Find Academic Supervisors

• Seismic Hazard Assessment
• Earthquake Risk Modeling
• Natural Disaster Risk Analysis
• +5 more

L. Oostwegel is a Researcher at GFZ German Research Centre for Geosciences, working within the Seismic Hazard and Risk Dynamics department. The researcher specializes in earthquake exposure modeling, building stock characterization, and multi-hazard risk assessment using geospatial data and open-source information. Oostwegel's research focuses on seismic hazard and risk assessment, with particular emphasis on building exposure modeling using OpenStreetMap and remote sensing data. Key research areas include earthquake risk modeling, flood risk assessment, landslide risk management, and the development of tools for multi-hazard risk assessment. The researcher has made significant contributions to understanding building stock characteristics at various scales, from individual buildings to global assessments. Analysis of recent publications reveals a strong focus on utilizing volunteered geographic information and Earth observation datasets for risk assessment. Oostwegel has developed methods for assessing the completeness of OpenStreetMap building data globally and has created tools like 'risk-calculator' for multi-hazard risk assessments. The research shows an interdisciplinary approach combining geophysics, geospatial analysis, and disaster risk reduction. Top-down or bottom-up in earthquake exposure modeling (2025) Safe Haven – Landslides: A Serious Game for Enhancing Risk Awareness (2025) A model of European buildings (2024) From Shelters to Skyscrapers: Worldwide Building Exploration (2024) Seismic loss assessment sensitivity study (2023) Oostwegel has collaborated extensively with researchers including Evaz Zadeh, T., Schorlemmer, D., and others across multiple projects focused on natural disaster risk assessment. The research has practical applications in urban planning, disaster risk reduction, and climate adaptation strategies, particularly for coastal megacities and areas prone to seismic activity.

• Sensitivity Analysis
• Stochastic Simulators
• Information Theory
• +3 more

Yu-Jay Huoh is a Researcher at the Department of Statistics, University of California, Berkeley. His work focuses on sensitivity analysis of stochastic simulators using information theory, aligning with the department's emphasis on computational statistics and probabilistic modeling. Education: Ph.D. in Statistics, UC Berkeley (2013), advised by Cari Kaufman. Research Interests: Specializes in computational methods for stochastic systems and uncertainty quantification. Applies information theory to analyze complex simulations in statistical computing. Professional Role: Currently serves as a Data Choreographer in Residence, contributing to data science initiatives and statistical computing projects.

Florian SteinkeView profile

Professor

• Energy Management Systems
• Machine Learning Applications in Energy
• Distributed Control Systems
• +5 more

Prof. Dr. Florian Steinke is a Professor and Head of the Energy Information Networks and Systems Department at Technische Universität Darmstadt. His academic career spans roles at Siemens Corporate Technology (2009–2016) and a PhD in machine learning at the Max Planck Institute for Intelligent Systems (2006–2008). His research focuses on algorithmic energy management, distributed control systems, machine learning applications in energy grids, and resilient smart grid design. Education: PhD in Machine Learning (Max Planck Institute for Intelligent Systems, 2006–2008) Diplom in Computational Physics (University of Tübingen & University of Washington, 1999–2005) Research Interests: Development of cyber-physical systems for energy grids Optimization of thermal-electric systems using game theory and stochastic control Integration of social media data for demand forecasting Cybersecurity measures against adversarial attacks on grids Recent work emphasizes probabilistic grid modeling, resilient energy market design, and AI-driven control strategies for Fourth Generation district heating grids. His platform ecosystem research aims to support the energy transition through data-driven solutions. Labs/Teams: Leads the Energy Information Networks and Systems research group, focusing on interdisciplinary projects combining automation, data science, and energy systems engineering.

