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Robert LaubacherView profile

Research Fellow

• Collective Intelligence
• Artificial Intelligence
• Organizational Design
• +3 more

Robert Laubacher is a Research Fellow at the MIT Center for Collective Intelligence within the Sloan School of Management , focusing on how technological innovations like generative AI and large language models are reshaping organizational practices, work structures, and social patterns. Education: B.A. in American Studies, Northwestern University M.A., Doctoral Coursework in Modern History, Harvard University His research explores: Developing ontologies for work activities inspired by biological taxonomy Enhancing human creativity through AI collaboration frameworks Building collective intelligence systems for global challenges like climate change and sustainable development Identifying foundational elements of collective intelligence Historical analysis of IT's impact on employment relationships Recent publications focus on human-AI co-creation , crowdsourced problem-solving , and organizational adaptation to digital transformation. His work has been featured in Harvard Business Review , Sloan Management Review , and ACM conference proceedings.

Yves Le TraonView profile

Professor

• Machine learning systems
• Software testing
• Software security
• +8 more

Dr. Yves Le Traon is a Full Professor of Computer Science at the University of Luxembourg, where he serves as Vice-Director of the Interdisciplinary Centre for Security, Reliability and Trust (SnT). He leads the 25-member SerVal research group (SEcurity, Reasoning and VALidation), focusing on software testing, security, and data-intensive systems. Previously, he chaired the CSC Research Unit (2013-2016) and pioneered model-driven engineering at INRIA. PhD and engineering degree in Computer Science from Institut National Polytechnique, Grenoble (1997) Former Associate Professor at University of Rennes (1998-2004) His research spans three main areas: innovative software testing and repair , Android security through static analysis and machine learning , and robust machine learning system design . Collaborations include industry leaders like PayPal, CREOS, and Cebi in fintech, smartgrid, and industry 4.0 domains. Awarded IEEE Fellow (2022) and Facebook Testing & Verification Research Award (2019) , he chairs editorial boards for STVR, SoSym, and IEEE Transactions on Reliability. His team has produced 20+ PhD graduates including Li Li (Monash University), Donia El Kateb (European Investment Bank), and Alexandre Bartel (SnT Research Associate). Commercial impact includes co-founding Datathings for runtime AI decision systems.

Lorenzo StriginiView profile

Professor

• Dependability Assessment
• Fault Tolerance
• Defense in Depth
• +5 more

Lorenzo Strigini is a Professor of Systems Engineering at City St George's, University of London , where he has been affiliated since 1995 and served as Director of the Centre for Software Reliability from 2012–2024. His research focuses on dependability assessment , fault tolerance , and defense in depth for safety, security, and reliability in computer-based and socio-technical systems. He has also explored high-speed networking during his earlier career at the Italian National Research Council (IEI-CNR) and as a visiting scientist at UCLA and Bell Communications Research.

• Power Systems
• Wind Power Integration
• Grid-Forming Converters
• +6 more

Peiyuan Chen is an Associate Professor at the Department of Electric Power Engineering, Chalmers University of Technology. He holds a B.Eng. from Zhejiang University (2004), an M.Sc. from Chalmers (2006), and a Ph.D. from Aalborg University (2010). His research focuses on power system operation and planning with wind power integration, emphasizing time series modeling, statistical analysis, and optimization. He contributes to projects on grid-forming converters, inertia estimation, frequency control, and renewable energy system stability. Research Interests: • Power Systems and Renewable Integration • Grid-Forming Converters and Stability Analysis • Time Series Modeling and Statistical Methods • Machine Learning for Energy Applications • Frequency Control and Synthetic Inertia Recent Publication Trends include studies on deep learning for heating load classification, wind turbine type optimization, fault ride-through capabilities, and inertia estimation in converter-dominated grids. His work bridges theoretical power system analysis with practical implementations in Nordic and European energy networks. Projects (2017-2024) include grants from the Swedish Energy Agency, Swedish Research Council (VR), and collaborations with institutions in Sweden, China, and Italy. Key areas: grid strength metrics, multiport converter applications, and citizen energy communities.

