Erdinç Altuğ serves as a Professor in the Department of Mechanical Engineering at Istanbul Technical University, specializing in advanced aerial robotics and control systems. His work bridges theoretical control methodologies with practical UAV applications. Research interests focus on Unmanned Aerial Vehicle design and fault tolerance Quadcopter dynamics and vertical takeoff systems Adaptive control for parametric variations Rapid prototyping of hybrid VTOL platforms His recent publications demonstrate consistent innovation in autonomous flight systems, particularly in fault-tolerant operational modal analysis and modular multi-drone configurations. Current projects include: Hibrit İnsansız Hava Aracı Ile Otonom Teslimat Sistemi Geliştirilmesi (TUBITAK, 2020-2022) Mini Jet Motorlu Dikine Kalkıp İnebilen Otonom Taşıyıcı Robot Geliştirilmesi (TUBITAK, 2018-2021) Kampüs içi ve bina içi ortamlarda çalışacak otonom taşıma aracı algoritmaları (SRP, 2018-2021) Supervising 26 graduate works, his research impacts both academic and industrial UAV development.
Dr. Ying Wang is a Professor in the Department of Electrical, Computer and Software Engineering at Ontario Tech University, part of the Faculty of Engineering and Applied Science. Her research focuses on RF/microwave circuits, millimeter-wave technology, antennas, and computer-aided design. She holds a PhD from the University of Waterloo (2000), and earlier degrees from Nanjing University of Science and Technology, China. Education: PhD (Electrical Engineering), University of Waterloo, 2000 Master of Applied Science (Electronic Engineering), Nanjing University of Science and Technology, 1996 Bachelor of Engineering (Electronic Engineering), Nanjing University of Science and Technology, 1993 Research Interests: Dr. Wang specializes in microwave filter design, millimeter-wave systems, antenna arrays, and neural network applications in electromagnetic optimization. Her work emphasizes practical implementations in wireless communication systems, satellites, and high-frequency devices. Recent projects include optimizing amplifier stability in CMOS technology and developing advanced multiplexing networks. Publications: Her publications span neural network modeling for microwave filters, millimeter-wave amplifier design, and multiplexer synthesis. Key themes include high-dimensional optimization techniques, scalable circuit models, and cross-disciplinary applications of artificial intelligence in electromagnetics. Professional Background: Prior to academia, Dr. Wang worked as a Senior Member of Technical Staff at COM DEV International (2000–2007), contributing to industrial microwave system development. She currently teaches courses such as Microwave and RF Circuits (ELEE 4750U) and Antenna Theory and Design (ENGR 5695G).
Jorma Kyyrä is a Professor at Aalto University's Department of Electrical Engineering and Automation. His primary research focuses on power electronics, wireless power transfer systems, and high-frequency converter design. He has contributed significantly to fields like switch mode power supplies, electromagnetic compatibility (EMC), and power factor correction. Key honors include the IEEE Education Society Chapter Achievement Award (2006) for Nordic Chapter. His work emphasizes practical applications in renewable energy integration, DC microgrids, and contactless charging systems. Recent research trends show strong focus on MHz-frequency wireless power transfer, model predictive control strategies for rectifiers, and high-efficiency DC-DC converter topologies. Publications span over 30 years, with notable contributions to the design of resonant converters, inductive power transfer systems, and energy storage solutions. Collaborative projects include investigations into multi-energy microgrids and adaptive control for industrial applications. His research often combines theoretical analysis with hardware implementation, evidenced by frequent conference participation at IEEE venues.
Miloš Matejić serves as an Associate Professor at the University of Kragujevac's Faculty of Engineering Sciences, within the Department of Mechanical Constructions and Mechanization. His academic position involves active research and teaching in mechanical systems design. Research interests center on advanced mechanical engineering topics including: Cycloid drive efficiency optimization and stress analysis Tribological testing methodologies and material interactions Computational modeling of gear systems using FEM Parametric design automation for mechanical components Composite materials performance in dynamic applications Recent publications demonstrate strong focus on experimental validation of cycloid reducers, novel tribometers, and material optimization. No awards, student advisories, or grant activities are documented in available sources. Institutional collaboration occurs through the Faculty of Engineering Sciences' laboratories and research centers.
