Prof. Dr.-Ing. Jörg Wallaschek is a Professor and Executive Director at the Institute of Dynamics and Vibration Research within the Faculty of Mechanical Engineering at Leibniz University Hannover. He leads the Executive Board of the Institute and serves on the board of the Collaborative Research Centre (CRC) 871: Regeneration of Complex Capital Goods . Additionally, he is a member of the Leibniz Research Centre Energy 2050 and the PhoenixD Cluster of Excellence , leading the Task Group S1: Macro Optical Systems . His research focuses on vibration dynamics , aeroelasticity , laser beam welding , ultrasonic technologies , and automotive optics . Key contributions include advancements in turbomachinery analysis, modal parameter estimation, and holographic optical systems for automotive applications. Prof. Wallaschek's work integrates experimental and computational methods to address challenges in mechanical systems, including friction damping, nonlinear vibrations, and material characterization. His interdisciplinary projects bridge mechanical engineering with photonics and energy systems. He holds leadership roles in multiple research initiatives and collaborates across departments to advance innovation in energy, manufacturing, and aerospace technologies.
Dr. Alexander Royal is an Associate Professor in the Department of Civil Engineering at the University of Birmingham, specializing in resilience and sustainability research. He holds a MEng from Loughborough University (2001) and a PhD in Geotechnical Engineering from the University of Birmingham (2006). His academic career includes roles as a Research Fellow (2005) and permanent academic staff member (2010). Research interests focus on cast in situ barriers for contaminated land remediation, trenchless utility installation technologies, erosion of fine-grained soils around leaking pipes, and geohazards. His work addresses challenges in subsurface interactions, utility network management, and sustainable infrastructure development. Notable projects include the Mapping the Underworld (MTU) initiative, which develops geophysical tools and multi-sensor platforms to reduce trenching costs, and the Cablepipe project exploring long-distance underground power transmission. Royal’s research has led to publications on utility detection, microtunnelling simulations, and HDD steering. He seeks doctoral students to explore cement-bentonite barriers, geotechnical factors in trenchless tech, and clay mineralogy impacts. He collaborates with industry bodies like the Pipeline Industries Guild and Midlands Geotechnical Society.
Sara Algeri is an Associate Professor in the Department of Statistics at the University of Minnesota – Twin Cities, within the College of Science and Engineering. Her research bridges statistical theory and applications in astrophysics and medical sciences, supported by active funding from the National Science Foundation. Her research expertise lies in developing statistically rigorous and computationally efficient methods for hypothesis testing, particularly in the presence of nuisance parameters and unknown backgrounds. Key areas include goodness-of-fit tests, sequential testing, and inference in high-dimensional or uncertain settings. Her work enables discoveries in physics, such as searches for dark matter and axions, while also contributing to public health through improved screening risk assessments. The trends in her recent publications reveal a strong focus on solving inverse problems in high-energy astrophysics and particle physics with innovative statistical frameworks. She develops methods applicable to data from instruments like HAWC and axion haloscopes, often under high background noise. Simultaneously, she applies statistical rigor to clinical questions, such as surgical outcomes and screening test risks, demonstrating interdisciplinary impact. Dr. Algeri is actively involved in collaborative research, as evidenced by her role as Co-Investigator on the NSF-funded project Computationally Tractable Inference for Multi-Messenger Astrophysics (2022–2025), which aims to develop scalable statistical tools for next-generation astrophysical data. While no formal students are listed, her collaborative publications suggest mentoring and advising activities. She has not received any explicitly mentioned scientific awards in the provided text. Her work contributes to broader scientific goals, including the UN Sustainable Development Goals, by advancing methodological tools for scientific discovery and health improvement. She collaborates with researchers across institutions and disciplines, particularly in astrophysics and medical statistics.
