Yang Lin is an Assistant Professor at the University of Rhode Island , affiliated with the Department of Mechanical, Industrial & Systems Engineering . His research focuses on Microfluidics , Acoustofluidics , and Organ-on-a-Chip technologies, with applications in Environmental Monitoring , Food Safety , and Human Health . Education : Ph.D. in Mechanical Engineering (2019) and M.S. in Mechatronic Engineering (2015) from the University of Illinois at Chicago, and B.S. in Mechanical Design Manufacturing and Automation (2012) from Beijing Information Science and Technology University. Research Interests include: Acoustofluidics : Developing non-invasive, biocompatible fluid manipulation techniques using acoustic bubbles and membranes. AI-Enhanced Diagnostics : Leveraging convolutional neural networks for sample-to-answer diagnostic systems in public health. 3D Printed Microfluidics : Expanding additive manufacturing for low-cost, complex physiological structures in healthcare. Environmental Microfluidics : Detecting microplastics and contaminants in water and food systems. Publications highlight advancements in 3D printed microneedles , machine learning for nanoplastic detection , and magnetofluidic biosensors . Lab Members include current Ph.D. students and alumni who have completed M.S. and B.S. degrees under his mentorship.
Muhammad Awais Bin Altaf is a researcher specializing in biomedical engineering, machine learning, and wearable technology. His work focuses on low-power embedded systems for neurological and cardiovascular monitoring, including EEG processors for seizure detection and PPG-based blood pressure classification. He has collaborated extensively with co-authors like Wala Saadeh and Jerald Yoo on IEEE journals and conferences. His research interests include Biomedical signal processing Wearable health devices Machine learning for medical diagnostics Energy-efficient hardware design Neurological disorder detection Embedded systems for clinical applications Recent publications highlight trends in shallow neural networks, autoencoders, and hardware acceleration for real-time health monitoring. Key subfields span seizure prediction, stress detection, and impedance-adaptive sensors. Collaborations include institutions in Germany, Finland, and Pakistan. His work often integrates open-source toolflows and industry-standard chip design techniques, emphasizing practical implementations for wearable environments. Contributions to HDR imaging algorithms and biomedical SoCs demonstrate interdisciplinary expertise in signal processing and healthcare technology.
Geir Mathisen serves as a Professor within the Department of Technical Cybernetics, Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). He is an active member of the Group for Industrial Computer and Instrumentation Systems, focusing on real-time systems integration and cyber-physical applications across industrial and energy domains. His educational background includes a Civil Engineering degree and a Doctorate (PhD), both earned from NTNU's Department of Technical Cybernetics, establishing foundational expertise in control systems and technical cybernetics. Professor Mathisen's research spans cyber-physical systems, deterministic networking, and distributed real-time systems with significant applications in smart grids and industrial automation. His work pioneers magnetic field energy harvesting for railway systems, edge-based fault detection for photovoltaic panels, and multi-robot coordination in sewing automation. Current investigations focus on power system state estimation, optimal power flow in smart grids, and deterministic communication channels for latency-sensitive applications. Analysis of his 2020-2024 publications reveals a strategic convergence of real-time computing with energy systems, particularly in railway energy harvesting and photovoltaic monitoring. His research consistently bridges theoretical advances in networking protocols with practical industrial implementations, emphasizing determinism and composability in distributed cyber-physical environments. Scientific Awards: No specific awards or fellowships were documented in the provided materials. Professor Mathisen actively supervises doctoral and master's students, including Johannes Schrimpf (2013 PhD thesis on industrial robot control), and offers project assignments as noted for fall 2021. His research is conducted through Norwegian collaborative projects on flexible distribution grids and smart grid services, though specific grant mechanisms remain unspecified in the source material. He contributes significantly to the Group for Industrial Computer and Instrumentation Systems at NTNU, which develops advanced solutions for industrial control, measurement systems, and cyber-physical integration, particularly in energy and manufacturing contexts.
