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Tom WenseleersView profile

Professor

• Theoretical Biology
• Evolutionary Biology
• Behavioural Ecology
• +7 more

Tom Wenseleers is a Professor at KU Leuven's Department of Biology within the Faculty of Science, where he leads the Laboratory of Socioecology and Social Evolution. His research spans theoretical and experimental approaches to evolutionary biology, with particular focus on social insect systems. Research spans social insects (ants, bees, wasps), microbes, viruses, and human systems Primary model organisms: social insects studying major evolutionary transitions Current projects examine caste determination, chemical communication, and evolutionary conflicts His research integrates theoretical modeling with experimental, behavioral, and comparative studies. Recent work combines genomic techniques and high-throughput GC/MS analysis to decipher chemical communication systems. Current trends show increasing interdisciplinary work spanning virology (SARS-CoV-2 variants), microbial ecology (antibiotic resistance), and robotics (pollinator behavior monitoring). The research demonstrates consistent application of evolutionary theory to diverse biological systems while maintaining social insects as the core model. Wenseleers actively mentors PhD students and postdocs, with recent graduates including Kamiel Debeuckelaere and Viviana Di Pietro. His lab receives substantial funding through multiple concurrent research projects, including Promotor roles on grants examining caste development in bee societies and microbial metabolite screening. The laboratory maintains strong international collaborations across Europe and South America. The lab operates within the Ecology, Evolution and Biodiversity Conservation unit at KU Leuven, with physical location at Naamsestraat 59, box 2466, 3000 Leuven. The research group maintains active outreach programs including science workshops for schools and public engagement events focused on insect conservation.

Peter K. KangView profile

Associate Professor

• Hydrogeology
• Groundwater Flow
• Reactive Transport
• +6 more

Peter K. Kang serves as Associate Professor & Gibson Chair of Hydrogeology in the Department of Earth & Environmental Sciences at the University of Minnesota. His research is affiliated with the St. Anthony Falls Laboratory and the Kang Research Group. Dr. Kang's research focuses on the physics of flow and reactive transport in porous and fractured media. His work combines theory, high-performance numerical simulations, machine learning, and visual laboratory experiments to understand how coupled processes control mixing and reactive transport across spatial scales (from pore to field scale). His research has applications in groundwater management, subsurface energy systems, and environmental protection. Current research opportunities include undergraduate projects in microfluidics laboratory experiments to visualize groundwater processes and fieldwork to characterize fractured aquifers. Dr. Kang is actively accepting both undergraduate and graduate research students into his research group. His methodological approach integrates multiple disciplines, with particular emphasis on developing predictive models for practical environmental applications through the innovative combination of computational methods and experimental validation.

• Geospatial Technology
• Machine Learning
• Natural Hazard Prediction
• +7 more

Dr. Husam Al-Najjar is a Lecturer at the School of Computer Science within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). He serves as the Course Director for the Bachelor of Information Systems (BIS) program. With expertise in geospatial technology and machine learning, Dr. Al-Najjar focuses on predicting and mitigating natural hazards and environmental issues to contribute to a sustainable digital earth. Dr. Al-Najjar earned his PhD from the University of Technology Sydney. Before joining academia, he worked in project management and has received numerous prestigious awards, scholarships, and grants throughout his career. Dr. Al-Najjar's research primarily centers on the application of machine learning techniques to address complex environmental challenges. His work spans geospatial AI, natural hazard prediction (particularly landslides and bushfires), and sustainable development. He has developed innovative approaches that integrate physical models with machine learning algorithms to improve prediction accuracy in data-scarce environments. His research also extends to remote sensing applications, urban planning, and smart city technologies, with a strong emphasis on practical solutions for real-world problems. Analysis of Dr. Al-Najjar's recent publications reveals a strong focus on applying explainable AI techniques to natural hazard prediction, particularly landslides. His work consistently bridges the gap between theoretical machine learning approaches and practical geospatial applications. He has made significant contributions to integrating physical models with AI, developing methods for handling imbalanced data through generative adversarial networks, and improving feature selection for remote sensing applications in environmental monitoring. Best Paper Award at the ISPRS Geospatial Week in Enschede, the Netherlands As an educator, Dr. Al-Najjar is actively involved in mentoring and teaching. He serves as Course Director for the Bachelor of Information Systems program and teaches courses in GIS, Information Systems, IS development methodologies, Design & Innovation, and Project Management. He welcomes prospective PhD candidates interested in his research areas and emphasizes the importance of detailed research proposals that demonstrate novelty and significance. His teaching philosophy focuses on fostering an engaging and inclusive learning environment that promotes student success and well-being. Dr. Al-Najjar is affiliated with 'The Trustworthy Digital Society' concentration at UTS and serves as a referee and holds editorial roles in respected journals. His work contributes to the development of geospatial AI frameworks that support decision-making in environmental management and disaster preparedness.

