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Wael El-DakhakhniView profile

Professor

• Resilient Infrastructure Systems
• Systemic Risk Management
• Data-Driven Modeling
• +4 more

Dr. Wael El-Dakhakhni is a Professor of Civil Engineering at McMaster University, holding the Martini, Mascarin and George Endowed Chair in Masonry Design. He serves as Director of the INTERFACE Institute and NSERC CaNRisk-CREATE program, focusing on systemic risk and resilience in complex systems. His research spans interdependent networks, data-driven modeling, and infrastructure resilience under climate and disaster scenarios. He leads the INViSiONLab, advancing AI-driven solutions for urban resilience through digital twins. El-Dakhakhni is a Fellow of the American Society of Civil Engineers and a Member of the Royal Society of Canada, with notable awards including the NSERC Discovery Accelerator Supplement (twice), Ontario Early Researcher Award, and John B. Scalzi Research Award. His work bridges academia and industry, contributing to codes, standards, and real-world applications in structural engineering and disaster response. Education: BSc (Ain Shams University, Egypt), MSc and PhD (Drexel University, USA). Research Interests: Complex systems simulation, systemic risk quantification, resilient infrastructure design, AI applications in urban planning, and climate resilience frameworks. His work integrates advanced machine learning, network theory, and physics-informed models to address multi-hazard challenges in energy, transportation, and urban systems. Key Projects: CITYDNA, McMasterDNA, and infrastructure digital twins for pandemic and climate crisis decision-support systems. He collaborates with governments and organizations to enhance infrastructure resilience through predictive analytics and adaptive strategies. Labs/Teams: INViSiONLab, McMaster INTERFACE Institute, NSERC-CaNRisk-CREATE program, and the Centre for Effective Design of Structures. His research has informed policy and standards in North America, emphasizing interdisciplinary solutions for systemic risk mitigation.

• Child Development
• Self-Regulation
• Diversity Science
• +6 more

Ying Zhang serves as an Assistant Professor in the Department of Psychology within Clarkson University's Lewis School of Health & Life Sciences, where her research examines developmental trajectories of cognitive, social, and emotional functioning from early childhood through emerging adulthood. Her work emphasizes contextual influences including cultural dynamics, socioeconomic conditions, and family relationships on self-regulation development. Her educational background includes: Ph.D. in Psychology from Syracuse University (2021) M.Ed. in Education from Jilin University (2012) B.Eng. in Engineering from University of Jinan (2008) Grounded in Diversity Science , Dr. Zhang investigates ecological influences on child self-regulation, particularly examining how intimate partner violence exposure, parental mental health, and socioeconomic disparities create pathways for risk and resilience. Her methodological approach integrates advanced statistical modeling with longitudinal designs, latent profile analyses, and machine learning techniques to analyze large secondary datasets, generating insights applicable to real-world interventions for vulnerable populations. Analysis of her 15 most recent publications (2023-2025) reveals three dominant research streams: (1) intricate mechanisms linking intimate partner violence to child self-regulation through parenting stress pathways, (2) socioeconomic and cultural influences on developmental outcomes using machine learning methodologies, and (3) diversity promotion in higher education contexts. Her work increasingly incorporates technology applications, including AI-based violence detection systems. Dr. Zhang actively involves undergraduate researchers in her projects, as evidenced by numerous student co-authorships, and teaches courses including Introduction to Psychology, Cultural Psychology, Diversity Science, and Prenatal/Child Development. Her office is located in Clarkson's Science Center (Box 5825) with open hours Mondays and Fridays 1-3:30pm.

