Amitabh Mishra is an Adjunct Professor at the University of Delaware. His research focuses on three core areas: computer-communication networks (wireless architectures, cross-layer design, mobile cloud computing), network performance analysis (stochastic models, numerical optimizations), and network security (vulnerability assessments, authentication protocols). He has contributed to interdisciplinary fields including IoT security, smart healthcare frameworks, and socio-technical systems analysis. His work spans technical domains like wireless sensor networks, tactical network management, and quantum dot material studies, alongside applied research in tourism economics, healthcare data analytics, and educational technology. Notable contributions include frameworks for energy-efficient physiological monitoring, secure IoT configurations, and machine learning-driven security protocols. Recent research highlights include: Developing secure mobile cloud computing paradigms Modeling Multipath TCP capacity bounds using stochastic theory Investigating AI applications for deepfake ethics and tourism marketing His publications span technical journals in computer networks, medical IoT systems, and interdisciplinary studies in cultural tourism and climate change resilience.
Twan Basten is a Full Professor in the Electronic Systems group at Eindhoven University of Technology (TU/e). He leads research on embedded and cyber-physical systems, focusing on model-driven design, computational models, and system dependability. He holds an MSc (1993) and PhD (1998) in Computing Science from TU/e, advancing from Assistant to Full Professor by 2009, and became the Electronic Systems group chair in 2013. His research spans international projects (FP5-7, H2020, ECSEL) and Dutch initiatives (STW, NWO, RVO), with over 200 publications and seven best paper awards. He has co-supervised 21 PhD students and actively participates in program committees and conferences. His work contributes to UN Sustainable Development Goals through innovations in smart systems. Education: MSc in Computing Science, TU/e (1993) PhD in Computing Science, TU/e (1998) Research Interests: Explores design methodologies for embedded systems, including scenario-based design, real-time scheduling, and performance analysis. Specializes in model-driven engineering and computational models to ensure system dependability. Active in projects like TRANSACT (real-time systems) and SAM-FMS (flexible manufacturing). Key Contributions: Co-author of 1 book and over 200 scientific publications Recipient of seven best paper awards Co-supervised 21 PhD degrees Senior member of IEEE and lifetime member of ACM Labs & Teams: Leads the Model-Based Design Lab and contributes to EAISI High Tech Systems initiatives. Collaborates on tools like TRACE4CPS for execution trace analysis and CReTS for vehicle platooning simulation.
Siamak Ravanbakhsh is an Associate Professor at McGill University's School of Computer Science and a Canada CIFAR AI Chair at Mila. His research focuses on machine learning, particularly representation learning with an emphasis on geometry, symmetry, and probabilistic inference. He has held academic positions at the University of British Columbia and was a postdoctoral fellow at Carnegie Mellon University. Education: B.Sc. in Computer Science, Sharif University of Technology M.Sc. and Ph.D. in Computer Science, University of Alberta (supervised by Russ Greiner) Postdoctoral Fellowship at Carnegie Mellon University (with Barnabás Póczos and Jeff Schneider) His research interests span geometric deep learning, equivariant networks, reinforcement learning, and AI for scientific applications. Notable contributions include work on symmetry-aware models, diffusion processes, and equivariant representation learning. Publications highlight advancements in causal abstraction, diffusion-based anomaly detection, and equivariant architectures for crystals and hierarchical structures. His work often bridges theory and application, emphasizing symmetry principles. Advising & Grants: Supervised over 20 graduate students and postdocs, including recent PhD graduates Daniel Levy and Mehran Shakerinava Active in mentoring M.Sc. and internship students He contributes to academic leadership roles at Mila and McGill, fostering interdisciplinary collaborations in AI research.