Fariba Moghaddam is an Ordinary Professor at HES-SO Valais-Wallis - Haute Ecole d'Ingénierie, leading the Power & Control orientation under the Institut Systèmes industriels. She specializes in renewable energy systems, control engineering, and educational technology, with a focus on solar energy optimization, remote laboratories, and sustainable development initiatives in the Global South. Her work spans academic leadership, research, and pedagogical innovation. Education: PhD in Control Engineering from École Polytechnique Fédérale de Lausanne (EPFL), 1996. Additional academic background in electrical and mechanical engineering. Research Interests: Solar tracking systems, photovoltaic-thermal (PVT) integration, machine learning applications in energy systems, remote experimentation platforms, and collaborative educational infrastructure. Recent projects include the Solar Energy Optimization via Remote Platforms and DEAR MENA (Digital Education & Research for MENA). Grants & Projects: Solar Energy Optimization (2022-2026): Swiss National Science Foundation-funded project developing cost-effective sun tracking solutions. DEAR MENA (2019-2024): Cluster fostering digital education partnerships between Swiss and MENA institutions. Sustainable Laboratories (2020-2021): Platform for redistributing lab equipment to emerging economies. MOOLs (2018-2021): Massive Open Online Laboratories for global engineering education. Labs & Infrastructure: Spearheaded the Remote Real-Time Research Platform for control engineering, enabling global access to lab equipment via secure web interfaces. Collaborates on IoT-integrated systems for remote experimentation and industrial automation. Awards: None explicitly listed, but recognized for contributions to sustainable energy and educational equity through numerous funded projects.
Raul G. Longoria is a Professor in the Department of Mechanical Engineering at the University of Texas at Austin, holding the General Motors Foundation Centennial Teaching Fellowship. He specializes in multi-disciplinary dynamic system modeling, vehicle dynamics, control systems, and AI-driven smart tools. His research integrates electromechanical systems, robotics, and biomedical engineering applications like cardiovascular assist devices. Education: B.S.M.E. and Ph.D. in Mechanical Engineering from UT Austin (1985, 1989). Leadership roles include membership in ASME, IEEE, SAE, and ISTVS. Research focuses on smart hand tools leveraging machine learning for skilled trades, superconducting systems, and energy storage solutions. Projects span automotive systems, medical device modeling, and robotics mobility. Publications highlight advancements in cardiovascular system simulation, robotic terrain interaction, and mechatronics. His work bridges theoretical modeling with practical applications, emphasizing innovation in both industry and healthcare. Recent research emphasizes socio-technical co-design with workers, AI ethics, and edge computing for industrial tools. His lab explores embedded sensing technologies to enhance worker precision and safety in manufacturing and maintenance contexts.
Maggie French is an Assistant Professor in the Department of Physical Therapy & Athletic Training at the University of Utah, where she has been employed since July 2023. Her academic journey includes a DPT from Emory University (2010-2013), a neurologic physical therapy residency at Johns Hopkins and University of Delaware, a PhD in Biomechanics and Movement Science from University of Delaware (2016-2021), and a postdoctoral fellowship at Johns Hopkins University School of Medicine (2021-2023). She is a Board-Certified Neurologic Clinical Specialist (2015-2025) and serves on the Education Committee for the American Society of Neurorehabilitation. Dr. French's research focuses on improving the value of post-stroke rehabilitation by understanding person and system level variability through three interconnected components: longitudinal measurement of function and healthcare utilization; characterizing variability in functional outcomes; and characterizing and improving the value of care. Her work employs learning health systems, data science, bioinformatics, and real-world data analysis, with growing applications of artificial intelligence in rehabilitation. She has published extensively on topics including locomotor learning after stroke, depression and physical activity relationships, video-based gait analysis, and precision rehabilitation frameworks. Her recent publications demonstrate a strong trend toward leveraging large-scale data and digital health technologies to understand rehabilitation outcomes. The research shows increasing integration of artificial intelligence methods, with several 2024-2025 papers focusing on responsible AI implementation, video-based pose estimation, and common data models for rehabilitation research. Board-Certified Neurologic Clinical Specialist (2015-2025) Faculty Fellow in One-U Responsible Artificial Intelligence Initiative (2025) NIH grant: Characterizing Post-Acute Physical Therapy Utilization and Outcomes After Stroke (2024-2029) University of Utah grant: Safety and Efficacy of Reduced Upper Extremity Restrictions after Lung Transplantation (2024-2026) As an educator, Dr. French teaches in both the entry-level DPT program and Rehabilitation Science PhD program at the University of Utah, with a teaching philosophy centered on fostering mutual respect, supporting self-discovery, and creating a culture of continuous learning. She actively mentors future clinicians and researchers, emphasizing preparation for leadership roles in rehabilitation.
