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Lucas BöttcherView profile

Assistant Professor

• Computational Science
• Applied Mathematics
• Disease Modeling
• +5 more

Lucas Böttcher is an Assistant Professor of Computational Science at the Frankfurt School of Finance and Management. He holds a PhD in Applied Mathematics and Computational Physics from ETH Zürich (2018), and has held academic appointments at ETH Zürich (Lecturer), UCLA (Research Scientist and Swiss National Fund Fellow), and a courtesy professorship at the University of Florida since 2023. Research focuses on modeling and control of high-dimensional stochastic processes, network analysis, and data assimilation for epidemics and healthcare systems Editorial board member of Nonlinear Dynamics and International Journal of Modern Physics C Recognized by major media including The Economist , Wall Street Journal , and the UN World Drug Report 2024 Research Trends : His recent publications (2023-2025) span computational medicine, network science, and machine learning applications. Key themes include: Controlling dynamical systems with neural networks and ODEs Modeling drug overdose epidemics and health disparities Optimizing epidemic response through test/vaccine allocation Developing physics-informed machine learning methods Studying organizational and societal resilience mechanisms Scientific Awards : Meritorious Reviewer Award 2024 (INFORMS Journal on Computing) SNF Early Postdoc.Mobility Fellowship 2020 MSc ETH Physics with Distinction 2015 Ardenne Physics Award 2011 DPG Abitur Award 2011

• Data Imputation
• Urban Mobility
• Transportation Policy
• +7 more

Sanaz Nikfalazar is a Teaching Fellow in the Department of Human Centred Computing at the Faculty of Information Technology, Monash University. Her work contributes to advancing knowledge in urban mobility, data imputation, and sustainable city development, with a strong alignment to UN Sustainable Development Goals, particularly Sustainable Cities and Communities. Research Interests: Her research spans missing data imputation using machine learning , urban mobility modeling , transport policy for autonomous vehicles , and informal housing practices in Australia . She employs computational techniques such as decision trees and fuzzy clustering to address data challenges in city planning. The trend in her recent publications shows a consistent focus on urban data analytics , combining data science with public policy to inform sustainable urban development. Her work bridges technology and societal challenges, particularly in transportation and housing. Scientific Contributions: Developed hybrid imputation methods for city mobility indices Analyzed transport policies for autonomous vehicles in Australian cities Explored informal housing patterns and their urban implications She has co-authored multiple peer-reviewed articles and conference papers. While no formal advising or grant information is available, her collaborative research indicates active engagement with experts in urban planning, data science, and policy analysis.

• Landscape and Environmental Governance
• Community Engagement in Natural Resource Management
• Participatory Processes in Agri-environmental Policy
• +12 more

Katrin Prager is a Reader (Associate Professor) in the Department of Geography and Environment at the University of Aberdeen’s School of Geosciences. She serves as Programme Director for the MSc Restorative Land Management and Rural Surveying and has held senior research positions at the James Hutton Institute and Humboldt University. Her academic work bridges landscape governance, rural development, and sustainable agriculture. Research Interests: Her research focuses on participatory processes in agri-environmental policy, community engagement in natural resource management, social-ecological resilience, and land manager behaviour. She investigates how co-creation, interdisciplinary collaboration, and innovative governance structures can support transitions toward sustainable land use. Recent Publications Trends: Her recent scholarly output emphasizes innovative contract models for environmental outcomes, results-based payments, institutional analysis of agri-environmental schemes, and the role of knowledge exchange in policy design. Her work is published in leading journals such as Land Use Policy , Global Environmental Change , and Soil Use and Management , reflecting a strong focus on policy-relevant, transdisciplinary environmental research. Editorial Roles: Guest Editor, Journal of Agricultural Extension and Education (2024/25) Editorial Board Member, Scottish Geographical Journal (2018–) Associate Editor, Land Degradation & Development (2018–2019); Editorial Board (2013–) Advising and Grants: Prager actively supervises PhD and Master’s students in human geography and environmental management. She has led and contributed to major funded projects including VISIONARY (EU Horizon Europe), CONTRACTS2.0 (Horizon2020), and FARM TREE (UKRI), demonstrating sustained success in securing competitive research funding and mentoring emerging scholars. Labs and Research Groups: While not formally affiliated with a single lab, her work is closely linked to interdisciplinary research initiatives at the University of Aberdeen, particularly those focused on sustainable land management, rural policy, and environmental governance. She collaborates with the Green Economy Research Centre and contributes to the Just Transition Lab’s efforts in sustainable geosciences.