Prof. Barbara Wohlmuth is a full professor in Numerical Mathematics at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. She leads the International Graduate School of Science and Engineering at TUM and has held professorships at Stuttgart, Darmstadt, and Berlin universities. Her research focuses on numerical simulation of partial differential equations, multiscale solvers, and coupled multi-field problems with applications in engineering. Education: Studied mathematics at TUM and Université Joseph Fourier in Grenoble, received her doctorate from TUM in 1995, and completed habilitation in Augsburg. Visiting professorships in USA, France, and Hong Kong. Research interests include discretization techniques, predictive modeling, and interdisciplinary collaboration with engineering disciplines. Notable achievements: 2012 Gottfried Wilhelm Leibniz Prize (Germany’s highest academic honor in sciences), 2005 Sacchi-Landriani Prize. Publications emphasize advanced numerical methods in fluid dynamics, geophysics, and biomedical engineering. Active in editorial roles for international journals and scientific committees across Europe and USA. Elected member of Bavarian and European Academies of Sciences. Key contributions include: Development of robust numerical algorithms for exascale simulations Pioneering work in coupled multi-physics modeling Innovative methods for computational contact mechanics Leadership in graduate education initiatives
Norbert Linke is an Adjunct Assistant Professor in the Department of Physics at the University of Maryland , affiliated with the Joint Quantum Institute . His research focuses on experimental quantum computing with trapped atomic ions, emphasizing quantum information processing, quantum algorithms enhanced by machine learning, quantum simulation, and quantum networking using entangled photons. He holds a Dipl. Phys. from the University of Ulm (2007) and a D.Phil. in Atomic & Laser Physics from the University of Oxford (2013), where he worked under David Lucas. Before joining UMD, he conducted postdoctoral research at Oxford. Education: Bachelor's (Dipl. Phys.): University of Ulm, Germany (2007) Doctorate (D.Phil.): University of Oxford, U.K. (2013) Research Interests: Quantum algorithms with machine learning integration Quantum simulation of complex systems (e.g., quantum chromodynamics) Quantum networking via Sr+ ion-photon entanglement Development of robust quantum hardware (e.g., 3D monolithic traps) His recent work explores quantum advantage in finance, blind calibration of quantum computers, and optical control of qubits. The lab emphasizes scalable architectures and hybrid analog-digital simulations. While no scientific awards are explicitly listed, his contributions to trapped-ion systems and quantum networking are widely recognized in the field. Norbert advises researchers at the Joint Quantum Institute and collaborates on grants related to quantum computing hardware and algorithms. His team’s lab focuses on advancing ion-trap technology and explores future applications in quantum communication and computation.
Keith Morrison is a Professor at the University of Reading, affiliated with the School of Mathematical, Physical and Computational Sciences and the Department of Meteorology. His research focuses on advanced remote sensing techniques, particularly synthetic aperture radar (SAR), for environmental monitoring and subsurface imaging. Institution: University of Reading School: School of Mathematical, Physical and Computational Sciences Department: Department of Meteorology Research Focus: Radar Remote Sensing, Soil Moisture, Peatlands, Subsurface Scattering His research interests lie in the development and application of radar systems for Earth observation. He specializes in SAR-based methods such as tomographic profiling, interferometry, and virtual bandwidth SAR (VB-SAR) to study soil moisture dynamics, peatland hydrology, forest structure, and subsurface features. His work bridges physics, signal processing, and environmental science, enabling improved understanding of ecosystem processes through microwave remote sensing. The analysis of his recent publications reveals a strong focus on C-band and S-band SAR applications, particularly in explaining anomalous backscatter due to subsurface scattering in dry soils and peatlands. He has pioneered techniques like VB-SAR for centimeter-scale vertical profiling from space, contributing significantly to the accuracy of soil moisture and vegetation parameter retrievals. His work increasingly integrates laboratory experiments, field studies, and satellite data for robust validation. Although no formal awards are listed in the provided text, his sustained contributions to high-impact journals such as IEEE TGRS, Remote Sensing of Environment, and International Journal of Remote Sensing suggest recognition within the scientific community. Morrison has supervised and collaborated with numerous researchers, including Edwards-Smith, Zwieback, Andre, and Bennett. While formal student advising is not detailed, his role as a senior author and frequent collaborator indicates mentorship responsibilities. He has been involved in projects related to peatland monitoring, forest biophysical retrieval, and SAR-based change detection, likely supported by research grants from UK and European funding bodies. He contributes to major international conferences such as IGARSS, EUSAR, and IEEE RadarCon, demonstrating active engagement in the global radar and remote sensing community. His work supports future advancements in satellite-based environmental monitoring, particularly in carbon-rich ecosystems like peatlands and forests.
