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Birgit KleinschmitView profile

Professor

• Remote Sensing
• Vegetation-Soil Interactions
• Environmental Planning
• +3 more

Prof. Dr. Birgit Kleinschmit is a Professor at the Technical University of Berlin specializing in Geoinformation in Environmental Planning and serves as Principal Investigator for the DFG Research Group FOR 2694 "Cosmic Sense", leading its Remote Sensing (RS) module focused on hydrological and environmental applications. Her research centers on spatio-temporal dynamics of vegetation-soil interactions using multi-temporal and multisensor remote sensing technologies, with extensive interdisciplinary collaboration across national and international projects. Key research domains include: Advanced remote sensing for environmental monitoring Land surface process modeling Spatio-temporal geospatial analysis Interdisciplinary environmental planning Prof. Kleinschmit actively contributes to major funded initiatives including: DFG: UWI (Urban Water Interfaces) DFG: UCaHS (Understanding Changes in the Hydrological System) BMBF: SuMaRiO (Sustainable Management of River Oases) INNOVATE (Innovative Water Management Technologies) These projects emphasize sustainable resource management through integrated geospatial approaches. No scientific awards were documented in the source material. Her advisory roles remain unspecified, though her leadership in consortium-based research demonstrates significant collaborative grant management. As core faculty within the Cosmic Sense team, she operates within the Remote Sensing module that integrates satellite and airborne data across multiple field sites to advance cosmic-ray sensing applications in hydrology.

• Supply Chain Logistics
• National Security
• Large-Scale Algorithm Design
• +5 more

Dr. Chase E Rainwater is Professor and Head of the Department of Industrial Engineering at the University of Arkansas College of Engineering, serving since 2009 while co-directing the Arkansas Security Research and Education Institute. A University of Arkansas alumnus with bachelor's and doctoral degrees in Industrial Engineering from UF, he commands significant research funding and leadership recognition. His educational credentials: B.S.I.E., University of Arkansas Ph.D., University of Florida Rainwater's research integrates large-scale optimization with critical applications in supply chain security, food safety systems, and national infrastructure protection. His work bridges machine learning, cybersecurity, and homeland security through $20M+ in NSF, DoD, and USDA-funded projects, developing algorithms for drug interdiction networks, poultry supply chain safety, and aerial image analysis. Recent publications reveal accelerating interdisciplinary convergence: 2021-2025 works combine computer vision (wildfire segmentation, solar PV monitoring) with operations research (supply chain security, network interdiction). This reflects his strategic pivot toward AI-driven solutions for environmental monitoring and critical infrastructure resilience while maintaining core logistics expertise. Major honors include: University of Arkansas Teaching Academy Fellow (2022) Five-time IE Department Outstanding Teacher (2012,2014,2015,2017,2022) IISE Innovation in Education Award (2021) College of Engineering Collaborative Research Award (2018) Arkansas Academy of IE Outstanding Faculty (2017,2022) FIRST Tech Challenge Compass Award (2017) He has advised six Ph.D. graduates while securing $20M+ in grants including EPSCoR DART ($4.3M), Cybersecurity Workforce Development ($4.6M), and Walmart food safety initiatives ($3.2M+). His leadership spans IISE Board of Trustees, INFORMS committees, and Arkansas FIRST robotics mentorship since 2015. Rainwater co-directs the Arkansas Security Research Institute while mentoring FIRST robotics teams for over a decade, including coaching Team USA at the FIRST Global Challenge. His departmental leadership focuses on data science integration and cross-college security initiatives.

