Prof. Dr. Stephan Schiemann is a faculty member at Leuphana University of Lüneburg specializing in Sports Science and Health Sciences . With over 122 publications and active research in physical education, strength training, and rehabilitation, his work focuses on biomechanics, muscle adaptation, and digital health interventions. Key Research Areas: Strength training, flexibility protocols, youth athlete development, inclusive sports, and digital health applications Projects: KITA GUT & GESUND, RoBaTaS (Rollstuhlbasketball), HeaLinGo (health-language integration) Research Trends Recent publications analyze: Strength-performance correlations across sports (basketball, cross-country skiing) Long-term stretching effects on muscle hypertrophy Measurement error differentiation in ultrasound diagnostics Comparative training methods for plantar flexors His work bridges theoretical development with practical applications in school sports and rehabilitation.
Dr. Mohammad Yazdani-Asrami is a Lecturer in Electrically Powered Aircraft and Operations at the Autonomous Systems & Connectivity (ASC) division of the James Watt School of Engineering, University of Glasgow. He leads research in electrification and cryo-electrification of transportation, particularly in aviation, leveraging applied superconductivity and AI techniques. His research interests span the Electrification and cryo-electrification of power and transportation systems Design of superconducting components (machines, cables, fault current limiters) for aviation Application of AI, machine learning, and big data in engineering and superconductivity Hydrogen electrolysis, production, and integration in aerospace and power networks His recent publications demonstrate a strong trend toward intelligent modeling and AI-driven solutions in superconducting technologies, with a focus on electric aircraft, fault protection, and thermal management using cryogenic fluids. Dr. Yazdani-Asrami has received notable scientific recognition, including: UK Royal Academy of Engineering Global Talent (2021) Young Professional of the Year, Cryogenic Society of America (2023) He actively supervises PhD students and hosts visiting researchers. His advising portfolio includes Alireza Sadeghi, Kerr Smith, Dedao Yan, Giacomo Russo, and Fábio Gregório. He has secured funding from the EPSRC, University of Glasgow, and CSC for PhD students. He also supports postdoctoral fellowships from the Royal Academy of Engineering, Leverhulme Trust, and Marie Skłodowska-Curie actions. He is involved in several research groups and collaborations, particularly within the Aerodynamics, Propulsion and Electrification group. His editorial roles include serving on the boards of Superconductor Science and Technology , World Journal of Engineering , Aerospace Systems , and others. He regularly contributes to major conferences such as the Applied Superconductivity Conference and the International Conference on Magnet Technology.
Ina Fichtner is a Professor at the Faculty of Digital Transformation of University of Applied Sciences HTWK Leipzig since 2022. Previously, she led the MINT department at the Institute for Applied Training Science (IAT) in Leipzig for 13 years (2009–2022), focusing on integrating mathematics, informatics, and natural sciences into sports research. Her work bridges computer science , biomechanics , and sports informatics , with extensive projects on athlete movement analysis, data systems (IDA), and digital tools for elite sports. PhD in Computer Science (2007) from TU Dresden and Leipzig University Diplom in Mathematics and Computer Science (2002) from Jena, Dresden, and Sheffield Her research spans data science , sports technology , and applied informatics , particularly in ski jumping , dive analysis , and athlete biomechanics . She has co-authored numerous publications in theoretical computer science and applied sports informatics , including studies on 3D body scanning , inertial sensors , and force-velocity profiling . She served as Alumni Representative and Treasurer of the Friends' Association at HTWK Leipzig, with memberships in German Mathematical Society and German Sports Science Association .
Sairaj Dhople is the Oscar A. Schott Professor in the Department of Electrical and Computer Engineering at the University of Minnesota. His research focuses on renewable energy systems, particularly modeling and control of grid-connected inverters, power-system reliability, and distributed energy resources. University: University of Minnesota Department: Electrical and Computer Engineering Academic Rank: Professor His work spans power systems, power electronics, and control theory, with recent publications examining grid-forming inverters, stability analysis, and hybrid computing solutions for optimization problems. Key research themes include: Equivalent-circuit modeling for renewable systems Large-signal stability assessment inverter-based resources Grey-box system identification of power networks Interoperability standards for grid-forming technologies Scientific awards include the Institute for Advanced Study Faculty Fellowship (2018). Current projects funded by the National Science Foundation and U.S. Department of Energy explore analog/hybrid computing and universal interoperability for grid-forming inverters (UNIFI Consortium). His Dhople Research Group investigates power-system architecture and sustainability challenges.
