Kwantae Kim is an Assistant Professor at the Department of Electronics and Nanoengineering within Aalto University's School of Electrical Engineering . He leads the Tiny Systems and Circuits (TSirc) Group , focusing on power-efficient analog/mixed-signal ICs for biomedical and neuromorphic sensor systems. IEEE Senior Member (2025) Collaborates with institutions across Europe, Asia, and America Specializes in ultra-low-power AI-embedded IoT platforms His research emphasizes Tiny, Sensory, Intelligent, and Wireless IoT systems through: Development of energy-efficient IC architectures Democratizing access to advanced chip design Hardware-software co-design for edge computing Recent publications highlight innovations in: Spoken-language-understanding SoCs Temporal-sparsity-aware keyword spotting Open-source silicon frameworks Awards include: 2025 IEEE Senior Member 2023 Best Poster Award (AICAS) 2019 Samsung HumanTech Silver Award Research partnerships span: Prof. Tobi Delbruck (UZH/ETH Zurich) Prof. Hoi-Jun Yoo (KAIST) Prof. Shih-Chii Liu (UZH) Prof. Sohmyung Ha (NYU Abu Dhabi)
Prof. Dr. Soeren Lienkamp is an Assistant Professor at the Institute of Anatomy , Faculty of Medicine , University of Zurich . His work bridges digital education and genetic research , focusing on enhancing medical teaching through innovative formats. Research Interests : Genetics, developmental biology, kidney disease modeling, CRISPR applications, digital medical education, and advanced microscopy. Methodologies : Combines Xenopus tropicalis models, deep learning , and bioengineering to study genetic kidney disorders and improve diagnostic tools. Publication Trends : His recent articles highlight predictable genome editing , 3D imaging technologies , and mechanistic insights into kidney and eye development. Earlier works focus on ciliary function , Wnt signaling , and metabolic stress in renal cells.
Thorsten Chmura is a Professor in the Department of Economics at Nottingham Business School, Nottingham Trent University. His work focuses on experimental and behavioral economics, utilizing laboratory and field experiments to address real-world challenges. He maintains collaborations within NTU’s Applied Economics and Policy Research Group, Public Service Management Research Group, and international partnerships across Europe, China, and the US. Chair of Industrial Economics at University of Nottingham (previous) Director, Centre for Research in the Behavioural Sciences (previous) PhD in Economics and Physics from University of Bonn Research interests span behavioral economics, experimental economics, game theory, and traffic modeling. His work examines decision-making under risk, wage discrimination, and behavioral responses in complex systems. Recent publications explore AVOD streaming economics (2024), social trading herding (2022), and toll road choice dynamics (2014). Key article trends include: Behavioral responses in financial markets Risk attitudes across 30 countries Cultural value impacts on loyalty programs Traffic flow simulations Game theory applications in coordination problems Experimental validation of economic theories
Mark S. Handcock is a Distinguished Professor in the Department of Statistics and Data Science at the University of California, Los Angeles (UCLA), where he leads research at the intersection of statistical methodology and applied problems in social sciences, epidemiology, and environmental science. His work bridges theoretical statistics with real-world challenges through innovative methodological development. His primary research interests encompass statistical models for social networks, network inference, methodology for hard-to-reach population surveys, spatial processes, demography, and environmetrics. Handcock has pioneered advances in exponential-family random graph models (ERGMs) and developed foundational R packages like ergm and tergm within the statnet suite, enabling sophisticated network analysis across disciplines. Analysis of his recent publications (2023-2025) reveals three dominant research thrusts: (1) Antarctic sea ice modeling using Bayesian reconstruction and temporal variability analysis, (2) epidemiological modeling of infectious disease transmission dynamics (particularly COVID-19), and (3) methodological innovations in network inference and causal analysis over stochastic networks. His work consistently integrates advanced computational statistics with domain-specific applications in climate science, public health, and social systems.
