Hee Sup Shin is an Assistant Professor in the School of Science and Engineering at the University of Missouri-Kansas City (UMKC). He holds a Ph.D. in Mechanical Engineering from Carnegie Mellon University (2021), an M.S. from the same institution (2015), and a B.S. from Korea University (2013). His research focuses on wearable robotics, flexible electronics, haptic systems, and biomedical devices. He leads the MiroLaboratory (https://mirolaboratory.github.io/), developing innovative technologies for healthcare monitoring and human-robot interaction. Shin's work integrates soft materials, advanced sensors, and wireless systems to create next-generation biomedical devices and robotic interfaces. Recent projects include skin-integrated wearables for sleep and respiratory monitoring, implantable bioelectronic systems, and modular sensor skins for UAVs. His research bridges mechanical engineering, materials science, and clinical applications, emphasizing practical biomedical solutions and aerospace engineering advancements. Key contributions include programmable thermal haptic interfaces for VR, soft airflow sensors for unmanned vehicles, and mechanoacoustic systems for pediatric health monitoring. His lab emphasizes translational research with applications in rehabilitation, aerospace, and wearable diagnostics. No scientific awards are explicitly listed, but his prolific publication record reflects strong academic engagement.
Antonio Couce Casanova is a Professor in the Department of Industrial Engineering at the Ferrol Engineering Polytechnic University College, part of the University of A Coruña (UDC) in Spain. His teaching spans multiple engineering programs including Automation and Industrial Electronics Engineering, Electrical Engineering, and various Master's degrees in Energy Efficiency, Occupational Risk Prevention, and Textile Technology. His research interests focus on renewable energy systems , particularly hydrokinetic and wind turbine technologies, energy efficiency certification, and building energy simulation. His work bridges theoretical engineering principles with practical industrial applications, especially in marine energy and sustainable manufacturing processes. Analysis of his publication record reveals a strong focus on biomimetic approaches to energy generation , with significant contributions to vertical axis wind turbine design and hydrokinetic energy systems. His more recent work (2022-2024) emphasizes meteorological data processing for building energy simulation and optimization of renewable energy systems using computational methods. Earlier work (2010-2015) focused on control systems and foundational renewable energy technologies. Dr. Couce Casanova has supervised numerous final degree and master's theses since 2013, with projects spanning energy rehabilitation, facility design, and industrial process optimization. His research has been supported by entities including Siemens Industry Software, the Galician regional government (Consellería de Economía e Industria), and NORVENTO INGENIERÍA S.L. He is actively involved in the INNOVACIONES MARINAS research unit, focusing on marine energy applications and sustainable industrial processes. His work demonstrates a consistent trajectory from theoretical control systems toward practical renewable energy solutions with marine applications.
Zervoudakis Konstantinos is a Researcher at the School of Production Engineering and Management, Technical University of Crete. His work focuses on computational intelligence, optimization algorithms, and their applications in education and product design. He holds a fixed-term research position and is based in Office G3.0.01, Building G3. Research Interests: His primary areas include developing nature-inspired optimization algorithms (e.g., flying fox, mayfly, and bees algorithms), applying computational methods to educational challenges like student psychological fitness assessment and group formation, and optimizing product line design using metaheuristics like Tabu Search and Differential Evolution. He also investigates the impact of ICT on education and special education teacher efficacy. Publications Trends: Recent works emphasize hybrid optimization algorithms for real-world problems (maintenance scheduling, product design), AI-driven educational tools for mental health assessment and learning grouping, and algorithmic solutions for multi-objective decision-making. His research bridges computational innovation with practical applications in education and engineering. Awards: None explicitly mentioned in the provided texts. Advising & Grants: No student advisees listed. No grants disclosed in the data.
