisciplinary research that connects engineering with socioeconomic factors in disaster contexts. There's a clear trend toward integrating machine learning techniques with traditional engineering approaches, particularly in modeling household displacement, economic recovery, and flood damage prediction. His work increasingly addresses the human dimension of disasters, examining how physical damage translates to social impacts and recovery timelines. Dr. Baker has received numerous prestigious awards recognizing his contributions to the field: William B. Joyner Lecture Award from the Seismological Society of America and Earthquake Engineering Research Institute (2023) PROSE Awards finalist for Seismic Hazard and Risk Analysis textbook (2022) Thorpe Medal from the European Council on Computing in Construction (2022) Walter L. Huber Civil Engineering Research Prize from the American Society of Civil Engineers (2018) CAREER Award from the National Science Foundation (2010) As an educator and mentor, Baker advises numerous doctoral and master's students while serving as Associate Dean for Faculty Affairs. His research group has secured significant funding for projects related to seismic risk, disaster recovery modeling, and infrastructure resilience. He has directed major initiatives like the Stanford Urban Resilience Initiative and co-founded the Haselton Baker Risk Group, demonstrating strong leadership in translating research into practical applications. Dr. Baker leads the Baker Research Group, which focuses on probabilistic approaches to disaster risk assessment and management. The group maintains active collaborations with government agencies, industry partners, and international research institutions to advance the state of knowledge in earthquake engineering and broader disaster resilience fields. Their work often involves developing innovative computational tools and frameworks that are made publicly available through GitHub repositories.
Rainald Loehner is a Distinguished Professor of Fluid Dynamics at George Mason University's Center for Computational Fluid Dynamics. Since 2003, he has led the Center for Computational Fluid Dynamics at George Mason University. He is currently a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS) for 2023, hosted by Professors Kai-Uwe Bletzinger and Roland Wüchner in the 'Adjoint-Based System Identification of Large-Scale Structures' Focus Group. Loehner received his Diplom Ingenieur (Maschinenbau) degree from the Technical University of Braunschweig, and his PhD and a DSc in civil engineering from the University College of Swansea, Wales. After teaching at Swansea for a year, he worked at the Naval Research Laboratory in Washington, DC, followed by a research professorship at George Washington University. He joined George Mason University as an associate professor and was promoted to full professor in 1995 and distinguished professor in 2004. With over 35 years of experience, Professor Loehner's research spans the complete pipeline of numerical solvers and simulation tools. His expertise includes pre-processing, grid generation, numerical methods, field solvers, parallel computing, adaptive mesh refinement, fluid-structure interaction, shape optimization, system identification, and computational crowd dynamics. His current work focuses on developing advanced field solvers for compressible and incompressible flows, acoustics, electromagnetic wave propagation, heat and mass transfer, structural mechanics, and fluid-structure interaction. Key application areas include blast mitigation, ship hydrodynamics, blood flow, contaminant transport, and pedestrian safety. Loehner's recent research output (2020-2024) shows a strong trend toward digital twin technology and adjoint-based methods for structural analysis and optimization. His publications focus on high-fidelity digital twins for detecting structural weaknesses, risk assessment in engineering systems, and optimization of sensor placement. His work bridges computational mechanics with machine learning approaches, particularly in system identification and inverse problems, demonstrating how computational methods can solve complex real-world engineering challenges. 2020: Ranked #15119 in the Stanford List of Most Influential Scientists of the World; #8 in Aerospace and Aeronautics 2010: Distinguished International Career Award, Argentine Association of Computational Mechanics 2008: Fellow, International Association for Computational Mechanics 2006: Associate Fellow, AIAA 2005: Honorary Professor, University of Wales Swansea 2005: Advisory Professor, Shanghai Jiao Tong University 2004: Distinguished Professor of Fluid Dynamics, George Mason University 1999: Computational Mechanics Achievements Award, Japan Society of Mechanical Engineering 1993: Doctor of Science in Civil Engineering, University College of Swansea 1979-1983: Studienstiftung des Deutschen Volkes (Top 1% of German Students) Professor Loehner has mentored numerous students through his work at George Mason University and has supervised research in computational fluid dynamics, structural mechanics, and related fields. His research has been supported by various grants from government agencies and industry partners, enabling the development of advanced simulation tools applied in aerodynamics, hydrodynamics, shock-structure interaction, and medical applications. His codes and methods have been widely adopted in industry and academia for applications ranging from aircraft and ship design to medical simulations and urban pathogen transmission modeling. Loehner leads the Center for Computational Fluid Dynamics at George Mason University, which focuses on developing cutting-edge computational methods for fluid dynamics and related multiphysics problems. The center works on strategic application areas including blast mitigation, ship hydrodynamics, blood flow simulation, and pedestrian movement modeling. As a TUM-IAS Fellow, he collaborates with the Chair of Computational Modeling and Simulation at TUM on adjoint-based system identification of large-scale structures, bringing together expertise in computational mechanics and digital twin technology to address complex engineering challenges.