Professor Richard Burke is a faculty member in the Department of Mechanical Engineering at the University of Bath, where he serves as Centre Director for the IAAPS Made Smarter Innovation: Centre for People-Led Digitalisation. His research focuses on automotive engineering systems, particularly turbocharger technology, internal combustion engines, electric vehicle powertrains, and thermal management systems. Professor Burke's research interests span a wide range of mechanical engineering topics related to sustainable transportation. His work encompasses turbocharger systems optimization, electric vehicle powertrain development, hydrogen fuel cell applications, and advanced engine control strategies. He has made significant contributions to understanding transient engine performance, thermal management in electric motors, and emissions reduction technologies. His research often bridges theoretical modeling with practical engineering applications, particularly in the automotive sector. His publication record shows a clear trend toward sustainable mobility solutions, with increasing focus on electrified powertrains, hydrogen technologies, and emission reduction strategies in recent years. His work demonstrates expertise in both traditional internal combustion engine optimization and emerging electric and hydrogen-powered vehicle technologies. The interdisciplinary nature of his research connects mechanical engineering principles with control systems, thermal dynamics, and energy management. Professor Burke appears to be actively supervising doctoral students and has contributed to engineering education through initiatives like the virtual engine laboratory for teaching powertrain engineering. His work has garnered significant citations, particularly in areas of turbocharger technology and electric vehicle systems, indicating substantial impact in his research fields.
Harsha Abeykoon is an Associate Professor in the Mechanical Engineering Department at Embry-Riddle Aeronautical University . His research focuses on Robotics , Reinforcement Learning , and Haptics , with applications in Bilateral Teleoperation , Biomedical Robotics , and Advanced Motion Control Systems . He has held academic positions at the University of Moratuwa, Asian Institute of Technology, and Plymouth State University. Ph.D. , Integrated Design Engineering, Keio University M.S. , Engineering, Keio University B.Sc. , Electrical Engineering, University of Moratuwa His research bridges Deep Learning and Haptics for teleoperation systems, Mobile Robots for navigation, and Disturbance Observers for friction compensation in DC motors. Recent work includes 3D Environmental Force Modeling and Monocular Camera-Based Object Localization . Dr. Abeykoon’s publications span Haptic Interfaces , Bilateral Control , and Reinforcement Learning . He has received multiple Outstanding Research Performance Awards from the University of Moratuwa and Best Paper Awards at EECon. Outstanding Research Performance Awards (2014-2023) Best Paper Awards (EECon 2021, 2024) Monbukagakusho Scholar (Japanese Government, 5 years) Keio Leading Lab Grant (3 years) IEEE SPARX Program Award (2024) He has led international collaborations via IEEE, including the ERASMUS+ Capacity Building Grant (€1 million) and served as Vice Chair and Secretary in IEEE Sections. Dr. Abeykoon mentors projects in Robot-Assisted Surgery and Smart Energy Systems .
BOGDANFFY Lorand is a Professor and Head of the Department of Automation, Computers, Electrical and Power Engineering at the Faculty of Mechanical and Electrical Engineering, UPET.RO. He holds a PhD and specializes in IoT systems, control engineering, renewable energy, and e-learning technologies. His research integrates simulation, modeling, and sensor networks into practical applications like smart urban management, solar tracking systems, and underground mining safety. His work spans interdisciplinary areas such as: IoT-enabled environmental monitoring for sustainable cities Computational methods in mining safety and risk analysis Development of e-learning platforms and educational software Advanced motor control systems and sensorless vector control His recent publications highlight trends in: Smart infrastructure and energy optimization Data-driven decision-making in insurance and risk management 360° imaging systems and mobile teleoperation No scientific awards are listed in the provided texts. His advising and grants focus on engineering education and industrial automation projects. He leads teams in developing control systems and renewable energy solutions.