Hamed RAHIMI NOHOOJI is a Postdoctoral researcher at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), specifically within the Automation department under Prof. Holger Voos's research group. He holds a Ph.D. from Curtin University (Australia, 2018) and has held research positions at the National University of Singapore, UC Louvain, University of Pisa, and the University of Birmingham. His research focuses on soft robotics , adaptive control systems , and human-robot interaction , with notable contributions to projects like the EU H2020 CYBERLEGs Plus Plus initiative and Singapore's A*STAR-funded soft gripper development. His work spans neuroadaptive control , reinforcement learning , and fault-tolerant systems , with applications in space robotics, wind turbine control, and collaborative robotics. With over 900 citations and an H-index of 18 (Google Scholar, 2023), his publications appear in top journals like Mechanical Systems and Signal Processing (IF 8.934) and Neurocomputing (IF 5.779). He has authored four Springer book chapters and served as a guest editor for journals including Frontiers in Robotics and AI and IEEE Transactions on Industrial Electronics . Research interests include: Soft Robotics : Design of compliant actuators, jamming grippers, and topology-optimized soft mechanisms Control Systems : Barrier Lyapunov functions, Nussbaum gain techniques, and neuroadaptive methods Human-Machine Collaboration : Adaptive trajectory optimization for safe human-robot interaction Space Applications : Soft robotics for extraterrestrial missions and compliant systems for space environments His recent articles emphasize constrained control systems , reinforcement learning for robotics , and lightweight gripper design for aerial platforms . His work often bridges theoretical control principles with practical robotic implementations in dynamic environments. Scientific achievements include a Student Travel Award at the 2016 Australasian Conf on Robotics and Automation. He has edited topical collections on Human-Robot Interaction and Soft Robotics , reflecting his leadership in shaping the field's research directions. Labs/Teams: Member of the Automation & Robotics Research Group at SnT, collaborating with Prof. Holger Voos and international partners on EU-funded projects.
Mark Minor is a Researcher in the Department of Mechanical Engineering at the University of Utah. His research focuses on wearable robots, virtual reality, soft robotics, haptics, and automated ground vehicles. He has contributed to projects like the MeLLO Data Library and developed haptic terrain display technologies. Key articles include work on augmented RF propagation modeling and digital spectrum twins, reflecting his interdisciplinary approach spanning robotics, control systems, and human-robot interaction. His recent work emphasizes autonomous vehicle control systems, soft robotic materials, and safety mechanisms in human-robot collaboration. Collaborations include contributions to the POWDER platform for radio dynamic zones and advancements in multi-sensory VR interfaces. Notable research themes include terrain modeling for robotics, bio-inspired mechanisms, and improving mobility technologies for assistive devices. His work integrates both hardware development and algorithmic innovation, particularly in haptic feedback and modular robotic systems.
Pamela Abshire is a Professor in the Department of Electrical and Computer Engineering and the Institute for Systems Research at the University of Maryland, College Park. She holds the rank of Fischell Institute Fellow and is affiliated with the Maryland Robotics Center, Brain and Behavior Institute, and Robert E. Fischell Institute for Biomedical Devices. Her work bridges VLSI circuit design and bioengineering, focusing on performance-resource tradeoffs in natural/engineered systems. Education: B.S. Physics (Caltech, 1992), M.S. and Ph.D. in Electrical Engineering (Johns Hopkins University, 1997 and 2001). Pre-UMD career included R&D roles at Medtronic (1992-1995). Research focuses on CMOS biosensors, low-power microsystems, and bio-inspired designs for applications like cell-based sensing, robotics, and medical devices. Notable projects include nose-on-a-chip odor detection systems, ant-like microrobots, and lab-on-CMOS platforms for real-time cell monitoring. Awards include IEEE Fellow (2018), NSF CAREER Award (2003), and 2021 University Distinguished Scholar-Teacher honor. Active in academic leadership roles including ADVANCE Professor (2020-2021) and editorial work for IEEE Transactions on Circuits and Systems. Grants include NSF funding for olfactory sensing, AFOSR bio-inspired flight tech, and DARPA CogniSense initiatives. Her Integrated Biomorphic Information Systems Lab collaborates on semiconductor innovation through partnerships like the Mid-Atlantic Semiconductor Collaborative. Labs/Teams: Leads the Integrated Biomorphic Information Systems Lab and contributes to Microelectronics at Maryland group. Co-develops biohybrid systems integrating CMOS, MEMS, and biological components.
