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Yar MuhammadView profile

Senior Lecturer

• Brain-Computer Interfaces
• Artificial Intelligence
• Machine Learning
• +1 more

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

• Transport Modelling
• Economic Analysis of Transport
• Sustainable Transportation
• +3 more

Anders Karlström is a Professor at KTH Royal Institute of Technology, specializing in Transport Modelling and Economics. His research focuses on sustainable transportation systems, emissions reduction, and energy efficiency. Key interests include activity-based modelling, dynamic discrete choice frameworks, and policy analysis for urban mobility. He has contributed to studies on travel behavior, infrastructure planning, and environmental impacts of transport systems across multiple international cities. His work integrates advanced methodologies such as recursive logit models, spatial regression, and machine learning for predictive analytics. Notable research areas involve evaluating weather variability effects on travel patterns, optimizing traffic state estimation with sensor data, and developing scenario-based models for future employment growth. Karlström collaborates with industries to enhance the competitiveness of sustainable transport solutions globally.

Örs LegezaView profile

Visiting Professor

• Quantum Physics
• Condensed Matter Physics
• Computational Physics
• +5 more

Örs Legeza is a physicist and scientific advisor at the Wigner Research Centre for Physics of the Hungarian Academy of Sciences in Budapest, leading the Strongly Correlated Systems Research Group. He holds a visiting professorship at Philipps University Marburg, Germany, and has held fellowships at institutions like ETH Zurich and LMU Munich. His research focuses on developing tensor network state (TNS) methods for strongly correlated quantum systems, with applications in condensed matter physics, quantum chemistry, and nuclear structure calculations. Education: PhD from Budapest University of Technology and Economics (1997). He has collaborated with European institutions such as FAU Erlangen-Nuremberg and has been an Alexander von Humboldt awardee. His work bridges quantum information theory and computational mathematics to advance simulations of complex quantum systems. Research interests include quantum phase transitions, magnetic properties in solids, and ultracold atomic systems. His methods push computational boundaries for larger systems, integrating techniques like density matrix renormalization group (DMRG) and matrix product states (MPS). Notable awards include the 2021 Academy Prize and 2018 Humboldt Research Award. Recent articles explore quantum crystal imaging, tensor network algorithms, and nuclear structure calculations. His work emphasizes interdisciplinary approaches to quantum many-body problems.

Luis F. Ayala H.View profile

Professor

• Advanced Reservoir Simulation
• Multiphase Flow in Porous Media
• Unconventional Reservoir Analysis
• +3 more

Luis F. Ayala H. is the Department Head and William A. Fustos Family Professor in the John and Willie Leone Family Department of Energy and Mineral Engineering at Penn State University. He holds dual summa cum laude degrees in Chemical and Petroleum Engineering from Universidad de Oriente (Venezuela), and M.S. and Ph.D. degrees from Penn State. His research focuses on computational fluid dynamics modeling of multiphase flow in unconventional reservoirs, hydrocarbon thermodynamics, and reservoir simulation. Education: Ph.D. (Petroleum and Natural Gas Engineering), Penn State University M.S. (Petroleum and Natural Gas Engineering), Penn State University Petroleum Engineering Degree, summa cum laude, Universidad de Oriente Chemical Engineering Degree, summa cum laude, Universidad de Oriente Research Interests: Advanced reservoir simulation, unconventional gas reservoir analysis (shale gas, tight sands), multiphase flow in porous media, hydrocarbon thermodynamics, and lattice Boltzmann methods. His work aims to improve predictive capabilities for unconventional reservoirs through quantitative modeling of multiphase transport dynamics. Key Awards: SPE Distinguished Member (2022) Fulbright-Colciencias Innovation Award (2016-2017) Howard B. Palmer Faculty Mentor Award (2022) Wilson Award for Excellence in Teaching (2008) Grants & Advising: He has led numerous research projects funded by industry and federal agencies, advising graduate students in energy systems and reservoir engineering. His administrative roles include service as executive editor for the SPE Journal and as an Administrative Fellow at Penn State’s Office of Research. Labs & Teams: His research group collaborates on projects involving advanced simulation tools for unconventional reservoirs, with a focus on multiphase flow dynamics and thermodynamic interplay in nano-pore systems.

