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Robert LagerströmView profile

Professor

• Cyber Security
• Information Systems
• Threat Modeling
• +6 more

Robert Lagerström is a Professor at KTH Royal Institute of Technology specializing in Industrial Information Systems. His research focuses on developing automated cyber security frameworks and threat modeling methodologies for large-scale IT environments. Key areas include vulnerability analysis of IoT, automotive systems, and critical infrastructure, with a particular emphasis on probabilistic modeling and simulation languages like vehicleLang and powerLang. He collaborates extensively with industry partners across automotive, banking, energy, and cloud sectors to validate practical security solutions. His work emphasizes model-based security analysis that avoids disrupting live systems, enabling safe testing of cyber defenses. Notable contributions include the securiCAD tool for enterprise cyber security management and the PatrIoT framework for agile threat research in IoT ecosystems. Lagerström's research integrates enterprise architecture modeling with security behavior analysis, addressing challenges in digitalization and agile development environments. He has pioneered methods for quantifying attack resilience through probabilistic threat simulations and has published widely on topics such as LPWAN security, SCADA system vulnerabilities, and ethical considerations in offensive cyber training. His work bridges theoretical cyber security concepts with real-world implementation challenges in both traditional IT and emerging technologies like blockchain and microservices architectures.

Jan DettmerView profile

Associate Professor

• Geophysical Inverse Problems
• Bayesian Methods
• Seismology
• +4 more

Dr. Jan Dettmer is an Associate Professor in the Department of Earth, Energy, and Environment at the University of Calgary's Faculty of Science. His research focuses on quantitative analysis of Earth structures through geophysical data inversion, specializing in Bayesian methods for uncertainty quantification. His work spans seismology, acoustical oceanography, and tsunami hazard prediction, with applications ranging from shallow seabed characterization to deep mantle structures. Research interests include: Probabilistic inversion methods for earthquake source parameters and earth structure Wave propagation modeling in complex media Computational algorithm development for large-scale inverse problems Integration of supercomputing (CPU/GPU clusters) in geophysical analysis Recent publications demonstrate strong focus on geophysical inversion techniques, computational methods, and applications to energy and environmental challenges. Awarded the Faculty of Science Research Award for early career excellence (2019).

• Trustworthy Machine Learning
• Automated Machine Learning
• Data-Driven Software Engineering
• +3 more

Dr. Huong Ha is a Senior Lecturer in Computer Science at RMIT University's School of Computing Technologies, located at the City Campus in Australia. Her research focuses on trustworthy machine learning, automated machine learning, and data-driven software engineering. She holds an ORCID ID (0000-0003-2463-7770) and is actively involved in supervising Masters and PhD students. Prior to her academic role, she worked as a Data Scientist at Freelancer International Pty Limited in Sydney from 2017 to 2018. Her research interests emphasize practical applications of machine learning in software systems optimization, including root cause analysis for microservices, Bayesian optimization techniques, and performance prediction for configurable software. She has published extensively in top conferences such as ICSE, AAAI, and NeurIPS, addressing challenges in high-dimensional optimization, system monitoring, and algorithmic efficiency. Current supervision projects include topics like Explainable AI in Human-Agent Planning, Fraud Detection on Blockchain networks, and High-dimensional Bayesian Optimization via Evolutionary Computation. Her work bridges theoretical advancements with real-world software engineering problems, contributing to robust and scalable solutions in AI-driven systems.

Duane McVayView profile

Professor

• Risk and uncertainty assessment
• Unconventional resource assessment
• Petroleum reservoir simulation
• +2 more

Duane McVay is a Professor and Albert B. Stevens Chair in Petroleum Engineering at Texas A&M University's College of Engineering, where he also serves as Associate Director of the Crisman Institute for Petroleum Research. His educational background includes a Ph.D., M.S., and B.S. in Petroleum Engineering from Texas A&M University. McVay's research focuses on: Risk and uncertainty quantification in energy systems Unconventional resource evaluation methodologies Integrated reservoir characterization techniques Petroleum reservoir simulation advancements Energy investment optimization under uncertainty His publications demonstrate consistent focus on uncertainty management in energy resource assessment, with recent work emphasizing Bayesian statistical methods, production forecasting improvements, and bias reduction techniques in reserves estimation. The research spans shale resources globally and addresses both technical and economic dimensions. Significant scientific honors include: Distinguished Member, Society of Petroleum Engineers (2007) Distinguished Lecturer, Society of Petroleum Engineers (2015-2016) Practice Award, Decision Analysis Society (2006) Member, Petroleum Engineering Academy of Distinguished Graduates (2015) McVay leads research initiatives at the Crisman Institute, collaborating with industry partners to address challenges in petroleum resource evaluation and management.