Akarsh Prabhakara is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin–Madison, with an additional affiliation in the Department of Electrical and Computer Engineering. He earned his Ph.D. from Carnegie Mellon University in 2024, where he worked under Professors Anthony Rowe and Swarun Kumar. Ph.D., Electrical and Computer Engineering, Carnegie Mellon University, 2024 B.Tech, Electronics and Communication Engineering, National Institute of Technology Karnataka, 2018 His research focuses on building high-fidelity wireless systems for perception and communication, particularly in cyber-physical and robotic applications. He explores machine learning-driven RF systems, novel communication paradigms, wireless-robotics integration, and embedded wireless sensing. His work aims to enable robust perception in challenging environments such as smoke or fog using millimeter wave radar and deep learning. His recent publications in CVPR, ICRA, MobiCom, and ICCV demonstrate a strong trend in using neural methods for radar simulation, super-resolution, and wireless intelligence. Key themes include implicit neural rendering for radar, end-to-end learning for perception, and high-resolution point cloud generation from low-cost sensors. His scientific contributions have been recognized through publications in top-tier venues, though specific awards are not mentioned in the provided text. He is actively involved in mentoring and recruiting students for research in wireless and robotics. He teaches courses such as Intro to Computer Networks and Big Ideas in Wireless: Perception and Communication . He leads research projects like RadarHD, which enables lidar-like perception from mmWave radar, and is developing tools and datasets for community use. His lab emphasizes practical, real-world applications of wireless systems in robotics and autonomous systems.
Luca Collini is a Professor at the Department of Industrial Systems and Technologies Engineering (DISTI) at the University of Parma. He teaches courses across both Mechanical Engineering and Management Engineering programs, including MACHINE DESIGN AB (I MOD) (3rd year, A.Y. 2025/2026), Mechanics of Materials and Structural Integrity (2nd year, A.Y. 2024/2025), and Principles of Mechanical and Structural Design (1st year, A.Y. 2024/2025). Research Interests: His work focuses on mechanical design optimization, additive manufacturing, and simulation techniques. Key areas include pneumatic actuator design , FDM 3D printing parameters , and Design-to-Value (DtV) methodology . He collaborates on projects involving virtual design integration, structural integrity analysis, and sustainable manufacturing practices. Key Publications Trends: Recent articles emphasize hybrid design approaches combining analytical formulas and simulations, multi-objective optimization using machine learning, and DtV strategies for industrial applications. Collaborative research with Maiocchi, Nicoletto, and colleagues demonstrates interdisciplinary focus on mechanical systems and advanced manufacturing.
Lifeng Yu is a Professor of Medical Physics at Mayo Clinic, holding a primary appointment as Consultant in the Department of Radiology. He specializes in CT physics, radiation dose optimization, and AI-driven imaging techniques. Dr. Yu earned his PhD in Medical Physics from the University of Chicago (2006), following degrees from Beijing University (BS, 1997; MEng, 2000). His research focuses on improving CT imaging through advanced reconstruction algorithms, photon-counting detectors, and quantitative image quality metrics. He chairs key committees such as the Radiological Society of North America's Physics Committee and SPIE's Medical Imaging Conference. Awards include Fellowships from AAPM (2018) and SPIE (2023), alongside the Reese-Hartman Award (2012). His work bridges clinical translation of technologies like multi-energy CT and AI-based denoising, aiming to enhance diagnostic accuracy while reducing radiation exposure. Dr. Yu's expertise spans CT system optimization, virtual clinical trials, and standards development. He has contributed over 300 peer-reviewed publications and holds patents for innovations in CT dose management and image reconstruction.