Dr. Scott L. Nykl is a Professor in the Department of Computer Science at the Air Force Institute of Technology (AFIT), part of the Graduate School of Engineering & Management. He is a leading researcher in computer vision, real-time 3D graphics, and autonomous aerial systems, with a focus on automated aerial refueling and navigation in GPS-denied environments. Education: Ph.D. in Computer Science, Ohio University (2008–2013), Summa Cum Laude, GPA: 4.0/4.0 M.S. in Computer Science, Ohio University (2011–2012), Summa Cum Laude, GPA: 4.0/4.0 B.S. in Software Engineering, University of Wisconsin–Platteville (2002–2006), Summa Cum Laude, GPA: 3.94/4.0 Dr. Nykl's research interests include computer vision, sensor fusion, interactive virtual worlds, and real-time 3D graphics, with applications in aerospace and defense. His work bridges simulation and real-world deployment, particularly in autonomous aerial refueling using stereo and monocular vision. He has pioneered techniques in pose estimation, occlusion mitigation, and sim-to-real transfer learning. His recent publications and projects show a strong trend toward robust, vision-based navigation systems for unmanned and manned aircraft, with emphasis on reliability, accuracy, and real-time performance. His work frequently appears in IEEE, AIAA, and ION venues, reflecting its high technical and operational relevance. Scientific Awards and Recognitions: 2024 Harold Brown Award – Highest U.S. Air Force scientific honor 2024 General Bernard A. Schreiver Award 2025 AETC Airmen of the Year Multiple Air Force Outstanding Scientist/Engineer Awards (2017–2023) Best Paper Award, ACM SIGGRAPH i3D 2013 Forbes' The Greatest Young Inventors in America (2012) NSF GK-12 Fellow (2006) Dr. Nykl has advised numerous graduate students and collaborated extensively on projects involving automated aerial refueling, 3D reconstruction, and cyber education. He has secured significant research funding, including a $100,000 Ohio Third Frontier grant. His work has led to multiple patents and technology transfers. He leads research integrating virtual worlds, digital twins, and augmented reality for both research and pedagogy. Laboratories and Research Teams: His work is conducted within AFIT’s research ecosystem, involving collaborations with the Air Force Research Laboratory (AFRL), Boeing, and academic partners. He leads projects under the Aerial Refueling Systems Advisory Group (ARSAG) and presents regularly at ION, AIAA, and IEEE conferences.
