Find Academic Supervisors

• Artificial Intelligence
• Big Data Analytics
• Bioinformatics
• +21 more

Dr. Mahakim Newton is a Lecturer in Data Science at the University of Newcastle, Australia, and an Adjunct Senior Research Fellow at Griffith University’s Institute for Integrated and Intelligent Systems. He holds a PhD in Computer and Information Sciences from the University of Strathclyde, alongside degrees from Bangladesh University of Engineering and Technology (BUET). His research focuses on artificial intelligence, machine learning, bioinformatics, and computer education, with notable contributions to protein structure prediction and IoT interoperability. Education: Ph.D., University of Strathclyde M.Sc.Engg., Bangladesh University of Engineering and Technology B.Sc.Engg., Bangladesh University of Engineering and Technology Research Interests: Dr. Newton’s work bridges AI and computational biology, including protein-ligand binding affinity prediction, bioinformatics algorithms, and IoT edge networks. He explores machine learning techniques for environmental informatics and drug discovery, emphasizing scalable solutions for complex problems. Teaching: He coordinates courses like Computing Project , Algorithms , and Deterministic and Stochastic Optimisation at the University of Newcastle, integrating practical and theoretical insights. Grants and Labs: Involved in interdisciplinary projects on water quality prediction and edge computing interoperability. His collaborations span academic and industrial partners, advancing applications in healthcare and environmental monitoring.

Mohammad REIHANEHView profile

Assistant Professor

• Operations Research
• Logistics and Supply Chain Management
• Optimization Algorithms
• +3 more

Professor Mohammad REIHANEH is an Assistant Professor of Operations Management at IÉSEG School of Management. He holds a Ph.D. in Operations Management from the University of Massachusetts (USA), an MSc in Industrial Engineering from Isfahan University of Technology (Iran), and a BSc in Applied Mathematics from Ferdowsi University of Mashhad (Iran). His research focuses on optimization algorithms, vehicle routing problems, scheduling theory, and logistics systems. He is a member of the LEM research group and has published extensively in top-tier journals such as the European Journal of Operational Research and the Journal of the Operational Research Society. Education: 2018: Ph.D., Operations Management, University of Massachusetts, USA 2012: MSc, Industrial Engineering, Isfahan University of Technology, Iran 2009: BSc, Applied Mathematics, Ferdowsi University of Mashhad, Iran Research Interests: Mohammad’s work emphasizes practical applications of optimization in logistics and healthcare systems. He develops exact algorithms (e.g., branch-and-price) and heuristic methods for complex routing problems, maintenance scheduling, and resource allocation in healthcare settings. His contributions bridge theoretical advancements in operations research with real-world operational challenges in industries like renewable energy (offshore wind farms) and humanitarian logistics. Publications: His recent work addresses cutting-edge challenges such as multi-period offshore wind farm routing, hemodialysis center scheduling, and food bank distribution optimization. He frequently collaborates with international researchers on projects involving vehicle routing, reliability engineering, and control chart design for manufacturing systems. Advising & Grants: No specific student advisees or grant details are listed in the provided materials.

Ye HongView profile

Researcher

• Geoinformatics
• Urban Planning
• Transportation Engineering
• +3 more

Dr. Ye Hong is a Researcher affiliated with the Institute of Cartography and Geoinformatics at ETH Zürich, specifically within the Department of Geoinformation Engineering. Their work focuses on geospatial data analysis, urban mobility modeling, machine learning applications in transportation systems, and sustainable urban planning. They contribute to open-source tools like Trackintel for mobility analysis and have published extensively on topics such as mobility data synthesis, traffic prediction, and privacy-preserving techniques. Research interests emphasize integrating multi-source geospatial data with deep learning to address challenges in urban sustainability, poverty reduction, and transportation efficiency. Their articles highlight innovations in trajectory generation, uncertainty quantification, and contextual-aware neural networks for spatial-temporal prediction. Dr. Hong’s publications explore the interplay between urban infrastructure, human behavior, and environmental impact. Key themes include accessibility measurement in cities, carbon footprint analysis, and the ethical implications of tracking mobility data. Their work bridges theoretical advancements in geoinformatics with practical applications in urban policy-making and infrastructure design. Notable contributions include frameworks for causal intervention in mobility data, methods to estimate poverty reduction efficiency using remote sensing, and analyses of tracking duration effects on location privacy. These efforts demonstrate a commitment to leveraging geospatial technologies for socially impactful research.