Jin-Song von StorchView profile

Professor

• Climate Physics
• Ocean Dynamics
• Climate Energetics
• +12 more

Prof. Jin-Song von Storch is Deputy Director of the Department of Climate Variability at the Max Planck Institute for Meteorology and a §17 Professor at the University of Hamburg. She leads the Energetics of Climate working group, focusing on ocean dynamics, climate energetics, and probabilistic climate prediction. Her research bridges oceanography and statistical physics to understand the inherent randomness in deterministic climate systems. PhD in Meteorology, University of Hamburg (1990) Diploma in Meteorology, University of Hamburg (1987) Vordiplom in Physics, University of Hamburg (1987) Her research interests include climate physics, ocean dynamics, climate energetics, statistical physics of climate, internal climate variability, and Earth system modeling. She investigates how mechanical energy from wind and tides supports diapycnal mixing and maintains overturning circulations, and how concepts from statistical physics can enhance probabilistic climate predictions. Her systems-oriented approach explores the randomness and irreversibility in deterministic climate models. Her recent publications span high-resolution climate modeling (ICON), ocean tides, Lorenz energy cycles, and wind-driven ocean dynamics. She contributes to advancing Earth system models and understanding energy transfers in the climate system. Heisenberg Program of the DFG (1999–2004) Scholarship from the Chinese government (1981–1987) She has advised PhD students such as Ssebandeke, J., and is involved in major modeling initiatives like ICON and MPI-ESM. She leads research on ocean energetics and contributes to international climate model intercomparisons. Her work integrates theoretical and modeling approaches to improve climate predictability. She is affiliated with the Max Planck Institute for Meteorology and the University of Hamburg, contributing to climate research through leadership, mentorship, and innovative modeling.

• Philosophy of Artificial Intelligence
• Cognitive Science
• Neuroscience
• +3 more

Carlos Zednik is an Assistant Professor for Philosophy of Artificial Intelligence at Eindhoven University of Technology , affiliated with the Industrial Engineering and Innovation Sciences department. He leads the Eindhoven Center for Philosophy of AI and participates in the alignAI (ERC) and ROBUST AI (NWO) consortia. His work bridges philosophy with AI and neuroscience, focusing on explainable AI (XAI), mechanistic explanation, and cognitive modeling. Education: BSc in Computer Science and Philosophy, Cornell University MSc in Philosophy of Mind, University of Warwick PhD in Cognitive Science, Indiana University Bloomington Zednik’s research investigates philosophical questions about biological and artificial intelligence, emphasizing: Methodological principles in cognitive psychology and neuroscience Norms and best practices for XAI in machine learning Knowledge representation in transformer models and large neural networks His recent publications explore the integration of cognitive models into XAI, the role of Bayesian reverse-engineering in cognitive science, and the mechanistic explanation of network neuroscience. Zednik also contributes to international standardization efforts through ISO/IEC TS 6254 and DIN SPEC 92001 . Scientific Awards include fellowships from: DAAD Alexander-von-Humboldt Foundation StandICT Fellowship Zednik supervises PhD students Zeynep Kabadere , Michela Ghezzi , Céline Budding , Miriam Gorr , and Hannes Boelsen , while mentoring postdocs Manuel Barbosa de Oliveira and Philippe Verreault-Julien . His teaching spans philosophy of AI, ethics of machine learning, and decision theory, with innovation projects on generative AI in higher education.

Andrea VandinView profile

Associate Professor

• Formal methods
• Qualitative and quantitative system modeling and analysis
• Domain Specific Languages
• +7 more

Andrea Vandin serves as an Associate Professor in the Formal Methods for Safe and Secure Systems section at DTU Compute, Department of Applied Mathematics and Computer Science, Technical University of Denmark. Her research integrates theoretical computer science with practical system analysis, focusing on formal verification methodologies for complex systems. Her primary research interests include: Formal methods for system verification Qualitative and quantitative modeling techniques Domain-specific language development Large-scale performance analysis Model reduction algorithms Chemical reaction network analysis Statistical model checking Recent publications demonstrate expertise in constrained lumping of chemical systems, network embedding through equitable partitions, and stochastic conformance checking. Her work bridges computer science, mathematics, and systems biology with applications in sustainable system design. She actively contributes to UN Sustainable Development Goals through formal verification approaches for safe and secure systems. Her research shows strong trends toward applying model reduction techniques to chemical reaction networks and agent-based systems, with increasing focus on statistical verification methods and probabilistic modeling. The integration of MultiVeStA with Mesa for Python agent-based models represents a significant contribution to verification capabilities in simulation environments.