Zhenman FangView profile

Associate Professor

• Hardware Acceleration
• Computer Architecture
• Big Data Computing
• +7 more

Dr. Zhenman Fang is an Associate Professor in the School of Engineering Science (Computer Engineering Option) and Associate Member in the School of Computing Science at Simon Fraser University, Canada. He founded and directs the HiAccel Lab, focusing on accelerator-rich architectures. His PhD (2014) is from Fudan University, China, with 15 months spent at the University of Minnesota. Prior to SFU, he was a Staff Software Engineer at Xilinx (2017-2019) and a postdoc at UCLA (2014-2017). His research spans: Hardware acceleration for ML, big data, genomics, and HPC FPGA-based customizable computing and near-data processing Compiler/runtime systems for heterogeneous platforms Performance/reliability optimization of accelerator-rich systems His recent publications (2024-2025) focus on FPGA acceleration for machine learning (e.g., on-device training, quantization), computational chemistry, image/video compression, database systems, and reconfigurable computing, demonstrating cross-domain applications of specialized hardware. Awards & Honors: Best Paper Awards: FPL 2024, MEMSYS 2017, TCAD 2019 Best Paper Nominations: ICCAD 2025, FCCM 2025, HPCA 2017, ISPASS 2018 SFU Research Excellence Horizon Award (2025) NSERC Alliance, CFI JELF, and Xilinx University Awards He advises 20+ PhD/Master's students in HiAccel Lab, focusing on accelerator design. Major grants include NSERC Alliance (2020) and CFI JELF (2019). The lab operates a 10-node cluster with FPGA/GPU infrastructure.

Clara StegehuisView profile

Associate Professor

• Random Graphs
• Network Science
• Theoretical Computer Science
• +4 more

Clara Stegehuis serves as Associate Professor in Mathematics of Operations Research, specializing in the mathematical foundations of complex networks. Her research establishes critical connections between theoretical graph models and real-world applications in wireless communications and chemical reaction systems, with 68 research outputs including 34 articles and 16 preprints. Her primary research domains include: Random Graph Theory and Scale-Free Networks Hypergraph Modeling and Sampling Algorithms Geometric Network Analysis Stochastic Processes on Networks Combinatorial Optimization for Network Structures Network Applications in Wireless Systems Recent publications (2023-2025) demonstrate methodological innovation in hypergraph sampling (Hypercurveball algorithm) and geometric network detection, while addressing practical challenges in mmWave networks, cellular resilience, and reaction network analysis. Her work consistently bridges abstract mathematical frameworks with engineering applications through rigorous probabilistic modeling. Dr. Stegehuis has supervised two graduate students and contributed two significant datasets to the research community. Though no specific awards are documented in the provided materials, her h-index of 9 and consistent publication output (10 papers in 2025) reflect substantial scholarly impact. Current research initiatives focus on multi-connectivity optimization for wireless networks, geometric structure detection in scale-free graphs, and applying random graph theory to model out-of-equilibrium chemical systems, often through interdisciplinary collaboration with domain experts.

Heather C. JanisseView profile

Professor

• Developmental Psychology
• Child Psychology
• Pediatric Obesity
• +5 more

Heather C. Janisse is a Professor in the Department of Psychology at Eastern Michigan University, serving as Faculty Advisor for Psi Chi/Psychology Club and teaching core courses including General Psychology, Child Psychology, and Developmental Psychology. Her research and academic work focus on developmental processes in at-risk youth within educational contexts. Education: Ph.D., Wayne State University M.A., Wayne State University B.S., Eastern Michigan University Dr. Janisse's research program centers on understanding how educational environments and parent-child dynamics shape social, cognitive, and health trajectories in vulnerable youth populations. She specifically targets health disparities among minority children through Head Start preschool interventions. Her primary research domains include: Developmental Psychology Child Psychology Pediatric Obesity Health Disparities Parent-Child Relationships At-risk Youth Development Social and Cognitive Development Head Start Interventions Analysis of her 15 most recent publications reveals consistent focus on improving outcomes for marginalized children through family-centered approaches. Her work spans pediatric obesity interventions, father involvement in chronic illness management, and technology impacts on preschool development, with strong emphasis on Head Start populations and African American communities to address systemic health inequities. Dr. Janisse mentors students through Psi Chi and the Parents for Healthy Kids (PHK) Lab, which operates a federally funded (NIH/NIDDK) obesity intervention trial for Detroit Head Start children. This project focuses on enhancing parent-child interactions and physical health outcomes through culturally responsive family engagement strategies. The PHK Lab in the Science Complex cultivates collaborative student-faculty research teams dedicated to advancing healthy development in minority children and families. Their work integrates physical activity promotion, nutritional awareness, and mental health support to create sustainable interventions for communities facing systemic health challenges.