Emad ElwakilView profile

Professor

• Infrastructure/Asset Management
• Simulation and Modeling
• Risk Management
• +5 more

Dr. Emad Elwakil is a Professor and Associate Head for Graduate Programs and Research at the Purdue Polytechnic Institute, Purdue University. He is a certified Professional Engineer (PE), Certified Cost Professional (CCP), and Project Management Professional (PMP) with extensive experience in infrastructure management, construction management, and organizational performance assessment. Education: Ph.D. in Construction Engineering and Management/Building Engineering, Concordia University, Montreal, Canada M.Sc. in Construction Engineering and Management/Civil Engineering B.Sc. in Civil Engineering Dr. Elwakil's research focuses on Infrastructure/Asset Management, Simulation and Modeling, Risk Management, Construction Management, Disaster Restoration & Reconstruction Management, and Water Infrastructure Management. His work integrates advanced computational methods like fuzzy logic, machine learning, and stochastic modeling to address complex challenges in construction and infrastructure systems. He has made significant contributions to understanding organizational performance in construction, water infrastructure management, and post-disaster recovery processes. His publications reveal a strong trend toward applying artificial intelligence and advanced modeling techniques to infrastructure management problems, particularly in water distribution systems, bridge and tunnel condition assessment, and disaster recovery housing. There's a clear progression from theoretical models to practical applications that address real-world infrastructure challenges with increasing sophistication in methodology. Scientific Awards: 2021 Purdue University Faculty Scholar 2019 Purdue University Seed of Success Award 2018 Purdue University ProSTAR's Featured Faculty Member 2018 Purdue Polytechnic Institute Exceptional Early Career for Outstanding Undergraduate Teaching Award 2018 The School of Construction Management Outstanding Faculty Awards (Discovery and Learning) 2017 The School of Construction Management Exceptional Early Career Teaching Award 2014 The School of Construction Management Outstanding Faculty in Discovery Award Dr. Elwakil has demonstrated exceptional mentorship, having chaired or co-chaired 7 PhD students and 28 thesis and non-thesis MSc students while serving on 11 additional graduate committees. His grant success is substantial, with nearly $1 million secured as sole principal investigator and $14.7 million as principal investigator with collaborators, demonstrating his ability to secure significant research funding for complex infrastructure projects. As Associate Editor for two ASCE journals and active reviewer for multiple prominent engineering journals, Dr. Elwakil plays a significant role in advancing scholarship in civil engineering and construction management. His industry experience with USAID projects, Caltrans, and Infrastructure Canada provides valuable practical context for his academic work.

Ridha KhedriView profile

Professor

• Cybersecurity
• Formal Methods in Software Engineering
• Information Security Ontology
• +6 more

Ridha Khedri is a Professor in the Department of Computing and Software at McMaster University . His research spans formal methods in software engineering, cybersecurity, information security ontology, network segmentation, and covert channels analysis. Full Professor since 2000 Contact: khedri@mcmaster.ca Research Interests : Prof. Khedri develops algebraic frameworks for software security, with recent work on network segmentation , ontology engineering , and covert channel detection . His interdisciplinary efforts include hybrid machine learning-ontology models for environmental predictions (e.g., river ice breakup) and digital twin healthcare systems . Article Trends : His 15 most recent works (2016-2025) focus on network security , knowledge representation , and formal verification . Notable trends include automated security testing , ontology modularization , and multi-context reasoning systems . Teaching : He has taught courses like Software Design (CAS 703), Discrete Mathematics (SFWRENG 2DM3), and Algebraic Methods in Software Engineering (CAS 738) since 2017.