• Maritime Decarbonization
• Sustainable Port Operations
• Maritime Autonomous Surface Ships (MASS)
• +5 more

Anas Alamoush is a Researcher at the World Maritime University (WMU), specializing in maritime decarbonization, sustainable port operations, and Maritime Autonomous Surface Ships (MASS). With a background as a ship captain in the Jordan Navy and academic credentials including a PhD in Maritime Affairs from WMU and an MSc in Shipping Management and Logistics, he contributes to high-impact projects like the EU Horizon Seanergy initiative and the IMO impact assessment of GHG reduction measures. His work bridges industry expertise with academic research in advancing maritime sustainability. PhD in Maritime Affairs, World Maritime University (WMU) MSc in Shipping Management and Logistics Anas focuses on sustainability frameworks for ports, technology integration in shipping, and decarbonization strategies. His research spans green shipping corridors, MASS systems, and policy barriers to sustainable maritime practices, often analyzing global case studies and stakeholder dynamics. Scientific Awards: Queen of Denmark Maregerate Second Medal for outstanding service in international affairs His articles reveal trends in port energy transitions, MASS adoption challenges, and policy integration for decarbonization. He actively reviews for peer-reviewed journals and participates in international projects addressing maritime safety and future work dynamics. Notable projects include the EU Bonus Baltimari (maritime safety), WMU/ITF Future of Work (technology integration), and EU Horizon Seanergy (port energy transition). These initiatives emphasize his role in advancing sustainable maritime innovation through collaboration.

Omid ChatrabgounView profile

Assistant Professor

• Uncertainty Quantification
• Bayesian Machine Learning
• Gaussian Processes
• +5 more

Dr. Omid Chatrabgoun serves as an Assistant Professor in Data Science and AI at the CEES School of Science, holding a PhD in Data Science from Shahid Chamran University (Iran, 2015). Previously affiliated with Malayer University, he teaches undergraduate and postgraduate courses including Statistical Learning, Data Science, and Machine Learning while actively supervising PhD students. His research focuses on uncertainty quantification and Bayesian machine learning for complex systems. Education: PhD in Data Science, Shahid Chamran University, Iran (2015) Dr. Chatrabgoun specializes in Modelling, Optimization, and Uncertainty Quantification (UQ) using (Deep) Gaussian processes, with applications spanning bioinformatics (Gene Regulatory Networks, Protein-Protein Interactions) and environmental engineering (coastal protection, flood modeling). He develops novel approaches for machine learning of linear operational equations to quantify uncertainty in highly complex systems, emphasizing computational efficiency through sparse approximations and kernel methods. His work bridges theoretical statistics with practical implementations in healthcare, environmental science, and industrial applications. Analysis of his 29 publications (2016-2025) reveals a consistent trajectory in Gaussian process methodologies, evolving from foundational graphical models to advanced deep Gaussian processes and probabilistic surrogate modeling. Key thematic clusters include bioinformatics network inference, environmental risk assessment, and computational optimization techniques, demonstrating interdisciplinary impact across computer science, statistics, and domain-specific applications. Dr. Chatrabgoun has secured research funding and conducted consultancy projects with the Iran National Science Foundation (INSF) and Research Institute for Grapes and Raisin (RIGR). His collaborative network spans international institutions, as evidenced by co-authorships across civil engineering, bioinformatics, and environmental science domains. Current projects involve developing machine learning frameworks for linear differential operators and expanding applications of pair-copula Bayesian models in high-dimensional data analysis. His research group actively explores uncertainty propagation in PDE-based models and spatio-temporal environmental simulations, maintaining strong industry-academia partnerships for knowledge exchange in data-driven decision systems.