Yuè LiView profile

Professor

• Computational Biology
• Machine Learning
• Population Genetics
• +4 more

Yuè Li is a Professor in the Department of Computer Science at McGill University, where he leads the Li Lab focused on machine learning applications in genomics and healthcare. His research develops computational methods for analyzing electronic health records (EHR), single-cell multi-omics data, and population genetics. Dr. Li teaches core courses including Applied Machine Learning (COMP 551), Machine Learning in Genomics and Healthcare (COMP 565), and Computer Programming for Life Sciences (COMP 204). His research interests span: AI methods for computational biology and translational healthcare Multi-modal EHR integration and clinical topic modeling Time-series health forecasting and trajectory analysis Single-cell transcriptomics and epigenomics Polygenic risk modeling and causal variant inference Regulatory genomics and functional annotation integration Publications demonstrate strong focus on transformer architectures for healthcare forecasting, Bayesian methods for genomic inference, and neural topic models for clinical phenotyping. Recent work emphasizes foundation models for single-cell data and federated learning for EHR analysis. Scientific Awards: KDD HealthDay2022 Best Paper Award for seed-guided topic modeling Dr. Li mentors graduate students and postdoctoral researchers working on machine learning applications in biomedical domains. Current lab members include Master's students Bo-Hong Wang, Claris Gu, Neda Esfehani, and Ruilin Wang, along with postdoctoral researcher Dr. Jun Bai. The Li Lab operates within McGill's School of Computer Science, developing computational frameworks to integrate heterogeneous biomedical data for improved disease understanding and clinical decision support.

• Data Mining
• Bioinformatics
• Machine Learning
• +3 more

Domingo Savio Rodríguez Baena is a Professor at Pablo de Olavide University, affiliated with the Department of Computer Languages and Systems. His research focuses on data mining, bioinformatics, and computational biology, with a particular emphasis on biclustering algorithms, gene co-expression networks, and high-performance computing applications. PhD in Engineering, Data Science, and Bioinformatics (2012) from Pablo de Olavide University His work spans interdisciplinary domains, including recommender systems , livestock behavior analysis , and biological data interpretation . Recent articles highlight his contributions to multi-GPU optimization , ensemble learning , and historical database construction . Key collaborations include the DATAi Intelligent Data Analysis and DASE Data Analytics Science & Engineering research groups. He has developed tools like the CyEnGNet–App for gene network visualization and BIGO for gene enrichment analysis. Contact: dsrodbae@upo.es

Andrew ElliottView profile

Senior Lecturer

• Statistics
• Data Analytics
• Modeling in Space and Time
• +8 more

Dr Andrew Elliott is a Senior Lecturer in the School of Statistics at the University of Glasgow. His research bridges Statistics , Computational Statistics , and Machine Learning & AI , with applications in Social & Urban Studies and Imaging, Image Processing & Image Analysis . Education: Not explicitly detailed in the text. Dr Elliott's work focuses on modeling in space and time , synthetic data generation , and network analysis . His recent publications explore fairness constraints in AI , agent swarms , and temporal network modeling , reflecting interdisciplinary applications in urban analytics and environmental science. His publications from 2014–2024 span network science , machine learning , image analysis , and geospatial studies . Key trends include privacy auditing , counterfactual explanations , and deep learning for network time series . Dr Elliott supervises doctoral students and has advised Zhengduo Zhao and Weiyue Zheng . He actively contributes to research groups in Statistics & Data Analytics and Machine Learning . Notable collaborations include work with Mihai Cucuringu and Gesine Reinert .

Sirojan TharmakulasingamView profile

Lecturer

• Machine Learning
• Artificial Intelligence
• Data Science
• +6 more

Dr. Sirojan Tharmakulasingam serves as a Lecturer and Research and Development Coordinator at the Signals, Information & Machine Intelligence lab within the Faculty of Engineering at the University of New South Wales (UNSW) Sydney. His work bridges theoretical machine learning with practical applications in edge computing and high-performance systems. His research spans multiple cutting-edge domains including machine learning, artificial intelligence, data science, edge computing, and high-performance computing. Dr. Tharmakulasingam specializes in developing next-generation inference models by integrating machine learning, signal processing, mathematical modeling, and computing across diverse data types including images, video, audio, and quantum molecular data. His work has significant implications for scientific computing, telecommunications, and healthcare applications. Analysis of his publication trends reveals a strong focus on practical AI implementations, with increasing emphasis on edge computing solutions, quantum applications, and energy-efficient models. His recent work demonstrates progression from foundational machine learning techniques toward specialized applications in scientific computing and real-time systems. Dr. Tharmakulasingam holds a Doctor of Philosophy from UNSW Sydney and a Bachelor of Science of Engineering from the University of Moratuwa in Sri Lanka. His academic journey reflects a strong foundation in both theoretical and applied engineering principles. As Research and Development Coordinator for the Signals, Information & Machine Intelligence lab, he oversees critical research infrastructure and collaborations. His work location in Room 447 of the EE&T Building (G17) places him at the heart of UNSW's engineering research ecosystem, with access to the Mark Wainwright Analytical Centre's extensive facilities.