Mohsen Heidari is an Assistant Professor in the Department of Computer Science at Indiana University, Bloomington. He is affiliated with the IU Quantum Science and Engineering Center (QSEc) and the NSF Center for Science of Information (CSoI). He previously held positions as a Visiting Assistant Professor at Purdue University and as a Postdoctoral Research Associate at CSoI. Ph.D. in Electrical Engineering (2019) and M.Sc. in Applied Mathematics (2017) from the University of Michigan His research focuses span quantum computing, theoretical machine learning, and information theory. Key themes include: Quantum algorithm design and sample complexity Fourier-based learning frameworks Quantum-classical duality in learning problems Information-theoretic approaches to biological systems Article trends show a strong emphasis on quantum-classical learning intersections (6/15 papers), Fourier analysis applications (5/15), and information-theoretic foundations (12/15). Notable venues include NeurIPS, IEEE Transactions, and ISIT. He directs research involving: Quantum Neural Network development Quantum measurement simulation Quantum data compression techniques Quantum algorithm implementation constraints
Tarmo Lipping is a Professor in the Department of Computer Science and Engineering at the Faculty of Information Technology and Electrical Engineering, University of Oulu. His work bridges computing sciences with biomedical engineering, environmental modelling, and data-driven societal applications. Doctor of Science (Technology), Information Technology – Awarded 14 Feb 2001 Master of Science (Technology), Information Technology – Awarded 10 Sept 1993 His research focuses on electroencephalography (EEG) , mental workload assessment , depth of anesthesia monitoring , and machine learning applications in healthcare and human-computer interaction. He also contributes to environmental informatics , particularly in land uplift modelling and radionuclide transport , aligning with UN Sustainable Development Goals. Recent publications highlight trends in transformer networks for EEG analysis , wearable HCI systems , data-driven food safety , and participatory municipal governance . His work integrates deep learning, signal processing, and real-world deployment. Scientific awards include: CIMO opettajavaihto (2017) Lipping has supervised numerous master’s students and served as an examiner in diverse topics including data vault modelling , telecom revenue estimation , and EEG hyperscanning . He has evaluated funding applications, acted as a journal reviewer (65 times), and contributed to editorial work. His activities reflect strong engagement in academic service and interdisciplinary research mentorship. He has contributed datasets on Fennoscandian land uplift , lake isolation , and archaeological shorelines to PANGAEA, supporting open science in geosciences and environmental history.
Tommy Löfstedt is an Associate Professor at Umeå University , affiliated with the Department of Computing Science and the Department of Mathematics and Mathematical Statistics. His research focuses on machine learning , computer vision , and medical image analysis , with applications in life sciences, radiation therapy, and biomedical imaging. He leads multiple research projects, including AI-driven delineation in radiation therapy, quantitative MRI for radiotherapy, and machine learning for plant nutrient uptake. Current research emphasizes structured regularization methods to improve model interpretability and robustness. Key applications include medical image segmentation , Alzheimer's classification , and uncertainty estimation in MRI . Recent publications highlight his work on morphological regularization , adversarial attack mitigation , and multi-task learning in medical imaging contexts. His projects span 2022–2026 with funding for pediatric oncology automation and gynecological cancer staging. Affiliated with both computing and mathematical departments, he bridges algorithm development with applied mathematical frameworks in medical and life science domains.
Xuan Zhang is an Associate Professor at the Department of Information and Communication Technology, University of Agder. His research focuses on Tsetlin Machines, learning automata, and their applications in machine learning, computer vision, and hyperspectral imaging. Research Trends: Zhang’s recent work includes developing interpretable machine learning models (e.g., Tsetlin Machines), optimizing convolutional architectures for image processing, and applying automata theory to solve multi-armed bandit problems and channel selection in cognitive networks. His field spans theoretical analysis and practical implementations in AI, remote sensing, and health informatics. Scientific Contributions Co-developed advanced Tsetlin Machine variants for XOR/NOT operator convergence, disease forecasting, and image restoration Published in journals like IEEE Transactions on Pattern Analysis and Machine Intelligence , Information Sciences , and Applied Intelligence Explored Bayesian pursuit algorithms, hierarchical learning automata, and particle swarm optimization techniques Contact: xuan.zhang@uia.no
Dr. Veysel Gümüş is an Associate Professor at Harran University's Faculty of Engineering, Department of Civil Engineering, where he has been since 2014. His research focuses on turbulence modeling, computational fluid dynamics, hydrological drought analysis, and time-series trend analysis. Licence (2003), Master's (2006), and Doctorate (2014) in Civil Engineering from Harran and Çukurova Universities. His research interests span hydrological drought , computational fluid dynamics , climate trend analysis , and GIS applications in hydrology . His recent work emphasizes drought risk assessment, wind speed trends, and fluid flow simulations using AI techniques. Publications since 2023 highlight his expertise in Mann-Kendall tests , copula-based drought analysis , and CMIP6 climate projections across Turkey and Morocco. He has supervised over 15 graduate theses and served as an editor/hakem for 10+ journals, including ASCE and Theoretical and Applied Climatology.