Scott L. Diamond is the Arthur E. Humphrey Professor of Chemical and Biomolecular Engineering and Bioengineering at the University of Pennsylvania's School of Engineering and Applied Sciences. He serves as Director of the Penn Center for Molecular Discovery, Director of the Penn Biotechnology Masters Program (one of the largest in the country with over 130 students), and Associate Director of the Institute for Medicine and Engineering (IME). His laboratory is located in the Roy and Diana Vagelos Laboratories at 3340 Smith Walk, 1020 Vagelos Research Laboratories, Philadelphia, PA. Diamond's research spans multiple interconnected fields in blood biology and biotechnology. His work focuses on mechanobiology, thrombolysis, coagulation, bioadhesion, gene therapy, drug/device development, proteomics, drug discovery, systems biology, and microfluidics. His laboratory has developed numerous specialized microfluidic devices for studying blood clotting under various flow conditions, including 8-channel devices for high-throughput clotting assays, side-view devices for clot structure analysis, stenosis devices for high shear clotting assays, and impingement-post devices for studying von Willebrand factor fibers. Diamond's research group has pioneered approaches to model and predict blood function using systems biology principles. His team has developed computational models that integrate reaction-transport phenomena with platelet signaling networks to predict thrombus formation under flow. These models have enabled the development of 'virtual blood' computer simulations that can predict the effectiveness of anticoagulation drugs for individual patients, contributing significantly to personalized medicine approaches in hemostasis and thrombosis. His extensive publication record demonstrates a consistent focus on understanding the fundamental mechanisms of blood clot formation and dissolution. Recent work has emphasized microfluidic approaches for point-of-care diagnostics, patient-specific modeling of platelet function, and the development of novel therapeutic strategies for thrombotic disorders. His research bridges engineering principles with clinical hematology to address significant challenges in cardiovascular medicine. NSF National Young Investigator Award NIH FIRST Award American Heart Association Established Investigator Award AIChE Allan P. Colburn Award George Heilmeier Excellence in Research Award Elected Fellow of the Biomedical Engineering Society (BMES) Diamond has secured significant research funding, including a $2.8 million NIH grant for 'Blood Systems Biology' and a $9.5 million NIH grant for the Penn Center for Molecular Discovery. His laboratory has developed numerous microfluidic devices for blood analysis and has collaborated extensively with clinicians and industry partners. Diamond has served on advisory committees for NSF, NIH, AHA, and NASA, and has consulted extensively for industry and government. With over 180 publications and patents, his work has significantly advanced the understanding of blood clotting mechanisms and the development of diagnostic and therapeutic approaches for thrombotic disorders.
Professor Dragan Jovcic is the Chair in Engineering at the University of Aberdeen's School of Engineering , where he has been a faculty member since 2004 and a full professor since 2012. Concurrently he serves as Director of the Aberdeen HVDC Research Centre , a role he has held since 2015. Education: PhD in Electrical Engineering, University of Auckland, 1999 Diploma Engineer in Control Systems, University of Belgrade, 1993 Postgraduate Certificate in University Teaching, University of Ulster, 2003 Research Interests: Professor Jovcic’s research centres on high-power electronics and HVDC transmission systems , with particular emphasis on the development of DC transmission grids that will enable large-scale integration of offshore wind energy . His work spans DC/DC converters , DC circuit breakers , modular multilevel converters (MMC) , flexible AC transmission systems (FACTS) , and advanced power system modelling and control . The overarching goal is to underpin the transition from fossil-fuel generation to renewable-dominated power systems, especially in the North Sea and European contexts. Publication Trends: Recent publications (2020-2023) reveal a strong focus on DC protection technologies —notably circuit breakers and energy absorbers—and on modelling methodologies for HVDC grids and offshore wind integration. The works address both theoretical advances (phasor and state-space models) and experimental validation (kV-level prototypes), reflecting a balanced portfolio of fundamental research and practical demonstration. Scientific Awards & Recognition: IEEE Fellow (2021) IEEE PES Distinguished Lecturer (since 2015) IET Fellow (2019) and Chartered Engineer (2018) Research Funding & Supervision: With over £5.5 million in external research income, Professor Jovcic is principal investigator or work-package leader on numerous EU Horizon Europe and EPSRC projects. He has supervised 9 PhDs to completion and is currently mentoring 2 PhD students and 2 post-doctoral fellows . Major grants include the €35 million PROMOTioN project on multiterminal DC networks and the €4 million MoWiLife project on wide-bandgap power electronics. Laboratory & Facilities: The Aberdeen HVDC Research Centre hosts a 0.9 kV DC grid demonstrator , 30 kW thyristor- and IGBT-based DC/DC converters , and 5 kV, 2 kA DC circuit breaker test benches , providing a world-class platform for experimental research and student training.