Bernt Sigve Aadnøy is a Professor in the Department of Energy and Petroleum Engineering at the University of Stavanger, Faculty of Science and Technology. His work bridges theoretical and applied petroleum engineering, with a strong emphasis on drilling operations, well design, and geomechanics. His research focuses on drilling optimization, wellbore stability, drilling fluids, and smart well systems. He has extensively studied the use of nanoparticles in drilling fluids, rate of penetration modeling, torque and drag in 3D wells, and closed-loop drilling optimization. His work integrates computational modeling, laboratory experiments, and field case studies. The recent publications highlight a consistent trend in applying advanced modeling techniques—including machine learning, finite element analysis, and stochastic optimization—to solve complex drilling challenges. Topics include nanoparticle-enhanced fluids, real-time mechanical specific energy minimization, and autonomous downhole control systems, reflecting a strong interdisciplinary approach combining petroleum engineering with data science and control theory. Bernt Sigve Aadnøy has collaborated with numerous researchers and students, contributing to advancements in drilling safety, efficiency, and sustainability, particularly in challenging environments such as the Arctic and deep-water reservoirs.
Silvia Komara is an Assistant Professor at the Faculty of Economic Informatics , University of Economics in Bratislava. She specializes in statistical modeling, econometrics, and insurance risk analysis, with a focus on contrast analysis, GLM parameters, and time series forecasting. Her work bridges theoretical statistics with practical applications in economic policy and digital skills development. Current: Assistant Professor at University of Economics in Bratislava (2019-) Past: Postdoctoral studies at VSB - Technical University in Ostrava (2014), Macquarie University (2015) Her research interests include: Generalized Linear Models (GLM) Logit modeling for social exclusion analysis Risk segmentation in motor insurance GARCH models for financial markets Time series analysis using ARIMA/ARIMAX Key research trends: Applications of statistical methods to economic policy analysis, insurance risk modeling, and macroeconomic forecasting. Notable collaborations include projects on poverty analysis ( Quality & Quantity , 2023) and work intensity studies ( Argumenta Oeconomica , 2023). Scientific contributions supported by: VEGA 1/0561/21: Impact of the COVID-19 crisis on businesses and employment VEGA 1/0038/23: Advanced statistical modeling in social economics VEGA 1/0410/22: Risk modeling for insurance companies VEGA 1/0166/20: Statistical methods in economic policy International collaborations include research visits to Macquarie University (2015), NOVA Technical University (2024), Gdańsk University of Technology (2024), and Erasmus+ teaching activities.
Maria-Veronica Ciocanel is an Assistant Professor of Mathematics (Primary) and Biology (Joint) at Duke University's Trinity College of Arts & Sciences, where she has held appointments since 2020. Her research sits at the intersection of mathematical biology, cell biology, and computational modeling, with a particular focus on intracellular transport mechanisms and protein organization in cells. Her research interests include mathematical cell and developmental biology, emergent dynamics in intracellular transport, multiscale modeling of filamentous protein organization, parameter inference for models of biophysics experiments, applied dynamical systems, and applied topological data analysis. She develops mathematical and computational techniques to uncover the basic functions of intracellular proteins in development and injury, with applications to RNA transport in frog oocytes, radial glial cells, and neurons. Her work combines differential equations, stochastic modeling, and topological data analysis to address fundamental questions in cell biology. Her recent publications demonstrate a consistent focus on mathematical approaches to biological problems, particularly in cytoskeleton dynamics, intracellular transport, and symmetry analysis in developmental systems. She also maintains an active research program in social justice applications, particularly analyzing racial disparities in federal sentencing through mathematical and statistical approaches. Lee A. Segel Prize for the Best Paper in the Bulletin of Mathematical Biology (2025) for 'Parameter identifiability in PDE models of fluorescence recovery after photobleaching' Red Sock Award (SIAG/Dynamical Systems) for best poster presentation Ciocanel actively mentors undergraduate students through various research programs including DOmath, PRUV, and Muser. Two of her former students, Anna and Niny, have gone on to become Assistant Professors at University of New Mexico and San Francisco State University, respectively. She has secured significant research funding including grants from the National Science Foundation and Pennsylvania State University for projects on microtubule behavior and membrane-actin cortex adhesion. She is also involved in the RTG: Training Tomorrow's Workforce in Analysis and Applications program as a Co-Principal Investigator. Her laboratory focuses on mathematical modeling of intracellular transport processes, with particular emphasis on microtubule dynamics in neurons and mRNA transport mechanisms. She collaborates extensively with experimental biologists at institutions including Brown University, Penn State University, and Duke University to ensure her mathematical models remain grounded in biological reality.