Jian Liu is an Associate Professor at the School of Computing, University of Georgia, leading the Mobile Sensing and Intelligence Security (MoSIS) Lab. Previously, he served as an Assistant Professor at the University of Tennessee, Knoxville. He holds a Ph.D. from Rutgers University and focuses on Trustworthy AI, Computational Sensing, and Human-Computer Interaction. Current Affiliation: School of Computing, University of Georgia Prior Affiliation: University of Tennessee, Knoxville Education : Ph.D. from Rutgers, The State University of New Jersey Dr. Liu’s research spans Trustworthy AI , Computational Sensing , and Intelligent Fitness Technologies , with publications in top venues like IEEE S&P/Oakland, ACM CCS, and ICML. His work addresses security vulnerabilities in mobile systems, federated learning, and acoustic-based sensing. Recent publications include HarmonyCloak (2025) for AI music copyright protection and mm-RunAssist (2025) for mmWave-based fitness analysis. Research trends emphasize AI ethics , privacy-preserving techniques , and innovative sensor applications . Scientific Awards : IChemE Biochemical Engineering Award (2023) ACM SIGMOBILE Research Highlights (2022) Professional Promise in Research and Creative Achievement Award (2025, University of Tennessee) Best Paper Awards at IEEE SECON (2017) and IEEE CNS (2018) Recognized in Stanford’s Top 2% Most Cited Scientists Dr. Liu mentors students in the MoSIS Lab, with lab members like Yi Wu joining the University of Oklahoma as a Tenure-track Assistant Professor. His research is supported by multiple NSF grants, including CSR and SaTC proposals, and industry partnerships like NVIDIA and Google. Media coverage includes BBC News , MIT Technology Review , and IEEE Spectrum , highlighting his work on AI-driven music protection and smart wearable technologies.
Jonas Örtegren is an Associate Professor and Senior Lecturer in the Department of Engineering, Mathematics and Subject Didactics at Mid Sweden University. He serves as the subject representative for Engineering Physics and program manager for both the Master of Science program in Engineering Physics (years 4-5) and the project-based Master by Research in Engineering Physics program. His research focuses on nanomaterials and functional surfaces for energy applications. Örtegren's research explores nanomaterials, functional materials, and surface science with applications in energy conversion (nanogenerators) and energy storage (batteries). His work spans triboelectric energy harvesting, plasmonic devices, battery electrode design, and sustainable energy solutions. Current projects include IMPHET (Innovation Environment for Advanced Materials and Processes with Sustainable Energy Applications) and aluminum-graphite dual-ion battery development. Analysis of his recent publications reveals strong focus on: Advanced battery technologies including silicon anodes and aluminum-ion systems Triboelectric nanogenerators using sustainable materials like wastepaper Plasmonic devices for optical applications Surface engineering and nanomaterial synthesis techniques Primary research trends show integration of energy harvesting with materials science and nanotechnology. Örtegren manages multiple research projects: Flexibla och hållbara fastfasbatterier (Flexible solid-state batteries) ALGCC (Graphene-coated aluminum current collectors) IMPHET innovation environment Aluminum-graphite dual-ion battery development He is affiliated with the FSCN Research Centre and supervises research in nanomaterials and energy systems. No specific students or awards are mentioned in the source material.
Daniel Leidner is a Cooperation Professor at the University of Bremen and a researcher at the German Aerospace Center (DLR) where he has been contributing to the Institute of Robotics and Mechatronics since 2011. He earned his doctorate in Artificial Intelligence and Robotics from the University of Bremen in 2017. Since 2017, he has led the Semantic Planning Group and the Fault-Tolerant Autonomy Architectures group at DLR, focusing on advanced task planning for autonomous robotic systems and enhancing the reliability of robotic operations in dynamic environments. Leidner's research interests span multiple areas in robotics and artificial intelligence. His work emphasizes developing robust and resilient robotic systems capable of autonomous operation in complex environments. He specializes in creating systems that can not only handle predictable scenarios but also flexibly respond to unforeseen events. His ERC Starting Grant project RECOVER.ME aims to equip robots with metacognitive abilities to autonomously manage hardware malfunctions by integrating formal reasoning with Vision-Language Models. This innovative approach enhances the resilience and efficiency of space robots, reducing the need for manual intervention during missions and leveraging insights from cognitive psychology for improved problem-solving capabilities. Leidner's research portfolio includes significant projects such as RECOVER.ME, FUTURO, EASE, OPERA, Smile2gether, Surface Avatar, and CoViPa. His publications demonstrate a strong focus on autonomous task planning, human-robot interaction, fault tolerance, and metacognitive capabilities in robotic systems. Recent publications highlight advancements in space teleoperation, assistive robotics, and cognitive reasoning for resilient robotic systems. ERC Starting Grant (2024) for project RECOVER.ME Georges Giralt PhD Award (Best European PhD Thesis in Robotics) Helmholtz Doctoral Prize MIT Technology Review Innovator under 35 Award Leidner has served as an advisor to the German Federal Government from October 2023 to July 2024, where he played a crucial role in developing a national strategy for AI-based robotics. His leadership in the Semantic Planning Group and Fault-Tolerant Autonomy Architectures group at DLR demonstrates his significant contributions to advancing robotic capabilities in challenging environments. His work bridges theoretical advances in cognitive robotics with practical applications in space exploration, healthcare, and industrial automation.
