Yasser Mohamed is a Professor in the Civil and Environmental Engineering Department at the University of Alberta . His academic and professional focus revolves around construction engineering, discrete-event simulation, and process optimization for industrial and tunneling operations. He has also explored knowledge engineering techniques and the application of TRIZ (Theory of Inventive Problem Solving) to construction processes. Email: yaly@ualberta.ca Location: 7-269 Donadeo Innovation Centre For Engineering, Edmonton, AB Courses Taught: CIV E 603 (Construction Informatics), CIV E 606 (Design and Analysis of Construction Operations) His research emphasizes modeling construction processes using discrete-event simulation to optimize performance and develop synthetic environments for construction operations. Recent publications, however, indicate a shift toward power systems, focusing on DC microgrids , grid-forming converters , and renewable energy integration . Scientific Awards: None explicitly mentioned in the provided data. Advising and Grants: No formal advisees listed. A co-applicant on a CRD grant (2007–present) for synthetic environments in construction simulation.
Dr. Tieming Liu is an Associate Professor in the School of Industrial Engineering and Management at Oklahoma State University , where he has served since 2005, first as Assistant Professor and then promoted to Associate Professor in 2011. His expertise bridges operations research, supply-chain coordination, healthcare analytics, renewable-energy policy, and production scheduling. Education Ph.D. in Transportation and Logistics, Massachusetts Institute of Technology, 2005 M.S. in Industrial Engineering and Management Science, Northwestern University, 2001 M.S. in Control Theory and Control Engineering, Tsinghua University, 2000 B.S. in Control Theory and Control Engineering, Tsinghua University, 1997 Research Interests Dr. Liu’s scholarship is organized around three pillars: Supply-Chain & Logistics: coordination contracts, inventory bounds, responsive pricing, channel rebates, and production flexibility under uncertainty. Healthcare Analytics: machine-learning models for diabetic retinopathy and sepsis risk prediction, clinical decision-support systems, and handling imbalanced EHR data. Energy & Sustainability: renewable portfolio standards, capacity coordination with renewable energy certificates, and incentive mechanisms for renewable and conventional generators. Recent methodological contributions include hidden Markov models for continuous mortality prediction, tree-augmented Bayesian networks for sepsis risk, and tensor-completion-driven convolutional networks for longitudinal medical data. Scientific Awards & Honors EJOR Reviewer Award, 2019 IEM Faculty Award, 2019 Halliburton Outstanding Faculty Award, OSU, 2014 Merrick Foundation Teaching Award, OSU, 2013 Riata/Koch Faculty Fellow, OSU, 2012 Lockheed Martin Teaching Award, OSU, 2011 Student Organization Faculty Advisor of the Year, OSU, 2010 Student Mentorship & Collaboration Dr. Liu has advised or co-advised a large cohort of doctoral and master’s students whose names appear as first or co-authors on his publications. His collaborative network spans MIT, Northwestern, IBM T. J. Watson Research Center, and multiple departments across OSU, fostering interdisciplinary projects that integrate operations research with real-world healthcare, transportation, and energy challenges. Laboratories & Teams He conducts research within the analytics and optimization laboratories of the School of Industrial Engineering and Management, directing projects funded by federal agencies and industry partners aimed at next-generation decision-support systems for healthcare providers, logistics operators, and energy market regulators.
Marc De Benedetti is an Assistant Professor, Teaching Stream in the Department of Computer Science at the University of Toronto's Mississauga campus within the Mathematical and Computational Sciences school. His work bridges computational methods with healthcare analytics, geophysical modeling, and educational innovation. He specializes in applying machine learning to medical datasets (e.g., SEER cancer data) for predictive modeling and has contributed to CAR T-cell therapy efficacy studies in oncology. His geoscience research focuses on spatial-temporal variability in climate systems, particularly in the Congo Basin and UK wind dynamics. De Benedetti also designs supplementary educational programs to support undergraduate STEM learners. His research emphasizes data-driven approaches to address challenges in healthcare, environmental science, and pedagogy. Research interests include: Machine learning applications in healthcare survival prediction Immunotherapy comparative efficacy analysis Spatial variability in climate and hydrology systems Wind energy potential modeling Innovative undergraduate physics/mathematics tutoring strategies His recent articles analyze treatment outcomes for blood cancers using CAR T-cell therapies, explore model resolution impacts on geophysical data accuracy, and develop predictive tools for cancer prognosis via SEER datasets. No scientific awards are explicitly mentioned in the provided materials. While no formal grants or student advisement records are listed, his work demonstrates strong engagement with both academic research and educational development initiatives. His research locations include the Congo Basin for hydroclimatic studies and the UK for wind energy modeling. Collaborations likely span medical institutions for oncology studies and climatological agencies for environmental data analysis.
