Harry Lahrmann is an Associate Professor and Research Group Leader at the Department of Construction, Urban and Environmental Engineering within Aalborg University's Faculty of Engineering and Science. He specializes in traffic safety research with a focus on cyclist-pedestrian interactions, vehicle inspection systems, and urban mobility solutions. Key Research Areas: Bicycle traffic, road safety analysis, traffic engineering, and data-driven transportation policy Recent Work Trends: Utilizes ambulance data and self-reporting mechanisms to identify hazardous road locations; investigates impact of vehicle inspection programs and cycling safety technologies Awards: 1994 - First prize in bicycle safety at intersections Advising & Grants: Supervises PhD students and secures funding from institutions like TrygFonden for projects such as "Better Data on Traffic Accidents." Labs & Teams: Leads the Traffic Research Group, collaborating with experts in infrastructure, hydraulic engineering, and environmental technology.
Dr. Heather L. Wilson is a Senior Lecturer in Immunity & Cardiovascular Disease at the School of Medicine and Population Health, University of Sheffield. With a DPhil in Pharmacology from Oxford and postdoctoral experience in immune cell biology, she specializes in macrophage dysfunction in aging and inflammatory diseases. Research: her work explores how macrophage function declines with age, exacerbates atherosclerosis, and drives autoimmune responses in diseases like lupus. She investigates human primary blood cells and uses zebrafish models to study IL-1β mechanisms. Teaching: she leads the MBChB Phase 3a Student Selected Component, co-deploys the MSc Reproductive Medicine Social Aspects module, and trains PhD/MSc students in cardiovascular cell biology. Grants: BBSRC, MRC, British Heart Foundation, Horizon 2020 Marie Curie ITN, Vivensa Foundation Awards: RCUK Fellowship (2005-2010), Fellow of the Higher Education Academy
Enrico Rukzio is a Professor at the University of Ulm, Germany, leading a prominent research group focused on human-computer interaction with particular emphasis on automotive user interfaces, mixed reality, and accessibility. His research spans multiple domains including automated vehicles, virtual reality, and sustainable interaction design, with consistent publication output in top-tier venues such as CHI, UIST, and AutomotiveUI. Professor Rukzio's research interests center around the intersection of human factors and emerging technologies. His work investigates how users interact with automated systems, particularly in transportation contexts, with significant contributions to external vehicle communication, in-vehicle interfaces for automated driving, and accessibility solutions for diverse user populations. His research group has pioneered methods for optimizing user interfaces through Bayesian optimization and has conducted extensive studies on user acceptance of automated vehicle technologies. His publication portfolio reveals a clear trajectory of research evolution from foundational HCI work to specialized applications in automated transportation systems. Recent work shows increasing focus on accessibility aspects of automated vehicles, particularly for users with visual impairments, as well as exploration of emerging domains like Urban Air Mobility. The research demonstrates strong methodological diversity, incorporating controlled experiments, field studies, and computational optimization techniques. Professor Rukzio has supervised numerous doctoral students who have become active researchers in the field, including Mark Colley, Pascal Jansen, and Luca-Maxim Meinhardt. His research group maintains strong international collaborations and has secured funding for multiple projects at the forefront of automotive user experience research. The group's work has practical implications for automotive manufacturers and technology developers creating next-generation transportation interfaces.