Naomi Eve Frankston serves as a PhD Research Fellow at the Institute of Physical Performance, Norwegian School of Sport Sciences, holding teaching responsibilities in Biomechanics while conducting specialized research in human movement analysis and injury prevention. Her academic credentials include: MS in Kinesiology with a major in Biomechanics from Indiana University-Bloomington, USA Bachelor of Science in Biology from Washington University in St. Louis, USA Her research program integrates clinical and performance biomechanics with focus on running injury mechanisms, orthopedic movement patterns, and sports-specific motion analysis. This work bridges laboratory-based gait analysis with practical applications in athletic performance optimization and injury rehabilitation protocols. Scientific Recognition: No formal scientific awards documented in institutional records Her academic service includes biomechanics instruction for sport science students, though no formal student advising or research grant management is indicated in current institutional documentation. Professional development includes prior laboratory experience in clinical biomechanics settings and industry research applications. Current research activities occur within the Institute's performance analysis infrastructure, leveraging prior experience from orthopedic biomechanics laboratories and athletic industry collaborations to advance sport injury prevention methodologies.
Palma Chillón Garzón is a full-time Professor at the University of Granada , affiliated with the Faculty of Sports Sciences and the Department of Physical Education and Sports . She is based at the campus located at Carretera de Alfacar, S/N, 18071 Granada, Spain. Research Interests include physical education, sports training, exercise physiology, and athletic performance. These areas align with her institutional affiliation and likely focus on optimizing physical activity, sports pedagogy, and health-related fitness. Contact & Tutoring: Phone: 958244374 Tutoring Schedule First Semester: Mondays & Wednesdays, 1:00 PM - 4:00 PM, Edf H Pb Second Semester: Mondays & Wednesdays, 1:00 PM - 4:00 PM, Edf H Pb
Alexandros Kontogiannis is a research fellow at the University of Cambridge, Department of Engineering, specializing in fluid dynamics and applied mathematics. His work combines Bayesian inference, machine learning, and physics-informed algorithms to solve inverse problems in magnetic resonance velocimetry (MRV) and fluid-structure interaction. EPSRC National Fellow in Fluid Dynamics Member of Energy, Fluids and Turbomachinery Division Research Focus: Development of digital twin frameworks that integrate MRV data with Navier-Stokes equations to reconstruct flowfields, infer rheological parameters in non-Newtonian fluids, and estimate hidden quantities like pressure and wall shear stress. Key innovations include: Physics-informed compressed sensing for sparse MRV data Simultaneous boundary shape and flowfield estimation Bayesian turbulence model parameter learning Scientific Awards: ASME Fluids Engineering Division Graduate Student Scholar (2021) Technical Chamber of Greece (TEE) Award (2018) Limmat Foundation Academic Excellence (2017) Mentzelopoulos Scholarship for international studies (2017) Greek State Scholarships Foundation Award (2012) Key Contributions: Algorithms for 3D flow reconstruction with adaptive discretization, viscous signed distance field regularization, and multi-objective aerodynamic shape optimization. His methodologies enable 27x reductions in MRI scanning time while maintaining diagnostic accuracy.
Kohei Nakajima is an Associate Professor at the Department of Intelligent Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo. He holds concurrent positions at the Department of Creative Informatics and the Next Generation Artificial Intelligence Research Center (AI Center). As an Endowed Chair in Advanced Artificial Intelligence Education, he leads the Physical Intelligence Lab, which focuses on the intersection of soft robotics, nonlinear dynamics, and physical computing. His research interests center on Physical Reservoir Computing (PRC), a paradigm that exploits the natural dynamics of physical systems for computation, with applications in soft robotics, spintronics, and quantum machine learning. Nakajima's work demonstrates how physical systems can inherently process information without traditional digital computation, leveraging phenomena like chaos, bifurcations, and embodied intelligence. Nakajima's publications reveal a strong focus on understanding how physical systems can perform computational tasks. His recent work spans from biological applications (jellyfish cyborgs, ostrich-inspired robotics) to fundamental theoretical advances in reservoir computing. The research demonstrates how physical phenomena can be harnessed for information processing, with implications for energy-efficient computing and novel robotic control paradigms. As the organizer of the Reservoir Computing Seminar, Nakajima has built a vibrant research community exploring the nature of information processing across disciplines. His lab actively recruits graduate students and postdocs, indicating strong research momentum and institutional support for his work in physical intelligence.