Yvette RichardsonView profile

Professor

• Atmospheric dynamics
• Mesoscale meteorology
• Synoptic meteorology
• +4 more

Yvette Richardson is a Professor in the Department of Meteorology and Atmospheric Science and Senior Associate Dean for Undergraduate Education at Penn State University's College of Earth and Mineral Sciences. Her research focuses on severe convective storms, with particular emphasis on tornadogenesis, storm rotation, and numerical modeling of supercell dynamics. Ph.D. in Meteorology from the University of Oklahoma Dr. Richardson investigates severe weather phenomena through both observational studies using mobile radars and numerical simulations. Key research areas include environmental shear effects on storm strength, cold pool dynamics, convective initiation processes, and thermodynamic observations in supercell thunderstorms. Her work has been integral to major field campaigns such as VORTEX2 and the International H20 Project (IHOP). Her recent research publications examine microphysics parameterization sensitivity in supercell models, thermodynamic observations via balloon-borne sensors, and environmental controls on storm-scale vorticity development. The studies span topics from tornadic supercell dynamics to hail scattering mechanisms and warning verification frameworks.

Henrikki TenkanenView profile

Assistant Professor

• Geospatial Analysis
• Urban Planning
• Transportation Accessibility
• +7 more

Henrikki Tenkanen is an Assistant Professor in the Department of Built Environment at Aalto University, specializing in Geoinformatics. His research focuses on geospatial analysis, urban planning, transportation accessibility, and open data applications for sustainable development. His primary research interests include Geospatial Analysis , Urban Planning , Transportation Accessibility , and Population Dynamics . Tenkanen's work integrates mobile phone data, social media, and open geospatial sources to understand urban environments, accessibility patterns, and carbon emissions. His research contributes significantly to UN Sustainable Development Goals related to sustainable cities and communities. Tenkanen's recent publications demonstrate strong trends in high-resolution spatial analysis of urban environments, with particular emphasis on transport equity , carbon emissions mapping , and rural population representation . His work combines advanced geocomputing techniques with practical urban planning applications, often developing open-source tools to enhance reproducibility and accessibility of geospatial research. As an active member of the academic community, Tenkanen serves as a peer reviewer for journals including Big Data & Society and Environment and Planning B, and participates in conference committees such as the International Conference on Location Based Services. His research has garnered significant attention, with multiple publications featured in news outlets and academic platforms. Tenkanen leads several major research projects including Geo-R2LLM (developing geographic large language models), Geoportti (open geospatial infrastructure), MAPICO (mapping commute-related carbon emissions), and LIH: Location Innovation Hub. His work bridges academic research with practical applications for urban planning and sustainable mobility.

• Offshore Renewable Energy Systems
• Wave Power Farm Modeling
• Extreme Weather Resilience
• +3 more

Malin Göteman is an Associate Professor at the Department of Electrical Engineering, Uppsala University. Her research focuses on offshore renewable energy systems, particularly modeling and optimizing large-scale wave power farms and analyzing their resilience to extreme weather conditions. Deputy Director, Center for Natural Disaster Studies (CNDS), Sweden Specialized in wave energy converter dynamics and hybrid offshore energy systems Collaborates on SPH-based numerical wave-current tanks and CFD validation Research Interests: She investigates wave energy farm interactions, hydrodynamic performance of floating platforms, extreme wave load modeling, and survivability strategies using machine learning. Her work spans renewable energy integration, coastal protection, and power system stability under extreme conditions. Recent Publications: Her 2025 articles address resilience of offshore energy systems to metocean extremes and reduced-order modeling via Bayesian design. Earlier works (2023-2024) cover SPH validations for floating wind-wave systems, neural network survivability approaches, and hybrid energy-water supply solutions. Collaborations: She works with international teams on projects like Lysekil wave energy test sites and DeepCwind floating platforms. Key areas include grid-connected wave parks, multi-fidelity surrogate modeling, and comparative studies on offshore wind dependencies.

Jochen GarckeView profile

Professor

• Numerical Methods in Science and Technology
• Machine Learning
• Sparse Grids
• +5 more

Prof. Dr. Jochen Garcke is a faculty member at the Institute for Numerical Simulation, University of Bonn, with a dual affiliation at Fraunhofer SCAI's Department of Numerical Data-Based Prediction. His work bridges numerical simulation and machine learning, focusing on high-dimensional problems, sparse grids, and optimal control. Key research themes: Sparse grids, machine learning for simulations, reinforcement learning, uncertainty quantification Teaching includes courses on Numerical Methods in Science and Technology and Scientific Computing , emphasizing practical machine learning applications. Recent publications explore hybrid models combining data-driven and physics-based approaches in automotive engineering, wind turbines, and geoscientific modeling. His group employs adaptive sparse grids, graph algorithms, and spectral methods to tackle challenges in crash simulations, fluctuating renewable energy systems, and turbulent flow analysis. Collaborations span Fraunhofer SCAI and industry 4.0 initiatives.