Dr. Sharmin Jahan serves as a tenure-tracked Assistant Professor in the Department of Computer Science at Oklahoma State University since August 2022. Her research centers on dynamic security assurance for autonomous systems (self-adaptive systems) through explainable AI models that interpret uncertain operational environments to enable autonomous security decision-making and compliance maintenance. Her educational background includes a Ph.D. and Master's in Computer Science from the University of Tulsa (2018-2021), and a B.Sc. in Computer Science and Engineering from Bangladesh University of Engineering and Technology (2007-2012). She teaches Introduction to Computer Security (CS 4243/5243) and leads Dr. Jahan's Lab focused on security for autonomous systems. Research interests span Explainable AI in Cyber Security, IoT Security, Self-Protecting Systems, and Micro-service Security. Her work develops frameworks that embed security awareness in dynamic systems, using XAI to interpret environmental uncertainty and maintain security compliance through autonomous adaptation. Current projects explore machine learning models for security analysis and XAI challenges in domain-specific security applications. Recent publications (2025-2020) demonstrate concentrated research on security assurance in self-adaptive systems, particularly for IoT and microservice architectures. Key trends include XAI-driven anomaly detection, security profile extraction from operational data, and risk-adaptive access control. Subfield specializations cover service mesh security, blockchain-based access frameworks, runtime trust evaluation, and autonomous threat containment. Scientific awards include: Principal Investigator for 2023 Arts and Sciences Summer Research Award on XAI-enhanced security awareness in autonomous systems Senior personnel on 2022 NSF RET Grant for Big Data and Machine Learning research experiences She advises M.Sc. student Masrufa Bayesh and teaches graduate/undergraduate security courses. Her lab actively investigates frameworks for security assurance in dynamic environments, with emphasis on IoT and microservice architectures requiring continuous adaptation to environmental changes while maintaining security compliance. Dr. Jahan's research team develops analysis and assessment models to determine security compliance degradation risks and optimal adaptation strategies, enhancing system resiliency through separate analytical frameworks integrated with her PhD-developed assessment methodology.
Prof. Victor Grigoras is a faculty member at the Technical University of Iași, holding the rank of Professor. He specializes in electrical engineering and computer science, focusing on signal processing, parallel architectures, and nonlinear dynamics in power systems. His research interests include smart grid technologies, renewable energy integration, and data-driven methodologies for grid optimization. He teaches courses such as 'Semnale, Circuite și Sisteme' and 'Algoritmi și Structuri Paralele de Calcul.' His research spans over 15 recent articles (2021–2025), emphasizing advancements in smart grid automation, machine learning applications in voltage quality analysis, and optimal power flow solutions for renewable integration. Notable trends include SCADA system improvements, energy storage strategies for prosumer grids, and IoT-based energy management. His work addresses challenges in grid reliability, power quality, and future urban grid resilience under high EV adoption scenarios. Prof. Grigoras has contributed to frameworks for electric vehicle charging station placement, hydropower plant optimization via data mining, and demand response mechanisms using smart metering. His methodologies often combine clustering techniques with fuzzy logic or metaheuristic algorithms to solve complex grid problems.
Chung-Wei Lin is an Associate Professor and Deputy Director at the Department of Computer Science and Information Engineering and the Graduate Institute of Networking and Multimedia at National Taiwan University. His research focuses on cyber-physical systems, particularly in the domains of connected and autonomous vehicles, system security, and design methodologies. He maintains active collaborations with industry partners including Toyota and has established himself as a leading researcher in intelligent transportation systems in Taiwan. Education: Ph.D. (2015) from Department of Electrical Engineering and Computer Sciences, University of California, Berkeley (Advisor: Alberto L. Sangiovanni-Vincentelli) M.S. (2007) from Graduate Institute of Electronics Engineering, National Taiwan University (Advisor: Yao-Wen Chang) B.S. (2005) from Department of Computer Science and Information Engineering, National Taiwan University Dr. Lin's research interests center on cyber-physical systems with specific focus on connected and autonomous vehicles, security mechanisms, and system design methodology. Before returning to NTU in 2018, he worked at Toyota InfoTechnology Center, USA, Inc. His recent projects cover diverse topics including systems engineering, formal verification for robustness and compatibility, runtime monitoring, and intelligent intersection management. His work bridges theoretical foundations with practical applications, addressing real-world challenges in transportation systems through innovative technical solutions. Analysis of Dr. Lin's recent publications (2023-2025) reveals a strong emphasis on intelligent transportation systems with particular focus on security challenges for connected vehicles, formal verification techniques for safety-critical systems, and novel control algorithms for vehicle coordination. His research demonstrates increasing integration of machine learning approaches, especially reinforcement learning, to address complex decision-making problems in transportation. The work spans multiple technical domains including control theory, networking, cybersecurity, and formal methods, reflecting the inherently interdisciplinary nature of cyber-physical transportation systems research. Selected Awards: 2016 Best Paper Award, ACM Transactions on Design Automation of Electronic Systems 2015 Most Accessed ESL Paper Best Paper Award, IEEE ISSREW 2016 workshop Best Paper Award, ICCD 2010 Best Paper Nominee, ASP-DAC 2015 Dr. Lin currently advises multiple Ph.D. and M.S. students, with research focusing on various aspects of cyber-physical systems for transportation. His group includes Ph.D. students Pintusorn Suttiponpisarn and I-Ching Tseng, as well as several M.S. students. His extensive publication record and numerous patents (over 20 granted) indicate significant research impact and likely substantial research funding from both government and industry sources. His research program demonstrates strong translational potential, with many concepts moving from theoretical foundations to practical implementations. Dr. Lin leads the Cyber-Physical Systems Laboratory at NTU, which focuses on research related to intelligent transportation systems. The lab conducts research in areas including vehicle control, intersection management, security mechanisms, and formal verification for cyber-physical systems. His team collaborates with researchers from various institutions globally, as evidenced by his extensive publication record with international co-authors from universities and research institutions in the United States, Japan, and Europe.