• Reinforcement Learning
• Machine Learning
• Multi-agent Systems
• +4 more

Matthew Taylor is an Associate Professor of Computing Science at the University of Alberta's Faculty of Science. He directs the Intelligent Robot Learning Lab (irll.ca) and holds a Canada CIFAR AI Chair at Amii, the Alberta Machine Intelligence Institute. His research spans reinforcement learning, multi-agent systems, robotics, and human-AI interaction, with emphasis on translating academic AI into industry applications. Dr. Taylor earned his PhD in 2008 and has held roles across academia and industry. His work focuses on human-in-the-loop AI systems, reward design, and multi-agent coordination. Notable contributions include frameworks for automated warehouse layout generation, decentralized energy resource coordination, and bio-inspired prosthetic control systems. Awards include his Amii fellowship and CIFAR chair. His lab develops real-world applications in areas like flight training augmentation, smart home systems, and robotics education. Current projects emphasize robustness in noisy environments and transfer learning across domains. Key Research Areas: Reinforcement Learning, Human-AI Collaboration, Multi-Agent Systems Lab: Intelligent Robot Learning Lab Affiliations: Amii (Alberta Machine Intelligence Institute)

• Semiconductor thin film materials and devices
• Material nano-characterization
• Electromechanical coupling characterization
• +3 more

Huang Boyuan serves as an Associate Professor and Doctoral Supervisor in the Department of Materials Science and Engineering at Southern University of Science and Technology (SUSTech). His research group operates within the Guangdong Provincial Key Laboratory of Information Functional Oxide Materials and Devices, utilizing advanced facilities including AFM, PLD, ALD, CVD, SHG, semiconductor analyzers, and cleanroom lithography equipment. Education: Ph.D. in Mechanical Engineering, University of Washington (2016-2020) B.S. in Physics, Nanjing University (2012-2016) Huang's research focuses on developing cutting-edge characterization techniques, particularly artificial intelligence atomic force microscopy (AI-AFM) and mechanically gated transistors (MGT). His work bridges materials science, nanotechnology, and artificial intelligence to investigate nanoscale multi-field coupling mechanisms in semiconductor materials. The research group actively explores two-dimensional thin film materials, experimental mechanics, and energy material interfaces, with particular emphasis on data-driven approaches to material characterization. Huang's publication portfolio demonstrates consistent high-impact output across materials science and nanotechnology domains, with increasing emphasis on AI-integrated characterization methods. His recent work shows strong interdisciplinary connections between materials physics, machine learning, and semiconductor device engineering, reflecting the growing importance of data science in advanced material characterization. Scientific Recognition: R&D 100 Awards Finalist (2020) Shenzhen Natural Science Award (2021) Shenzhen Overseas High-level Talent (2020) National Excellent Self-financed Overseas Student Scholarship (2020) Featured in MIT Technology Review Huang has successfully secured funding for eight research projects, including the NSFC Original Exploration Plan project and Major Research Plan Integration Project. His research group actively mentors graduate students and postdoctoral fellows, with several students receiving prestigious awards and scholarships. The team maintains strong industry connections, with two authorized patents achieving technology transfer. The research group operates within state-of-the-art facilities at SUSTech, maintaining collaborative relationships with national research initiatives including the National Major Scientific Research Instrument Development Projects. Current recruitment efforts focus on doctoral candidates and research assistants specializing in two-dimensional thin film materials, experimental mechanics, and energy material interface studies.