Chad A. ShawView profile

Professor

• Statistics
• Genomics
• Systems Biology
• +3 more

Chad A. Shaw is a Professor at Baylor College of Medicine and a Joint Professor of Statistics at Rice University. He serves as Director of the D2K Laboratory at Rice, and holds additional roles including Investigator at the Jan and Dan Duncan Neurological Research Institute and Faculty Member at the Baylor College of Medicine’s STaR Center and Quantitative & Computational Biosciences program. His expertise spans statistical genomics, bioinformatics, and molecular genetics. Education: BS in Mathematics (Duke University, 1995); PhD in Statistics (Rice University, 2001). Research Interests: Focuses on statistical methods for genomics, including next-generation sequencing analysis, structural variation studies, and clinical exome sequencing. Key contributions include probabilistic models for mutation transmission in human genetic diseases, copy-number variation analysis, and functionalization of genetic variants. His work bridges computational statistics with clinical applications, addressing challenges in rare disease diagnostics and Mendelian disorder modifiers. Publications: Over 200 peer-reviewed articles, including foundational studies on recurrence risk in genetic disorders and mechanisms of structural variation. Recent work emphasizes Bayesian modeling in sequencing assays, CNV-driven genetic diseases, and prenatal genomic analysis. Awards/Grants: No explicit awards mentioned, but his research has been cited ~17,000 times. Active in grant-funded projects across computational and clinical genomics. Advising: Trained 5 PhD students and advised over 10 others, with students contributing to areas like high-dimensional regression and software tools for variant prioritization. Labs/Teams: Leads the D2K Laboratory at Rice, fostering interdisciplinary research in data science and computational biology.

Menglong GuanView profile

Assistant Professor

• Behavioral Economics
• Experimental Economics
• Applied Microeconomic Theory
• +3 more

Menglong Guan is an Assistant Professor of Business Economics at the Department of Risk Management, Smeal College of Business, Pennsylvania State University. He received his Ph.D. in Economics from the University of California, Santa Barbara in 2024. His research focuses on Behavioral and Experimental Economics, with a specialization in information economics, complexity, and bounded rationality. His work examines strategic decision-making under complexity, probabilistic reasoning, and information demand dynamics. Education: PhD in Economics, UC Santa Barbara (2024). Research interests include experimental methodologies to study decision-making under uncertainty, cognitive limitations in strategic interactions, and the design of trustworthy institutions. His recent publications explore complexity in strategic choices, probabilistic vs. strategic errors, and information bundle selection in decision processes. He has contributed to journals like Games and Economic Behavior . No scientific awards explicitly listed. Advising no listed students. Active in research collaborations with scholars such as Ryan Oprea and Sen Geng. Laboratory work involves experimental economics setups to model real-world decision scenarios, emphasizing empirical validation of theoretical models.

Catarina Pinto MoreiraView profile

Adjunct Associate Professor

• Quantum Probabilistic Models
• Machine Learning
• Deep Learning
• +3 more

Dr. Catarina Pinto Moreira is an Adjunct Associate Professor in the School of Computer Science at Queensland University of Technology (QUT). She holds a PhD in Information Systems and Computer Engineering from the University of Lisbon and specializes in quantum probabilistic models, machine learning, and explainable AI. Her research focuses on developing non-classical probabilistic graphical models for decision-making, particularly in medical and cognitive contexts. She has been recognized with awards such as the Dean's Award for Excellence in Teaching (2018) and the Centre for Data Science 2020 Excellence Award. Dr. Moreira is an Associate Editor for BMC Bioinformatics' 'Artificial Intelligence in Bioinformatics' section, emphasizing applications of machine learning in biological data. She actively supervises PhD students in areas like interpretable AI and predictive process analytics. Her academic roles include teaching at QUT and the University of Leicester, where she contributed to courses in information systems, finance, and artificial intelligence. Her work bridges quantum cognition, medical decision support, and human-centered AI, with publications spanning journals like Behavioral and Brain Sciences and Entropy . She has secured grants totaling $20,000 for research in Explainable AI and causality. Dr. Moreira's contributions to AI ethics, multimodal learning, and adversarial attacks reflect her commitment to advancing trustworthy AI systems.