Elena Niculina Dragoi is a Lecturer at the Faculty of Chemical Engineering and Environmental Protection 'Cristofor Simionescu' at Gheorghe Asachi Technical University in Iasi, Romania. Her academic work integrates Artificial Intelligence and Machine Learning tools for solving complex problems in Chemical Engineering and Environmental Protection . With over 30 published papers and six active research projects, her contributions span process optimization, nanomaterials, and sustainable technologies. Teaches Applied Informatics (Years 1 & 4) and Artificial Intelligence at the Faculty of Chemical Engineering Contributes to Programming Engineering at the Faculty of Computer Science, University 'Alexandru Ioan Cuza' Engaged in interdisciplinary courses at the Faculty of Automatic Control and Computer Engineering Research Interests : Elena's work focuses on modelling and optimization (90% emphasis) of chemical processes using AI methodologies, with cross-disciplinary applications in environmental engineering (70%) and chemical engineering (95%). Her recent publications highlight innovations in: 3D-printed nanocomposite adsorbents for pollutant removal Metaheuristic optimization algorithms for industrial processes Hydrogen generation via nanocatalysts Electrochemical biosensors for environmental and health monitoring AI-driven wastewater treatment systems Green chemistry applications in pharmaceutical and dye removal
Bo An is a President's Chair Professor and Head of the Division of Artificial Intelligence at the College of Computing and Data Science , Nanyang Technological University, Singapore . He also holds a courtesy appointment as Professor at the School of Physical & Mathematical Sciences and serves as Director of the Centre of AI-for-X. Previously, he was a Nanyang Assistant Professor (2014-2018), Associate Professor at the Chinese Academy of Sciences (2012-2013), and Postdoctoral Researcher at the University of Southern California (2010-2012). His academic journey began with B.Sc. and M.Sc. degrees from Chongqing University, followed by a Ph.D. in Computer Science from the University of Massachusetts, Amherst (advised by Victor Lesser). Research Interests : Artificial Intelligence Multiagent Systems Computational Game Theory Reinforcement Learning Automated Negotiation Optimization Research Impact : Applications in infrastructure security (deployed by US Coast Guard and Federal Air Marshals), e-commerce, sensor networks, and financial technology. Over 150 publications in top venues like AAMAS, IJCAI, AAAI, ICML, NeurIPS, KDD, and ACM/IEEE Transactions. Scientific Recognition : 2010 IFAAMAS Victor Lesser Distinguished Dissertation Award 2012 INFORMS Wagner Prize 2018 & 2022 Nanyang Research Awards 2017 Microsoft Collaborative AI Challenge IEEE Intelligent Systems 'AI's 10 to Watch' (2018) Leadership Roles : Editor-in-Chief of IEEE Intelligent Systems, Associate Editor for AIJ, JAAMAS, and ACM Transactions. Served as General Co-Chair for AAMAS'23 and Program Chair for IJCAI'27.
Om P. Damani is a Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay. He serves as Faculty In-Charge of the Sustainable Development unit of the Center for Policy Studies and is also associated with the Centre for Technology Alternatives for Rural Areas (CTARA). His work bridges computer science with social development challenges, focusing on practical applications for rural communities. Dr. Damani's research interests span Technology for Development of the bottom 80%, System Dynamics: Modeling and Simulation for Social Development, System Architecture, and Data Science. His work demonstrates how computational approaches can address complex development challenges through projects like GramDrishti (for detecting rural infrastructure in satellite images), JalTantra (for optimizing water distribution networks), and FAI (Farm Assessment Index for holistic farming practice evaluation). His publications reveal a consistent focus on applying computer science to solve real-world problems in water management, agricultural systems, and rural infrastructure. His research has been recognized with significant awards including the IIT Bombay Industrial Impact Award 2010, IIT Bombay Impactful Research Award 2019, and Best Poster Award at Agriculture Science Congress 2017. Dr. Damani has successfully translated theoretical research into practical tools that address development challenges, particularly in water resource management and agricultural systems. As an educator, he has mentored numerous PhD students including Chintan Tundia, Shreenivas Kunte, Nikhil Hooda, Sivamuthu Prakash Murugan, Dipak L. Chaudhari, Prateek Kapadia, and Manoj K. Chinnakotla. His teaching portfolio includes courses on System Dynamics: Modeling and Simulation for Development (CS 752), Program Derivation (CS 420), and ICT for Development. Dr. Damani's educational background includes a Ph.D. in Computer Sciences from the University of Texas at Austin (1994-1999), B.Tech. in Computer Science and Engineering from IIT Kanpur (1990-1994), and prior professional experience at IBM T J Watson Research Lab and Akamai Technologies.