Michał PrzewoźniczekView profile

Professor

• Evolutionary Computation
• Multi-objective Optimization
• Many-objective Optimization
• +12 more

Michał Przewoźniczek is a Professor at Wrocław University of Science and Technology (PWr), actively contributing to the fields of evolutionary computation, optimization, and artificial intelligence. He is a key member of multiple research teams, including the Metaheuristics Team, Machine Learning Team, and Advanced Data Analysis Methods Team, reflecting his broad interdisciplinary impact. His research focuses on Evolutionary Computation , Multi- and Many-objective Optimization , Linkage Learning , Problem Decomposition , and Hybridization of metaheuristic methods, with applications in industrial process planning, network optimization, and real-world decision-making. He leads significant research projects such as greybox optimization (2023–2026) and multi-objective evolutionary methods with linkage learning (2021–2026), demonstrating sustained grant support and scientific leadership. The analysis of his recent publications reveals a consistent trend in advancing parameter-less evolutionary algorithms, empirical linkage learning, and scalable distributed implementations using containerization (e.g., Docker). His work bridges theoretical algorithm development with practical applications in optical networks, manufacturing, and distributed computing. His scientific honors include: Best Paper Nomination at the Genetic and Evolutionary Computation Conference (GECCO) Associate Editor, IEEE Transactions on Evolutionary Computation (since January 2023) Prof. Przewoźniczek supervises graduate students as a thesis advisor and collaborates extensively with researchers such as Piotr Dziurzanski, Marcin Komarnicki, Krzysztof Walkowiak, and Leandro Soares Indrusiak. He is actively involved in major conferences like GECCO, CEC, and CORES, and his work is indexed in Google Scholar, ResearchGate, and ORCID (0000-0003-2446-6473). He is affiliated with research groups focusing on Machine Learning, Computer Networks, Advanced Data Analysis, and Metaheuristics, indicating a strong collaborative and team-based research environment.

• Urban Mobility
• Smart Cities
• Transportation Policy
• +7 more

Sanaz Nikfalazar is a Teaching Fellow in the Department of Human Centred Computing at Monash University. Her work spans urban mobility, data science, and transportation policy, with a focus on smart cities and sustainable urban development. Her research interests include urban mobility , data imputation , autonomous vehicles , and informal housing practices in Australian cities. She applies computational methods such as decision trees and fuzzy clustering to real-world urban challenges, contributing to policy-relevant insights in transportation and city planning. The recent trend in her publications shows a strong interdisciplinary focus, combining computer science techniques with urban policy analysis. Her work contributes to UN Sustainable Development Goals related to sustainable cities and communities. Scientific contributions include several peer-reviewed articles and conference papers. Notable outputs include research on city mobility indices, missing data imputation, and transport policy for autonomous vehicles. She has collaborated with researchers across institutions on topics related to urban informality and transportation systems. While no formal advising or grant information is available, her consistent research output indicates active engagement in academic and applied research projects.

• Environmental Decision Making
• Climate Risk
• Adaptation and Resilience
• +17 more

Dr Simon Jude is a Reader in Environmental Decision Making at the Cranfield Environment Centre, Cranfield University. His research integrates environmental science, engineering, computing, and policy to develop tools and frameworks for improved decision-making under uncertainty. He leads the Cranfield University Urban Observatory (UKCRIC/EPSRC) and previously led the Decision Science Group and the NERC/ESRC DREAM Centre for Doctoral Training. He is a member of the Office for Environmental Protection's College of Experts, highlighting his influence in environmental governance. Research Interests: Climate risk, adaptation, and resilience in infrastructure and communities Development of urban observatories and living laboratories using sensor technologies Marine environmental risks related to climate change and renewable energy Natural capital accounting and environmental informatics Transdisciplinary and stakeholder-led environmental decision science His recent publications (2019–2024) show a strong trend in applying data analytics, machine learning, and network modeling to urban water systems, climate adaptation governance, and marine spatial planning. He frequently collaborates with interdisciplinary teams across environmental science, engineering, and social science domains. Scientific Leadership and Grants: Lead, Cranfield University Urban Observatory (UKCRIC/EPSRC) Former Centre Manager, DREAM CDT (NERC/ESRC) Former Leader, Decision Science Group, Cranfield Environment Centre Member, Office for Environmental Protection’s College of Experts He has secured and led major research initiatives funded by UKRI councils (NERC, ESRC, EPSRC) and collaborates with key agencies including DEFRA, Environment Agency, and water utilities. While no formal awards are listed, his leadership roles reflect recognition of his scientific contributions. He advises PhD students through doctoral training programs and supervises research on climate adaptation, risk, and urban systems. He has no listed part-time status and is actively engaged in research, policy, and academic leadership.