Professor Peter Fussey holds a dual role at the University of Sussex as a Professor in the School of Engineering and Informatics and Associate Dean for Global and Civic Engagement at the Faculty of Science, Engineering and Medicine. He leads the Energy and Materials Engineering Research Centre, focusing on Thermal Systems , Battery and Vehicle Energy Management , Model Predictive Control , and Data Analytics . Previously, he spent over 25 years at Ricardo UK, leading the Control Department and developing advanced control systems for hybrid/electric vehicles and thermal management. His career also includes rail acoustics research at British Rail Research and SNCF. Education: DPhil in Engineering from the University of Oxford MA in Engineering from the University of Cambridge Research Interests: Professor Fussey’s work spans automotive and mechanical engineering , with a focus on low-emission powertrains , energy management systems , and advanced control algorithms . His recent projects include optimizing EV cabin climate control for extended range, geofencing for smart mobility, and neuro-fuzzy modeling for thermal systems. He has also pioneered applications of model predictive control (MPC) in selective catalytic reduction (SCR) systems and engine combustion optimization. Teaching: Courses include Low Emission Vehicle Propulsion , Vehicle Dynamics , and supervising Formula Student projects. His teaching emphasizes practical applications of control theory and sustainability. Labs/Teams: Leads the Energy and Materials Engineering Research Centre , fostering interdisciplinary research in sustainable energy and advanced propulsion systems.
Dr. Laleh Tafakori is a Senior Lecturer in the Department of Statistics and Analytics at RMIT University's School of Science. Her research focuses on the intersection of statistical modeling, machine learning, and their applications in healthcare, finance, and environmental science. She is affiliated with the university's City Campus and can be contacted at laleh.tafakori@rmit.edu.au . Her teaching interests include Applied Analytics , Statistical Inference , Time Series Analysis , Mathematical Statistics , Stochastic Processes , and Probability Theory . She actively supervises research projects in areas such as: Healthcare modeling (e.g., diabetes onset prediction, maternal risk assessment) Environmental data analysis (e.g., extreme precipitation estimation via satellite data) Financial risk analysis (e.g., Value-at-Risk forecasting, credit portfolio management) Machine learning applications in complex networks and smart grids Her recent research trends emphasize predictive modeling for public health challenges, leveraging advanced statistical techniques (copula models, functional volatility) and machine learning (CNNs, random forests). She collaborates with institutions worldwide, addressing issues like Saudi Arabia's healthcare indicators and European financial systemic risk. While no formal awards are listed, her work demonstrates impactful contributions to interdisciplinary data science. Dr. Tafakori has advised over 16 students on topics ranging from diabetes epidemiology to edge computing optimization. Her research outputs include 28+ peer-reviewed articles, with a focus on methodological innovation and real-world problem-solving. She maintains active engagement in collaborative projects without explicitly listed grants.
Thivya KANDAPPU is an Assistant Professor at the School of Computing and Information Systems, Singapore Management University (SMU). Her research focuses on mobile and wearable computing systems for human cognition monitoring, memory modeling, and technology-enhanced learning. Education: PhD in Computer Science, University of New South Wales (2014) Research Interests span: Human-Centric Wearable Systems Cognitive State Analysis Event-Based Vision & Sensing Privacy in Pervasive Systems Health & Wellbeing Applications Smart City Mobility Analytics Selected Publications demonstrate expertise in eye tracking, cognitive monitoring, and privacy-preserving wearables, with recent work appearing in NeurIPS and ACM IMWUT. Research Grants include projects funded by MOE and A*STAR on cognitive dynamics, privacy-aware systems, and multimodal travel analytics. Professional Service involves organizing AutoMLPerSys 2025, and serving on TPCs for ACM MobiSys 25, IEEE PerCom 25, and ICDCN 25.