Calin Belta is a Professor in the College of Engineering at Boston University , with joint appointments in Mechanical Engineering, Systems Engineering, and Electrical and Computer Engineering. His research bridges control theory and formal methods, focusing on controller synthesis and automatic verification of hybrid systems with applications in robotics and systems biology . Education: Ph.D. in Control Theory, University of Pennsylvania His work emphasizes temporal logic specifications for ensuring safety and correctness in autonomous systems, particularly through control barrier functions (CBFs) , reinforcement learning , and model predictive control . Applications span from microrobotics to autonomous driving and biomolecular modeling . His recent articles (2025–2024) highlight advancements in safe control algorithms for autonomous vehicles, adaptive CBFs , temporal logic-guided learning , and microrobotics for cell manipulation. Common themes include formal verification , robustness , and human-in-the-loop safety . Scientific Awards: AFOSR Young Investigator Award (2008) NSF CAREER Award (2005) He also contributes to academia as a Senior Member of IEEE and Associate Editor for journals like SIAM Journal on Control and Optimization and IEEE Transactions on Automatic Control . His lab develops computational tools for safety-critical control in complex environments.
Marc Sachon is a Full Professor and Director of the Department of Operations, Information, and Technology at IESE Business School, University of Navarra . He serves as Academic Director for Advanced Management Programs (AMP) and specialized courses like Successful Change Management and Industry 4.0 . His leadership extends to the annual IESE Automotive Industry Conference (since 1986) and consulting engagements with global firms such as BMW Group , Phoenix Group , and Traton Group . PhD in Industrial Engineering and Engineering Management from Stanford University MBA from IESE Business School Master's in Aerospace Technology from University of Stuttgart His research focuses on operations strategy , particularly in the automotive industry , and the impact of Industry 4.0 on manufacturing and logistics. He explores how digital transformation reshapes value chains, emphasizing human-machine collaboration and supply chain resilience. His work spans academic journals like IEEE Transactions and business publications like IESE Insight , with case studies on companies such as Porsche and Netflix . Professor Sachon has received multiple teaching awards and maintains an active consulting practice across industries including airlines , pharmaceuticals , and logistics . He previously worked at Airbus and IBM , and currently advises a mobility startup board. His recent publications highlight trends in electric mobility , 3D printing , and supply chain sustainability .
Jennifer Dy is a Distinguished Professor at Northeastern University with joint appointments in Electrical and Computer Engineering and Khoury College of Computer Sciences. As Director of AI Faculty at the Institute for Experiential AI, she leads research in machine learning, computer vision, and explainable AI. Her work spans biomedical applications (COPD phenotyping, neuroimaging) and fundamental algorithms (active learning, continual learning). She holds a PhD from Purdue University and is an AAAI Fellow. Research Focus: Dy develops methodologies for robust and interpretable machine learning, including techniques for model stability in continual learning, dependency-aware active learning, and axiomatic explanation frameworks. Her applied research advances diagnostic tools using Raman spectroscopy, CT imaging, and multi-omics biomarker discovery. Awards: Recognized with the NSF CAREER Award, Faculty Research Team Award, and AAAI Fellowship for contributions to unsupervised learning and medical AI. Publication Trends: Recent articles demonstrate strong cross-disciplinary integration, combining theoretical advances in explainability/robustness with applications in healthcare, wireless systems, and particle physics. Methodological themes include optimal transport theory, probabilistic modeling, and transformer architectures.