Omprakash GnawaliView profile

Associate Professor

• Internet of Things
• Smart Devices
• Wireless Sensor Networks
• +3 more

Omprakash Gnawali is an Associate Professor in the Department of Computer Science at the University of Houston, with expertise in Internet of Things, wireless sensor networks, and artificial intelligence. His research focuses on advanced networking protocols, mobility analysis, and safety monitoring systems. Postdoctoral work at Stanford University PhD in Computer Science from University of Southern California Masters and Bachelors from Massachusetts Institute of Technology His research interests include Ultra-Wideband (UWB) localization, network protocol design, edge computing for monitoring systems, and mobile sensor networks. He leads the Networked Systems Laboratory , where he develops frameworks like the Collection Tree Protocol and CodeDrip for efficient data dissemination. Recent publications highlight trends in UWB-based safety monitoring, routing optimization in dual-radio networks, and deception detection in cybersecurity. He has secured NSF Student Travel Grants for ACM SenSys conferences in 2016 and 2017. Scientific Awards NSF Student Travel Grant (2017) NSF Student Travel Grant (2016) He actively mentors students in research projects and teaches courses such as Research Methods in Computer Science and Computer Networks . His service roles include Technical Program Committee memberships and chairing the TinyOS Network Protocol Working Group.

• Natural and Artificial Lighting
• Solar Energy (Passive and Active)
• Building Technology
• +3 more

Jean-Louis Scartezzini is an Honorary Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC) and the Solar Energy and Building Physics Laboratory (LESO-PB). His research focuses on natural/artificial lighting, solar energy systems, and building technology, with a strong emphasis on energy efficiency and sustainability. Director of LESO-PB since 1994 Founded and led several institutes, including the Institute for Infrastructure, Resources, and Environment (2002–2009) Doctorat in Physics from EPFL (1986) Extensive international collaborations, including visiting roles at NUS (2009) and LBNL/UCLA (1988) Research interests include: - Daylighting and lighting control systems - Passive/active solar technologies - Urban microclimate and energy systems - Stochastic simulation and predictive control Recent work addresses climate change impacts on energy systems, urban sustainability, and machine learning applications in energy optimization. Key publications span lighting health impacts, renewable integration, and microclimate modeling Awards include the European Solar Prize (2001/2002) and Walsh-Weston Bronze Medal (1998) Mentored over 20 PhD students, many leading in academia and industry (e.g., Marilyne Andersen at EPFL, Flavio Foradini at E4Tech).

• Advanced Semiconductor Packaging
• Thermal Management
• Co-Design and Multiphysics Modelling
• +3 more

Christopher Bailey is a Professor of Advanced Semiconductor Packaging and Director of the Centre for Advanced Semiconductor Packaging at Arizona State University (ASU). He previously served as Professor of Computational Mechanics & Reliability and Associate Dean for Research at the University of Greenwich, UK. At ASU, he leads research on advanced semiconductor packaging, including roles as Principal Investigator (PI) and Co-Investigator (Co-I) on major projects such as the SRC-funded Thermo-Mechanical Modelling and US Chips Act initiatives (e.g., SWAP-Hub, SHIELD, ITSI). His research focuses on semiconductor packaging reliability, thermal management, co-design methodologies, and multiphysics modeling. Education: MBA (Technology Management), Open University, UK PhD, Thames Polytechnic, UK Research Interests: Advanced Semiconductor Packaging Thermal Management Solutions Co-Design and Multiphysics Modeling Reliability of Electronic Components His work integrates computational mechanics, materials science, and engineering to address challenges in high-reliability electronics. Recent projects emphasize predictive modeling for semiconductor packaging failures under thermal-mechanical stress. Awards: IEEE Region 8 Europe Award (2024) IEEE David Feldman Award (2022) Visiting Professorships at IIT Kharagpur (2018/2022) and Hong Kong (2018) Service & Leadership: Former President of IEEE Electronics Packaging Society (2020–2021) Associate Editor for IEEE Transactions on Components, Packaging, and Manufacturing Technology Conference Leadership (e.g., Program Chair for IEEE PAINE 2024) He has secured over $40M in research funding and authored 400+ archival papers, with expertise spanning industry collaborations (e.g., BAe Systems, Rolls Royce) and government advisory roles (EPSRC Peer Review College, UK Research Excellence Framework).