Elio Tuci is a Professor at the Faculty of Computer Science , University of Namur , Belgium (since 2022). Prior roles include Senior Lecturer at Middlesex University London (2016–2018) and Lecturer at Aberystwyth University (2010–2016). He holds a PhD in Computer Science and Artificial Intelligence from the University of Sussex (2004) and a Master in Experimental Psychology from Sapienza University of Rome (1996). His research lies at the intersection of bio-inspired robotics , computational intelligence , and collective decision-making . He designs control mechanisms for autonomous agents to operate in complex environments, drawing inspiration from biological systems. Key themes include agent-environment interaction, communication in multi-robot systems, and the relationship between morphological structure and behavior. Recent work involves robot swarming models for Caenorhabditis elegans behavior; synchronization mechanisms in e-puck2 robots; evolutionary dispersal strategies under information costs. He co-organizes the WIVACE workshops and leads projects like BABOTS (swarming biological robots) and AUTOMATic (urban traffic management). His research has been featured in media outlets discussing robotics and self-driving technology. He advises PhD students and collaborates with teams at the Namur Digital Institute (NADI) and Namur Institute for Complex Systems (naXys) . Grants and projects focus on autonomous systems, transgenic organisms, and complex network synchronization.
Malte von Scheven is a Senior Researcher and Deputy Director at the Institute of Structural Analysis and Dynamics at the University of Stuttgart. He holds a Dr.-Ing. degree (2009) and specializes in adaptive structures, fluid-structure interaction, and computational mechanics. Research Focus: Redundancy matrices for structural assessment, high-performance computing, actuator placement optimization Teaching: Finite element methods, computational mechanics, nonlinear structural analysis Leadership: Deputy Director since 2006, conference organizer for ECCOMAS and SMART symposia His work bridges structural mechanics with bio-inspired design, including studies on sea urchin skeletons as models for segmented shells. He has supervised numerous theses on SFRP composites, topology optimization, and adaptive systems. Scientific Engagement: Published 15+ papers on redundancy matrices and FSI Organized mini-symposia at international conferences (ECCOMAS 2024, SMART 2023) Active in university governance through Faculty Council and TIK committee Recent research investigates mechanical modeling of adaptive structures, with applications in civil engineering and architectural geometry. His redundancy matrix framework provides novel performance indicators for robust design and assemblability assessment.
Giovanni Iacca is an Associate Professor at the University of Trento's Department of Information Engineering and Computer Science (DISI), where he serves as Coordinator of the Master's Degree in Computer Science and Deputy Director of the Information Engineering and Computer Science Doctoral School. He leads the Distributed Intelligence and Optimization Lab (DIOL) and teaches courses including Computer Architectures, Introduction to Machine Learning, Bio-Inspired Artificial Intelligence, and Optimization Techniques across multiple academic programs. PhD in Computer Science, University of Jyväskylä, Finland (2011) MSc in Computer Engineering, Technical University of Bari, Italy (2006) Professor Iacca's research focuses on the intersection of evolutionary computation, machine learning, and optimization with applications in distributed systems and robotics. His work spans from theoretical foundations of memetic computing and multi-objective optimization to practical implementations in soft robotics, embedded systems, and healthcare applications. Recent efforts emphasize interpretable AI, particularly in reinforcement learning contexts, where his team develops methods to make decision processes transparent while maintaining performance. His research bridges the gap between fundamental algorithmic development and real-world engineering challenges, with over 15 years of industrial experience in optimization applied to engineering, logistics, and scheduling. Analysis of his recent publications reveals a strong trend toward interpretable AI systems, particularly in reinforcement learning contexts, with significant contributions to federated learning optimization, evolutionary neural architecture search, and applications in healthcare scheduling. His work consistently combines evolutionary algorithms with modern machine learning techniques to solve complex optimization problems across diverse domains including soft robotics, batteryless edge computing, and supply chain management. Scientific Awards: EvoApplications Best Paper Award (2017) UKCI AWARENESS Best Paper Award (2012) IEEE CIS Outstanding Student-Paper Award (2011) Professor Iacca actively supervises a large research group with numerous PhD students across multiple doctoral programs, including Information Engineering and Computer Science, Industrial Innovation, and the National PhD in Artificial Intelligence for Society. His lab has secured significant research funding through collaborations with industry partners and international research consortia. Recent grants support work on interpretable reinforcement learning, federated optimization, and applications of evolutionary computation in healthcare and robotics. He has also been appointed to editorial roles for prestigious journals including IEEE Transactions on Evolutionary Computation and Evolutionary Intelligence. The Distributed Intelligence and Optimization Lab (DIOL) under Professor Iacca's leadership comprises over 30 researchers including postdocs, PhD students, and master's students. The lab maintains strong international collaborations and has developed specialized expertise in evolutionary computation, interpretable AI, and optimization for embedded systems. Current projects include work on the EIC Pathfinder Challenge "Awareness Inside," development of methods for batteryless edge intelligence, and applications of evolutionary algorithms to healthcare scheduling problems.