Barbara Bigliardi is an Associate Professor at the Department of Engineering and Architecture, University of Parma, with national scientific qualification for full professor in Economic-Management Engineering (SSD ING-IND/35). She serves as President of the Management Engineering Program at University of Parma and Director of Bachelor's/Master's programs at University of San Marino, co-leading double-degree initiatives between the two institutions. Over 100 publications (57 SCOPUS-indexed) with H-index=20 Editorial roles: Cambridge Scholars Publishing (2019), MDPI Sustainability, Sci, European Journal of Innovation Management Key research areas: Open Innovation, Technology Transfer, Food Industry Innovation, Industry 4.0, Supply Chain Sustainability Recent Publications (2024-2025) demonstrate leadership in: Industry 4.0 integration with circular economy AI applications in public administration and healthcare Digitalization of food supply chains Green startup resource orchestration Simulation-based optimization in remanufacturing Sustainable additive manufacturing Scientific Recognition : 2005 Emerald Highly Commended Award 2013 Most Cited Paper in Trends in Food Science & Technology 2019 Highly Cited Paper in Review of Policy Research Research Leadership includes: National Observatory on Start-ups (President since 2021) National Observatory on Reputation (Vice President since 2019) INAIL-funded mobile risk assessment systems (2018-2020) INAF space technology transfer projects (2018-present) Academic Contributions : Supervised over 300 theses Deputy Coordinator of Industrial Engineering Doctoral Program Director of Management Engineering Programs (Parma & San Marino) Founder of academic spin-offs: Sistemi per il marketing di contenuto S.r.l. (2016-present), Univenture SrL (2006-2010)
Dr. Dandolo Flumini is a Researcher at the Zurich University of Applied Sciences (ZHAW), School of Engineering, specializing in Applied Complex Systems Science. His research focuses on artificial life, morphological computation, blockchain applications, and computational modeling. He serves as team member or project lead in multiple interdisciplinary initiatives including Bio-HhOST (bio-hybrid tissues), Agroforestry Carbon Token System, and blockchain-based voting solutions. His primary research interests include: Complex Systems Science : Emergent behaviors in biological and artificial systems Morphological Computation : Physical systems performing computational tasks Artificial Chemistry : Programmable chemical systems using droplet networks Blockchain Applications : Decentralized finance and voting systems Computational Ethics : Responsible implementation of AI and modeling Flumini's recent publications (2019-2023) demonstrate strong focus on microfluidic systems, droplet agglomeration physics, programmable chemistry, and ethical AI. His work frequently appears in artificial life and computational modeling venues, with increasing emphasis on real-world applications in sustainability and decentralized systems. He maintains active collaborations through the Applied Complex Systems Science research group at ZHAW, contributing to projects involving microfluidic device design, blockchain architectures, and bio-hybrid tissue engineering.
Roberto Garello is a Tenured Associate Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino . He specializes in Communication Systems , Satellite Networks , and Channel Coding , with a focus on 5G/6G Technologies and Non-Terrestrial Networks . His work aligns with the School of Master’s Programmes and Lifelong Learning . Research Interests: Satellite communications systems, Direct-to-Satellite IoT constellations, Mega-constellation services in space, and physical layer advancements for 5G/6G. Teaching: Offers courses like Information Theory for Data Science , Communication and Network Systems , and Space Exploration and Resources , while supervising Applied Signal Processing Laboratory . Projects: Leads initiatives such as DitDSSS (satellite localization), RESTART (future telecommunications), and TESL@ (ICT energy efficiency). Scientific Awards: Received Best Paper Awards at CTRQ 2010 and COCORA 2013. Students: Supervises PhD candidates including Alessandro Compagnoni, Agbotiname Lucky Imoize, and Riccardo Tuninato, focusing on topics like Wireless Communication, Machine Learning, and Non-Terrestrial Networks. Publications: His recent work explores OTFS vs. OFDM, spectrum sensing algorithms, MIMO with cylindrical arrays, and 5G NTN synchronization, reflecting trends in satellite IoT and machine learning integration.
