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Dukka KC is an Adjunct Professor in the Department of Computer Science at Michigan Technological University and a member of the Institute of Computing and Cybersystems (ICC). His research focuses on computational data science with applications in bioinformatics, computational biology, and health informatics, particularly leveraging machine learning and high-performance computing to develop predictive tools for protein and nucleic acid modifications. Ph.D., Informatics, Kyoto University, 2006 M.Inf., Informatics, Kyoto University, 2003 B.Eng., Computer Science, Kyoto University, 2001 Research interests include: Developing GPU-accelerated bioinformatics tools (e.g., GPU-I-TASSER) Predicting post-translational modification sites using deep learning (e.g., DeepNGlyPred, DeepRMethylSite) Machine learning approaches for malonylation, succinylation, and sulfenylation site prediction High-throughput analysis of next-generation sequencing data Interdisciplinary projects in biometrics, cybersecurity, and disaster prediction Recent publications highlight a strong trend in applying deep learning to protein structure and function prediction, GPU-parallelization for computational efficiency, and machine learning for both biological and cybersecurity applications. The lab also emphasizes cross-domain collaborations and the development of scalable bioinformatics workflows. Grants and funding include projects like the President's Convergence Science Initiative (PI, $300K), NSF III grants for protein function prediction ($111K), and multi-institutional collaborations on biometric test-beds and synthetic biology research. The KC Lab at Michigan Tech specializes in integrating computational data science with molecular biology, focusing on protein/RNA/DNA modification site prediction and contributing to large-scale proteome analysis through machine learning-driven pipelines.

• Bioinformatics
• Data Science
• Machine Learning
• +4 more

Elisa Perrone is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology. Her research focuses on dependence modeling, copula theory, and their applications in fields such as public transport analysis, environmental risk assessment, and renewable energy forecasting. Academic Rank: Assistant Professor University: Eindhoven University of Technology (TU/e) Department: Mathematics and Computer Science Elisa’s work explores discrete copulas, zero-inflated data, optimal experimental design, and uncertainty quantification. She has contributed to modeling dependence structures in complex datasets, particularly in transportation systems and climate science. Recent research outputs highlight copula-based statistical post-processing for weather forecasts, analysis of multi-way contingency tables, and uncertainty reduction in LED health management. Her publications span top-tier journals and conferences in statistics and applied mathematics. Scientific Award : Second Best Poster Presentation Award (2015) Elisa actively organizes workshops like the Eurandom Workshop on Dependence Modeling and contributes to editorial activities. She teaches courses on linear statistical models, regression models, and dependence modeling.

• Statistics
• Mathematical Statistics
• Dependence Modeling
• +4 more

Tyler Schartel serves as an Associate Research Scientist in Conservation Ecology at the Illinois Natural History Survey, University of Illinois Urbana-Champaign. His research integrates computational methods with field ecology to address freshwater conservation and invasive species challenges. Research interests span: Freshwater mussel biodiversity and ecosystem health Machine learning applications for species distribution modeling Invasive pest risk assessment (vineyard moths, Xylella vectors) Protected lands effectiveness for conservation targets His work emphasizes algorithmic rigor in ecological informatics and climate-driven disease forecasting. Recent publications reveal strong trends in computational ecology: improving machine learning stability for cross-dataset species-richness modeling (2025), optimizing Maxent background selection for freshwater systems (2024), and developing climate-informed risk frameworks for agricultural pests (2022-2023). Key methodological contributions focus on predictor discriminability metrics and vector tolerance thresholds.

• Freshwater Ecology
• Conservation Biology
• Invasive Species Management
• +3 more