• Energy Harvesting
• Sensor Electronics
• Neural Probes
• +3 more

Prof. Dr.-Ing. Yiannos Manoli is the Director of the Fritz Huettinger Chair of Microelectronics at the University of Freiburg, Germany. He holds a Dr.-Ing. from Gerhard Mercator Universität and has held academic positions at the University of Saarland and the Fraunhofer Institute. His research focuses on energy-harvesting electronics, sensor systems, and biomedical applications. He has published over 400 papers and contributed to innovations in low-power mixed-signal CMOS circuits. Education: B.A. (summa cum laude) in Physics and Mathematics, Lawrence University (1978) M.S. in Electrical Engineering and Computer Science, University of California, Berkeley (1980) Dr.-Ing. in Electrical Engineering, Gerhard Mercator Universität (1987) Research Interests: His work spans energy harvesting for IoT and automotive systems, analog-to-digital converters, neural probes, and biomedical applications. Notable contributions include advancements in ultra-low-voltage circuits and sensor read-out systems. Recent projects involve tactile sensors with machine-learning-based processing and neural probe interfaces. Awards & Recognition: Best Paper Award at ESCCIRC (1988) MSE-2007 Award for SpicyVOLTsim Excellence in Teaching Award (2010) IEEE Life Fellow Leadership & Service: Served as Dean of the Faculty of Engineering (2008–2015) and co-director of the Hahn-Schickard-Institut. Contributed to the establishment of the INATECH department. Active in editorial roles for IEEE journals and conferences like ISSCC and IEDM. Labs & Collaborations: Lead the Microelectronics Professorship at IMTEK, collaborated with industry partners like Intel and Motorola, and engaged in international research networks including the Thomas Mann House residency (2018).

K.R. AptView profile

Professor

• Computer Science
• Game Theory
• Network Optimization
• +7 more

Krzysztof Apt is a Professor at the University of Amsterdam and a CWI-Fellow. His academic work focuses on theoretical computer science, game theory, and formal verification of algorithms. He is affiliated with the Networks and Optimization department at the University of Amsterdam. Research Interests His research spans multiple fields including: Logic and formal verification Game theory and mechanism design Network optimization algorithms History of computer science Programming methodology Distributed systems Scientific Contributions His recent publications (2021–2025) cover loop termination proofs, historical analyses of computer science pioneers, coordination games on graphs, and foundational work in Hoare logic. The research combines theoretical rigor with applications in machine learning and distributed systems. Awards and Grants NWO TOP grant (2013) for combining machine learning with game theory

• Electrical Power Systems
• Modeling and Simulation of Power Systems
• Voltage Stability
• +7 more

Dr. Francisco Miguel Echavarren Cerezo is an Associate Professor in the Department of Electrical Engineering and Systems at the Higher Technical School of Engineering, Comillas Pontifical University, and a key researcher at the Technological Research Institute (IIT). He has been with the institute since July 2001 and holds a PhD in Engineering and Computer Science from the same university, awarded in 2006. Education: PhD in Engineering and Computer Science, Comillas Pontifical University, 2006 Industrial Engineering (Electrotechnology), Comillas Pontifical University, 2001 His research focuses on the modeling, analysis, and simulation of electrical power systems , with particular emphasis on voltage stability, constraint management, protection systems, and the integration of renewable energy sources. His work bridges theoretical development and practical applications in modern power grids, including AC/DC hybrid systems and island networks. The most recent publications highlight a strong trend in advanced power flow algorithms , stability analysis in hybrid AC/DC systems , and integration of renewable energy . His work spans both algorithmic innovation (e.g., holomorphic embedding methods) and real-world applications in the Spanish and European power systems. Scientific Contributions: Active researcher in EU and nationally funded projects (e.g., TED2021-130610B-C22, PID2021-125628OB-C21) Principal investigator in industrial projects for Iberdrola, Endesa, Repsol, and Red Eléctrica de España Organizer of major conferences like ICREPQ'23 He has supervised multiple PhD students and delivered training courses for industry professionals on smart grids, HVDC, and power system simulation tools. His work is regularly published in high-impact journals such as IEEE Transactions on Power Systems and Electric Power Systems Research . Laboratories and Research Teams: Active member of the power systems research group at IIT-Comillas Collaborates with institutions like REE, Iberdrola, and international universities Involved in EU-funded initiatives on grid resilience and renewable integration