Thomas KirschsteinView profile

Professor

• Logistics Management
• Supply Chain Sustainability
• Operations Research
• +4 more

Introduction Prof. Dr. Thomas Kirschstein is a Professor at Martin Luther University Halle-Wittenberg (MLU), affiliated with the Faculty of Law, Economics and Business, and a researcher at the Center for Economics of Materials (CEM), Fraunhofer-Zentrum IMW. His work focuses on logistics, emission management, and robust statistical methods. He holds a PhD in Economics (2014) and a habilitation in eco-oriented logistics management (2020). Education 2002–2003: Diploma in Mathematics at MLU 2003–2008: Diploma in Economics at MLU 2014: PhD in Economics (thesis: 'Integrated Supply Chain Planning in Chemical Industry') 2020: Habilitation (thesis: 'Papers on the Eco-oriented Management of Logistics Operations') Research Interests Kirschstein's research integrates logistics and environmental sustainability, including emission reduction in freight transport, robust statistical methodologies, and supply chain optimization. His work combines simulation-based planning with empirical analysis, addressing challenges in complex production networks and intermodal transportation. Awards and Recognition Christian Wolff Award (2020) for habilitation thesis Werner Kern Award (2013/2014) for production management research Best Paper Award (2013) from the Faculty of Law and Business Administration Teaching and Academic Contributions He teaches courses in production management, logistics, and data science. Notable roles include: Lecturer: 'Production & Logistics' (2014–2019) Organized conferences like Logistics Management 2019 and Robust Statistics 2014 Labs and Collaborations Active collaborations with Dow Olefinverbund GmbH and Fraunhofer IMW, focusing on integrated logistics systems and robust estimator development.

• Traffic Safety
• Public Health
• Adolescent Behavior
• +3 more

Kaigang Li is an Adjunct Assistant Professor at Yale School of Medicine (Yale University), specializing in public health and traffic safety research. His work focuses on adolescent and young adult behaviors related to impaired driving, alcohol-related risks, and preventive strategies. He collaborates with interdisciplinary teams to model complex behavioral systems and evaluate intervention outcomes. Research interests include traffic safety, substance abuse prevention, and community health initiatives. Notable projects involve system dynamics modeling of impaired driving trends and mixed-methods assessments of social-environmental factors influencing risky driving behaviors. Dr. Li has contributed to studies on parental monitoring's protective effects against recurrent drunk driving and feasibility trials of community gardening health impacts. Recent publications (2024-2025) emphasize systems approaches to address alcohol-impaired driving fatalities and the role of social contexts in youth decision-making. His work is supported by collaborations with institutions like the DrivSim research group, focusing on translational traffic safety solutions. Labs/Teams: Active contributor to the DrivSim initiative, applying systems science to traffic safety challenges.

Longbing CaoView profile

Professor

• Artificial Intelligence
• Data Science
• Machine Learning
• +7 more

Professor Longbing Cao is the Distinguished Chair in Artificial Intelligence and Director of the Frontier AI Research Centre at Macquarie University. He holds dual PhDs in Artificial Intelligence and Computing Science, an MSc in Communication, and a Bachelor's degree in Electrical Engineering. His research spans AI, data science, behavior informatics, and enterprise innovation, with over 400 publications and significant industry collaborations. Education: PhD in Pattern Recognition and Intelligent Systems, Graduate University of Chinese Academy of Sciences PhD in Computing Science, University of Technology Sydney MSc in Communication Bachelor's in Electrical Engineering Research Interests: Focuses on AI systems, federated learning, time series forecasting, and humanoid robotics. His work integrates theoretical advancements with real-world applications in finance, healthcare, and smart cities. Recent Articles: Explores cutting-edge topics like generative AI (ProgDiffusion), federated learning (FedSI), and pandemic modeling (waning effect of interventions). These contributions highlight advancements in both foundational AI and applied data science. Awards: Eureka Prize (2019), ACM Distinguished Scientist (2019) Grants & Projects: Leads major initiatives such as the Federated Omniverse Facilities for Smart Digital Futures (2024-2025) and Data Complexity and Uncertainty-Resilient Deep Variational Learning (2024-2027). Prior roles include Founding Director of UTS Advanced Analytics Institute (2011-2014). Labs & Teams: Directs the Frontier AI Research Centre and collaborates with global institutions like Microsoft, Commonwealth Bank, and Shanghai Stock Exchange.