Ivan BratkoView profile

Professor

• Machine Learning
• Knowledge-Based Systems
• Qualitative Modelling
• +5 more

Ivan Bratko is a Professor of Computer Science at the University of Ljubljana's Faculty of Computer and Information Science. He founded the Artificial Intelligence Laboratory in 1985 and served as its head until 2017, remaining an active member. Until 2002, he also directed the AI group at the Jožef Stefan Institute. His academic journey includes B.Sc., M.Sc., and Ph.D. degrees in electrical engineering and computer science, all from the University of Ljubljana. Bratko's research spans machine learning, knowledge-based systems, qualitative modeling, intelligent robotics, heuristic programming, and computer chess. His work focuses on learning from noisy data, combining learning with qualitative reasoning, constructive induction, Inductive Logic Programming, and applications in medicine and dynamic system control. He has authored over 200 scientific papers and influential books including Prolog Programming for Artificial Intelligence (third edition, 2001), KARDIO: A Study in Deep and Qualitative Knowledge for Expert Systems (MIT Press, 1989), and Machine Learning and Data Mining: Methods and Applications (Wiley, 1998). His publication portfolio demonstrates consistent contributions to AI, with recent work emphasizing argument-based machine learning, qualitative modeling applications, and medical AI systems. These publications reveal strong interdisciplinary connections between theoretical AI and practical applications in environmental science, healthcare, and robotics. Fellow of the European Coordinating Committee for Artificial Intelligence (ECCAI) Member of the Slovene Academy of Arts and Sciences (SAZU) Former editorial board member of Artificial Intelligence , Machine Learning , Journal of AI Research , and other leading journals Co-founder and first chairman of the Slovenian AI Society (SLAIS) Bratko has secured numerous research projects including ARRS programs on artificial intelligence (2009-2020), the PARKINSCHECK project for Parkinson's disease detection, and European projects like X-MEDIA and XPERO. His laboratory serves as the central hub for AI research at the University of Ljubljana, fostering collaborations across medical, environmental, and industrial domains. He has mentored numerous researchers and maintained active collaborations through visiting positions at institutions including Edinburgh University, University of New South Wales, and Delft University of Technology.

Laure ZannaView profile

Professor

• Climate Dynamics
• Physical Oceanography
• Machine Learning
• +4 more

Laure Zanna is the Joseph B. Keller and Herbert B. Keller Professor in Applied Mathematics at New York University, with joint appointments in the Department of Mathematics at the Courant Institute and the Center for Data Science. Her research bridges physical oceanography, climate physics, and machine learning. PhD (2009), Harvard University MSc (2003), Weizmann Institute of Science BSc (2001), Tel Aviv University Her work focuses on understanding the ocean's role in climate systems, combining numerical simulations , turbulence modeling , machine learning , and data-driven approaches to study processes like ocean heat uptake , carbon cycling , and sea level rise . She leads M²LInES, an international collaboration applying scientific machine learning to improve climate models. The 15 most recent publications highlight trends in deep learning for climate emulation , stochastic parameterization , and causal inference in spatiotemporal climate fields . Key areas include mesoscale eddy modeling , air-sea flux uncertainty , and multi-scale climate interactions . Nicholas P. Fofonoff Award (2020), American Meteorological Society Zanna’s leadership in M²LInES and her interdisciplinary approach to climate modeling emphasize the integration of data science and geophysical fluid dynamics . She collaborates with institutions like GFDL and WHOI, advancing methodologies for climate prediction and ocean reanalysis .

Sergio CampobassoView profile

Senior Lecturer

• Computational Fluid Dynamics (CFD)
• Renewable Energy
• Turbomachinery Design
• +6 more

Dr. Sergio Campobasso is a Senior Lecturer in Renewable Energy Engineering at the School of Engineering, Lancaster University , where he has worked since January 2013. He holds a Postgraduate Diploma in Turbomachinery from the Von Karman Institute (Belgium, 1996) and a PhD in Numerical Analysis from Oxford University (2004). Education: Postgraduate Diploma in Turbomachinery (Von Karman Institute), PhD in Numerical Analysis (Oxford) Professional Experience: BMW-Rolls Royce (aerodynamicist/stress engineer), Rolls-Royce CFD UTC (Oxford), Cranfield University (uncertainty propagation), Glasgow University (wind turbine research) His research focuses on advancing Computational Fluid Dynamics (CFD) technologies for wind and gas turbine design, incorporating stochastic uncertainty from manufacturing and environmental variability. Key projects include: Development of harmonic balance Navier-Stokes solvers for unsteady flows Low-speed preconditioning methods for mixed-speed flow analysis Novel boundary conditions for complex turbomachinery geometries Hybrid parallelization techniques for high-performance computing His recent publications (2020-2024) emphasize blade erosion modeling, offshore wind turbine dynamics, and machine learning applications for energy loss prediction. Current research projects (2021-2024) address predictive maintenance, emissions reduction, and multi-rotor system optimization.