Ronald G. Larson serves as the George Granger Brown Professor of Chemical Engineering and A. H. White Distinguished University Professor at the University of Michigan's College of Engineering, with additional appointments in Mechanical Engineering and Macromolecular Science & Engineering. His research leadership spans multiple departments within the Chemical Engineering Division, where he directs the Larson Lab focused on fundamental and applied soft matter physics. His research program investigates complex fluids through computational and theoretical frameworks, emphasizing polymer physics, rheology, and molecular simulations. Key thrusts include polymer melt processing, biomembrane dynamics, colloidal systems, and polyelectrolyte coacervation. The group employs advanced techniques like Brownian dynamics, coarse-grained modeling, and multiscale simulation to address challenges ranging from industrial polymer processing to biomedical applications. Recent publications (2023-2025) reveal strong momentum in rheological modeling of complex fluids, with particular emphasis on self-healing materials, wax deposition in pipelines, and crystallization mechanisms. The work bridges fundamental molecular insights with industrial applications, demonstrating consistent high-impact output across polymer science, soft matter physics, and chemical engineering domains. The Larson Lab operates as a collaborative hub within the Chemical Engineering Department, leveraging computational resources to advance understanding of fluid mechanics and material properties. Current projects integrate machine learning with traditional modeling approaches, reflecting the group's commitment to methodological innovation while maintaining strong connections to experimental validation and real-world engineering problems.
Dr. Qingbo Sun is a researcher at the Department of Materials Physics, Australian National University, specializing in advanced materials for energy and electronic applications. His work focuses on defect engineering, dielectric materials, and photovoltaic effects in nanocrystalline systems. Research interests include: Defect-driven local symmetry breaking Colossal dielectric permittivity Photocatalytic heterojunctions High-pressure material transformations Doping strategies in semiconductors Nonlinear electric polarization Research trends from his publications highlight innovations in TiO2-based photocatalysts, SnO2 dielectrics, and ferroelectric heterostructures. Collaborations span materials synthesis, computational modeling, and international experimental studies. His work is cited extensively in Scopus with 294 citations.
Dr. Joyoung Lee is an Associate Professor in the Department of Civil and Environmental Engineering at New Jersey Institute of Technology (NJIT). He previously served as Laboratory Manager at the Federal Highway Administration's Saxton Transportation Operations Laboratory. His research focuses on Connected Vehicle (CV) systems, including applications in traffic management, signal control optimization, and autonomous vehicle infrastructure integration. Dr. Lee holds a Ph.D. (2010) and M.S. (2007) in Transportation Engineering from the University of Virginia, and a B.S. (2000) in Transportation Engineering from Hanyang University. His work emphasizes CV-based solutions for real-time traffic systems, cooperative vehicle-infrastructure systems (CVIS), and autonomous vehicle integration. Notable achievements include the 2019 IEEE CAVS Best Paper Award and multiple best paper recognitions from PTV User Group Meetings. His research also addresses traffic safety through innovations like the Virtual Guide Dog system for visually impaired pedestrians and advanced traffic monitoring frameworks using LiDAR and computer vision. Education: Ph.D., Transportation Engineering, University of Virginia (2010) M.S., Transportation Engineering, University of Virginia (2007) B.S., Transportation Engineering, Hanyang University (2000) Dr. Lee's research interests span smart city infrastructure, edge computing for traffic systems, and sustainable transportation solutions. He has pioneered algorithms for cooperative intersection management, automated platooning systems, and federated learning-based traffic optimization. His work bridges theoretical models with real-world implementation through partnerships with FHWA and industry stakeholders. Key contributions include development of the Cumulative Travel-Time Responsive (CTR) traffic signal control system, smart arrival notification systems for paratransit services, and advanced microsimulation calibration techniques. His lab focuses on translating CV data into actionable strategies for safer, more efficient transportation networks. Awards: IEEE CAVS Best Paper Award (2019) ASCE Grand Challenge Innovation Contest Honorable Mention (2017) PTV VISSIM Best Paper Awards (2012, 2008) Excellence in Research Award (University of Virginia, 2011) Ongoing projects include semi-decentralized graph neural networks for traffic forecasting and low-cost LiDAR-based traffic monitoring systems. His work addresses critical challenges in autonomous vehicle integration, incident management, and infrastructure resilience through interdisciplinary collaborations.