Kevin Vinsen is a Senior Research Fellow at the University of Western Australia, working in the Data Intensive Astronomy (DIA) Program of the International Centre for Radio Astronomy Research (ICRAR) since 2009. He is also affiliated with the UWA Defence and Security Institute and holds an ORCID ID of 0000-0001-5332-3784. His work focuses on translating ICRAR software capabilities into practical industry applications across diverse domains. His research interests include: Peta-scale systems High-performance Computing Machine Learning applications in multiple fields Wave and weather forecasting Digital Assistive Technologies Agricultural applications of ML Large language models Vinsen heads the Translation and Impact work of the DIA team and leads the development of Machine Learning systems. His current projects include ML for wave forecasting on the NW shelf, wind and temperature forecasting, honey traceability and provenance, and digital assistive technology for people with disabilities. His work contributes to UN Sustainable Development Goals related to industry, oceans, agriculture, food, disability, and defense. His research output demonstrates a strong trend toward applying machine learning techniques to solve real-world problems across astronomy, environmental science, agriculture, and disability support. This interdisciplinary approach showcases the versatility of his computational expertise across scientific and social domains. Vinsen has an h-index of 11 with 621 citations across 33 research outputs. As the ICRAR/UWA Summer Studentship Co-ordinator, he mentors emerging researchers and contributes to building research capacity. His collaborative network spans multiple institutions and research areas, reflecting his ability to bridge academic research with practical applications.

• Machine Learning
• High-performance Computing
• Peta-scale systems
• +7 more

Dr. Jose Escribano is a Lecturer in Aviation & Logistics at the Department of Civil and Environmental Engineering within the Faculty of Engineering at Imperial College London. His research focuses on humanitarian logistics optimization, AI-driven airspace management, and urban resilience strategies. He holds a First Class Honours bachelor’s degree (2015) and a PhD (2021) from Imperial College London. Dr. Escribano is affiliated with the Centre for Transport Engineering and Modelling and the Transport Systems and Logistics Project D-Risk SHIFT. His academic qualifications include a BEng in Engineering and a PhD in Civil Engineering, both from Imperial College London. His professional affiliations include the Institution of Civil Engineers, Chartered Institute of Logistics and Transport, and the American Institute of Aeronautics and Astronautics. He has received the 2023 Transportation Research Board Best Paper Award and a JSPS Fellowship for urban evacuation modelling. Dr. Escribano’s research integrates stochastic modelling, machine learning, and simulation to address challenges in humanitarian response, UAV coordination for disaster relief, and airspace safety. His work emphasizes endogenous value-of-information analysis and the application of cutting-edge technologies to enhance societal resilience. He has collaborated with the United Nations World Food Programme on UAV deployment models for humanitarian contexts. His recent publications span topics like air traffic network resilience, autonomous vehicle optimization, and last-mile delivery mechanisms. He advises doctoral candidates in transportation systems, logistics, and air traffic management, offering opportunities for PhD research in these domains.

• Humanitarian Operations
• Air Traffic Management
• Logistics Optimization
• +7 more

Indranil Bose is a Distinguished Professor and Head of the Area of Excellence in AI, Data Science, and Business at NEOMA Business School, with a Ph.D. in Management from Purdue University. He specializes in business analytics, big data, and digital transformation, leveraging expertise from prior roles at IIM Calcutta, University of Hong Kong, University of Florida, and UT Arlington. Education: Ph.D. in Management (Purdue University, 1997) M.S. (Purdue University) M.S. (University of Iowa) B.Tech (IIT Kharagpur) Research Focus: His work spans predictive analytics, social media impact on business, innovation management, cybersecurity, fintech, and AI ethics. Key themes include consumer behavior in digital ecosystems, AI-driven decision systems, and ethical implications of emerging technologies. Publication Trends: Recent articles emphasize AI ethics, digital misinformation, and predictive modeling in finance/healthcare, reflecting a focus on societal impacts of technology. Methodologies include deep learning, sentiment analysis, and causal inference. Awards & Recognition: Winner, EFMD 2017 Case Competition Ranked 29th globally for Information Systems research productivity (2005–2014) Editorial Leadership: Senior Editor for Decision Support Systems and Pacific Asia Journal of the AIS ; Associate Editor for Information & Management , Journal of the AIS , and Communications of the AIS .

• Business Focused Predictive Analytics
• Social Media for Business Transformation
• Management of Innovation
• +5 more

Henrik Myhre Jensen is a Professor at the College of Engineering , Aarhus University, specializing in Mechanics of Materials , Solid Mechanics , and Mechanical Engineering . His research focuses on fracture mechanics, composite materials, and computational modeling of structural behaviors. Research Focus Fracture mechanics in composites and layered materials Computational modeling of kink band propagation Surface wear and coating technologies Ultrasound imaging applications in mechanical systems Notable Contributions Henrik has contributed to understanding crack propagation in cantilever beams, developed numerical methods for simulating delamination in composites, and explored buckling instabilities in solids. His recent work connects machine learning (holomorphic neural networks) to traditional fracture mechanics problems. Key Projects MAGFLY (2017-2021): Magnets for Flywheel Energy Storage InnoVacc (2009): Pressure Testing of Vacuum Chambers Simulation of composite structures (2011-2020): Micro-mechanical modeling