Anna RaffertyView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Educational Assessment
• +5 more

Anna Rafferty is an Associate Professor of Computer Science at Carleton College , where she has been teaching since 2014. She earned her Ph.D. from the University of California, Berkeley, under Professor Tom Griffiths, and holds an MS from Stanford University. As the chair of the Computer Science Department, she plays a pivotal leadership role in shaping academic programs. Research Interests : Her work focuses on applying machine learning and artificial intelligence to enhance education. Key projects include: Developing ML models for educational assessment that provide granular insights into student understanding Using inverse reinforcement learning to diagnose algebra misconceptions via Emmy's Workshop Optimizing game-based assessments for efficient knowledge diagnosis Building adaptive platforms for chemistry education with automated feedback systems Exploring bandit algorithms to dynamically improve online educational content Teaching : She teaches foundational CS courses (CS111, CS254) and advanced topics like natural language processing and machine learning . Her commitment to education extends to outreach programs, including teaching at Girls Inc. and volunteering for Expanding Your Horizons and Women in Computer Science and Electrical Engineering at Berkeley.

Amin RahimianView profile

Assistant Professor

• Networks
• Data Science
• Decision Sciences
• +9 more

Amin Rahimian is an Assistant Professor at the University of Pittsburgh , affiliated with the Swanson School of Engineering and leading the Sociotechnical Systems Research Laboratory . He is also associated with the Intelligent Systems Program and the Institute for Cyber Law, Policy, and Security . PhD in Electrical and Systems Engineering (University of Pennsylvania) Master’s in Statistics (Wharton School) His research spans networks, data, and decision sciences , focusing on robust autonomy , control of large-scale networked systems , satellite constellations , and decision support in mission-critical applications . His work bridges applied probability, statistics, algorithms, and decision/game theory to address challenges in online social networks, public health, e-commerce, cyberinfrastructure, and warfare . Recent publications highlight his expertise in differential privacy, network resilience, and social contagion dynamics. Awards include the Meta Foundational Integrity Research Award (2023) and Pitt Cyber Accelerator Grant (2022) . He has served on program committees for conferences like ACM Economics and Computation and IISE Annual Conference.

Sally McCleanView profile

Professor

• Process Mining
• Business Process Management
• Activity Recognition
• +9 more

Professor Sally McClean is an academic at Ulster University's School of Computing , holding the title of Professor of Mathematics . Her research spans computational mathematics, machine learning, and process mining, with applications in healthcare, IoT, and smart cities. Focus areas include Process Mining , Activity Recognition , and Lead Toxicity Prediction Funded by organizations like The Royal Society and Invest Northern Ireland Her work contributes to the UN Sustainable Development Goals in health and technology domains. Recent publications analyze smart home IoT systems, emergency department logistics, and federated process mining privacy frameworks. Awarded Honorary award (2021) for heartbeat rhythm analysis Received TM Forum Catalyst Industry Contribution (2022)

• Energy Economics
• Electricity Market Regulation
• Risk Management in Energy Systems
• +3 more

Carlos Batlle López is an Associate Professor at the Instituto de Investigación Tecnológica (IIT) of the ICAI School of Engineering , Comillas Pontifical University . He specializes in Energy Economics and Electric Power Systems Regulation . From 2011 onwards, he has been a Visiting Scholar at the Massachusetts Institute of Technology (MIT) , contributing to the MIT Energy Initiative . Additionally, he serves as an Electricity Advisor at the Florence School of Regulation , directing the FSR Summer School on Regulation of Energy Utilities . PhD in Industrial Engineering , Comillas Pontifical University His research focuses on electricity market operations , generation supply security , and risk management , with particular emphasis on wholesale/retail market mechanisms and renewable energy integration . Over his career, he has led >40 research projects and consulted for governments/industry in >25 countries. Recent publications analyze topics including: Risk aversion in energy investments Capacity mechanisms in EU markets Hydro resource management Smart grid regulation Renewable energy auctions His work spans both theoretical modeling (e.g., probabilistic market simulations) and practical implementation (e.g., Colombian generation expansion models). Scientific recognition includes multiple Sexenio awards from Spain's Ministry of Science for excellence in engineering research. He has served as co-author in 30+ peer-reviewed articles across journals like Energy Policy , IEEE Transactions on Power Systems , and Renewable & Sustainable Energy Reviews .