Emiliano Gelati is a Research Fellow at the Department of Geosciences, University of Oslo, Norway, specializing in hydrology and remote sensing applications. He holds a PhD from the Technical University of Denmark and has extensive postdoctoral and industry experience across institutions in Italy, France, and Spain. His research focuses on hydrological modelling, water resources optimization, irrigation systems, and climate change impacts. He teaches courses on hydrology and geophysical data science. Education: PhD, Environmental Engineering, Technical University of Denmark (2007–2010) MSc/BSc, Environmental Engineering, Politecnico di Milano, Italy (2000–2006) Research Interests: Hydrological extremes and drought analysis Integration of remote sensing data into hydrological models Water resources management under climate change Modelling irrigation sustainability and crop-water interactions Land surface model validation and uncertainty assessment Articles Trends: His recent work emphasizes inter-disciplinary platforms for climate-ecosystem modelling, global water resource assessments, and satellite-based monitoring solutions. Key contributions include improving irrigation area mapping in Europe and evaluating drought trends under global warming scenarios. Advising & Grants: Supervised MSc theses on hydrologic extremes and hydrometeorological trends. Collaborated on EU-funded projects addressing water-energy-food nexus challenges and Mediterranean sustainability. Labs/Teams: Active in the Section for Geography and Hydrology (GeoHyd) and the Hydrology and Water Resources research group at the University of Oslo. Engaged with international teams like the European Joint Research Centre (JRC).
James Allison is an Associate Professor in both the Industrial and Enterprise Systems Engineering and Aerospace Engineering departments at the University of Illinois at Urbana-Champaign. He is also affiliated with the Carl R. Woese Institute for Genomic Biology. His research focuses on systems engineering, control systems design, thermal management systems, and optimization methodologies. Dr. Allison has contributed to advancements in fluid-based thermal management systems, floating offshore wind turbine control co-design, and AI-driven design optimization. His work emphasizes interdisciplinary approaches, blending mechanical, aerospace, and computational engineering principles. Key research areas include design automation, graph neural networks for system architecture exploration, and reliability-based co-design of complex systems. He has pioneered methodologies for extracting design knowledge from optimization data and advancing multifunctional structures for attitude control in aerospace systems. Awards: NSF CAREER Award (2017) Labs/Groups: Involved in the development of tools like LGR-MPC and SS-MPC for Model Predictive Control, and the WEIS toolset for offshore wind turbine analysis. Dr. Allison’s recent publications highlight contributions to thermal management system configurations, control strategies for offshore renewable energy systems, and topology optimization techniques. His research bridges theoretical advancements with practical applications in aerospace, energy, and manufacturing sectors.
Jon Blower is an academic affiliated with the University of Reading's Department of Geography and Environmental Science. His work focuses on climate data management, environmental data visualization, and geospatial technologies. He has contributed to projects like the C3S ECEM climate service, the CHARMe metadata initiative, and the GODIVA2 web mapping system. His research spans cloud computing applications in environmental science, oceanographic data dissemination, and urban density analysis. He has authored over 40 peer-reviewed publications and technical reports, including foundational work on Climate Forecast metadata conventions and cloud-based GIS systems. Blower has led interdisciplinary efforts in integrating environmental data quality frameworks and developing open-source tools for geospatial analysis. Education & Background: While specific educational details are not explicitly stated, his extensive contributions to environmental science and computing imply advanced training in these fields. His work often bridges computer science and environmental applications, suggesting a multidisciplinary academic background. Research Focus: Key areas include: Data interoperability standards (e.g., CF metadata conventions) Climate service development for energy policy Interactive environmental data visualization systems Cloud computing for large-scale environmental datasets Oceanographic data synthesis and quality frameworks Recent Trends in Publications: Recent work emphasizes urban systems analysis (2023), renewable energy-climate integration (2018), and cloud infrastructure for environmental science (2014/2016). Earlier contributions focused on ocean data systems (2009-2012) and grid computing for climate modeling (2006-2009). Awards & Grants: No specific awards listed, but his sustained contributions to major projects like GODAE and MELODIES reflect institutional recognition. Active in grant-funded initiatives related to environmental data infrastructure. Labs/Teams: Collaborates with teams at the National Oceanography Centre, NASA, and European climate initiatives. Core contributor to projects managed by organizations like ESA and the UK's Natural Environment Research Council (NERC).