Cao Haishan is an Associate Professor at Tsinghua University, affiliated with the Department of Energy and Power Engineering in the School of Mechanical Engineering. His research focuses on cryogenic cooling systems, high heat flux thermal management, and the physics of amorphous ice formation and phase transitions. He leads a research group supported by the National Natural Science Foundation of China and industry partners including Huawei, Midea, and Lenovo. Ph.D., Mechanical Engineering, University of Twente, 2013 M.Sc., Chemical Engineering, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 2009 B.Sc., Chemical Engineering, Zhejiang University, 2006 Dr. Cao's research spans three major areas: cryogenic cooling (including micro cryocoolers and sorption systems), high heat flux electronic cooling (especially with non-condensable gases), and the formation and transformation of amorphous water ice. His work combines theoretical modeling, computational simulation, and experimental validation, often at micro and nano scales. He applies principles from thermodynamics, fluid dynamics, and materials science to solve engineering challenges in refrigeration and thermal control. The recent publications reflect a strong trend toward interdisciplinary research, integrating machine learning for heat transfer prediction, computational screening of MOFs for cryogenic switches, and fundamental studies of ice nucleation on various substrates. The articles span journals in physics, engineering, materials, and applied thermal sciences, indicating broad impact across multiple domains. Notable scientific awards include: Gustav and Ingrid Klipping Award (2016) Cryogenics Best Paper Award (2017) Annual Teaching Excellence Award, Tsinghua University (2023) Excellent Supervisor Award, Tsinghua University (2024) Multiple First Prize Advisor awards in national student contests on energy saving Dr. Cao has been principal investigator on several grants, including projects funded by the National Natural Science Foundation of China on amorphous ice lifetime and micro-cryocooling for semiconductor chips. He has also led industry-university collaborations with Huawei, Midea, and Lenovo. He advises graduate students and leads a research team focused on next-generation cooling technologies. He serves on editorial boards for Journal of Refrigeration , Vacuum and Cryogenics , and Energies , and has chaired sessions at major international conferences such as ICEC-ICMC and ACTS. His research group operates within the Institute of Thermophysics at Tsinghua University, leveraging facilities in the Lee Shau Kee Science and Technology Building. The team collaborates with national laboratories and international institutions, particularly maintaining ties with the University of Twente. Current efforts are directed toward ultra-low vibration cooling, efficient separation of non-condensable gases, and extending the stability of amorphous ice for cryobiological applications.
Alan Mantooth is a Distinguished Professor holding the Twenty-First Century Research Leadership Chair in Engineering within the Department of Electrical Engineering at the University of Arkansas, Fayetteville. He serves as Director of the National Center for Reliable Electric Power Transmission (NCREPT), Executive Director for GRAPES (NSF I/UCRC) and SEEDS (DoE Center), and Deputy Director of the NSF Engineering Research Center for Power Optimization of Electro-Thermal Systems (POETS). His educational background includes: B.S. in Electrical Engineering, University of Arkansas M.S. in Electrical Engineering, University of Arkansas Ph.D. in Electrical Engineering, Georgia Institute of Technology Dr. Mantooth's research centers on analog/mixed-signal IC design, power electronics CAD, and semiconductor device modeling with emphasis on harsh-environment applications. His pioneering work in silicon carbide (SiC) and gallium nitride (GaN) power systems has enabled high-temperature operation for electric vehicles and renewable energy infrastructure, significantly advancing reliability in extreme conditions. His 2025 publications reveal strong trends toward AI-driven power electronics (e.g., SolarFormer++ for PV profiling), wide-bandgap device modeling (β-Ga2O3, SiC), and innovative packaging solutions. Key themes include reliability engineering for extreme environments, multi-physics optimization, and explainable AI for safety-critical power systems. Major scientific recognition includes: IEEE Fellow (2009) for power electronic device modeling Three R&D 100 Awards (2009, 2014, 2016) for SiC power modules IEEE Power Electronics Society Technical Achievement Award (2019) Multiple university teaching/research awards including SEC Faculty Achievement Award (2015) As an exceptional mentor (UA Outstanding Mentor 2006-2008), he co-founded Lynguent and Ozark Integrated Circuits. His centers NCREPT, GRAPES, and SEEDS have secured major funding from NSF, DoE, and industry partners, supporting over 350 refereed publications and numerous patents. Current research focuses on AI-enhanced power electronics, recyclable packaging, and next-generation wide-bandgap device characterization. He leads the NCREPT test facility and multi-institutional teams developing grid-connected power electronic systems, secure energy delivery architectures, and thermal management solutions for high-power-density applications, with direct impact on electric transportation and renewable energy integration.
Sophie S. Berkman is an Assistant Professor in the Department of Physics & Astronomy at Michigan State University. Her research focuses on experimental particle physics, particularly neutrino interactions and the development of liquid argon time projection chamber (LArTPC) detectors. Her work involves precision measurements of neutrino-argon cross sections critical for the Deep Underground Neutrino Experiment (DUNE). She contributes to Fermilab's MicroBooNE and ICARUS detectors within the Short-Baseline Neutrino program, analyzing data to understand neutrino properties and detector performance. Her research spans charged-current and neutral-current interactions, pion production mechanisms, and searches for physics beyond the Standard Model through sterile neutrino and dark sector investigations. Recent publications demonstrate leadership in neutrino interaction vertex reconstruction using deep learning, liquid argon purity monitoring, and supernova neutrino detection capabilities. Her work on trigger systems and software development directly supports DUNE's operational readiness and scientific objectives in neutrino oscillation physics. She actively participates in international collaborations including DUNE, MicroBooNE, and ICARUS, contributing to detector calibration, event reconstruction algorithms, and cross-section measurements essential for next-generation neutrino experiments.