• Computer Network Security
• Mobile Computing
• Cloud Computing
• +4 more

Dr. Dijiang Huang is an Associate Professor in the School of Computing and Augmented Intelligence at Arizona State University (ASU). He joined ASU in 2005 after completing his Ph.D. in Telecommunications and Computer Networking from the University of Missouri-Kansas City (2004). His research focuses on cybersecurity, mobile computing, and cloud computing, supported by grants from the National Science Foundation (NSF), Office of Naval Research (ONR), and industry partners like HP. He has received prestigious awards, including the ONR Young Investigator Award and HP Innovative Research Award. Education: B.E. in Telecommunications, Beijing University of Posts and Telecommunications (1995) M.S. in Computer Science, University of Missouri-Kansas City (2001) Ph.D. in Telecommunications and Computer Networking, University of Missouri-Kansas City (2004) Research Interests: Huang’s work emphasizes secure communication protocols, privacy-preserving techniques, and resilient network architectures. He has pioneered frameworks like Secure Group Communication (SeGCom) and Attribute-Based Cryptography , addressing challenges in VANETs, SDN, and edge computing. His recent projects include developing Waterfall for SDN security and SmartDefense for DDoS mitigation. Grants & Awards: ONR Young Investigator Award (2008) HP Innovative Research Award (2008) NSF grants for secure mobile cloud frameworks and cyber-physical systems Professional Contributions: Huang has served as a reviewer for journals like IEEE Transactions on Wireless Communications and conferences such as ACM MobiArch. He co-developed the Open Human-Robotic Mobile Networking and Security Testbed (OHReST) and the Virtual Laboratory (VLab) for cybersecurity education.

Weiwei LinView profile

Associate Professor

• Structural Engineering
• Civil Engineering
• Materials Science
• +5 more

Weiwei Lin is an Associate Professor in the Department of Civil Engineering at Aalto University, specializing in structural engineering with a focus on bridge systems, composite materials, and structural health monitoring. His research explores fatigue behavior of steel structures, seismic performance of composite systems, and innovative repair techniques. He holds a PhD from Waseda University (2012), MSc from Southeast University (2009), and BEng from Southwest Jiaotong University (2006). Key research areas include: steel-concrete composites, bridge redundancy evaluation, replaceable energy dissipaters, and AI-driven infrastructure diagnostics. Lin leads projects like CCU Structure (EU Horizon Europe) and RCF Mobility initiatives, focusing on sustainable construction and material recyclability. He has published 120+ peer-reviewed articles and secured 6 major grants. Lin has received prestigious awards including the IABMAS Young Award (2014) and Outstanding Reviewing Award (2017). His lab collaborates globally, hosting researchers from institutions like Israel Institute of Technology and Tsinghua University. Current work emphasizes crowdsourcing-based bridge monitoring and physics-guided AI frameworks for infrastructure diagnostics.

Konstantinos SpiliopoulosView profile

Professor

• Stochastic Processes
• Applied Mathematics
• Probability Theory
• +7 more

Konstantinos Spiliopoulos is a Professor and Director of Statistics at Boston University's Department of Mathematics and Statistics, part of the College of Arts & Sciences. He leads research in Applied Mathematics and Probability and Statistics groups, focusing on stochastic processes, machine learning, and mathematical finance. His research interests include stochastic analysis of complex systems, multiscale phenomena, and their applications to neural networks, PDEs, and financial modeling. Notable areas of study involve mean-field limits, rare event simulation, and asymptotic methods in stochastic differential equations. He has received grants such as DMS-EPSRC funding for analyzing online training algorithms in recurrent and deep neural networks. His work bridges theoretical advancements with practical applications in data science and computational methods. Spiliopoulos maintains an active presence in interdisciplinary research, addressing challenges in systemic risk, network dynamics, and optimization. His contributions span from fundamental probability theory to applied problems in engineering and finance.

Thomas Michael SattichView profile

Associate Professor

• Geopolitics
• Sustainability Transition
• Energy Policy
• +4 more

Thomas Michael Sattich is an Associate Professor at the University of Stavanger , affiliated with the Faculty of Social Sciences and Department of Media and Social Studies . His research focuses on geopolitical implications of sustainability transitions, particularly energy policy interdependence and EU-China relations. PhD in Political Science and Economics Expertise in qualitative methods, mixed methods (cluster analysis), and data visualization Lead researcher on EU-funded digital climate negotiation simulation projects His recent publications examine renewable energy geopolitics, industrial policy competition, and sustainability education frameworks. Methodologically, he specializes in Qualitative Comparative Analysis (QCA) and network analysis, contributing to debates on energy security and regional cooperation.