Antoine Sanner is a Researcher affiliated with the Institute of Construction Materials (Institute of Building Materials) at ETH Zurich, part of the College of Architecture, Civil and Environmental Engineering. His work focuses on computational mechanics of building materials, surface topography analysis, and adhesion science. He co-developed the dtool and dserver systems to enhance FAIR data practices in research. Research interests include the structural behavior of cement-based materials, mechanisms of material adhesion under varying surface conditions, and multiscale analysis of surface roughness effects. He has also contributed to engineering tools for characterizing surface topography across scales, emphasizing the creation and use of 'digital surface twins' for material studies. His publications consistently explore topics at the intersection of materials science and mechanical engineering, with recent work advancing understanding of surface adhesion dynamics and cement curing processes. No scientific awards are explicitly mentioned in the provided texts. As part of the Professorship for Computational Mechanics of Building Materials, Antoine contributes to research infrastructure and computational methods development. He has no listed advisees or grants, and his role appears to be full-time with no indication of part-time status. He collaborates on projects involving advanced materials characterization, particularly in polycrystalline diamond coatings and bearing component analysis, leveraging both experimental and computational methodologies.
Dr. Minh-Son Pham is a Senior Lecturer in Materials at Imperial College London, specializing in additive manufacturing and materials design. He obtained his Doctor of Science from ETH Zurich and leads research on metal 3D printing, material degradation, and meta-materials. His research investigates microstructure-property relationships in additively manufactured alloys, particularly nickel superalloys and high-entropy alloys. Recent work explores damage-tolerant architected materials inspired by crystal microstructures, with applications in aerospace and biomedicine. Publications demonstrate consistent focus on process-microstructure-property relationships in additive manufacturing, combining experimental characterization with computational modeling. Research spans fundamental materials science to industrial applications with aerospace and medical device partners. Awards include the 2024 TMS Young Innovator Award. He teaches fracture mechanics and additive manufacturing courses while coordinating international exchange programs. Current projects involve industrial collaborations on alloy design for 3D printing and meta-material development.
Prof. Dr. Armando Walter Colombo is a Professor at the Department of Technology, Electrical Engineering and Informatics at the University of Applied Sciences Emden/Leer. He leads the Institute I2AR as Scientific Director and coordinates the DAAD/DAHZ Binational Master in Industrial Informatics with the Universidad Tecnológica Nacional-FRRe in Argentina. His research focuses on Cyber-Physical Systems (CPS), Industrial Digitalization, Industry 4.0, and Smart Manufacturing. Key areas include asset administration shells, IoT integration, and sustainable industrial automation. He holds IEEE Fellow status and is a Distinguished Lecturer for the IEEE Systems Council. He has pioneered educational frameworks like T-CHAT and contributed to standards alignment (RAMI4.0, IEEE Industrial Agents). Recent publications emphasize Industry 4.0 compliance, digital twins, and AI in logistics. Responsibilities: DAAD Master Coordinator, Institute I2AR Director, and International Relations Officer. Awards: IEEE Fellow, Distinguished Lecturer (IEEE Systems Council). Grants & Partnerships: DAAD-funded binational programs, EU-funded PERFoRM projects. His work bridges academia and industry through platforms like the ICPS-based Digital Factory Lab, addressing SME digitalization and sustainable automation.