David Minh is an Associate Professor of Chemistry and the Robert E. Frey, Jr. Endowed Chair in Chemistry at Illinois Institute of Technology (IIT), affiliated with the Lewis College of Science and Letters. He serves as Associate Director of the Center for Interdisciplinary Scientific Computation (CISC). His research focuses on computational chemical biology, developing methods to predict protein dynamics and molecular interactions for structure-based drug design. Education: Ph.D. in Chemistry, University of California, San Diego M.S. in Chemistry, University of California, San Diego B.A. in Chemistry, University of California, Berkeley Research Interests: Dr. Minh's group specializes in computational methods to study small molecule-biological interactions, including: Structural mechanisms of G protein-coupled receptors (GPCRs) and signaling proteins Advanced binding free energy calculations incorporating entropy Enhanced sampling in molecular simulations Bayesian statistical integration of experimental data Modeling bacterial metabolic enzymes and inhibitor development Articles Trends: Recent work emphasizes antiviral drug discovery (e.g., SARS-CoV-2 protease inhibitors), Bayesian analysis of binding data, and computational tools like AlGDock for free energy predictions. Collaborations with biologists (e.g., Oscar Juárez) drive antibiotic discovery targeting pathogenic bacteria. Advising & Grants: Leads interdisciplinary projects funded by NIH and industry partnerships. Mentors students in computational modeling and experimental validation. Active in open science initiatives like the D3R Grand Challenge in drug design. Labs & Teams: Directs the Minh Computational Chemistry Lab at IIT, focusing on molecular simulations, machine learning, and interdisciplinary collaborations to address biomedical challenges.
Professor Wenfei Fan is a Chair of Web Data Management at the University of Edinburgh since 2006. He holds adjunct roles at Huawei Edinburgh Research Laboratory and the International Research Center on Big Data at Beihang University. His research focuses on database systems, theory, big data, data quality, and constraint applications. He is a Fellow of the ACM and Royal Society of Edinburgh, and recipient of the ERC Advanced Grant (2015). His work includes foundational contributions to XML constraints, data quality management, and graph databases, with over 160 top-tier publications and 8 patents. Education: BSc and MSc from Peking University (1985, 1988); PhD from the University of Pennsylvania (1999). Professional Roles: Editor for TODS, TCS, VLDBJ, and TBD; PC Chair for PODS, CIKM, APWeb, and others. Key awards include the Roger Needham Award (2008), Yangtze River Scholar (2007), and multiple best paper awards at SIGMOD, PODS, VLDB, and ICDE. He has led over 15 grants totaling €6M and advised students who all received top conference awards. His labs and initiatives drive industry collaboration, with deployed solutions at Huawei for big data query optimization.
Dan Hu is a Lecturer in Mechanical Engineering at the CEES School of Engineering. He specializes in biomechanical modeling, vehicle dynamics, and estimation algorithms. His research focuses on human movement analysis, musculoskeletal systems, and advanced control systems for automotive applications. He is currently accepting PhD students in two projects: developing predictive modeling tools for personalized rehabilitation devices and creating experiment-free clinical diagnosis systems using machine learning. Dr. Hu holds a Doctorate in Biomechanics & Automotive Engineering from Jilin University (2014), where his thesis addressed musculoskeletal modeling of drivers' hands. He also earned an MSc in Automotive Engineering (2009), focusing on Kalman filter applications for vehicle state estimation. His research interests span biomechanical engineering, computational modeling of human motion, and vehicle state estimation using extended Kalman filters. Notable contributions include 3D whole-body walking models, metatarsophalangeal joint kinematics studies, and biomimetic prosthetics design. His work bridges biomechanics with automotive engineering, aiming to improve healthcare technologies and vehicle safety systems. Dr. Hu has peer-reviewed for journals like Scientific Reports , IEEE Transactions on Neural Systems , and Computer Methods in Biomechanics . His research collaborations span biomechanics, robotics, and automotive control systems. Current projects emphasize predictive modeling and clinical diagnosis innovation.
Thomas Lecuit is a Professor at Collège de France , leading the Dynamics of Living Systems Chair . His research combines developmental biology , biophysics , and systems biology to study morphogenesis—the physical and informational processes driving biological form from cells to organisms. He heads a team at the Institut de Biologie du Développement in Marseille and directs the Turing Center for Living Systems , an interdisciplinary hub integrating physics, mathematics, and computational approaches. ERC Advanced Grant (2024) Chair of Excellence in Biology-Health (2024) Liliane Bettencourt Prize for Life Sciences (2015) CNRS Silver Medal (2015) His work reveals how mechanical forces , genetic programs , and self-organization govern tissue morphogenesis, dendrite development, and morphogenetic waves. Publications demonstrate predictive models for biological reproducibility and evolution, with applications in cell biology , neuroscience , and biological information theory . Lectures at Collège de France and international institutions explore themes like information flow and computation in living systems .