• Mechanics of Materials
• Solid Mechanics
• Mechanical Engineering
• +4 more

Jeff Sadler is an Assistant Professor in the Department of Biosystems & Agricultural Engineering at Oklahoma State University, where he also serves as an Extension Specialist for Water Resources with OSU Extension. He leads the WaDE (Water Data and Education) Lab, focusing on data science and machine learning applications in water resources. Education: PhD in Civil and Environmental Engineering, University of Virginia (2019) MS in Civil Engineering, Brigham Young University (2015) BS in Civil Engineering, Brigham Young University (2013) Research Interests: Jeff’s research lies at the intersection of data science and water resources. He specializes in machine learning, particularly physics-guided and process-aware deep learning, for modeling stream temperature, water quality, flood dynamics, and hydrological forecasting. His work emphasizes real-time decision support, reproducible modeling, and integrating domain knowledge into data-driven systems. Recent Research Trends: His recent publications demonstrate a strong focus on advanced deep learning architectures (e.g., graph neural networks, recurrent models), data assimilation, multi-task learning, and surrogate modeling for environmental systems. Applications center on the Delaware River Basin and coastal Virginia, with implications for climate change adaptation and infrastructure resilience. Scientific Awards: No awards explicitly listed in the provided text. Advising and Grants: Jeff mentors graduate students and supervises master's and doctoral research. He is actively funded through multiple grants from the USDA, NOAA, and USGS, supporting projects in water quality monitoring, rural health, evapotranspiration forecasting, and integrated hydrological modeling. Labs and Teams: He leads the WaDE Lab, which develops data-driven tools for water resource education and management. He has collaborated extensively with researchers from the U.S. Geological Survey, University of Virginia, and other institutions on cyberinfrastructure, reproducible modeling, and environmental machine learning.

• Water resources engineering
• Surface water hydrology
• Data science
• +7 more

Andreas Groll is a Professor at the Technical University of Dortmund, affiliated with the Department of Statistical Methods for Big Data under the Faculty of Statistics. His research focuses on variable selection, regularization techniques in generalized linear models, categorical data analysis, and sports statistics, particularly predicting international soccer and tennis tournaments. He leads a working group including researchers like Dr. Daniel Horn and Dr. Rouven Michels. Key research areas include semiparametric regression and event data analysis. Recent work explores machine learning applications in sports analytics and healthcare, such as predicting hospital readmissions and modeling environmental data. Groll has published extensively in journals like Journal of Quantitative Analysis in Sports and Statistical Modelling .

• Variable Selection and Regularization
• Generalized Linear Models
• Event Data Analysis
• +3 more

Jonathan L. Goodall is a Professor of Civil and Environmental Engineering and Director of the Link Lab at the University of Virginia. His research focuses on hydroinformatics, urban hydrology, and flood modeling, leveraging data science and cyber-physical systems to enhance resilience in coastal urban environments. He leads efforts in reproducible environmental modeling through integration of platforms like HydroShare, emphasizing open science and computational workflows. His work includes advancing machine learning techniques for real-time flood forecasting, integrating IoT and sensor networks for smart city applications, and assessing climate change impacts on coastal communities. Notable contributions involve developing surrogate models for flood prediction in Norfolk, VA, and studying compound flooding effects using hydrodynamic modeling paired with crowdsourced data. Collaborations span universities, national labs, and agencies like CUAHSI, focusing on environmental data interoperability and infrastructure resilience. Goodall’s research often addresses socio-technical challenges in urban flood management, combining engineering solutions with community engagement strategies. His lab explores reinforcement learning for real-time stormwater control, IoT education initiatives, and geospatial tools for flood vulnerability analysis. Projects frequently involve interdisciplinary teams and emphasize reproducibility through containerized environments and metadata standards.