Tina ShoaView profile

Associate Professor

• Battery Technology
• Electrochemical Engineering
• Energy Storage Systems
• +3 more

Tina Shoa is an Associate Professor in the School of Sustainable Energy Engineering at Simon Fraser University. She holds a Ph.D. in Electrical Engineering from the University of British Columbia (2010), an M.Sc. from the University of Manitoba (2004), and a B.Sc. from Iran University of Science and Technology (2000). Her research focuses on battery performance modeling, electrochemical methods for fault detection, sustainable battery manufacturing, and AI-based diagnostics. Education: Ph.D., Electrical Engineering, University of British Columbia, 2010 M.Sc., Electrical Engineering, University of Manitoba, 2004 B.Sc., Electrical Engineering, Iran University of Science and Technology, 2000 Research Interests: Battery performance modeling, analysis, and optimization Electrochemical and ultrasound-based battery fault detection Sustainable battery manufacturing processes AI-driven battery diagnostics Teaching and Courses: Advanced Battery and Fuel Cell Technologies Power Plant Systems Smart Grids Practicum SEE 354 D100 Energy Storage (Summer 2025) Patents: Battery State-of-health Determination upon charging (US Patent 11079437B2, 2022) Battery State-of-health Determination using multi-factor normalization (US Patent 10,302,709, 2019) Apparatus and Method for testing electrochemical systems (US Provisional Patent 62/994687, 2020) Key Contributions: Her work integrates electrochemical principles and AI to advance battery diagnostics and sustainable energy storage solutions. She has authored over 15 publications in top-tier journals and conferences, addressing battery aging, state estimation, and novel manufacturing techniques.

Brett Jason HallamView profile

Associate Professor

• Photovoltaic Engineering
• Silicon Solar Cells
• Renewable Energy Systems
• +3 more

Associate Professor Brett Jason Hallam is a Scientia Fellow at the University of New South Wales (UNSW), affiliated with the School of Photovoltaic and Renewable Energy Engineering. His research focuses on advancing solar panel manufacturing, particularly improving efficiency and reducing costs through innovations like selective emitter formation, laser processing, and hydrogen passivation. Key areas include understanding hydrogen passivation mechanisms to address defects in silicon solar cells and developing next-generation technologies such as TOPCon and SHJ cells. Education: PhD in Photovoltaic Engineering (UNSW, 2014), BE (First Class Honours and University Medal) and BSc in Physics/Oceanography (UNSW, 2009). He has held research placements at Fraunhofer Institute (Germany), Oxford University (UK), and IMEC (Belgium). Research Interests: Advanced hydrogenation for solar cells, material challenges for terawatt-scale PV deployment, reducing silver consumption, and sustainable manufacturing. Current projects include ARENA-funded initiatives on high-efficiency solar cell technologies and next-generation selective emitters. Notable Awards: 2018 Scientia Fellow, 2017 NSW Premier Prize for Energy Innovation, and 2016 Ulrich Goesele Young Scientist Award. His work addresses global energy transition goals, aiming for net-zero emissions by 2050. Industry Experience: Consultant for Suntech Power and Technology Transfer Team at UNSW, collaborating with companies like LONGi and Jinko. He advises on silicon solar cell technologies and has 16 patents. Labs/Teams: Active in UNSW’s PV research groups, focusing on scalable solar solutions and material science innovations.

• Radiomics
• Medical Imaging Analysis
• Machine Learning in Healthcare
• +7 more