Uğur Ufuk KÖRPE serves as a full-time Researcher in the Department of Electrical and Electronics Engineering at Ahi Evran University's Faculty of Engineering and Architecture since 2021. His academic foundation includes comprehensive education from Karabük University across all degree levels. Academic Background: PhD in Electrical and Electronics Engineering (2022), Karabük University MSc in Electrical and Electronics Engineering (Thesis, 2020-2022), Karabük University BSc in Electrical and Electronics Engineering (2015-2020), Karabük University Specializing in Electrical Machines and Energy Conversion with emphasis on Control Theory and Applications, Dr. KÖRPE's research focuses on advanced control methodologies for electric motors. His work addresses critical challenges in motor drive systems including parameter variations, magnetic saturation effects, and thermal dependencies through innovative predictive and adaptive control frameworks. Current investigations integrate machine learning techniques with traditional control paradigms to enhance motor performance in electric vehicle applications. Publication analysis reveals concentrated expertise in model predictive control (MPC) variants for permanent magnet and induction machines, with recent work (2021-2025) demonstrating progressive sophistication from basic MPC implementations to reinforcement learning-enhanced adaptive systems. Key thematic developments include unscented Kalman filter integration for real-time parameter estimation and deep reinforcement learning for handling nonlinear motor characteristics. Research Funding: Principal Researcher for ‘Speed Control Implementation in Brushless AC Motors’ (2022-2023), funded by Higher Education Scientific Research Projects Collaborative work primarily occurs with Karabük University researchers including Ozan Gülbudak and Mustafa Gökdað, forming a consistent research team across six publications between 2021-2025. Current investigations focus on robust control solutions for electric vehicle propulsion systems under varying operational conditions.
Waldemar Milej serves as a Lecturer in the Department of Power Electronics and Automation of Energy Conversion Systems at the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology in Kraków, Poland. His position falls within the assistant professors group at the institution. His research spans Power Electronics, Electrical Engineering, and Energy Conversion Systems with specialized focus on thermal phenomena in electrical machines, motor control systems, and fault diagnosis of induction motors. Additional interests include photovoltaic energy systems, grid-connected inverter control, and e-learning applications in engineering education. His work demonstrates strong integration of theoretical modeling with practical industrial applications. Analysis of his 15 publications (2001-2019) reveals persistent emphasis on thermal behavior in synchronous and induction machines, torque ripple mitigation in brushless DC motors, and stator-voltage-based diagnostic techniques. His research consistently bridges electromagnetic design, thermal management, and control strategies for power electronic systems, with notable contributions to transient state analysis and renewable energy integration.
Professor Jerome Jouffroy is affiliated with the University of Southern Denmark under the Faculty of Engineering and Science , specializing in Mechanical and Electrical Engineering . His work bridges theoretical control systems research with practical applications in robotics, marine engineering, and mechatronics. Research Focus: Control systems design, autonomous vehicles, marine stabilization, and 3D-printed mechatronics. Collaborations: Active in international networks, particularly in drone cooperative control and marine systems. Projects: Supervised research on drone load handling and energy-efficient UAVs. His publications highlight advancements in quadrotor control , modulating function methods , and marine vessel stabilization . Recent work explores tilt-rotor energy efficiency and cooperative drone systems. Despite extensive media engagement (e.g., defense industry AI applications, offshore wind drone servicing), no formal scientific awards are listed. Teaching includes Control Systems and interdisciplinary team projects.