• Artificial Intelligence
• Machine Learning
• Data Science
• +3 more

Dr. Atie Kia is a Lecturer in Computing Technologies at the School of Science, Computing and Emerging Technologies, Swinburne University of Technology. Her work bridges artificial intelligence, sustainable development, and inclusive education, with a focus on impactful research and innovative teaching. PhD in Engineering (Machine Learning and Data Science) from the University of Melbourne Master of IT (Network & Security) from Deakin University Bachelor of Applied Computing from Southern Cross University Her research applies AI and data science to social good, particularly sustainable development and SDG prioritization. She has developed frameworks for SDG resource allocation, presented to the United Nations Committee of Experts on Global Geospatial Information Management (UN-GGIM), and focuses on inclusive education for neurodiverse learners. Dr. Kia has published in top-ranked journals, including the Best Journal Paper Award (IEGRC 2023) , and her work on SDG prioritization has garnered significant citations. Her research spans machine learning, policy analysis, and systems thinking. Teaching Excellence Acknowledgment (2019) Student Experience Award (2020) Star Award for Student Experience, Accountability & Value Creation (2022) Recipient of multiple academic honors at Deakin University She has supervised over 80 student IT projects (36 Master’s and 50 Bachelor’s) and collaborates with industry partners on real-world challenges. Her teaching spans software development, cybersecurity, data science, and game programming.

• Business Analytics
• Computer Information Systems
• Predictive Analytics
• +5 more

Richard V. McCarthy is a Professor of Business Analytics and Information Systems and Associate Dean for the School of Business at Quinnipiac University. His work bridges academia and industry through predictive analytics, data management, and virtual team pedagogy. BS, Central Connecticut State University MBA, Western New England College DBA, Nova Southeastern University Dr. McCarthy's research focuses on healthcare analytics, financial fraud detection, and data science pedagogy. He applies machine learning to prevent post-surgical falls, combat money laundering, and enhance educational strategies. His publications emphasize predictive modeling techniques, including decision trees, neural networks, and regression analysis, across healthcare, finance, and information systems domains. Computer Educator of the Year, International Organization for Computer Information Systems (2019)

• Machine Learning
• Computer Science
• Engineering
• +7 more

David Côté is an academic researcher with significant contributions to the application of machine learning and artificial intelligence in network operations and optical communications. His work focuses on automating network decision-making, fault localization, and predictive maintenance using advanced AI techniques. He has collaborated with researchers like S. Shirmohammadi and Ayşe Rumeysa Mohammed. Research Interests Machine learning for network anomaly detection Reinforcement learning in network automation Deep learning for video QoE forecasting Optical network monitoring Stateless action recommenders Publications Overview His research spans from 2018 to 2023, showcasing applications of supervised, unsupervised, and deep learning in real-world network environments. Key areas include network automation, fault detection, and predictive analytics.