Helmut MeissView profile

Assistant Professor

• Biological Pest Control
• Weed Management
• Sustainable Agriculture
• +3 more

Helmut Meiss serves as an Assistant Professor at the University of Lorraine's University Institute of Technology (IUT) Nancy-Brabois, where he directs the DUT Biology with Agronomy option study program. His research focuses on conservation biological control of crop pests and weeds through natural enemies within innovative cropping systems, bridging ecological science and agricultural practice. Dr. Meiss's work emphasizes biological pest control and weed management in sustainable agriculture, investigating how landscape features, crop rotations, and biodiversity enhance ecosystem services. His research integrates field studies with predictive modeling—particularly machine learning—to develop practical tools for farmers, as evidenced by his leadership in the ARENA project which establishes observation networks for natural pest regulation. Analysis of his 15 most recent publications reveals consistent themes in cereal aphid regulation, weed dynamics in cropping systems, and data-driven approaches to agri-environmental indicators. His work demonstrates a strong trajectory toward generic predictive models for pest control and the integration of biocontrol into farm-level decision-making, with increasing emphasis on ecosystem service quantification since 2017. Dr. Meiss co-supervises PhD candidate Eric STELL on predicting conservation biological pest control in cereal crop mixtures. His involvement in the ARENA project indicates collaborative grant-funded research, though specific funding sources aren't detailed in available materials. He actively contributes to farmer advisory systems through biodiversity indicators and crop management tools. As part of a multidisciplinary research group organized around three thematic axes, he participates in the ARENA network for pest biocontrol assessment and engages in public outreach including school workshops, biodiversity exhibitions, and science-society initiatives like the 'Grand Nancy Terre de pollinisateurs' project.

Qipei MeiView profile

Assistant Professor

• Civil Engineering
• Artificial Intelligence
• Robotics
• +4 more

Qipei Mei is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Alberta's Faculty of Engineering. With an MSc in Computer Science and a PhD in Structural Engineering, he bridges civil engineering with artificial intelligence to enhance infrastructure productivity and sustainability. His research spans AI-driven design automation, robotics for construction safety, and IoT-based condition assessment. PhD, Structural Engineering - University of Alberta (2020) MSc, Computer Science - Georgia Institute of Technology (2018) MSc, Structural Engineering - University of Alberta (2014) B.E., Civil Engineering - Huazhong University of Science and Technology (2011) Mei's work focuses on three key areas: leveraging data-driven methods for design automation, applying sensing/robotics to construction operations, and using digital twins for infrastructure assessment. His team explores generative AI for housing design, robotic construction in remote communities, and smart monitoring systems. Recent publications highlight advancements in: lateral capacity prediction for monopile foundations, transformer-based architectural layout analysis, large language models for building code compliance, vision-language models for safety hazard detection, and sensor networks for bridge monitoring. These works demonstrate interdisciplinary integration of AI, structural engineering, and IoT. Mei actively collaborates with diverse researchers and welcomes graduate students to his Smart Infrastructure Technologies (SITE) Research Group, part of the Infrastructure and Human Tech Lab (IHT-Lab). He teaches advanced topics in structural and civil engineering while pursuing industry-funded projects through NSERC, CFI, and Alberta Innovates.

• Transportation Engineering
• Location Optimization
• Geospatial Information Science
• +4 more

Tae J. Kwon is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Alberta . His research spans Winter Transportation Engineering , Intelligent Transportation Systems (ITS) , Traffic Optimization , and Geospatial Information Science . Developed LoRWIS , a web-based decision support system for road weather sensor optimization Recipient of multiple awards including ITS Canada Excellence in Research and Development Award (2024) , KOFST Scientist of the Year (2024) , and Early Career Research Award (University of Alberta, 3 times) Active in professional committees like the TRB Surface Transportation Weather Research Group (2016–present) Research Highlights : Winter Road Maintenance : Advanced geostatistical and deep learning techniques for road condition monitoring Location Optimization : Pioneered models for RWIS sensor placement using hybrid geostatistical methods Intelligent Transportation Systems : Focused on big data applications for traffic safety and connected vehicles Climate Resilience : Integrated ecological vulnerability into fire hazard modeling Scientific Awards : ITS Canada Excellence in Research (2024) KOFST Scientist of the Year (2024) Donald Stanley Best Paper Award (2023) AKCSE Early Achievement Award (2021) University of Alberta Early Career Research Award (2020) Great Supervisor Award (2019) Teaching & Leadership : Teaches courses like CIV E 419 (Transportation Engineering: Highway Planning and Design) and CIV E 612 (Transportation Planning: Methodology and Techniques) Leads the Geospatial Transportation System & Sensing Solutions Lab (GeoTrans) , mentoring PhD students Juan Cuellar and Jiehao Bi Collaborates with agencies including Alberta Transportation , NSERC , and Iowa DOT