Paolo Motto Ros is a Researcher specializing in biomedical engineering, wearable systems, and low-power electronics. He has extensive experience in event-driven signal processing , functional electrical stimulation , and biocompatible sensor design , with a focus on human-machine interfaces and implantable devices. His research interests include: Biomedical instrumentation Wireless power/data transmission Surface electromyography (sEMG) Low-complexity embedded systems Plant impedance monitoring Neuroprosthetics Recent publications highlight collaborations with institutions on piezoelectric skin sensors , CMOS neural implant circuits , and plant health monitoring systems . His work spans applications in healthcare, robotics, and environmental technology.
Silvia Curteanu is a Professor at the Faculty of Chemical Engineering and Environmental Protection of the Gheorghe Asachi Technical University of Iasi, Romania. Her academic career spans over 40 years, with roles ranging from researcher to PhD supervisor in chemical engineering and applied informatics. She holds a PhD in Chemical Engineering (1998) and a License in Chemical Engineering (1981). University: Gheorghe Asachi Technical University of Iasi School: Faculty of Chemical Engineering and Environmental Protection Department: Department of Chemical Engineering Academic Rank: Professor Research Interests: Specializing in artificial intelligence applications for chemical processes, Silvia Curteanu has developed methodologies using neural networks , genetic algorithms , and hybrid models for tasks like process modeling, optimization, and inverse problem solving. Her work addresses polymerization , bio-processes , electrochemical treatments , and molecular design , with notable contributions to soft sensors and multi-objective optimization . Scientific Output: With over 192 papers (133 ISI-indexed), 23 books/chapters, 14 patents, and 32 research grants, her articles focus on neural network topology , biologically inspired algorithms , and chemical process optimization . Her work has been published in journals like Journal of Chemical Engineering , Environmental Science and Pollution Research , and Applied Soft Computing . Scientific Awards: Best Paper Award (2016) for 'Performance Comparison of Different Regression Methods for a Polymerization Process with Adaptive Sampling' Grants & Projects: 32 research grants, including international collaborations 11 projects as director (1 international) Advising: Acted as PhD supervisor since 2005, mentoring students in chemical engineering and AI applications. Her laboratory collaborates with institutions like Oxford University and Aristotle University of Thessaloniki.
Sambriddhi Mainali serves as the Undergraduate Programs Director and Assistant Teaching Professor in the Department of Computer Science at the University of Missouri-St. Louis within the College of Arts and Sciences. Holding a Ph.D. in Computer Science from the University of Memphis (2021), she bridges computational theory with biological applications through her research and teaching. Education: Ph.D. in Computer Science, University of Memphis, 2021 Dr. Mainali's research program focuses on computational biology and bioinformatics, employing advanced machine learning, information theory, and molecular computing techniques to solve genomic challenges. Her work spans pathogenicity prediction, genomic sequence analysis, phenotype forecasting, and environmental DNA profiling, with particular emphasis on dimensionality reduction methods and species identification systems. This interdisciplinary approach integrates computer science fundamentals with biological data to advance precision medicine and biodiversity conservation. Analysis of her 14 publications from 2017-2022 reveals consistent innovation in genomic data science, with recent work emphasizing deep learning for DNA structure analysis (2022), information-theoretic dimensionality reduction (2021), and universal genomic positioning systems (2017-2020). Her research trajectory demonstrates increasing sophistication in applying computational frameworks to complex biological questions, particularly in translating genomic sequences into phenotypic predictions and environmental assessments. As Undergraduate Programs Director, she oversees curriculum development and student mentorship in computer science, maintaining office hours Tuesdays and Wednesdays 1:30-3:30 PM in ESH 313 with Zoom availability. Her contact details include email smbtk@umsl.edu and phone (314) 516-5239.