Kaiyan Qiu is the Berry Family Assistant Professor of Mechanical Engineering at Washington State University , leading interdisciplinary research at the intersection of 3D printing , artificial organs , and flexible electronics . His work combines advanced manufacturing techniques with biomedical innovation. Ph.D. in Fiber Science & Biopolymers from Cornell University (2012) Postdoctoral experience at University of Minnesota, Princeton University, and Dartmouth College Research focuses on: Medical Applications : 3D printed presurgical organ models (prostate, aortic root) with integrated sensors Wearable Electronics : Stretchable tactile sensors and photodetectors for health monitoring Biomimetic Systems : Bioinspired actuators and surfaces for robotics Advanced Printing : Multimaterial and multiscale additive manufacturing Recent publications address machine learning-enabled 3D printing , biomimetic device fabrication , and direct printing on freeform surfaces . Collaborations with Medtronic Inc. and Visible Heart Laboratory at UMN demonstrate clinical impact. Honors include: Berry Family Assistant Professor (2021) Best of 2018 article selection National Textile Center Competition Awards Liu Memorial Scholarship Qiu's lab develops customized 3D printing systems with motion control, ink dispensing, and monitoring subsystems for biomedical and robotics applications.
Christina Busing is a Full Professor for Combinatorial Optimization at RWTH Aachen University, a position she has held since 2021. Previously, from 2016 to 2021, she served as a Junior Professor for Robust Planning in Medical Care at the same institution. She leads the Teaching and Research Group on Combinatorial Optimization, contributing significantly to the academic community through her research and teaching activities. Her educational background includes Mathematics studies at WWU Münster, Universidad Comlutense de Madrid, and the Technical University of Berlin. Her doctoral work was completed under the supervision of Prof. Möhring, focusing on Recoverable Robustness in Combinatorial Optimization. She has also held postdoctoral positions at institutions in Aachen, Lancaster, and Vienna. Professor Busing's research spans multiple areas of optimization theory and application. Her work focuses on optimization under uncertainty, robust optimization, scenario generation, combinatorial optimization, complexity theory, optimality criteria, and both exact and heuristic algorithms. She has made significant contributions to applying these theoretical frameworks to practical problems in healthcare, energy systems, and transportation networks. Her interdisciplinary approach bridges theoretical computer science with real-world operational challenges. Her extensive publication record demonstrates a consistent focus on robust combinatorial optimization, with recent work emphasizing applications in healthcare systems, particularly in patient-to-room assignment, primary care scheduling, and pharmacy services. She has developed novel methodologies for handling uncertainty in optimization problems, including recycling valid inequalities and designing consistent decision frameworks for two-stage optimization problems. RWTH Aachen Brigitte Gilles Award for contributions to the advancement of women in science (2022) RWTH Aachen FAMOS Award for excellent family-friendly leadership (2019) RWTH Aachen university-wide Best Teaching Award (2018) TU Berlin Best Diploma Excellence Award in Mathematics (2008) German National Scholarship (Cusanuswerk) (2003-2007) Professor Busing serves on multiple program committees including EURO (2019), INOC (2018), and ESA (2017). She is also involved with the UnRAVeL Graduate College since 2018. Her teaching portfolio includes courses on Graph and Network Optimization, Mathematical Heuristics for Discrete Optimization Problems, and Combinatorial Optimization. She has developed problem-based learning approaches for heuristic methods in decision problems across mathematics, computer science, and industrial engineering. Her research group actively collaborates on interdisciplinary projects addressing operational issues in healthcare, energy systems, and transportation networks, developing mathematically optimized solutions for complex real-world challenges.
Shayan Mehraeen serves as an Assistant Professor within the Department of Physics, Chemistry and Biology (IFM) at Linköping University, actively contributing to the Sensor and Actuator Systems (SAS) research group and Bionics and Transduction Science unit. His work addresses the critical need for wearable assistive technologies in aging populations through innovative exoskeleton development. His research focuses on electroactive polymer-based textile actuators that combine flexibility, lightweight construction, and silent operation. By integrating novel textile designs with polymer science, he develops wearable systems capable of active body movement assistance, with applications spanning medical rehabilitation and human-machine haptic interfaces. This interdisciplinary approach bridges material science, biomechanics, and assistive technology engineering. Analysis of his 2025 publications reveals concentrated advancements in conducting polymer actuators, particularly PEDOT-based systems. Key innovations include optimizing double-coiled yarn architectures, evaluating anisotropic fabric impacts on 3D-printed components, and pioneering wet-spinning techniques for core-sheath artificial muscles—collectively enhancing performance metrics for real-world wearable robotics. Scientific Awards: No awards documented in source materials Advising and Grants: Student mentorship details and grant funding information not specified in available documentation Labs and Teams: Integral member of the multidisciplinary Sensor and Actuator Systems (SAS) group conducting research from transduction materials to soft robotics, and the Bionics and Transduction Science unit exploring intersections of biology, material science, and microsystem technology.