Lars Kotthoff is the Templeton Associate Professor and Derecho Professor at the University of Wyoming's School of Computing, Department of Electrical Engineering and Computer Science. He leads the MALLET lab, focusing on meta-algorithmics, learning, and large-scale empirical testing. His research integrates AI and machine learning to develop robust systems, particularly in algorithm selection, configuration, and automated machine learning (AutoML). He has held sabbaticals and collaborations globally, including at the University of Warsaw and NASA Ames. Awards include the Templeton Endowed Chair and Open Source Machine Learning Award. His work bridges computational mechanics, materials science, and optimization heuristics. Research interests include Bayesian optimization for materials science, automated parameter tuning, and interpretable machine learning models. Key projects include optimizing laser-induced graphene production and developing the mlr3 framework. Grants include NASA EPSCoR funding for in-space manufacturing and a Microsoft grant for biomedical imaging. He advises graduate and undergraduate students, mentors Google Summer of Code projects, and organizes workshops/conferences like COSEAL and Dagstuhl seminars. Publications span automated algorithm design, performance benchmarking, and interdisciplinary applications. His work emphasizes making machine learning accessible to non-experts through tools like Auto-WEKA and the mlr3 ecosystem. Current projects include NASA-funded advanced electronics manufacturing and collaborations with institutions worldwide.
Janek Thomas is a researcher actively contributing to the fields of Machine Learning and Automated Machine Learning (AutoML). His work focuses on hyperparameter optimization, multi-objective algorithms, and improving the interpretability of machine learning models. Collaborating with institutions like TU Munich and LMU Munich, he has authored over 35 publications since 2016. Key contributions include the AMLB benchmark suite for AutoML systems and foundational research on multi-objective hyperparameter tuning. His research bridges theoretical advancements with industrial applications, emphasizing robust model verification and scalable optimization techniques.
Roland YAP Hock Chuan serves as an Associate Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS), and previously held the role of assistant director at The Logistics Institute Asia Pacific (TLI-AP). His academic foundation was built at Monash University, Australia, where he earned comprehensive qualifications in Computer Science. Education: Ph.D. in Computer Science, Monash University, Australia M.Sc. in Computer Science, Monash University, Australia B.Sc. (Honours) in Computer Science, Monash University, Australia Research Focus: Renowned for pioneering the CLP(R) system that revolutionized Constraint Programming, Prof. Yap's work now spans artificial intelligence, security, programming languages, and social networks. His research bridges theoretical rigor with real-world applications, particularly in data analytics and knowledge compilation for industrial systems. Publication Evolution: His scholarly output reveals a strategic progression from foundational constraint logic programming (1990s) to cutting-edge security mechanisms and AI-driven solutions (2010s). Recent works demonstrate expertise in robust search algorithms, memory protection systems, and social network defense strategies, reflecting continuous adaptation to emerging computational challenges. Scientific Recognition: Best student paper award at SCA 2011 for social network research Best student runner-up paper at CIKM 2009 1995 Australian Computer Science Ph.D. Prize (1996) Praxa Computer Prize for theoretical computing achievements (1985) Academic Leadership: Prof. Yap directs the Tier 1 research project 'Investigating Product Configuration as Knowledge Compilation,' developing accelerated industrial solvers through innovative KC integration. His teaching portfolio includes advanced security courses (CS4239/5439/6231), shaping next-generation cybersecurity expertise. Mentoring excellence is evidenced by multiple student award-winning publications in top-tier conferences. Collaborative Infrastructure: Through his leadership role at TLI-AP, he contributes to NUS's interdisciplinary logistics research ecosystem, connecting computer science with supply chain innovation and operational optimization challenges.