Aumber Abbas is a researcher at Newcastle University specializing in advanced materials for sustainable energy and environmental applications. His work spans nanotechnology, catalysis, and biomass conversion, with significant contributions to carbon-based nanomaterials and electrochemical systems development. His research focuses on synthesizing graphene quantum dots from biomass waste for environmental sensing and remediation applications, developing catalytic processes for CO 2 utilization and cyclic carbonate production, and engineering electrochemical systems including vanadium redox flow batteries. Recent work emphasizes waste-derived functional materials for optical security, anti-counterfeiting, and wastewater treatment through nanoporous catalyst design and plasma-based tar removal technologies. Analysis of recent publications (2024-2025) reveals a dominant trend in sustainable nanomaterial engineering, particularly biomass-waste-derived carbon structures with tailored optical and catalytic properties. His work integrates experimental validation with simulation approaches to address energy storage, environmental remediation, and security applications, demonstrating strong interdisciplinary collaboration within Newcastle's engineering research community.
Yafang Cheng is Director of the Aerosol Chemistry Department at the Max Planck Institute for Chemistry since 2024, with concurrent appointments as Guest Professor at Peking University (2023-) and Distinguished Guest Professor at University of Science and Technology of China (2021-). Her research integrates experimental methods , multi-scale modeling , and machine learning to advance understanding of aerosol particle dynamics and their impacts on air quality , public health , and climate change . Ph.D. in Environmental Sciences (Peking University, 2007) B.Sc. in Environmental Sciences (Wuhan University, 2001) Her work focuses on reactive nitrogen chemistry , aerosol acidity , black carbon effects , and planetary boundary layer interactions . She has developed novel instrumentation for aerosol analysis and pioneered machine learning applications in atmospheric science. Recent publications emphasize black carbon mitigation strategies (One Earth 2023), aerosol microdroplet pH (Chem 2023), and SARS-CoV-2 transmission modeling (Science 2021). These studies demonstrate interdisciplinary approaches spanning environmental chemistry , climate physics , and public health policy . Fellow: AAAS (2023), AGU (2022) Joanne Simpson Medal (AGU, 2022) Science Breakthroughs of the Year (Falling Walls, 2021) Highly Cited Researcher (Web of Science, 2021-2022) Minerva Outstanding Female Scientist Award (2014) She has mentored 38 early-career researchers (21 postdocs, 17 PhD students) who have achieved professorships , tenured positions , and international awards . Her institutional leadership includes initiating academic exchange programs between European and Chinese institutions.
Ingrid Mann is a Professor in Space Physics at the UiT The Arctic University of Norway , Department of Physics and Technology. She leads and participates in multiple externally funded research initiatives including the Cosmic dust injection into the upper Earth atmosphere , MXD 2 rocket project to study the mesosphere , and EISCAT Research infrastructure project . ORCID: 0000-0002-2805-3265 Member of research group Space Physics Member of projects: Intermittent fluctuations in physical systems , Maxidusty-2 , CASCADE , Codia , Boosting Space Business , and Forskningsparken 1 A216 Her research spans space and atmospheric physics , focusing on dusty plasmas , cosmic dust dynamics , and polar atmosphere interactions . She employs spacecraft observations , EISCAT radar , rocket experiments , and machine learning for data analysis. Recent publications highlight cosmic dust detection with Parker Solar Probe and Solar Orbiter , PMSE multilayer properties , and dust impact signal modeling . Her work integrates radar , optical , and spacecraft data to understand polar atmospheric systems. She teaches FYS-2000 Kvantemekanikk , FYS-2019 Sun, Planets, and Space , and supervises G-Chaser student rocket projects . Her research group contributes to EISCAT_3D infrastructure and interplanetary dust modeling . Co-edited books: Nanodust in the Solar System (2012) Small Bodies in Planetary Systems (2008) Modern Meteor Science (2005)