Dr Alexei Lisitsa is a Senior Lecturer at the University of Liverpool specializing in theoretical computer science and mathematical structures. His work bridges automated reasoning, machine learning, and topological mathematics with applications in cybersecurity and network analysis. Active since at least 2008, he maintains a robust research profile with recent publications through 2025. His research focuses on computationally intensive problems in algebraic topology and formal methods. Key contributions include developing machine learning techniques for braid invariants, automating group theory proofs, and creating knot diagram analysis frameworks. This work consistently integrates experimental mathematics with computational verification, demonstrating innovative cross-disciplinary approaches to complex topological challenges. Analysis of his 2023-2025 publications reveals accelerating integration of neural networks with symbolic reasoning systems. His team pioneers applications of deep learning to traditionally intractable problems in braid theory and group presentations, while maintaining strong foundations in formal verification methods for temporal logic systems. Dr Lisitsa actively secures competitive research funding: Flexible Querying of Encrypted Graph Databases (Innovate UK, 2020) KTP with ValueChain Enterprise Systems (Innovate UK & VALUECHAIN, 2018-2022) Future AI and Robotics for Hub Space (FAIR-SPACE) (EPSRC, 2017-2021) Machine Learning for recognising tangled 3D objects (Leverhulme Trust, 2020-2023) Engineering Autonomous Space Software (EPSRC, 2008-2012) Discrete analogues of dynamical systems (NATO, 2008-2010) He supervises postgraduate research in network security and movement pattern analysis, with thesis topics including temporal logic-based intrusion detection systems and large-scale network prediction methodologies.
Tom Knowles is a Lecturer in Robotics at the University of the West of England , affiliated with the School of Engineering . He teaches technical subjects such as C Programming , Matlab Programming , Python Programming , Robotics , and Neuroscience . Education: MSc BSc Mr. Knowles bridges conventional and spiking neural networks to develop artificial systems that mimic biological capabilities, particularly in navigation. His work explores how mammalian brains represent space and models robust, flexible behaviors through robotics, intersecting Robotics , Neuroscience , and Artificial Intelligence . Publications trends highlight biomimetic robotics , spiking neural networks , and computational neuroscience . His research integrates machine learning , bio-inspired models , and sensory reconstruction to solve navigation challenges, with collaborations at the University of Sussex and Technische Universität München (TUM) . Collaborations: University of Sussex TUM (Technische Universität München) Contact: Email: Tom.Knowles@uwe.ac.uk Phone: +44117 965 6261
Professor Yasuhiro Hayashi is a distinguished academic at Waseda University's School of Advanced Science and Engineering, where he serves as Professor in the Faculty of Science and Engineering and Director of the Smart Society Technology Integration Research Institute (ACROSS). He holds leadership positions including Vice President of the Institute of Electrical Engineers of Japan (since 2025.06) and Chair of the Utsunomiya Zero Carbon Promotion Council (since 2023.02). His educational background includes a Ph.D. from Waseda University (1993), following undergraduate and graduate studies at the same institution. Prior academic appointments include Associate Professor at University of Fukui (2000-2009) and Lecturer at Ibaraki University (1997-2000). Professor Hayashi's research focuses on energy management systems, power engineering, and renewable energy integration. His work spans critical areas including optimization of advanced electrical energy systems, cooperative operation and control of distributed generation, photovoltaic systems integration, CO2 emissions reduction, and climate change mitigation. He has made significant contributions to smart grid technology, demand-side management, and zero-carbon transition initiatives, with particular emphasis on practical implementation through smart meter data analysis and real-world demonstration projects. His publication record is extensive, with 365 papers according to Scopus (citation count: 3,423, h-index: 27) and even more according to Google Scholar (citation count: 12,332, h-index: 51, i10-index: 253). His recent work demonstrates strong focus on practical applications of smart meter data for urban carbon intensity analysis, electric transportation integration, and advanced control strategies for distributed energy resources in real distribution networks. Best Paper Award from Big Earth Data Journal (2024) Best Paper Award for 'Development of Evaluation Process for Demand-Side Resilience against Power Outage' (2023) The Commendation for Science and Technology by the Minister of Education (2019) Multiple Waseda Research Awards (2014-2019) Distinguished Paper Award from the IEE of Japan (2008) Professor Hayashi serves on numerous high-impact committees including as Special Commissioner of the Electricity and Gas Trading Surveillance Commission under METI, Chair of the Energy-Resource-Aggregation-Business-Forum at ACROSS, and leadership roles in developing Japan's smart grid standards and distributed energy resource policies. His work bridges academic research with practical policy implementation in Japan's energy transition, with recent focus on sector coupling between transportation and energy systems, advanced data analytics for grid operations, and practical implementation strategies for achieving Net Zero emissions in urban environments.