Adrien Depeursinge is a Professor at HES-SO Valais-Wallis - Haute Ecole de Gestion, affiliated with the School of Economics and Services and the Management Information Systems department. His research focuses on radiomics, personalized medicine via image-based analysis, and clinical workflow optimization. He leads the development of the QuantImage platform, a physician-centered web-based tool for radiomics research, and contributes to radiomics standardization efforts through initiatives like the Image Biomarker Standardization Initiative (IBSI). His work emphasizes machine learning applications in healthcare, including tumor segmentation, biomarker extraction, and improving diagnostic accuracy through computational models. Key research themes include: 1) Radiomics – developing quantitative imaging features for cancer diagnosis/prognosis; 2) Medical Imaging Analysis – advancing texture-based models, multi-modal fusion, and automated lesion detection; 3) Physician-AI Collaboration – designing user-centric tools for clinical integration. His contributions span neuro-oncology (brain metastases), head-and-neck cancer, and multiple sclerosis imaging. Publications emphasize methodological advancements (e.g., kernel optimization in CNNs, steerable detectors) and clinical validation (e.g., reproducibility of radiomics features across imaging protocols). He collaborates with institutions like the University Hospital of Lausanne (CHUV) and international teams on projects like the HECKTOR challenge for PET/CT tumor segmentation. His work bridges technical innovation with clinical impact, aiming to translate radiomics into actionable clinical tools. QuantImage v2, his flagship tool, enables no-code development of machine learning models using clinical imaging data. Research also includes phantom-based validation of radiomics features and addressing challenges in feature stability across imaging modalities. Current projects explore improving contour quality for radiomics studies and optimizing AI explainability in medical decision-making.

Tayfun GünelView profile

Professor

• Microwave Circuits
• Radar Systems
• Antennas
• +12 more

Tayfun Günel is a Professor in the Department of Electronics and Communication Engineering at Istanbul Technical University (ITU) , Faculty of Electrical and Electronics Engineering. He holds a PhD (1993), MSc (1988), and BSc (1986), all from ITU. His research spans microwave circuits, radar systems, antennas, and optimization using genetic algorithms and soft computing. His research interests include Microwave Circuits , Radar and Antennas , Optimization , and Genetic Algorithms . His work focuses on impedance matching, microstrip antennas, noise modeling, and metamaterial-based microwave components. He has taught courses such as Electromagnetic Fields, Radar Systems, and Satellite Communication Systems. The recent publications reflect a strong trend in microwave circuit design , antenna miniaturization , and the application of evolutionary algorithms (genetic algorithms, PSO) and machine learning (neural networks, SVR) in electromagnetic design and optimization. There is a consistent focus on practical microwave components like transmission lines, patches, and amplifiers, often using nanomaterials (e.g., carbon nanotubes) and metamaterials . His work bridges theoretical modeling with computational optimization for real-world RF and radar applications. Email: gunelmur@itu.edu.tr Professor Günel has supervised 2 completed PhD theses, 2 ongoing PhD theses, 23 completed master's theses, and 1 ongoing master's thesis, demonstrating a significant contribution to student mentoring. There are no specific grants or funding sources mentioned in the provided text. He is affiliated with research in microwave systems and antenna design , likely operating within the broader research ecosystem of the Electronics and Communication Engineering Department at ITU, which includes labs such as the Microwave Systems and Antennas Laboratory and the Radar and Microwave Technologies Research Laboratory.

Luis A. Ricardez-SandovalView profile

Associate Professor

• Process Systems Engineering
• Multiscale Modelling
• Computer-aided Catalyst and Materials Design
• +7 more

Luis A. Ricardez-Sandoval is an Associate Professor in the Department of Chemical Engineering at the University of Waterloo and holds a Tier II Canada Research Chair in Multiscale Modelling and Process Systems. His research group develops advanced computational tools for optimizing chemical processes across multiple scales. Doctorate: Chemical Engineering, University of Waterloo (2008) MASc: Chemical Engineering, Instituto Tecnologico de Celaya (2000) BASc: Chemical Engineering, Instituto Tecnologico de Orizaba (1997) The research group focuses on multiscale modelling and process systems engineering , particularly for CO2 capture , energy systems , and heterogeneous catalysis . Their work combines advanced mathematics, machine learning , and uncertainty analysis to optimize chemical processes before physical implementation. Recent publications emphasize dynamic system optimization under uncertainty, multiscale simulation , and CO2 conversion technologies . Key methodologies include probabilistic uncertainty quantification and economic predictive control . Scientific Awards : 1997: First Place, XII National Creativity Contest 1998: Best Student Award, Instituto Tecnologico de Orizaba 1999: Third Place, XIV National Creativity Contest 2000: J.M. Smith Award for Best MASc Student He has collaborated with international institutions like CONACyT-Mexico, China Scholarship Council, and Universidad de Los Andes. His teaching includes graduate courses in process control, optimization, and computer-aided design.