Alberto Colotti is a Professor at the Zurich University of Applied Sciences (ZHAW), School of Engineering, where he heads the Power Electronics and Drives team within the Institute of Mechatronic Systems. He serves as a lecturer for Power Electronics and Drive Systems and maintains an active research profile in power conversion technologies. His research spans power electronics, electric drives, and mechatronic systems with emphasis on wide-bandgap semiconductors (GaN/SiC), motor design for traction applications, and thermal management in power converters. Key specialties include synchronous reluctance machines, IPMSM development, and educational test bench design for instructional purposes in drive systems. Publications from 2014-2024 reveal consistent innovation in power converter topologies and machine design, with recent work focusing on instructional test benches (2024) and SiC thermal modeling (2019). His research bridges theoretical modeling with experimental validation, particularly in gallium nitride applications and traction drive systems for small vehicles. Colotti has led multiple research projects: Control platform for gallium nitride-based power converter (Project leader, completed) Sigrid – Smart Interlocking Grid (Project leader, completed) Adastec (Project leader, completed) Lisrel (Project leader, completed) Holistic mechatronic design for a sonic toothbrush (Deputy project leader, completed) Multipol Resolver with passive Rotor (Deputy project leader, completed) He directs the Power Electronics and Drives team at ZHAW's Institute of Mechatronic Systems, which specializes in advancing power electronics research through both simulation and experimental validation of next-generation drive systems.
John N. Randall serves as Adjunct Professor at the University of Texas at Dallas' Jonsson School of Engineering since 2012, concurrently holding CEO roles at Zyvex Labs and Executive VP positions at Teliatry and NanoRetina. With 40+ years in micro- and nano-fabrication, his industry leadership has attracted $48M in research contracts and generated $750M+ in product revenues through innovations in semiconductor manufacturing and quantum technologies. Dr. Randall earned his academic foundation at the University of Houston: PhD in Electrical Engineering (1981) MS in Electrical Engineering (1977) BS in Electrical Engineering, Cum Laude (1975) His research spans semiconductor lithography (optical, e-beam, ion-beam, x-ray, STM-based) and quantum devices/circuits, with significant contributions to MEMS, atomic layer epitaxy, and metrology. This interdisciplinary work bridges fundamental nanofabrication with quantum electronic applications, driving advancements in nanoscale device manufacturing for semiconductor and computing industries. Analysis of his publication history reveals a clear evolution from 1980s foundational work in ion beam lithography and quantum dot structures toward 1990s applications in optical proximity correction and MEMS. The consistent focus on precision nanofabrication techniques demonstrates a trajectory from theoretical quantum phenomena to industrial semiconductor manufacturing solutions. Dr. Randall's scientific recognition includes IEEE and AVS Fellowships, University of Houston's Distinguished Engineering Alumni Award, and leadership honors as Conference Chairman for premier nanotechnology forums. His early career accolades encompass National Collegiate Judo Championships and academic distinctions. Fellow of the IEEE (2015) Disinguished Engineering Alumni Award – University of Houston (2010) Fellow of the AVS (2009) Elected Distinguished Member of Texas Instruments’ Technical Staff (2000) Conference Chairman roles for Gordon Conference (1998) and EIPB Conference (1995) National Collegiate Judo Champion (1974-1975) His $48M research funding at Zyvex directly enabled commercially successful nanotechnology products, while his advisory roles with University of North Texas, Girl Scouts of NE Texas, and UT Dallas demonstrate commitment to education and industry collaboration. Though student mentoring isn't explicitly documented, his academic appointments and conference leadership indicate substantial field guidance. As Zyvex Labs CEO, Randall leads atomic-precision manufacturing initiatives building on his MIT/TI-era nanofabrication breakthroughs. His current focus on quantum computing applications extends his legacy of transforming fundamental research—like room-temperature quantum circuits and 80nm lithography—into industry-shaping technologies across semiconductor, medical, and computing sectors.