Jianning Dong is an Assistant Professor at Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science. He specializes in electrical machine design and power electronics with focus on applications for electric vehicles and renewable energy systems. His educational background includes: BSc from Southeast University, Nanjing, China (2010) PhD from Southeast University, Nanjing, China (2015) Post-doctoral researcher at McMaster Automotive Resource Centre, McMaster University (2016) Dong's research focuses on electrical machine design and power conversion systems with particular expertise in permanent magnet machines and wireless power transfer technologies. His work bridges theoretical analysis with practical applications in electric vehicle propulsion and renewable energy integration. Key research areas include: High-speed permanent magnet machines Wireless power transfer systems for electric vehicles Acoustic noise analysis of electrical machines Waste heat recovery systems Electric motors for home appliances and electric vehicles Electric generators for wind turbines His recent publications demonstrate a strong focus on optimizing wireless EV charging systems, with particular attention to efficiency, power quality, and system design. The work spans fundamental research on modulation techniques, compensation topologies, and system integration for high-power applications. Dr. Dong actively participates in collaborative research projects: ECS4DRES: Electronic Components and Systems for flexible, coordinated and resilient Distributed Renewable Energy Systems (active) PROGRESSUS: Highly efficient and trustworthy electronics for next generation energy supply infrastructure (completed) He has supervised 5 students and regularly presents tutorials on EV charging technologies at international conferences. His work is closely aligned with the TU Delft Wind Energy Institute (DUWIND), contributing to interdisciplinary research in sustainable energy systems.
Sanjoy Paul is an Associate Professor at the University of Technology Sydney (UTS) Business School, specializing in supply chain management and operations research. He holds roles as Associate Editor of Business Strategy and the Environment and Global Journal of Flexible Systems Management . His research focuses on supply chain resilience, risk modeling, and sustainable practices, with applications to global disruptions like pandemics and IT outages. Paul has published in top-tier journals such as the European Journal of Operational Research and secured grants from government bodies including the Department of Defence. Education and Career: Prior to UTS, he worked at RMIT University and Bangladesh University of Engineering and Technology. He holds a PhD from UNSW, recognized with the Stephen Fester Prize for outstanding thesis. His career spans academic roles from Lecturer (2017) to Senior Lecturer (2019) before his current position since 2023. Research Contributions: Paul’s work bridges theoretical models and real-world applications, including recovery frameworks for supply chains during crises and strategies for sustainable practices in post-pandemic contexts. He frequently advises media on supermarket pricing, supply chain disruptions, and business strategies, appearing in outlets like The Guardian and ABC News . Awards and Recognition: His honors include the ASOR Rising Star Award, Research with Relevance Award, and inclusion in the top 2% global scientists (2020–2023). He has contributed to policy debates on supermarket competition, EV market dynamics, and Australia’s industrial strategies.
Na (Luna) Lu is a Professor of Civil and Construction Engineering at Purdue University, with a courtesy appointment in Materials Engineering. She also serves as Vice President for Industry Partnerships and holds the Indiana ACPA Professorship in Concrete Paving and Materials Science. Her research focuses on power systems, inverter-based resources, microgrid control, grid resilience, and smart grid technologies. Key areas include stability analysis, renewable energy integration, and advanced control strategies for resilient distribution systems. Dr. Lu’s work bridges academia and industry, emphasizing practical applications of power electronics and cyber-physical systems. She has contributed extensively to topics like inverter dynamics, hybrid AC/DC microgrids, and real-time charging infrastructure. Her research highlights include AI-driven black-box modeling for photovoltaic systems, region-based stability analysis using machine learning, and optimal power flow in inverter-dominated grids. She has also explored resilience-enhancing strategies for coastal communities using marine energy resources and dynamic microgrids. Dr. Lu’s interdisciplinary approach combines electrical engineering, materials science, and computational methods to address challenges in modern power systems. Publications emphasize data-driven approaches, stability augmentation, and control system optimization. Awards include her endowed professorship reflecting industry recognition. Her administrative role underscores her commitment to industry-academia collaboration, fostering innovation in energy and infrastructure sectors.