• Air Pollution
• Particulate Matter Source Apportionment
• Chemometrics
• +5 more

Philip K. Hopke, Ph.D., serves as an Adjunct Professor in both the Department of Public Health Sciences and Department of Environmental Medicine at the University of Rochester School of Medicine and Dentistry. With a distinguished career spanning over five decades since earning his Ph.D. from Princeton University in 1969, Dr. Hopke has established himself as a global leader in environmental chemistry and air pollution research. Ph.D. in Chemistry, Princeton University (1969) M.A. in Chemistry, Princeton University (1967) B.S. in Chemistry, Trinity College (1965) Dr. Hopke's pioneering work focuses on particulate matter source apportionment , where he developed innovative methodologies like dispersion-normalized positive matrix factorization (DN-PMF). His research integrates chemometrics , atmospheric chemistry , and environmental epidemiology to address critical air quality challenges worldwide. Recent projects span China's megacities, Iran's water crisis, and New York State's vehicle emission regulations, with particular emphasis on health impacts of source-specific pollutants. Analysis of his 15 most recent publications reveals consistent focus on advanced source apportionment techniques , health outcomes linked to air pollution , and policy evaluation using sophisticated statistical modeling. His work increasingly incorporates machine learning approaches while maintaining rigorous chemical characterization of environmental samples. Scientific Recognition Fellow of both the Air and Waste Management Association (2021) and American Association for Aerosol Research Recipient of the Lifetime Achievement Award in Chemometrics (2014) Albert Nelson Marquis Lifetime Achievement Inductee (2018) Multiple international awards including the Fissan-Pui TSI International Aerosol Research Award (2018) Dr. Hopke maintains an exceptionally active research program with collaborators across six continents, evidenced by his 2025 publications from China, Iran, Kazakhstan, and the United States. His work bridges fundamental atmospheric science with practical public health applications, particularly in evaluating regulatory impacts on air quality and health outcomes.

• Forecasting
• Time Series Analysis
• Inventory Management
• +5 more

Nikolaos Kourentzes is a Professor of Informatics at the University of Skövde , specializing in forecasting and operations research. His work bridges theoretical advancements in time series analysis with practical applications in supply chain management, tourism demand, and renewable energy forecasting. Academic Rank: Professor Department: Department of Information Technology Research Interests: His research focuses on hierarchical and temporal forecasting methodologies, integrating macroeconomic indicators into demand planning, inventory optimization, and machine learning applications. He explores forecast reconciliation, shrinkage estimators, and the role of expert judgment in predictive analytics. Recent Publications: Highlights include advances in hierarchical forecasting with leading indicators, probabilistic forecasts during crises like the pandemic, and complex smoothing techniques. His work spans journals such as Omega , International Journal of Forecasting , and European Journal of Operational Research . Collaborations: Kourentzes collaborates with researchers globally, including George Athanasopoulos, Rob Hyndman, and Robert Fildes, across domains like tourism analytics, tire industry forecasting, and public health modeling.

• Critical Care Medicine
• Risk Prediction in ICU
• Chronic Critical Illness
• +4 more

Jason Shahin is an Assistant Professor at the Department of Medicine , Faculty of Medicine and Health Sciences , McGill University . He serves as an Investigator at the Research Institute of the McGill University Health Centre (RI-MUHC) , specifically within the Translational Research in Respiratory Diseases Program . His academic and clinical work bridges critical care medicine and respiratory medicine at the McGill University Health Centre (MUHC) . Research Focus: Dr. Shahin specializes in risk prediction in intensive care units (ICU) , with particular emphasis on prediction tools for complex populations , including the chronic critically ill and potential organ donors . His work integrates clinical data science with translational approaches to improve ICU outcomes. Article Trends: His publications span critical care medicine , sepsis , mechanical ventilation , and organ donation . Recent studies focus on machine learning applications , probiotics in infection prevention , and blood conservation strategies in ICU settings. Key Collaborations: He contributes to large-scale trials like the LOVIT , PROSPECT , and FORECAST studies, collaborating with networks such as the Canadian Critical Care Trials Group and REVA Network .

• Wireless Communication
• Machine Learning
• Signal Processing
• +4 more

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.

Abolfazl SimorghView profile

Researcher

• Aircraft Trajectory Optimization
• Climate Change Mitigation in Aviation
• Robust Control
• +3 more