Maria BarrufetView profile

Professor

• Reservoir Engineering
• Enhanced Oil Recovery
• CO2 Storage and Capture
• +4 more

Dr. Maria Barrufet is a Professor of Petroleum Engineering at Texas A&M University, holding roles as Assistant Department Head for Staff Administration and Director of Online Learning in Petroleum Engineering. She also serves as the Baker Hughes Endowed Chair and Affiliated Faculty in Chemical Engineering. Her research focuses on reservoir engineering, enhanced oil recovery (EOR), and CO2 capture/storage technologies, with expertise in multiphase flow and shale reservoir simulation. Education: Ph.D. in Chemical Engineering, Texas A&M University (1987) M.S. in Chemical Engineering, Universidad Nacional de Salta, Argentina (1983) B.S. in Chemical Engineering, Universidad Nacional de Salta, Argentina (1979) Research Interests: Integration of capillary pressure and thermodynamics for reservoir fluid analysis Simulation of near-critical fluids and compositional reservoirs Equations of state (EOS) modeling for multiphase equilibria CO2 storage and EOR mechanisms in unconventional reservoirs Flow assurance and leak detection in multiphase systems Publications Trends: Her recent work emphasizes CO2 sequestration in shale reservoirs, molecular sieving phenomena in nanoporous media, and advanced EOR techniques for unconventional resources. Key themes include phase behavior modeling, reservoir simulation of complex fluid systems, and energy-efficient production strategies. Awards: Charles Crawford Distinguished Service Award (2006, 2012-2013) Faculty Fellow, Texas A&M Engineering Experiment Station (2004-2005) Assessing Technology in Teaching Award (2004) Advising & Teaching: As a leader in online education, Dr. Barrufet has pioneered distance learning initiatives in petroleum engineering. Her academic leadership includes staff administration roles and curriculum development for international programs.

• Organizational Learning
• Technological Innovation
• Biopharmaceuticals
• +3 more

Russell Seidle, PhD, is an Associate Professor of Strategy and International Business at Suffolk University. His research focuses on organizational learning dynamics in technological innovation, particularly within biopharmaceutical and medical device sectors. He employs qualitative methodologies to explore how firms leverage knowledge sources for new product development. Dr. Seidle holds a PhD and MBA from McGill University and an Honors Bachelor of Commerce from the University of Ottawa. His work has been recognized with awards including the Emerald Literati Award (2020), Douglas Nigh Award (2020), and WAM Past Presidents Best Paper Award (2017). His research interests span organizational learning, innovation management, and cross-border practices. Recent work examines digital transformation challenges and wicked global problem-solving through experiential learning frameworks. He has contributed to journals such as Technological Forecasting and Social Change , Journal of World Business , and International Journal of Innovation Management . Awards: Literati Award, Douglas Nigh Award, WAM Best Paper, Knut Holt Award Consulting: Worked with organizations like Manulife, Teleflex, and Rapid Snack