Alexia Auffèves serves as a First Class Research Director (DR1) at the French National Centre for Scientific Research (CNRS) and holds a Visiting Research Professor position at the Centre for Quantum Technologies (CQT), National University of Singapore. She directs the CNRS International Research Lab MajuLab and co-founded the Quantum Energy Initiative (QEI), an interdisciplinary global consortium investigating the energy footprint of quantum technologies. She completed her experimental PhD under Nobel laureate Prof. Serge Haroche. From 2017-2022, she led the QuantAlps center for quantum science in Grenoble before launching the Quantum Energy Initiative in 2022. Dr. Auffèves pioneers research at the intersection of quantum energetics, quantum optics, and quantum foundations. Her work establishes fundamental principles for quantifying energy costs in quantum information processing, bridging theoretical physics with philosophical inquiry. Recent focus includes developing energy efficiency metrics for quantum processors and analyzing thermodynamic constraints in quantum measurements. Her 2023-2025 publications reveal a cohesive research trajectory centered on quantum thermodynamics. Key contributions include establishing energy cost frameworks for quantum measurements, demonstrating reservoir-free decoherence mechanisms, and linking quantum negativity to anomalous energy exchanges. These works consistently integrate theoretical modeling with experimental validation through collaborations with leading quantum hardware groups. She leads multiple high-impact projects including BACQ and HQI (French Quantum Strategy), NGap (NRF), and OECQ (French Public Bank of Investment), involving industry partners like Alice&Bob, Quandela, and EDF to optimize quantum processor energy efficiency. Dr. Auffèves mentors a multinational research team comprising Kiarn Laverick, Kian Hwee Lim, Samyak Prasad, Nathan Shetell, Harshit Verma, Hanlin Nie, and PhD student Tejas Acharya. Her group operates within MajuLab and the Quantum Energy Team, driving collaborative research across France, Singapore, and international institutions.
Professor Emma Bond is the Pro Vice-Chancellor for Research and Knowledge Exchange and Professor of Socio-Technical Research at the University of Suffolk. As a key member of the Executive and Senior Leadership Team, she drives the university's research agenda, oversees research institutes/centers, and leads preparations for the Research Excellence Framework (REF) 2027/8. She is a Senior Fellow of the Higher Education Academy with over 20 years of teaching and research experience. Her research examines online risks for vulnerable groups , including image-based abuse, domestic violence, child safeguarding, and digital participation. She develops participatory methodologies to engage marginalized communities and collaborates with organizations like the UK Safer Internet Centre and Marie Collins Foundation. Key projects include Home Office-funded evaluations of online safety resources and the development of the Higher Education Online Safeguarding Self-Review Tool . Her publications focus on digital society ethics , online harassment policy, and socio-technical vulnerabilities. Recent work critiques legislative gaps in revenge pornography and advocates for rights-based online safeguarding frameworks. Scientific Awards: Senior Fellow, Higher Education Academy Fellow, Royal Society for Arts, Manufactures and Commerce Advising & Grants: She supervises 10 PhD students researching technology's role in domestic abuse, non-binary youth experiences, and health professional training. She secured grants from the Home Office, Office for Students, and UK Safer Internet Centre for projects on digital civility, online harassment, and child exploitation prevention. Labs & Teams: Directs the Research and Knowledge Exchange Directorate, coordinating cross-university research culture and partnerships with UK policing bodies, NGOs (e.g., Amnesty International), and international consortia like the Health Literacy in Childhood and Adolescence group.