• Hydroinformatics
• Urban Hydrology
• Flood Modeling
• +3 more

Kwaku Ohene-Asare is a Lecturer in Business Analytics at De Montfort University, UK, within the School of Leadership, Management and Marketing. He holds a PhD in Operational Research and Management Science from the University of Warwick, an MSc in Economics and Finance (with distinction) from Loughborough University, and a BSc in Economics (first-class honors) from the University of Ghana-Legon. He also completed a certificate in Decision Science and Machine Learning at MIT, USA. He has held visiting professorships at Warwick University and Stellenbosch University and plays a senior lecturer role at the University of Ghana. His educational background includes: PhD in Operational Research and Management Science, University of Warwick, UK (2012) MA in Decision Science and Machine Learning, MIT, USA MSc in Economics and Finance, Loughborough University, UK (Distinction) BSc in Economics, University of Ghana-Legon (First Class) PGCAP (Part 1), University of Warwick, UK (2009) Certificate in Nonparametric & Bootstrap Methods, Sapienza University of Rome, Italy (2012) Kwaku's research interests span business analytics, management science, artificial intelligence, data science, machine learning, economic efficiency, productivity analysis, data envelopment analysis (DEA), stochastic frontier econometrics, and their applications in energy, finance, insurance, and credit unions. He has developed a research-based DEA course at the University of Ghana and pioneered the advanced quantitative research methods course for PhD students since 2015. His work integrates cutting-edge computational techniques and econometric modeling to address real-world economic and business challenges. The recent trend in his publications shows a strong focus on efficiency and productivity analysis across sectors—particularly in energy, banking, and insurance—using advanced non-parametric and parametric methods. He frequently applies DEA, Malmquist indices, and stochastic frontier models to assess performance in African and ECOWAS economies, with a growing emphasis on sustainability, undesirable outputs, and dynamic efficiency. His work bridges theoretical rigor with practical policy implications. His scientific awards include: Global Leadership Award (2021) DFID Shared Scholarship Scheme Award (2004) Doctoral Research Scholarship, Warwick Business School (2007) He has received multiple research grants, primarily from the University of Ghana Business School (UGBS), as Principal Investigator, including projects on data science and machine learning, energy productivity, banking efficiency, and multinational operations. He has supervised PhD students through course development and research mentorship. His consultancy work includes efficiency analysis for the National Petroleum Authority, Ghana, and market entry feasibility studies for international firms. He is affiliated with the Centre for Enterprise and Innovation (CEI), the Institute for Sustainable Economics, and the Institute of Energy and Sustainable Development (IESD) at DMU, where he contributes to interdisciplinary research on sustainable economic development. He is an active member of professional societies including the Operational Research Society (UK), INFORMS, Association of European Operational Research Societies, British Academy of Management, Productivity Analysis Research Network (USA), and the Economic Society of Ghana.

• Business Analytics
• Management Science
• Artificial Intelligence
• +16 more

Lin Cai is a Professor in the Department of Electrical & Computer Engineering at the University of Victoria, Canada. She holds prestigious fellowships including NSERC Steacie, IEEE, CAE, and Royal Society of Canada. Her research focuses on wireless communications, networking, and mobile computing, with emphasis on protocols for multimedia and IoT systems. She has led projects in vehicular networks, UAV-assisted systems, and federated learning for edge intelligence. Dr. Cai has advised over 20 students, many of whom have received awards and prominent roles in academia and industry. She has authored numerous high-impact papers, secured grants from NSERC, CFI, and industry partners, and serves in leadership roles at IEEE and educational institutions. Notable contributions include work on congestion control, network security, and autonomous systems. Education: BEng (Nanjing U. of Sci. & Tech.), MASc/PhD (University of Waterloo) Affiliations: IEEE Vehicular Technology Society Board of Governors, IEEE ComSoc Distinguished Lecturer Awards: 2020 IEEE N2Women 'Star in Networking', RSC Fellow 2024, Best Paper Awards (ICC 2008, WCNC 2011) Research Labs: Connected Autonomous Vehicles (CAV) Lab, AI-driven Networking Group Her work integrates networking, AI, and control theory to address challenges in 6G, IoT, and smart transportation. She actively promotes diversity through initiatives like the 'Riko-chan' STEM manga series.