Edward OttView profile

Professor

• Chaos Theory
• Nonlinear Dynamics
• Wave Chaos
• +4 more

Edward Ott is a **Distinguished University Professor of Electrical Engineering and Physics** at the **University of Maryland, College Park**, affiliated with the Institute for Research in Electronics and Applied Physics (IREAP) and the Institute for Systems Research (ISR). He holds a bachelor's degree in electrical engineering from Cooper Union, and master's and doctoral degrees in electrophysics from Polytechnic Institute of Brooklyn. After postdoctoral work at Cambridge University and a professorship at Cornell University, he joined UMD in 1979. His research spans **chaotic dynamics**, **wave chaos**, and **networked systems**, with a focus on chaos control, fractal basin boundaries, and applications of machine learning in hybrid atmospheric models. He authored *Chaos in Dynamical Systems*, a foundational textbook, and co-edited *Coping with Chaos*. His work bridges nonlinear dynamics with practical applications in meteorology, fluid dynamics, and electromagnetism. Recent research emphasizes hybrid models combining physics-based simulations with machine learning to improve weather prediction, network inference, and system stability analysis. His articles reflect interdisciplinary advances in reservoir computing, data assimilation, and quantum graph experiments. Ott's contributions include pioneering chaos theory applications in engineering, plasma physics, and biological systems. He actively explores machine learning's role in forecasting complex spatiotemporal dynamics and understanding tipping points in non-stationary systems.

Wei QianView profile

Associate Professor

• High-dimensional statistics
• Sequential decision making
• Model selection
• +10 more

Wei Qian is an Associate Professor of Statistics at the Department of Applied Economics and Statistics, University of Delaware, affiliated with the College of Agriculture & Natural Resources. He is also an affiliated faculty member of the Data Science Institute at the University of Delaware. Education: Ph.D. in Statistics, minor in Mathematics, University of Minnesota, Twin Cities, 2014 M.S. in Chemistry, Stanford University, 2008 B.S. in Chemistry, Peking University, Beijing, 2006 His research focuses on high-dimensional statistics, sequential decision making, model selection, dimension reduction, nonparametric and semiparametric estimation, statistical computing, actuarial statistics, and deep learning. His work addresses complex data analysis challenges across domains like insurance, epidemiology, and network modeling. Recent publications emphasize machine learning applications in Hypergraph Neural Networks Multi-armed Bandit Problems Gender Equity in Hiring Covid-19 Epidemiological Modeling Interbank Network Risk Analysis High-dimensional Censored Data Scientific Awards: JPMC Fellow, Institute for Financial Services Analytics, University of Delaware (2017 -- present) Grants: NSF DMS funding support

• Energy Market Modeling
• Energy Forecasting
• Risk Analysis
• +3 more

Karina Trindade Ribeiro serves as a Researcher at the Technological Research Institute (IIT) of Comillas Pontifical University within the School of Engineering (ICAI), having joined the institution on July 1, 2025. Her position is categorized under RF4.P4.02 research fellowship level. She holds a Bachelor's degree in Physics from the University of Valencia and a Master's degree in Optics and Photonics (specializing in Solar Energy) from the Erasmus Mundus Europhotonics program, completed primarily at Karlsruhe Institute of Technology (Germany) with a semester at Aix-Marseille Université (France). Her research concentrates on medium- and long-term energy market modeling and planning, energy forecasting, risk analysis, and regulatory frameworks. She applies physical modeling and data analysis techniques to complex energy systems, with expertise spanning MATLAB, Python, GAMS, and VBA for optimization and simulation tasks. Applied Photonics Award (Fraunhofer IOF) for Master's thesis on renewable hybrid device modeling She has collaborated with major institutions including the European Commission (Blue Book Internship 2024), Karlsruhe Institute of Technology, Fraunhofer IOSB, and University of Valencia. Her current project involves analysis of hourly business variable dynamics for Endesa SA (March-December 2025). She maintains fluency in English (C2), Spanish (native), Portuguese (native), Catalan (C1), and basic French proficiency.