Giuseppe Carbone is a Full Professor at the Department of Mechanics, Mathematics & Management, Politecnico di Bari, Italy. His research bridges theoretical and experimental mechanics, specializing in tribology, contact mechanics, viscoelastic materials, adhesion phenomena, and biomimetic engineering. He employs advanced numerical simulations and experimental methodologies to address complex mechanical challenges. Primary research domains include: Fundamentals of viscoelastic contact mechanics and friction dynamics Surface engineering for tribological optimization Biomimetic approaches for material and system design Advanced lubrication systems and bearing technologies Analysis of his 15 most recent publications (2024-2025) reveals a consistent focus on viscoelastic material behavior under dynamic contact conditions. Key trends include: Integration of numerical and experimental techniques for contact mechanics Innovations in adhesion/friction modeling for rough surfaces Applications in robotics, automotive systems, and bio-inspired engineering Development of novel computational methods for tribological systems
Erhan KURT is a Lecturer at the Faculty of Engineering and Architecture , Kırşehir Ahi Evran University , since 2024. Previously, he worked as a Research Assistant at Nuh Naci Yazgan University from 2012 to 2024. PhD in Electrical and Electronics Engineering, Erciyes University (2023) MSc in Electronic Engineering, Erciyes University (2014) BSc in Electrical and Electronics Engineering, Erciyes University (2011) His research focuses on Electromagnetic Field Analysis , Microwave Engineering , and Artificial Intelligence Applications in antenna design. He has contributed to optimization algorithms for antenna array synthesis, sidelobe suppression, and pattern nulling. A review of his 15 most recent articles reveals trends in Antenna Array Optimization using bio-inspired algorithms (e.g., Ant Lion, Salp Swarm, Differential Evolution) and applications of AI in microwave engineering. Key subfields include Computational Electromagnetics , Beamforming , and Algorithmic Antenna Design . He has collaborated extensively with researchers such as Kerim Güney , Suad Başbuğ , and Mustafa Türkmen on interdisciplinary projects involving signal processing and antenna systems.
Maria Vahdati is an Associate Professor at the University of Reading, where she serves as Head of the Sustainable Energy, Environment and Engineering Programme and Programme Director of the MSc Renewable Energy: Technology & Sustainability since 2009. Her work focuses on renewable energy systems, Net Zero design, and low-carbon technologies, with a strong emphasis on interdisciplinary research. Academic Qualifications: PhD in Mechanical Engineering (University of Bristol) BSc in Chemical Engineering (University of Birmingham) Research Interests include wind, hydro, solar, and biomass energy systems design, ground-source heat pump/PV hybrids, and sustainable construction materials. She explores the integration of renewable energy into urban infrastructure and the development of biomass-based solutions for energy generation. Recent Trends in her 15 most recent publications span life cycle assessment, virtual power plants, Net Zero building frameworks, and bio-inspired sustainable materials. Her work addresses decentralized waste-to-compost systems, energy efficiency in commercial kitchens, and climate adaptation strategies for buildings. Scientific Awards: Senior Fellow of the Higher Education Academy (SFHEA) Chartered Engineer Editorial Board Member for Renewable Energy Journal Postgraduate Supervision covers diverse topics such as virtual power plants, municipal waste management, natural gas as transportation fuel, adobe brick stabilization, UK commercial kitchen energy reduction, and residential demand flexibility. She contributes to teaching modules in renewable energy, thermodynamics, and carbon management. Research Affiliations include the University of Reading's Energy and Environmental Engineering Research Group , with projects on decarbonising heating networks and green infrastructure for schools.