Keith DeckerView profile

Associate Professor

• Multi-Agent Systems
• Artificial Intelligence
• Distributed Artificial Intelligence
• +9 more

Keith Decker is an Associate Professor and JPMorgan Chase Fellow in the Department of Computer and Information Sciences at the University of Delaware's College of Engineering. He holds multiple affiliated faculty positions at the Artificial Intelligence Center of Excellence, Data Science Institute, Center for Bioinformatics and Computational Biology, and Institute for Financial Service Analytics. His research spans several key areas of computer science with a focus on Multi-Agent Systems , Distributed Artificial Intelligence , Computational Organization Design , and Bioinformatics . His work bridges theoretical foundations with practical applications in finance, healthcare, and information systems. Dr. Decker's publications reflect trends in distributed AI with emphasis on coordination technologies, agent communication, and information gathering systems. His work has evolved from foundational multi-agent coordination theory to applications in bioinformatics, financial services, and health informatics. DARPA special recognition award for foundational research in coordination technologies Dr. Decker has advised numerous graduate students and led significant research projects including automated genetic annotation, coalition management for electric vehicle-to-grid power systems, and machine learning for automated health coaching. His interdisciplinary work demonstrates strong connections between theoretical AI research and practical applications across multiple domains. He maintains active leadership roles in the academic community, having served as program co-chair for the International Conference on Autonomous Agents and Multi-Agent Systems and other major AI workshops.

Marc SchmittView profile

Researcher

• Artificial Intelligence in Finance
• Business Analytics
• Digital Ecosystems
• +5 more

Dr. Marc Schmitt serves as a Research Associate in the Department of Computer Science at the University of Oxford while concurrently leading as Managing Director of the DEIM Research Institute in Germany. His interdisciplinary work bridges academic research and industry applications across artificial intelligence, cybersecurity, and financial systems. Academic Background: PhD in Computer and Information Sciences (AI in Finance), University of Strathclyde MSc in Quantitative Finance, University of Strathclyde MSc in Software Engineering, University of Oxford BA in Business Administration, Technische Hochschule Nürnberg Georg Simon Ohm Dr. Schmitt's research focuses on AI-driven decision-making at the intersection of finance, business analytics, and cybersecurity. His work examines how intelligent systems integrate into organizational structures while addressing systemic risks in digital ecosystems. Recent investigations include generative AI threats in social engineering, no-code AutoML applications, and policy frameworks for AI-enhanced security systems. His publications demonstrate consistent methodological innovation across theoretical and applied domains. Analysis of his publication trajectory reveals growing emphasis on generative AI security implications (2024-2025), with foundational work in business analytics applications (2023). The research shows strong interdisciplinary connections between computer science, financial economics, and human-centered design principles, reflecting his unique background spanning technical and business domains. Prior to academia, Dr. Schmitt held strategic positions including Senior IT Partner for Equity Finance at Siemens Financial Services and management consulting roles at d-fine and Deloitte, where he advised Fortune 500 companies on digital transformation and risk management. His industry experience directly informs his research approach, emphasizing practical implementation challenges alongside theoretical innovation.