W. Pratt RogersView profile

Associate Professor

• Mine Safety and Health
• Machine Learning in Mining
• Data Analytics
• +2 more

W. Pratt Rogers is an Associate Professor in the Department of Mining Engineering at the University of Utah's College of Engineering since 2016, concurrently serving as Mining Safety Assistant Program Director at the Rocky Mountain Center for Occupational and Environmental Health (RMCOEH). He is co-developing a new Mining Safety master's program launching in fall 2024 to address critical miner safety challenges in Region 8 and the Western U.S. His research integrates data science and mining engineering to solve safety issues, with core expertise in operator fatigue management through IoT wearables and machine learning models blending subjective/objective data. Recent work focuses on stope stability analysis using probabilistic approaches for imbalanced datasets, rare earth elements extraction from coal resources, and ethical supply chains for conflict minerals using blockchain-inspired frameworks. Publication trends reveal strong computational intelligence adoption across mining safety, with 12 of 15 recent articles applying AI/ML techniques to fatigue tracking, haul truck operations, and safety training. Key thematic clusters include real-time health monitoring systems, optimization of surface mining processes through lean methodologies, and ethical resource management frameworks. Dr. Rogers has chaired 16 graduate committees and secured NIOSH funding for operator fatigue research. His industry background includes VP of Product Development at MISOM Technologies and site engineering for Luminant Mining, complementing his academic role as chair of SME's Safety and Health division. At RMCOEH, he contributes to the Center's mission through the Mining Safety program, leveraging partnerships with industry stakeholders to translate research into practical safety solutions for Western U.S. mining operations.

• Control Systems
• Energy Management
• Distributed Optimization
• +4 more

Yutaka Iino serves as an Associate Professor at Waseda University's Advanced Collaborative Research Organization for Smart Society (ACROSS) and Research Institute for Advanced Network Technology (RIANT). With over 35 years of professional experience, he transitioned from a long career at Toshiba Corporation (1984-2018) where he worked on energy management and energy-saving technologies, to his current academic position focusing on smart society element technologies including power, transportation, and information systems. Doctor of Engineering from Tokyo Institute of Technology (2015) Master's in Electrical Engineering from Waseda University (1984) Bachelor's in Electrical Engineering from Waseda University (1982) Professor Iino's research focuses on control and system engineering with particular emphasis on energy management systems and distributed optimization. His work bridges theoretical control systems with practical applications in smart grids, electric transportation, and distributed energy resources. He has pioneered approaches in decentralized optimization, modeling techniques for energy systems, and demand science applications in power networks. His research interests include Di-centralized and distributed optimization, Distributed Energy Resource management, System Control Theory, and Demand Science. Analysis of his recent publications reveals a strong focus on practical applications of distributed energy resources, particularly in the context of electric buses, photovoltaic integration, and grid congestion management. His work demonstrates a consistent trajectory toward developing market-oriented solutions for energy systems, with increasing emphasis on machine learning techniques for parameter optimization and federated learning for smart inverter control. The research spans multiple domains including electrical engineering, transportation systems, and control theory, with a clear practical orientation toward solving real-world energy challenges. SICE Paper Award, No.1995 for 'Cyber-physical Optimal Traffic Signal Control Based on a Macroscopic Traffic Model and Its Verification Using Microscopic Traffic Simulator' Best Paper Award of IEEJ Transactions on Electrical and Electronic Engineering 2021 for 'Evaluation of Mutual Effect between Power and Traffic Aiming Electrified Urban Transportation System Using Integrated Light Rail Transit and Distribution System Model' Professor Iino has secured research funding through Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research, including current projects on cooperative next-generation distribution system operation methods for mass introduction of photovoltaic power generation (2023-2027) and hybrid model analysis methods with data and physical models for automatic energy analysis (2020-2023). His committee memberships include roles in IEEE, Japan Society of Energy and Resources, The Institute of Electrical Engineers of Japan, and The Society of Instrument and Control Engineers (where he holds Fellow status). At Waseda University, Professor Iino contributes to the research ecosystem through the Advanced Collaborative Research Organization for Smart Society, where he leads efforts in developing practical energy management solutions that bridge theoretical control systems with real-world applications in smart grids and transportation electrification. His work exemplifies the integration of academic research with industry applications, leveraging his extensive industrial experience at Toshiba to inform his current academic pursuits.