Xuming He is an Associate Professor at the School of Information Science and Technology (SIST), ShanghaiTech University, where he leads the PLUS Lab. His research spans computer vision and machine learning with a focus on developing algorithms that operate effectively under limited supervision and evolving data conditions. His core research interests include weakly-supervised and few-shot learning for scenarios with sparse annotations, continual learning frameworks for knowledge retention during sequential task acquisition, semantic segmentation techniques for scene understanding, and multimodal vision-language representations. He emphasizes interpretable machine learning to build transparent AI systems capable of human-understandable reasoning, addressing critical challenges in model trustworthiness and deployment reliability. Recent publications reveal strong trends toward novel class discovery in long-tailed recognition scenarios, physics-informed generative modeling for scientific applications, and robust segmentation under distribution shifts. His work increasingly integrates large language models for multimodal reasoning while maintaining focus on efficiency in resource-constrained environments like robotic grasping and medical imaging analysis. He actively mentors students, having supervised Qian He to PhD completion and Chuanyang Hu to Master's degree in 2023. He welcomes prospective graduate students through ShanghaiTech's Computer Science & Technology program and offers undergraduate research projects requiring minimum six-month commitments. The PLUS Lab under his direction drives innovation in learning under supervision constraints, with recent work spanning medical tumor analysis, cross-view geolocation, photonic computing, and semiconductor design verification. The lab's research bridges theoretical advances with practical applications across healthcare, robotics, and scientific discovery domains.
Dr. Magdalena Schreter-Fleischhacker works at the Technical University of Munich within the Professorship of Simulation for Additive Manufacturing . Her research focuses on physics-based computational modeling of coupled liquid-powder-gas dynamics in metal additive manufacturing, including melt pool dynamics and powder-gas interactions . She specializes in multi-phase flow modeling using cut-element and diffuse interface methods with continuous/discontinuous Galerkin schemes . She also develops constitutive models for quasi-brittle materials like 3D printed concrete and rock, incorporating anisotropy , gradient-enhanced damage mechanics , and micropolar continua . Her computational work leverages matrix-free algorithms and parallel computing , with significant contributions to the deal.II finite element library . Research Interests Physics-based computational modeling of coupled liquid-powder-gas dynamics in additive manufacturing Multi-phase flow simulation using sharp/diffuse interface methods Advanced constitutive modeling for quasi-brittle materials (rock, soils, 3D printed concrete) High-performance computing and matrix-free algorithms Notable Contributions Development of consistent diffuse-interface models for melt-vapor dynamics Improvements to continuum surface flux models in additive manufacturing Formulation of gradient-enhanced damage-plasticity models for geological materials Principal contributor to the deal.II library (version 9.6) Supervised Student Projects Johannes Resch (2024): DG-based thermo-hydrodynamic melt pool simulations Julian Brotz (2024): DEM-FEM coupling for fluid-powder interaction Andreas Ritthaler (2024): Matrix-free cutDG formulation for complex flows Tinh Vo (2023): Laser modeling for melt pool simulations Scientific Awards ERC Starting Grant recipient
Lorenz Dörschel is an Adjunct Professor (Lehrbeauftragter) at the Institute of Automatic Control at RWTH Aachen University. He holds the academic title PD Dr.-Ing. habil, signifying post-doctoral research qualifications. His position is part-time, focusing on advanced control theory and applications. His primary research interests include: Control of distributed parameter systems (e.g., fluid dynamics, thermal processes) Model predictive control for industrial and automotive systems Parameter space methods for robust controller design Model reduction techniques for complex nonlinear systems Dörschel's recent publications (2018-2024) demonstrate broad applications across biomedical engineering, renewable energy, automotive systems, and industrial automation. His work consistently integrates mathematical rigor with practical implementations, emphasizing advanced control methodologies like nonlinear MPC, Lyapunov-based design, and Bayesian optimization. A recurring theme is the development of computationally efficient control strategies for distributed parameter systems. No scientific awards, student advising relationships, or research grants are documented in the available information.
Jie Chen is an Assistant Professor in the Department of Mechanical Engineering at Virginia Tech's College of Engineering. Their research bridges machine learning with engineering analysis and design under uncertainty, focusing on process-structure-property-performance relationships. PhD, Mechanical Engineering (2022) – Arizona State University MS, Civil Engineering (2018) – Beihang University BS, Civil Engineering (2015) – Beihang University Research interests include: physics-informed machine learning, uncertainty quantification, predictive maintenance, materials design, and advanced manufacturing. The SEAD Lab develops methods to integrate engineering analysis into stochastic machine learning algorithms and uses AI for knowledge discovery in uncertain environments. Recent publications emphasize: Digital twin frameworks combining machine learning and Bayesian optimization Graph neural networks for high-entropy alloy and molecular mixture property prediction Physics-guided neural networks for fatigue life analysis of additively manufactured alloys Uncertainty quantification in imbalanced regression tasks and multi-fidelity data fusion Real-time imaging of polymer deformation mechanisms The lab actively mentors students, including PhD candidate Yisheng Lu, and manages projects in predictive maintenance, fatigue modeling, and materials design.