Jian Tang is an Assistant Professor at HEC Montréal and a core member of the Montreal Institute for Learning Algorithms (MILA). His research focuses on graph representation learning, generative models, and their applications in drug discovery and material science. Prior to this, he was a postdoctoral researcher at the University of Michigan and Carnegie Mellon University, and a researcher at Microsoft Research Asia (2014-2016). He has received several prestigious recognitions, including the Canada CIFAR Artificial Intelligence Chairs (CCAI Chair) and best paper nominations at WWW’16. His work on LINE (WWW’15) was recognized as the most cited paper in its year. Tang’s research spans theoretical foundations and practical systems, such as GraphVite for scalable graph embedding and TorchDrug for drug discovery. Key research interests include geometric deep learning for molecular structures, generative models for protein design, and neural-symbolic reasoning for knowledge graphs. He actively collaborates with leading biological labs and leverages industry partnerships for GPU resources. Recent publications emphasize molecular property prediction, 3D conformation generation, and algorithmic reasoning frameworks. He has secured grants from IBM/MILA, Amazon, and the National Research Council Canada, supporting projects like molecular pretraining and geometric representation learning. Tang teaches courses on graph representation learning and deep learning, and mentors a vibrant team of PhD and master’s students. His lab has developed impactful software tools like LINE, PTE, and LargeVis, widely used in the research community.
Carolina Osorio is a Professor at HEC Montréal, holding the Scale AI Research Chair in Artificial Intelligence for Urban Mobility and Logistics. She is affiliated with the Department of Decision Sciences and is a member of the Group for Research in Decision Analysis (GERAD) and the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT). Her research focuses on transportation optimization, urban mobility, and data-driven simulation-based methods. She has been recognized among the world’s most influential researchers in 2023 and 2024. Education: Ph.D. in Mathematics, École Polytechnique Fédérale de Lausanne (EPFL) M.Sc. in Statistics, University College London (UCL) Bachelor’s in Engineering, École nationale supérieure d'informatique et de mathématiques appliquées de Grenoble (ENSIMAG) Research Interests: Her work emphasizes scalable transportation modeling, simulation-based optimization, and AI applications for urban logistics. She develops methods for large-scale network analysis, traffic demand estimation, and sustainable urban mobility solutions. Key areas include traffic signal optimization, car-sharing service design, and high-dimensional stochastic systems. Publications: Recent articles highlight advancements in scalable traffic demand estimation, Bayesian optimization for transportation systems, and simulation-based toll optimization. Her work addresses challenges in global highway networks, urban congestion dynamics, and multi-city calibration. Awards: Scale AI Research Chair (Artificial Intelligence for Urban Mobility and Logistics) Recognition as a world-leading researcher in transportation science Advising & Grants: Osorio collaborates on projects funded by Scale AI and leads research initiatives through GERAD and CIRRELT. Her supervision activities include teaching courses such as Decision Analysis and Sample Efficient Optimization at HEC Montréal. Labs & Teams: She contributes to interdisciplinary teams at GERAD and CIRRELT, focusing on integrating advanced analytics into urban transportation systems.
Dr. Yar Muhammad is a Principal Lecturer in Computer Science at the University of Hertfordshire's School of Physics, Engineering & Computer Science. His research develops Brain-Computer Interface applications using AI/ML techniques for healthcare. He holds a PhD in ICT (Tallinn University of Technology) and dual master's degrees. Research Leadership: Supervised PhD students: Nimra Memon (fault-tolerance in web services), Dmytro Zabolotnii (agent behavior prediction), Mahir Gulzar (context-aware modeling) Accepts self-funded PhD candidates in BCI/AI applications Awards: Young Investigator Award (Springer/IFMBE, 2014) Best Paper Award Runner-up (26th ISSC 2015) Professional Recognition: Fellow of Higher Education Academy IEEE Senior Member Editorial board member for multiple journals