Dean ChatfieldView profile

Associate Professor

• Supply Chain Management
• Business Simulation
• Operations Management
• +3 more

Dean Chatfield is an Associate Professor in the Department of Information Technology & Decision Sciences at Old Dominion University , where he teaches and conducts research in supply chain management and simulation modeling. Education: Ph.D. in Management Science and Information Systems, Pennsylvania State University (2001) M.S. in Management Information Systems, Pennsylvania State University (1993) M.B.A. in Finance, Pennsylvania State University (1991) B.S. in Management Systems, Rensselaer Polytechnic Institute (1989) Research Interests focus on supply chain dynamics and inventory system optimization , particularly examining phenomena like the Bullwhip Effect , stockout propagation , and risk amplification in multi-echelon systems. His work employs discrete-event simulation and agent-based modeling to develop decision support frameworks for complex supply chain scenarios, including cascading failures and stochastic lead times. Representative Publications include simulation studies on network characteristics affecting supply chain resilience (International Journal of Production Economics, 2020), crossover-aware inventory policies (Transportation Research Part E, 2018), and quantifying information-sharing impacts on demand amplification (Production and Operations Management, 2004). His research has secured over $834,238 in grants , including a $551,438 NSF CAREER award for simulation-based inventory modeling. Scientific Honors: 2005 Best Application Research Paper, Decision Sciences Institute Grants address technology gaps in transportation logistics and mathematical frameworks for service-driven systems. He has developed specialized simulation tools like SISCO and SCML to support industry applications.

Naonori UedaView profile

Research Professor

• Machine Learning
• Artificial Intelligence
• Physics-Informed Deep Learning
• +5 more

Naonori Ueda is a Research Professor and Deputy Director at RIKEN Center for Advanced Intelligence Project. He also serves as a Visiting Fellow at NTT Communication Science Laboratories, Research Supervisor for Mathematical Information Platform at Japan Science and Technology Agency (JST), and Visiting Professor at Kobe University's Graduate School of System Informatics. His distinguished career spans academia, government research institutions, and industry collaboration, with significant contributions to advancing artificial intelligence and machine learning applications across multiple scientific domains. Dr. Ueda's research interests focus on the intersection of machine learning, artificial intelligence, and physical sciences. He specializes in physics-informed deep learning approaches that integrate governing physical equations with neural network architectures. His work spans geophysical data analysis, remote sensing applications, computational seismology, and environmental monitoring systems. He has pioneered methods for crustal deformation modeling, earthquake prediction, tsunami inundation forecasting, and satellite imagery analysis using advanced machine learning techniques. His research demonstrates how AI can solve complex scientific problems by bridging the gap between data-driven approaches and physical domain knowledge. His publication record reveals a strong trend toward applying machine learning to solve real-world geophysical and environmental challenges. His recent work shows increasing sophistication in physics-informed neural networks that incorporate domain-specific knowledge into deep learning architectures. The publications span high-impact journals like Nature Communications, demonstrating the interdisciplinary significance of his work. His research consistently focuses on practical applications of AI for disaster prevention, environmental monitoring, and scientific discovery. Fellow of IEICE (Institute of Electronics Information and Communication Engineers) Member of Japan Prize field review committee Selection Committee Member for Brilliant Female Research Award (The Jun Ashida Award) Member of Kyoto Prize Selection Committee Dr. Ueda has secured substantial research funding through multiple government-sponsored projects including RIKEN Pioneering Project 'Prediction Science,' JST AIP Acceleration Research projects on weather prediction and drug discovery, and AMED-funded medical research initiatives. His leadership extends to serving as Sub-project Director for Japan's Moonshot R&D Project. He actively mentors researchers through his roles at RIKEN, NTT, and various academic institutions, fostering the next generation of AI scientists. As Deputy Director of RIKEN Center for Advanced Intelligence Project, Dr. Ueda leads one of Japan's premier AI research initiatives. He also serves on the Advisory Board of Kobe University's Mathematical and Data Science Center and Kyoto University's Graduate School of Informatics. His leadership extends to coordinating the AI Seminar at Osaka Industrial Association and supervising the Keihanna 'Edison Society' at the International Institute for Advanced Studies, demonstrating his commitment to bridging academic research with industrial applications.