• Wireless Communications
• Networking
• Mobile Computing
• +7 more

Shixiang (Woody) Zhu is an Assistant Professor in Data Analytics at the Heinz College of Information Systems and Public Policy, Carnegie Mellon University. He holds a PhD in Machine Learning from Georgia Institute of Technology (2022) and B.S./M.S. in Computer Science from Beijing University of Posts and Telecommunications (2017). His research bridges machine learning, operations research, and statistics, focusing on sequential modeling, human-AI collaboration, and energy systems operations. He has received awards including the IEEE Power & Energy Society Best Paper Award (2025) and was a finalist for the INFORMS Wagner Prize (2021). Education : PhD in Machine Learning, Georgia Tech (2017–2022) B.S./M.S. in Computer Science, BUPT (2010–2017) His research emphasizes spatio-temporal data analysis , decision making under uncertainty , and applications to energy systems, healthcare, and public policy. Notable projects include optimizing police zone design (Wagner Prize finalist) and enhancing grid resilience through robust optimization. He actively collaborates with institutions like Argonne National Laboratory and NSF-funded projects. Awards : Best Paper Award, IEEE Power & Energy Society (2025) Gen-AI Fellows (2024) Finalist, INFORMS Wagner Prize (2021) Advising & Grants : Advises PhD students Zekai Fan, Wenbin Zhou, and others Recipient of Block Center Seed Grant (2024), NSF funding (2024) His work spans energy resilience, public policy optimization, and causal inference in social systems. He co-leads the INFORMS Data Mining Society and reviews for top journals like Operations Research and Management Science.

• Machine Learning
• Data Science
• Operations Research
• +7 more

Gonzalo Nápoles is an Assistant Professor at Tilburg University's School of Humanities and Digital Sciences, Department of Cognitive Science and AI. He holds a Doctoral Degree in Computer Science from Rough Cognitive Networks (2014–2017). His research focuses on AI applications in cognitive modeling, pattern classification, and neural networks with interdisciplinary applications in healthcare, finance, and social sciences. Key research areas include Fuzzy Cognitive Maps, data augmentation techniques for neuroimaging, and interpretable machine learning systems. He actively contributes to UN Sustainable Development Goals related to education and innovation. Recent work explores AI ethics, financial risk assessment using dynamic networks, and sensory processing disorder analysis through neural networks. Education: Doctoral Degree in Computer Science, 2017 (Thesis: Rough Cognitive Networks) Prize-winning research includes Best Paper Awards at CIARP 2021 and IWAIPR 2023. He collaborates internationally, hosting academic visitors and serving on multiple PhD committees. Current projects involve stock prediction using graph neural networks and fMRI data augmentation methodologies. Awards: Best Paper Award - CIARP 2021 Best Paper Award - IWAIPR 2023 Nápoles advises on PhD theses in cognitive science and AI applications. His work bridges theoretical advancements with practical implementations in healthcare, finance, and urban systems.

• Artificial Intelligence
• Machine Learning
• Cognitive Science
• +5 more

Eirik Valseth is an Associate Professor of Scientific Computing at the Norwegian University of Life Sciences (NMBU), Department of Data Science. He holds concurrent roles as a research associate at the Oden Institute, University of Texas at Austin, and an affiliated researcher at Simula Research Laboratory (Department of Numerical Analysis and Scientific Computing). His expertise lies in advanced finite element methods for PDEs with applications in flood modeling and hydropower systems. Current Affiliation: NMBU (Norwegian University of Life Sciences) Secondary Affiliations: Oden Institute (UT Austin), Simula Research Laboratory Research interests span numerical methods for challenging PDE systems, including: Stabilized finite element formulations Hurricane storm surge and riverine flood modeling Hydropower infrastructure analysis Computational mechanics and applied mathematics His recent publications (2024–2025) emphasize flood risk assessment (compound flooding, dam breaks, dredging impacts), advanced numerical methods (isogeometric analysis, stochastic finite elements, graph-grammar algorithms), and environmental applications (pollution transport, pathogen distribution, mosquito population dynamics after hurricanes). Key trends include cross-disciplinary integration of physics-aware machine learning and robust hydrodynamic simulation tools. Valseth's work extends to software development (e.g., WAVEx for spectral wave models, SWEMniCS for coastal circulation) and large-scale modeling frameworks like the ADCIRC unstructured mesh model for US coasts. Collaborative projects involve institutions such as University of Texas at Austin, Simula, and NOAA.

• Scientific Computing
• Numerical Analysis
• Applied Mathematics
• +5 more