Abolfazl Simorgh is a researcher at Charles III University of Madrid's Department of Aerospace Engineering, specializing in climate-optimized aviation systems. His work bridges mathematical control theory with practical climate impact mitigation, focusing on robust trajectory optimization under environmental and operational uncertainties. He leads development of open-source tools for sustainable flight planning while contributing to major European aviation initiatives. Education: B.Sc. in Control Engineering (2017) M.Sc. in Control Engineering (2020) Ph.D. in Aerospace Engineering from Charles III University of Madrid Dr. Simorgh's research centers on developing mathematical frameworks that reconcile aircraft trajectory optimization with climate impact reduction. His expertise spans robust control systems, optimization under uncertainty, and climate modeling integration, with particular emphasis on non-CO₂ emissions. His methodology addresses both CO₂ and non-CO₂ climate forcing mechanisms through computationally efficient algorithms that account for weather variability and climate metric uncertainties. This work directly supports aviation's decarbonization by providing operational strategies that reduce environmental footprint without prohibitive cost increases. Analysis of his 15 most recent publications reveals a cohesive research trajectory focused on operationalizing climate-optimal flight planning. His work consistently integrates climate science with aerospace engineering through robust optimization frameworks, demonstrating particular innovation in handling multiple uncertainty sources (weather, climate models, emissions). The publications cluster around three interconnected themes: 1) Development of open-source computational tools (ROOST, CLIMaCCF), 2) Network-scale implementation of climate-aware air traffic management, and 3) Risk analysis of climate mitigation strategies. This body of work establishes new methodological standards for quantifying and minimizing aviation's total climate impact. Scientific Awards: Luis Azcárraga Aeronautical Innovation Award for collaborative research impact Best Paper Award (2022) from a high-impact aerospace journal Dr. Simorgh secures significant research funding through European Commission projects including FlyATM4E (climate-optimized flight planning), ALARM (aviation emissions reduction), and RefMAP (sustainable aviation pathways). His grant portfolio emphasizes practical implementation of climate mitigation strategies, with strong industry-academia collaboration. He mentors junior researchers through project teams and has developed three major open-source Python libraries (CLIMaCCF, ROOST, ROC) that have become community standards for climate impact assessment in aviation research. His current work focuses on scaling climate-optimized trajectories to continental airspace while addressing operational constraints and economic viability. He leads a research group focused on climate-aware air traffic management, developing the ROOST simulation framework for GPU-accelerated trajectory optimization and the CLIMaCCF library for standardized climate metric calculations. His team collaborates with European air navigation service providers and aircraft manufacturers to transition research into operational practice, with current projects emphasizing real-time implementation and regulatory compliance frameworks.

Yeo Howe LimView profile

Professor

• Water Resources Engineering
• Hydrologic Modeling
• Open Channel Hydraulics
• +5 more

Dr. Yeo Howe Lim is a Professor and Department Chair of Civil Engineering at the University of North Dakota, with a focus on water resources engineering. He serves as Graduate Program Director for Civil and Environmental Engineering, teaching courses in fluid mechanics, hydrology, and applied hydraulics. Education: BEng & MEng from University of Canterbury, PhD from Memorial University of Newfoundland Research Areas: Open channel hydraulics, flood frequency analysis, streambank stabilization, urban stream revitalization, cold region hydrodynamics His research explores climate change impacts on flood patterns, hydrodynamic modeling of wetlands, and innovative use of Unmanned Surface Vehicles for aquatic studies. Recent publications emphasize lithium extraction technologies in oilfields, distributed Muskingum flood routing models, and optimization algorithms for cold climate water systems. Scientific awards include the Dean’s Outstanding Faculty Award (2013), ASCE Outstanding Reviewer recognition (2009), and the Institution of Civil Engineers (UK) Overseas Prize (2001). He has supervised numerous graduate students in hydrological modeling, including Mohammed Almousa and Vahid Atashi. Current projects involve HYCAT technology for bridge scour assessment, LiDAR bathymetry modeling, and climate change adaptation tools for cold region water management. His work bridges computational hydrology, hydraulic structure design, and sustainable water resource solutions.

Damian FlynnView profile

Associate Professor

• Renewable Energy Integration
• Power System Stability
• Wind Energy
• +7 more

Dr Damian Flynn is an Associate Professor at the UCD School of Electrical and Electronic Engineering , University College Dublin. His research focuses on the challenges of integrating renewable energy sources like wind and solar into power systems while maintaining stability and reliability. Research Themes: High renewable penetration, grid-forming converters, energy storage, and smart grid technologies. Collaborations: EirGrid, Glen Dimplex, Electricite de France, and General Electric. Dr Flynn’s work addresses the technical and economic feasibility of transitioning to 100% renewable energy systems, particularly for islanded grids like Ireland’s. His models explore scenarios for 2030–2050, emphasizing the need for adaptive infrastructure and policy frameworks. Key challenges include balancing unpredictable renewable supply with demand, managing grid congestion, and leveraging technologies such as electric vehicles and blockchain for system stability. Recent Publications highlight trends in grid-forming converter design, renewable curtailment reduction, and multi-carrier energy systems. He investigates solutions like transportable storage and dynamic line rating to enhance grid flexibility. Scientific Awards: Smurfit Kappa Newman Fellowship Award.