• Artificial Intelligence
• Machine Learning
• Data Science
• +4 more

Laura E. Brown is Associate Professor of Computer Science at Michigan Technological University, also serving as Associate Dean for Data Science Initiatives in the College of Computing and Director of the university’s M.S. and B.S. Data Science programs. She is a core member of the Institute of Computing and Cybersystems (ICC), the Ecosystem Science Center (ESC), and the Center for Agile Interconnected Microgrids (AIM). Education Ph.D., Biomedical Informatics — Vanderbilt University M.S., Biomedical Informatics — Vanderbilt University M.S.E., Electrical Engineering & Computer Science — University of Michigan B.S., Engineering — Swarthmore College Research Interests Laura’s scholarship spans artificial intelligence, machine learning, and data science, with theoretical advances tightly coupled to high-impact applications in energy systems (microgrids, power analytics), health and biomedical informatics, and computer science pedagogy. She is passionate about translating algorithmic innovations into real-world deployments that improve sustainability, clinical decision-making, and equitable education. A second major strand of her work centers on educational innovation: developing automated code-critiquing systems, studying their influence on student self-efficacy—especially for women in engineering—and leading campus-wide initiatives such as Carpentries workshops and ICPC programming competitions. Publication Trends Brown’s 2018–2025 publication record reveals sustained productivity in three overlapping domains: (1) AI/ML methodology—hardware prefetching, neural architectures, transfer learning; (2) energy analytics—forecasting coincident peak load, solar irradiance, battery degradation; and (3) computing education—automated feedback, gender studies, curriculum design. Recent emphasis has shifted toward educational technology, with multiple 2023–2025 papers on code critiquers and their psychological impact. Research Funding & Grants NSF Revolution through Evolution: $699 K, PI (2015–2019) DoD Distributed Agent-Based Management of Agile Microgrids: ~$1 M, co-PI (2013–2017) NSF Adaptive Memory Resource Management in a Data Center: $299 K, PI (2014–2017) NSF Microdevice for Rapid Blood Typing without Reagents: $300 K, co-PI (2016–2019) Google Explore CS Research Workshops: $18 K PI / $35 K co-PI (2018–2020) Student Engagement & Leadership Laura co-advises Women in Computing Science (WiCS) , organizes Michigan Tech’s ICPC regional site, and has led Google-sponsored undergraduate research workshops, Summer Youth Programs, and multiple Carpentries training events.

Hugo BuddelmeijerView profile

Researcher

• Astronomy
• Astrophysics
• Scientific Visualization
• +6 more

Hugo Buddelmeijer is an active Researcher at the Faculty of Science and Engineering , University of Groningen , affiliated with the Kapteyn Astronomical Institute . He specializes in astronomical data management, scientific visualization, and cosmological surveys. Research Interests : Galaxy evolution, photometric redshifts, data lineage, and visualization techniques for large-scale surveys like KiDS and Euclid. Technical Expertise : Development of simulation tools (e.g., ScopeSim), data catalog systems, and query-driven visualization frameworks. Contributions : Key participant in the KiDS and Euclid missions, focusing on optical/near-infrared imaging and dark matter analysis. His work emphasizes integrating machine learning with astronomical data. Collaborations : Extensive international partnerships with institutions like the European Southern Observatory and projects involving Very Large Telescope observations.

• Statistical Machine Learning
• Bayesian Nonparametrics
• Mixture Models
• +13 more

Keefe Murphy is a Lecturer in Statistics within the Department of Mathematics and Statistics at Maynooth University , Faculty of Science & Engineering, and is affiliated with the Hamilton Institute . He is an active researcher in statistical machine learning, Bayesian nonparametrics, and clustering/classification of complex, high-dimensional data. Education: PhD in Statistics, University College Dublin MSc in Statistics, University College Dublin BSc in Economics & Mathematics, University of Limerick Research Interests: His work centres on developing and extending statistical methodologies for supervised and unsupervised learning , with emphasis on mixture models, latent variable models, Bayesian nonparametrics, and computational statistics . He actively contributes novel algorithms and software implementations, including the R packages IMIFA , MoEClust , and MEDseq available on CRAN. Current projects include extensions to Bayesian Additive Regression Trees (BART) , handling missing data , modelling multivariate count data , and variable selection in model-based clustering. Publication Profile: His recent publications (2021–2025) demonstrate a clear trajectory in advancing Bayesian machine learning methods, with contributions to Gaussian process BART models , sparse factor analysis , and educational data mining . Collaborative work spans learning analytics and multi-omic prostate cancer biomarker discovery , illustrating broad interdisciplinary impact. Awards & Recognition: Distinguished Dissertation Award (The Classification Society, 2020) Service & Advising: He serves as Associate Editor for Statistical Analysis and Data Mining and on departmental committees (Course Committee, PR Committee). He has successfully supervised PhD student Mateus Maia (graduated 2024) and currently teaches modules such as Advanced R Programming , Introduction to Data Science , and Nonparametric Statistics . Labs & Collaborations: He is affiliated with the Hamilton Institute , which fosters interdisciplinary research in applied mathematics and statistics, providing a collaborative environment for advancing computational and methodological statistics.