Yi MeiView profile

Associate Professor

• Evolutionary Computation
• Combinatorial Optimisation
• Genetic Programming
• +10 more

Dr. Yi Mei is an Associate Professor and Associate Dean (Research) at the Faculty of Engineering, Victoria University of Wellington, New Zealand. He serves as Programme Director for Computer Science & Computer Graphics within the School of Engineering and Computer Science and is affiliated with the Centre for Data Science and Artificial Intelligence (CDSAI). Dr. Mei's research focuses on evolutionary computation for combinatorial optimisation , with specific expertise in genetic programming , automatic algorithm design , explainable AI , and multi-objective optimisation . His work bridges theoretical foundations with practical applications in transportation, healthcare, manufacturing, and aquaculture. His publication portfolio shows a clear trajectory toward increasingly complex real-world applications of evolutionary computation, with recent work focusing on emergency medical dispatch optimization , dynamic transportation systems , and explainable AI for routing problems . The research demonstrates strong interdisciplinary connections between evolutionary computation, machine learning, and domain-specific optimization challenges. Victoria University of Wellington Ki te Pae - Research Excellence Award 2024 Multiple Best Paper Awards at ACM Genetic and Evolutionary Computation Conference (GECCO) from 2022-2024 HUMIES Silver Award at GECCO 2023 IEEE Transactions on Evolutionary Computation Outstanding Associate Editor (2024, 2025) Fellow of Engineering New Zealand and IEEE Senior Member Dr. Mei has successfully supervised numerous PhD and Master's students whose research has received recognition, including the ACM SIGEVO Dissertation Award. He leads significant research projects funded by MBIE Endeavour Smart Idea Fund ($1M NZD), New Zealand Royal Society Catalyst Leaders Fund ($150K NZD), and industry partners like Honda Research Institute Europe. His service contributions include editorial roles for top journals including IEEE Transactions on Evolutionary Computation and leadership positions in IEEE Computational Intelligence Society. Dr. Mei leads the Evolutionary Computation for Combinatorial Optimisation Group (ECCO) and the Evolutionary Computation and Machine Learning Research Group (ECRG) at Victoria University of Wellington. He also chairs the IEEE Taskforce on Evolutionary Scheduling and Combinatorial Optimisation (TESCO), fostering international collaboration in evolutionary computation applications.

• Transport Planning
• Sustainable Infrastructure
• Resilient Infrastructure
• +4 more

Fulvio Lopane serves as Assistant Professor in Transport Planning at Northeastern University London within the Centre for Apprenticeships, holding academic credentials spanning civil and environmental engineering disciplines. His educational foundation includes: Undergraduate degree in Civil and Environmental Engineering MSc in Civil Engineering PhD in Civil Engineering with specialisation in multi-objective spatial optimisation of infrastructure services Research focuses on urban science and mobility systems through sustainable infrastructure networks, human mobility patterns, and urban modelling. His work develops spatial optimisation frameworks and land use analysis tools specifically targeting resilient, equitable transport networks responsive to urban population dynamics. Current investigations integrate spatial interaction platforms with infrastructure service evaluation methodologies. Professional engagement includes teaching comprehensive transport planning curricula across theoretical and applied domains. No formal advisee relationships or research grants are documented in available materials. Operational activities occur within Northeastern University London's Centre for Apprenticeships framework, though specific laboratory facilities or research teams remain unmentioned in source documentation.

Fang LiuView profile

Assistant Professor

• Software Engineering
• Artificial Intelligence for Software Engineering
• Program Understanding
• +3 more

Fang Liu is an Assistant Professor at the School of Computer Science & Engineering, Beihang University, China. She has made significant contributions to the field of software engineering, particularly in the intersection of artificial intelligence and software development practices. Dr. Liu received her Ph.D. in Computer Science from Peking University (Sep. 2017 - Jul. 2022), supervised by Prof. Zhi Jin and Prof. Ge Li. Prior to that, she earned her B.S. in Computer Science from Chongqing University (Sep. 2013 - Jul. 2017). Her research interests focus on AI for Software Engineering, including program understanding and generation, program repair, and applications of Large Language Models to software development tasks. Dr. Liu teaches Compiler Technology as a compulsory course at Beihang University (Fall 2024) and Programming in Cangjie Language as an elective course (Spring 2025). Her recent publications demonstrate a clear trend toward leveraging large language models for various software engineering tasks, with a particular emphasis on code editing, program repair, and code translation. Her research spans both theoretical advancements in model architectures and practical applications to real-world software development challenges, with numerous publications in top-tier venues like ASE, ICSE, and FSE. Distinguished Paper Award at ICPC'20 for "A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning" Dr. Liu actively participates in the software engineering research community as a program committee member for major conferences including ASE, FSE, and ICSE. Her work has significant implications for improving developer productivity, software quality, and the integration of AI technologies into the software development lifecycle.