• Airport Operations
• Transportation Simulation
• Air Traffic Management
• +4 more

Miguel Mujica Mota is a Senior Lecturer at the Faculty of Technology, National Autonomous University of Mexico (UNAM), and a member of the Centre of Applied Research Technology. His research focuses on airport operations, multimodal transport systems, and simulation modeling. He has expertise in analyzing capacity challenges in multi-airport systems, particularly in Mexico City, and developing decision support systems for airport security and resource allocation. His work integrates sustainability and efficiency, addressing topics like environmental reporting in airlines and post-pandemic airport recovery strategies. Research Contributions: Dr. Mujica Mota has published extensively on airport capacity optimization, multimodal transport integration, and simulation-based methodologies. Key projects include the X-TEAM D2D initiative for door-to-door travel and the IMHOTEP project for smart passenger flow management. His work often involves collaboration with institutions like Schiphol Airport and the H2020 EU framework. Research Interests: Airport terminal design, air traffic management, simulation modeling, multimodal logistics, and sustainable aviation. Awards: A-BOOST Research Fund (2020) Beste paper award EMM2018 X-TEAM D2D Project Recognition (2020) Activities: Organized conferences like the 2023 EUROSIM Simulation Seminar and served on committees for events such as the 2024 Multilog Conference. Grants & Projects: Involved in EU-funded initiatives like H2020, focusing on multimodal integration and sustainable transport solutions. His research also explores climate change impacts on infrastructure and simulation-based validation approaches.

Josh McDermottView profile

Professor

• Auditory Neuroscience
• Psychology
• Machine Learning
• +5 more

Josh McDermott is a Professor in the Department of Brain and Cognitive Sciences at MIT and an Associate Investigator at the McGovern Institute. He holds roles as Associate Department Head and Principal Investigator of the Laboratory for Computational Audition. His work bridges psychology, neuroscience, and engineering to study auditory perception, with a focus on sound interpretation, hearing impairment treatments, and machine hearing systems. Education includes a B.A. from Harvard (summa cum laude), an MPhil from University College London, and a PhD from MIT. Postdoctoral training included NYU and the University of Minnesota. Research interests encompass computational principles of sound perception, natural sound statistics, music cognition, and machine hearing. Key areas include sound localization, auditory scene analysis, and the role of generative models in perception. Recent publications highlight advancements in auditory neural networks, cross-cultural music perception, and noise schema processing. Awards include the Troland Research Award, BCS Excellence in Advising, and NSF CAREER Award. Advising includes over 20 graduate students and postdocs, with notable contributions to auditory neuroscience and machine learning. Major grants support projects on auditory models and sensory systems. The lab develops tools like cochleagram generation and headphone screening software. The Laboratory for Computational Audition operates at MIT, focusing on biological and computational approaches to hearing. Collaborations span engineering, psychology, and neuroscience to advance understanding of auditory processing.

Christiane BarzView profile

Professor

• Operations Research
• Revenue Management
• Stochastic Dynamic Optimization
• +3 more

Christiane Barz is a Professor of Mathematics at the University of Zurich's Institute for Business Administration since 2016. Previously, she held academic roles at the UCLA Anderson School of Management, the Chicago Booth School of Business, and the Technical University (TU) Berlin. Her research focuses on stochastic dynamic systems, Markov decision processes, and their applications in revenue management. She emphasizes making mathematical tools accessible and practical for real-world problem-solving, particularly in optimizing decision-making under uncertainty. Education includes a degree in industrial engineering and a doctorate from the University of Karlsruhe (TH), Germany. Her career path includes postdoctoral research at the University of Chicago's Booth School of Business and roles as an Assistant Professor at UCLA. She combines academic excellence with balancing family life, advocating for gender equity in STEM fields. Her research explores risk-sensitive decision-making frameworks, dynamic pricing models for transportation and healthcare, and optimizing resource allocation in complex systems. Recent work includes applications in FlixBus, air cargo networks, and improving patient admission scheduling in hospitals. Barz's teaching philosophy prioritizes demystifying mathematics for students, encouraging critical engagement rather than fear of complexity. She collaborates with industry partners to apply operations research methods to real-world challenges, emphasizing both theoretical rigor and practical relevance.

Seth BlumsackView profile

Professor

• Electricity and Natural Gas Industries
• Energy Infrastructure Resilience
• Network Science
• +4 more

Seth Blumsack is a Professor at the Pennsylvania State University in the Department of Energy and Mineral Engineering and serves as Director of the Center for Energy Law and Policy . He holds an Adjunct Research Professor position at the Carnegie Mellon Electricity Industry Center and is affiliated with the Santa Fe Institute as an External Faculty member. His research spans energy economics , power grid reliability , and complex infrastructure networks . Key projects include: Interdependent natural gas and electricity systems analysis Governance of regional transmission organizations Smart grid consumer behavior studies Power grid reliability tools development He has secured funding from the U.S. National Science Foundation , Department of Energy , Environmental Protection Agency , and private industry. His Best paper award at Hawai’i International Conference on System Sciences (2011) and John T. Ryan, Jr. Fellowship (2011-17) highlight his scientific recognition. Publications emphasize electricity market deregulation , energy infrastructure resilience , and consumer response to smart grid technologies . His work has been cited in major media outlets like The New York Times and The Los Angeles Times , and he has consulted for National Renewable Energy Laboratory , U.S. Department of Energy , and other industry stakeholders.