• Welding Metallurgy
• Physical Metallurgy
• Additive Manufacturing
• +10 more

Leijun Li, PhD, P.Eng., is a Professor in the Department of Chemical and Materials Engineering at the University of Alberta, where he also serves as Chair. With a career spanning institutions including Rensselaer Polytechnic Institute, University of Northern Iowa, and Utah State University, he specializes in physical metallurgy , welding metallurgy , and additive manufacturing . His research focuses on microstructure characterization, mechanical properties, and modeling of non-equilibrium phase transformations during welding and AM processes. Current affiliations: University of Alberta, American Welding Society, ASM International Research themes: Additive manufacturing of alloys, Corrosion science, Pipeline metallurgy, Phase transformations, Welding robotics He has received multiple AWS Hobart Awards (4 times) and Savage Awards (2 times) for his work on pipeline welding and metallurgy. His group has published extensively on topics including delta-ferrite retention in Grade 91 steel, inverse bainite transformations , and welding defect analysis . Recent projects include NSERC Alliance Missions Grant for rare earth mineral recovery and Alberta Innovates Ecosystem Program for advanced manufacturing. Key collaborators: Dr. Tom Lienert, Dr. Xiaoying Fang, Dr. P-Q Xu Labs: Rooms 2-158/3-133 (CME Building), Office 12th Floor DICE Building

Camelia E. HostinarView profile

Associate Professor

• Developmental Psychology
• Health Psychology
• Social Psychology
• +4 more

Camelia E. Hostinar is an Associate Professor in the Department of Psychology at the University of California, Davis. She is affiliated with the Center for Mind and Brain and the Center for Poverty and Inequality Research. Her research focuses on how social environments influence health through stress-response systems, particularly linking early-life stress to long-term disease trajectories and protective processes. Ph.D., University of Minnesota, Institute of Child Development (2013) M.A., Child Psychology, University of Minnesota (2010) B.S., Psychology, Towson University (2008) Dr. Hostinar's research spans childhood poverty, early-life adversity, and their impacts on physical and mental health. She integrates endocrine, immune, genetic, and behavioral measures to study stress buffering mechanisms and self-regulatory skill development. Her recent work also addresses environmental health risks like air pollution and pandemic-related stress. Recent publications show interdisciplinary trends combining developmental science, neuroimmunology, public health, and machine learning. Key topics include social buffering of stress, neuroinflammation, childhood adversity biomarkers, policy-relevant environmental research, and pandemic-induced developmental disruptions. 2024 Lab Safety Award, UC Davis 2022-2025 Dean’s Faculty Fellow 2023 Distinguished Undergraduate Teaching Award 2022 Boyd McCandless Award 2019 SRCD Early Career Contributions 2015 APS Rising Star Award 2014 NIH F32 Postdoctoral Fellowship As director of the Social Environment and Stress (SES) Lab, she mentors graduate students in studying social-environmental influences on health. Her teaching includes developmental psychology, health psychology, and research methods courses.

Katharina EggenspergerView profile

Researcher

• Automated Machine Learning (AutoML)
• Hyperparameter Optimization (HPO)
• Tabular Machine Learning
• +1 more

Katharina Eggensperger is an Early Career Research Group Leader at the University of Tübingen , leading the AutoML for Science group within the Cluster of Excellence Machine Learning for Science . She previously completed her Ph.D. at the University of Freiburg under Frank Hutter and Marius Lindauer (2022), and actively contributes to the AutoML community through open-source tool development and competition leadership. Co-developer of AutoML.org tools Faculty member of IMPRS-IS Chair for multiple AutoML workshops/conferences (2019-2025) Her research focuses on automated machine learning (AutoML) with specific attention to: AutoML Systems Hyperparameter Optimization Tabular Machine Learning Scientific Applications of ML She has organized multiple AutoML schools and conferences, including serving as Program Chair for AutoML 2024 and Non-archival Track Chair for AutoML 2025. Her work emphasizes making machine learning accessible through automation while maintaining scientific rigor and interpretability, particularly for tabular data applications. Katharina actively recruits PhD students through IMPRS-IS and collaborates with institutions like the University of Freiburg and Cyber Valley .