Tobias Gerken is an Assistant Professor in the Integrated Science and Technology (ISAT) department at James Madison University's College of Integrated Science & Engineering. He holds a Ph.D. in Environmental & Atmospheric Science from the University of Bayreuth (Germany) and a Diplom in Environmental Science from the same institution. His research focuses on land-atmosphere interactions, including surface flux dynamics of water, energy, and trace gases between ecosystems and the atmosphere, and their impacts on weather and climate. Notable areas include flash drought mechanisms in the Northern Great Plains, agricultural land management effects on rainfall, and carbon dioxide exchanges in tropical ecosystems like the Amazon rainforest and Tibetan Plateau. Gerken has held prior appointments as an Assistant Research Professor at Penn State University and Research Associate at Montana State University. His work spans interdisciplinary projects funded by the National Science Foundation (NSF), NASA, and the Department of Energy (DOE), including studies on urban lightning patterns, climate modeling via ACT-America missions, and Tibetan Plateau ecosystem dynamics. He teaches courses on environmental science, sustainability, and applied data analysis. His research outputs emphasize climate change impacts, air pollution dynamics, and hydroclimatology. Notable studies include analyzing lightning frequency in urban regions, quantifying methane emissions from bison herds, and evaluating carbon cycle models using airborne observations. Gerken actively contributes to editorial roles in journals like Agricultural and Forest Meteorology and participates in the National Ecological Observation Network (NEON). Media engagements include discussions on wildfire smoke effects, climate destruction mitigation strategies, and agricultural climate impacts in the Great Plains. His work bridges field experiments, computational modeling, and policy-relevant climate science.
Michela Zedda is an Associate Professor at the Department of Mathematical, Physical and Informatics Sciences of the University of Parma . Her research focuses on complex geometry, differential geometry, and geometric analysis, with particular emphasis on Kähler and Sasakian manifolds, geometric flows, and quantization techniques. Her work includes studies on isometric immersions of locally conformally Kähler manifolds, stability in Lie group actions, J-flow dynamics on Sasakian manifolds, and Berezin-Engliš quantization of Cartan-Hartogs domains. Recent contributions address convergence properties of geometric flows and asymptotic expansions in geometric quantization. Professional Activities: She organizes workshops such as PREDICT 2025 and Informal Geometry Workshop in Paradiso 2025 , and participates in international conferences on complex and differential geometry.
Niko Nevaranta serves as an Associate Professor (Tenure Track) in the Department of Electrical Engineering at Lappeenranta-Lahti University of Technology (LUT), specifically within the School of Energy Systems. His academic journey began at LUT where he earned his B.Sc. (2010), M.Sc. (2011), and D.Sc. (2016) degrees in electrical engineering. His career path includes a visiting researcher position at KTH Royal Institute of Technology in Stockholm (2018) and a post-doctoral researcher role funded by the Academy of Finland (2019-2022). Nevaranta's research focuses on data-driven modeling and control systems for electromechanical and energy applications. His primary expertise lies in Industrial Informatics for Energy System Disruption, with specific interests in high-speed rotating machinery, system identification, parameter estimation, and intelligent control systems. His work bridges theoretical control engineering with practical industrial applications, particularly in the energy transition sector. Analysis of his recent publications reveals a strong emphasis on active magnetic bearing technology, rotor dynamics, and high-speed machinery applications. His research increasingly incorporates data-driven approaches and machine learning techniques to address complex control challenges in energy systems. The publications demonstrate a clear progression from fundamental control theory toward practical implementation in real-world energy conversion systems. As an educator, Nevaranta has developed innovative teaching materials for control engineering, including MATLAB-based tools and virtual learning environments. His educational contributions focus on making complex control concepts accessible to undergraduate students through interactive and visual learning approaches. Nevaranta works within the laboratory of Control Engineering and Digital Systems at LUT, where he leads research on advanced control methodologies for energy systems. His current projects involve high-speed machine technology, particularly in applications related to biomass and waste heat recovery, as well as emerging technologies in direct air capture systems.