Prof. Dr. Sven Feurer is a Research Professor of Marketing at Bern University of Applied Sciences' Business School, leading the Institut Marketing & Global Management. His roles include Co-Institutsleiter and Head of the Marketing Group since 2022. He holds a PhD from the University of Mannheim and has held academic positions at Karlsruhe Institute of Technology and Vanderbilt University. His research focuses on consumer behavior, pricing strategies, innovation management, and marketing ethics, with prominent publications in Journal of Marketing , Journal of Consumer Research , and Journal of Product Innovation Management . His research explores topics such as radical innovation adoption, technology acceptance, and pricing fairness. Notable projects include studying AI's influence on price perception and consumer reactions to personalized pricing. He collaborates internationally, including with Vanderbilt University's Owen Graduate School of Management. Feurer’s 20+ peer-reviewed articles address dynamic energy tariffs, B2B social media use, and consumer responses to really new products. He co-authored the 2024 book Price fairness . His work bridges academic rigor and practical marketing challenges, emphasizing ethical considerations in pricing and innovation.
Dr. Robert Vogt-Ardatjew is a researcher specializing in Radio Systems , with a focus on Electromagnetic Compatibility (EMC) , Risk Management , and Software Defined Radio (SDR) . His work spans Shielding Effectiveness , Propagation Channel Modeling , and Frequency-Selective Emission Detection , contributing to both academic research and practical EMC engineering solutions.
Barb Cutler is an Associate Professor in the Computer Science Department at Rensselaer Polytechnic Institute (RPI) and an affiliated faculty member at EMPAC (Experimental Media and Performing Arts Center). She specializes in computer graphics, interactive visualization, computational geometry, and open-source educational software. Previously, she was a student and Post-Doctoral Lecturer at MIT's Department of Electrical Engineering and Computer Science. Education: PhD in Electrical Engineering and Computer Science, MIT, August 2003 MEng in Electrical Engineering and Computer Science, MIT, June 1999 Research Interests: Her research spans computer graphics , interactive visualization , computational geometry , and the development of open-source educational tools . She has made significant contributions to architectural daylighting design, spatially augmented reality, and procedural modeling. Her work often integrates advanced visualization techniques with practical applications in education and architecture. Scientific Awards: NSF Early CAREER Award (2009) Rensselaer School of Science Outstanding Teaching Award (2017) Rensselaer Trustees' Outstanding Teacher Award (2019) Teaching & Advising: Barb Cutler has taught a wide range of courses including Data Structures, Advanced Computer Graphics, Interactive Visualization, and Computational Geometry. She has advised numerous PhD and MS students, many of whom have gone on to positions at leading companies and institutions. She is currently advising Evan Maicus, a PhD student in Computer Science. Lab & Projects: She leads the development of Submitty , an open-source homework submission, autograding, and TA grading system used widely in computer science education. Her lab focuses on innovative educational technologies and advanced visualization systems.
Andrew J. Parkes is an Assistant Professor (Lecturer) in Operational Research and Computer Science at the University of Nottingham , affiliated with the School of Computer Science and the COL research group (formerly ASAP). He has been a member of the LANCS Initiative's executive committee and co-organized multiple streams at international conferences like OR61, EURO 2019, and OR60. His research spans optimization techniques, heuristic methods, and applications in timetabling and financial modeling.
Kevin MICHENEAU is a Teacher-Researcher at CESI School of Engineering, affiliated with the LINEACT research laboratory in Guipavas, France. His work bridges building energy systems and experimental particle physics, focusing on data-driven optimization of smart buildings and dark matter detection. Education: PhD in Subatomic Physics, University of Nantes (2018): "Study of residual electrons in the XENON100 experiment" Master's degree in Research in Subatomic Physics, University of Nantes (2014) Research Focus: Dr. MICHENEAU develops advanced models for building energy performance with emphasis on occupancy behavior impact and smart control systems . His methodology combines sensor fusion and multi-objective optimization to balance energy efficiency with occupant comfort. Previously, he contributed to XENON dark matter experiments through signal reconstruction and background modeling. Publication Evolution: His research trajectory shows a strategic pivot from particle physics (2017-2019) to building energy systems (2024), applying rigorous data analysis techniques across domains. The 2024 MPC optimization study demonstrates transferable methodology from high-precision physics to sustainable engineering. Mentorship: Currently supervising PhD candidate BOURGOIN on "Towards modeling the impact of occupancy on the energy behavior of smart buildings" (2023-2026). Research Ecosystem: Member of the "Engineering and Digital Tools" team within LINEACT, teaching Computer Science, Mechanics, and Physics across preparatory and engineering cycles while contributing to PhD training at University of Nantes.