Jan Martin NordbottenView profile

Professor

• Applied Mathematics
• Porous Media
• Geological CO2 Storage
• +4 more

Jan Martin Nordbotten is a full-time Professor at the Department of Mathematics, University of Bergen (UiB), with adjunct positions at Princeton University and NORCE. His research focuses on applied mathematics, particularly in porous media, CO2 storage, fluid dynamics, and interdisciplinary applications in hydrology, biomedicine, and ecology. He completed his PhD at UiB in 2004 and became Norway's third youngest professor in 2007. His work emphasizes numerical methods, multiscale modeling, and experimental validation. Affiliations: UiB (full-time), Princeton (adjunct), NORCE (adjunct) Research Group: Center for Sustainable Subsurface Resources Research interests span mathematical modeling of subsurface processes, including flow in fractured media, geomechanics, and phase-field fracture. Notable contributions include analytical and numerical solutions for CO2 leakage, multiphase flow, and development of tools like DarSIA for image processing in porous media. Publications highlight advancements in mixed-dimensional models, finite element methods, and experimental validation of CO2 storage forecasts. His work bridges theoretical mathematics with practical applications in energy and environmental systems.

Xiaofan YuView profile

Assistant Professor

• Embedded Systems
• Edge AI
• Neuromorphic Computing
• +5 more

Xiaofan Yu is an Assistant Professor in the Department of Electrical Engineering at the University of California, Merced. He holds a Ph.D. (2025), M.S. (2020), and B.S. (2018) from the University of California, San Diego and Peking University, respectively. His research focuses on embedded systems , edge AI , and neuromorphic computing , with applications in IoT, federated learning, and hyperdimensional computing. ML&Systems Rising Star (2024) CPS Rising Star (2023) EECS Rising Star (2022) His work addresses on-device AI for real-world IoT deployments, reliability-driven sensor networks , and next-generation edge intelligence . Recent publications highlight advancements in federated learning (TIOT 2025), multimodal sensor interaction (IMWUT 2025), and noise-resilient sensor systems (Sensors 2024). Key subfields include hyperdimensional computing , asynchronous distributed training , and resource-efficient edge models . Dr. Yu actively mentors students across institutions and programs, including the Early Research Scholarship Program (UCSD) and ENLACE Summer Research Program. He has advised projects on smart elderly monitoring , LLM-based sensor reasoning , and hyperdimensional algorithm optimization . Collaborations span UCSD, TUM, and Stanford, with industry partnerships in IoT design automation (RelIoT simulator) and biomedical applications (bladder fullness restoration system).

Markus VinczeView profile

Associate Professor

• Robotics
• Computer Vision
• Human-Robot Interaction
• +5 more

Markus Vincze is an Associate Professor at the Institute of Automation and Control Engineering (ACIN) at Vienna University of Technology (TU Wien). He founded the Vision for Robotics (V4R) group in 1996 to advance robotic perception, particularly in real-world environments and homes. His work focuses on cognitive computer vision techniques for robotics. Education: Diplom in Mechanical Engineering (1988) and PhD (1993) from TU Wien; M.Sc. (1990) from Rensselaer Polytechnic Institute. V4R coordinates EU projects like ActIPret, robots@home, HOBBIT, and national initiatives like vision@home. Markus has edited a book on Robust Vision with Gregory Hager and authored 62 peer-reviewed journal articles and over 400 reviewed publications. His recent research explores zero-shot 6D pose estimation, sim-to-real transfer, and transparent object detection. Markus has served as program chair for ICRA 2013 and organized HRI 2017 in Vienna. He has advised numerous students and secured grants from the Austrian Academy of Sciences for work at HelpMate Robotics and Yale's Vision Laboratory. The V4R group leads innovations in robotic vision, including frameworks for synthetic data generation (Unrealgensyn), depth completion (CAGT), and educational robotics applications for sustainability. Their work spans household robotics (RH3), agricultural robotics (EdgeSoil), and human-robot collaboration.