• Cognitive Ergonomics
• Human Factors
• Human-Technology Interaction
• +3 more

Dr. Felix Dreger is a Research Associate in the Experimental Ergonomics department at the Leibniz Research Centre for Working Environment and Human Factors (IFADO) in Dortmund, Germany. He works under the leadership of Prof. Dr. Edmund Wascher in the Cognitive Ergonomics Working Group, focusing on human-technology interaction, automation systems, and work design. Dr. Dreger holds a B.Sc. and M.Sc. in Psychology from Eberhard Karls University of Tübingen, with additional academic experience at the University of Connecticut and Delft University of Technology. His educational background includes specialized training in cognitive and media sciences from the University of Duisburg-Essen. His research interests center on cognitive ergonomics, human-technology interaction, and human factors in automation and robotics. Dr. Dreger specializes in studying how humans interact with complex systems, particularly in industrial settings involving robotic collaboration, crane operations, and forestry machinery. His work examines learning processes, workload assessment, and feedback design in human-machine systems. Analysis of Dr. Dreger's publication record from 2020-2025 reveals a strong trajectory in human-robot collaboration research, with increasing focus on industrial applications. His work spans multiple domains including forestry operations, crane control systems, and multi-human multi-robot collaboration frameworks. The research demonstrates expertise in both theoretical frameworks like cognitive ergonomics and practical applications in workplace settings. Dr. Dreger has extensive international research experience, including collaborations with Virginia Tech Transportation Institute and Delft University of Technology. His work has been supported by EU-funded projects including Marie Curie Skłodowska RISE and EU Horizon initiatives like FELICE and EU-SOPRANO. His laboratory work focuses on experimental ergonomics, utilizing methodologies including hierarchical task analysis, neuroergonomics with mobile EEG, and human factors assessment in real-world industrial settings. The research team he works with at IFADO maintains strong connections with both academic institutions and industry partners to ensure practical relevance of their findings.

Patrick CoiraultView profile

Professor

• Automatic Control
• Nonlinear Systems
• Multi-agent Systems
• +5 more

Patrick Coirault is a Full Professor in Automatic Control and Systems at the University of Poitiers' Institute of Technology. He is affiliated with the LIAS research laboratory, with operations at both ENSIP in Poitiers and ISAE-ENSMA. His research spans theoretical and applied control systems with significant contributions to multiple engineering domains. Professor Coirault's research interests encompass nonlinear control systems, multi-agent formation control, chaotic system synchronization, and practical applications in automotive systems. His work in hybrid vehicle control addresses critical challenges including torque ripple reduction, combustion control, and energy management. He has also made significant contributions to antenna array synchronization, power systems control, and wind engineering applications. His methodological approach combines theoretical advances in control theory with practical implementation in real-world systems, often bridging traditional model-based control with emerging data-driven techniques. Analysis of his recent publication record reveals a clear evolution toward distributed control systems, particularly for power networks and multi-agent coordination. His 2023-2025 publications demonstrate increasing integration of machine learning techniques with traditional control approaches, exemplified by work on data-enabled predictive control for LPV systems. The applications of his research span automotive systems, power grids, wireless communications, and aerodynamics, showcasing the versatility and impact of his control methodologies across multiple engineering disciplines. Professor Coirault maintains active collaborations within the LIAS research laboratory and with international partners. His publication record in top-tier journals including Automatica , IEEE Transactions on Automatic Control , and Control Engineering Practice demonstrates his sustained contribution to the field of control systems engineering over several decades.