Professor Song Young-min is joining the Department of Electrical Engineering at Korea Advanced Institute of Science and Technology (KAIST) as a Professor, with his appointment beginning July 1, 2025. His research focuses on developing innovative bio-inspired optical systems for robotics, with particular expertise in biomimetic cameras and neuromorphic vision systems. Professor Song's research interests span flexible optoelectronic devices and nanophotonics, with specific applications in biomimetic cameras for intelligent robots , optoneuromorphic devices and systems , nanophotonics-based reflective displays , and infrared-controlled radiative cooling devices . His work bridges electrical engineering, materials science, and biological inspiration to create energy-efficient vision systems that reduce computational demands. His recent publications demonstrate significant advancements in bio-inspired vision technology, particularly in feline-inspired vertical pupil systems that improve object tracking stability and cuttlefish-inspired W-shaped pupil designs for uneven lighting conditions. These innovations show how hardware improvements can substantially reduce energy consumption in robotic vision systems. Professor Song has established significant research collaborations with institutions including MIT (working with Frédo Durand on the Artificial Compound Eyes with Artificial Intelligence project), EPFL, and Northwestern University. His work has been published in high-impact journals including Nature, Science Robotics, and Science Advances. His laboratory focuses on developing next-generation vision systems that integrate biological inspiration with cutting-edge optical engineering, with applications ranging from surveillance robots to autonomous vehicles. Current projects include improving wide-angle imaging capabilities and developing more efficient optic flow processing systems for drone navigation.
Dr. Xinyu Zhang is a Research Fellow at the Australian Institute for Machine Learning (AIML), University of Adelaide's Faculty of Sciences, Engineering and Technology. Her research bridges computer vision and machine learning, focusing on image/video generation, self-supervised learning, and multimodal retrieval for human-centric AI applications. Zhang's current investigations include: Causal representation learning and multimodal integration Bayesian deep learning frameworks Video generation with temporal consistency Lightweight detection transformers Unsupervised person re-identification Analysis of recent publications reveals strong emphases on generative modeling innovations (especially video synthesis), efficient transformer architectures for real-time applications, and self-supervised representation learning. Her work frequently addresses the alignment between latent representations and human perception across vision-language tasks. Dr. Zhang co-supervises graduate students on projects involving multi-agent 3D scene generation and knowledge transfer in low-supervision learning. She serves as conference reviewer for premier venues including CVPR, ICCV, and NeurIPS, contributing to the advancement of computer vision research.
Jose Miguel Reynolds Barredo is an Associate Professor and Director of the Doctorate in Plasmas and Nuclear Fusion at Carlos III University of Madrid. His research focuses on plasma physics, magnetohydrodynamics (MHD), and energy systems resilience. He leads studies on stellarator reactor design, plasma confinement optimization, and the integration of renewable energy into power grids. His work spans advanced MHD equilibrium solvers (e.g., SIESTA, FLIPEC) and fusion device optimization for ITER and Wendelstein 7-X. He also investigates climate impacts on renewable energy efficiency and power grid stability under high renewable penetration. Notable contributions include HVDC grid segmentation strategies and non-axisymmetric plasma transport modeling. Key Areas: Fusion reactor design, MHD stability, power grid resilience, climate-energy interactions Tools: SIESTA, FLIPEC, GENE, OPA cascading blackout model Projects: Doctorate in Plasmas and Nuclear Fusion, W7-X bootstrap current studies, climate-energy system interdependencies Research emphasizes computational plasma physics and interdisciplinary energy solutions, blending theoretical, numerical, and applied engineering approaches.
Mateja Novak is an Assistant Professor at AAU Energy, Aalborg University, Denmark, within the Department of Applied Power Electronic Systems under the Faculty of Engineering and Science. Her research focuses on model predictive control, multilevel converters, machine learning, and reliability of power electronic systems, contributing to sustainable energy systems and renewable energy integration. She holds a Ph.D. from Aalborg University (2020) and an M.Sc. from Zagreb University (2014). Previously, she was a Postdoc at AAU Energy (2020-2023) and a visiting researcher at Kiel University (2018) and Danfoss (2023). Notable achievements include the EPE Outstanding Young EPE Member Award (2019) and 2nd place in the 2021 IEEE-IES Student and YP Competition. Her work spans projects like ALL2GaN (2023-2026) and AI-Power (2022-2027), addressing GaN IC solutions and AI-driven power electronics advancements. She is actively involved with IEEE societies including the Power Electronics Society and IEEE Women in Engineering. Her research outputs emphasize control strategies for power electronics, reliability analysis, and optimization techniques. Key areas of exploration include thermal stress balancing in converters, statistical model checking, and multiobjective control algorithms. Collaborations with industry partners like Danfoss and academic institutions like Kiel University underscore her interdisciplinary approach to advancing power electronics technology.
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