Mohsen HeidariView profile

Assistant Professor

• Quantum Computing
• Quantum Algorithms
• Theoretical Machine Learning
• +3 more

Mohsen Heidari is an Assistant Professor in the Department of Computer Science at Indiana University, Bloomington. He is affiliated with the IU Quantum Science and Engineering Center (QSEc) and the NSF Center for Science of Information (CSoI). He previously held positions as a Visiting Assistant Professor at Purdue University and as a Postdoctoral Research Associate at CSoI. Ph.D. in Electrical Engineering (2019) and M.Sc. in Applied Mathematics (2017) from the University of Michigan His research focuses span quantum computing, theoretical machine learning, and information theory. Key themes include: Quantum algorithm design and sample complexity Fourier-based learning frameworks Quantum-classical duality in learning problems Information-theoretic approaches to biological systems Article trends show a strong emphasis on quantum-classical learning intersections (6/15 papers), Fourier analysis applications (5/15), and information-theoretic foundations (12/15). Notable venues include NeurIPS, IEEE Transactions, and ISIT. He directs research involving: Quantum Neural Network development Quantum measurement simulation Quantum data compression techniques Quantum algorithm implementation constraints

Vahid JamaliView profile

Assistant Professor

• Resilient Communications
• Wireless Communications
• 6G Networks
• +4 more

Prof. Vahid Jamali is an Assistant Professor and Head of the Resilient Communication Systems Group at the Technical University of Darmstadt, Germany. His research focuses on resilient communications, 6G wireless systems, bio-inspired molecular communication, and reconfigurable intelligent surfaces (RIS). He holds a Doctoral Degree from Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany, and has served as a postdoctoral researcher at Princeton University and FAU. Education PhD in Communication Systems, FAU (2019) Visiting Researcher at Stanford University (2017) Research Assistant at FAU's Institute for Digital Communications (2013-2019) Research Interests Resilient Networks : Emergency networks, RIS-based systems, and resilience-by-design architectures. Wireless Innovations : 6G technologies, holographic MIMO, and joint communication-sensing systems. Bio-inspired Systems : Molecular communication modeling using biological principles like diffusion and chemical reactions. Recent Work Trends His 2024-2025 publications emphasize RIS optimization (e.g., temperature-aware phase shifts, fast beam switching) and molecular communication (e.g., Poisson channel identification, bio-inspired receiver designs). Emerging themes include AoI-based RIS reconfiguration and integrated sensing-communication-powering (ISCAP) for IoT. Lab Activities He leads the Resilient Communication Systems Group, exploring cutting-edge RIS hardware (e.g., liquid crystal implementations) and theoretical foundations for future wireless systems.

Kyusang LeeView profile

Associate Professor

• Optoelectronics
• Thin-Film and Flexible Devices
• Neuromorphic Computing
• +5 more

Kyusang Lee is an Associate Professor in the Electrical and Computer Engineering and Materials Science and Engineering departments at the University of Virginia. His research focuses on optoelectronic devices, neuromorphic computing, and smart sensors, emphasizing applications in solar energy conversion and flexible electronics. He holds a B.S. from Korea University (2005), M.S. from Johns Hopkins University (2009), and Ph.D. from the University of Michigan (2014). He conducted postdoctoral research at the University of Michigan and MIT. Education: B.S., Electrical Engineering, Korea University, 2005 M.S., Electrical and Computer Engineering, Johns Hopkins University, 2009 Ph.D., Electrical Engineering and Computer Science, University of Michigan, 2014 Postdoctoral Fellowships: University of Michigan (EECS), MIT (Mechanical Engineering) His research interests span thin-film and flexible optoelectronics, neuromorphic computing architectures, and AIoT-enabled smart sensors. Notable contributions include remote epitaxy techniques for semiconductor membrane integration and solar-tracking concentrator designs. His work bridges materials science and device engineering to advance energy-efficient optoelectronics and bioinspired systems. Key Research Themes: Organic/inorganic optoelectronic devices for solar energy Flexible and stretchable electronics Neuromorphic hardware for edge computing Gas sensing and bioinspired sensor systems Lee’s publications reflect interdisciplinary innovation, with recent work on ferroelectric transistors, neuromorphic vision systems, and high-efficiency photovoltaics. He received the NSF CAREER Award (2020) and AFOSR YIP Award (2023).