Scott KeatingView profile

Lecturer

• Seismic Inversion Methodology
• Uncertainty Quantification
• Numerical Optimization
• +4 more

Dr. Scott Keating is a Lecturer at ETH Zurich’s Department of Earth and Planetary Sciences (D-EAPS), affiliated with the Institute of Geophysics. His research focuses on seismic inverse problem methodologies, particularly uncertainty quantification, numerical optimization, and the development of automated workflows for regional inversion updates. Current projects include full-waveform inversion of ambient noise measurements and CO2 storage monitoring using advanced sensor technologies like distributed acoustic sensing (DAS) and accelerometers. His work integrates cutting-edge computational methods, such as probabilistic inversion frameworks and adjoint-based optimization, to address challenges in subsurface imaging and parameter estimation. Applications span environmental seismology, carbon sequestration, and reservoir characterization, with a strong emphasis on practical, cost-effective solutions for real-world problems such as CO2 storage validation. Publications highlight contributions to elastic full-waveform inversion (FWI), including multiparameter analysis using combined geophone and DAS data, and innovative techniques like targeted nullspace shuttling to enhance inversion reliability. His research also addresses data sparsity and modeling uncertainties, advancing methodologies for robust subsurface monitoring and decision-making.

• Systems Engineering
• Aircraft Design
• Value-Driven Design
• +6 more

Dr. Peter Hollingsworth is a Senior Lecturer in Mechanical and Aerospace Engineering at The University of Manchester. He holds a PhD in Aerospace Engineering from Georgia Institute of Technology (2004), alongside a Bachelor of Engineering (1999) and Master of Science (2000). His research focuses on Value-Driven Design methodologies, systems engineering, and reducing aviation's environmental impact. He co-chairs the AIAA Value Driven Design Program Committee, advocating for standardized taxonomies and industry education. His work spans aircraft design optimization, climate-conscious technologies, and UAV applications for emissions monitoring. Key collaborators include the US Joint Planning and Development Office (JPDO), IATA, and CAAFI. He leads the Aerospace Engineering research group, overseeing projects on space systems design, small satellite constellations, and sustainable aviation fuels. His interdisciplinary approach bridges academia, industry, and policy to address global environmental challenges. Education: PhD in Aerospace Engineering, Georgia Institute of Technology, 2004 Master of Science in Aerospace Engineering, Georgia Institute of Technology, 2000 Bachelor of Engineering in Aerospace Engineering, Georgia Institute of Technology, 1999 Research Interests: Value-Centric Design Frameworks Climate Impact of Aviation Alternative Fuels and Propulsion Systems Unmanned Aerial Systems (UAS) for Environmental Monitoring Space Systems Architecture and Certification Grants and Projects: Principal Investigator for multiple aerospace engineering projects, including small satellite deployment and UAV emissions studies Contributed to initiatives like the US Commercial Aviation Alternative Fuels Initiative (CAAFI) Co-lead on AIAA Value Driven Design Program Committee Labs and Teams: Aerospace Research Institute at The University of Manchester Space Systems Research Group

Bentley OakesView profile

Assistant Professor

• Digital Twins
• Model Transformations
• Software Engineering
• +6 more

Bentley Oakes is an Assistant Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He holds a Ph.D. (2018) and M.Sc. (2015) from McGill University, and a B.Sc. (2013) from the University of Manitoba. His research focuses on Digital Twins , Model Transformations , and Knowledge Representation for cyber-physical systems. Ph.D. in Computer Science, McGill University, Canada M.Sc. in Science, McGill University, Canada B.Sc. in Computer Science, University of Manitoba, Canada His work bridges Model-Driven Engineering and Artificial Intelligence to advance the rigorous development of digital twins. Key research areas include: Digital Twin Engineering : Frameworks for systematic development, reporting, and validation. Knowledge Representation : Ontologies and contracts for modeling domain expertise. Model Transformations : Symbolic execution and debugging of ATL/DSLTrans transformations. Verification : Formal methods for cyber-physical systems and co-simulations. Oakes has published extensively in MODELS , MSR , TOSEM , and SoSyM . His recent studies on rationale extraction in open-source software and DevOps approaches for built assets highlight interdisciplinary innovations. He has been recognized for Outstanding Reviewing Contributions , receiving multiple Best Reviewer Awards at leading software engineering venues. At Polytechnique Montréal, he teaches courses such as: INF6900AE : Scientific and Technical Communication I INF7900AE : Scientific and Technical Communication II LOG6953FE / LOG6310E : Digital Twin Engineering LOG8371E : Software Quality Engineering