Pavlo KrokhmalView profile

Professor

• Stochastic Optimization
• Decision Making Under Uncertainty
• Risk Analysis
• +12 more

Pavlo Krokhmal is a Professor in the Department of Systems and Industrial Engineering at the College of Engineering, University of Arizona. He serves as the Director of Industrial Engineering and is a member of the Graduate Faculty. He has previously held academic positions at the University of Iowa and the University of Florida. Education: PhD in Operations Research, University of Florida, Gainesville, Florida, United States PhD in Mechanics of Solids and Applied Mathematics, Kyiv National Taras Shevchenko University, Kyiv, Ukraine MS in Applied Mathematics and Mechanics, Kyiv National Taras Shevchenko University, Kyiv, Ukraine His research focuses on stochastic optimization, risk analysis, and decision-making under uncertainty, with applications in financial engineering, network resilience, and renewable energy systems. He also contributes to multidisciplinary optimization and cooperative control. His work bridges applied mathematics, engineering, and operations research. The most recent publications reflect a strong trend in risk-averse optimization under uncertainty, especially in network structures, energy systems, and combinatorial problems. His work integrates advanced mathematical modeling, stochastic programming, and computational algorithms. Topics frequently include risk measures like CVaR, p-cone programming, and PDE-constrained optimization with stochastic inputs. Scientific Awards and Honors: Diploma in the Competition of Young Scientists and Students for the Best Research Project, National Academy of Sciences of Ukraine, Spring 1997 Soros Student Award, International Soros Science and Education Program, Fall 1994 Scholarship for scientific and academic achievements, National Academy of Sciences of Ukraine, Spring 1994 Air Force Summer Faculty Fellowship Award (multiple years: 2011, 2012, 2014, 2018, 2019) NRC Senior Research Associateship Award, National Research Council, Spring 2015 Donald E. Bently Faculty Fellowship of Engineering, University of Iowa, Fall 2013 Recognition for Excellence in Teaching, College of Engineering, University of Iowa (2010, 2013) Dr. Krokhmal has been actively involved in advising graduate students and leading research projects funded by agencies such as the Air Force Office of Scientific Research. His collaborations span across institutions and disciplines, including work with researchers at the University of Florida, University of Iowa, and military research labs. He has served on editorial boards and contributed to academic leadership through journal editorials and peer review. His research is conducted within interdisciplinary teams focusing on optimization, risk modeling, and complex systems. These teams often involve mathematical modeling, algorithm development, and simulation for real-world applications in defense, energy, and infrastructure resilience.

Xin XingView profile

Assistant Professor

• Medical Imaging
• Machine Learning
• Neuroscience
• +3 more

Xin Xing is an Assistant Professor at the University of Nebraska at Omaha, College of Information Science & Technology, Department of Computer Science. Their research bridges machine learning, neuroscience, and biomedical applications. Education: Ph.D. in Computer Science (2023), University of Kentucky M.S. in Information Technology (2016), University of Stuttgart B.S. in Communications Engineering (2011), Shandong University Research Interests: Medical Imaging AI: Developing models for Alzheimer's disease diagnosis using 3D PET/MRI and transformers (e.g., ADViT, CAT-XPLAIN) Gut-Brain Axis: Investigating microbiome impacts on neurodegeneration, particularly in APOE4 carriers Computer Vision: Innovating diffusion models, self-supervised learning, and attention mechanisms for biomedical and geospatial applications Publication Trends: Recent work focuses on neuroimaging biomarkers for Alzheimer's, microbiome interventions, and scalable vision-language architectures. They integrate cutting-edge ML techniques with clinical data analysis. Collaborations: Engaged in interdisciplinary projects spanning genetics, nutrition, and traffic safety. Their work appears in journals like Communications Biology and explores real-world applications such as roadway hazard detection via satellite imagery.

• Hydrological Modeling
• Environmental Engineering
• Bayesian Inference
• +3 more

Professor Dmitri Kavetski is a faculty member in the School of Architecture and Civil Engineering at the University of Adelaide, specializing in Environmental Engineering. His work focuses on developing advanced hydrological models and Bayesian methods for uncertainty analysis, including the Bayesian Total Error Analysis (BATEA) framework. He collaborates internationally with institutions like the Swiss Federal Institute for Aquatic Science and Technology (EAWAG) and the US National Center for Atmospheric Research (NCAR). His research emphasizes probabilistic predictions, numerical algorithms, and improving water resource management through robust modeling techniques. Research interests include Bayesian inference, hydrological modeling, numerical analysis, and climate impacts. His contributions to frameworks like BATEA have been applied in operational forecasting systems, such as the Australian Bureau of Meteorology’s seasonal predictions. He is actively involved in supervising postgraduate students and has secured significant research funding. Publications highlight advancements in probabilistic hydrology, subseasonal forecasting, and model adequacy testing. His work bridges theoretical developments with practical applications, addressing challenges in ungauged basins and ephemeral catchments. Collaborations span global institutions, reflecting his commitment to advancing environmental engineering through interdisciplinary approaches.

Arndt HildebrandtView profile

Professor

• Civil Engineering
• Offshore Engineering
• Hydraulic Engineering
• +3 more

Prof. Arndt Hildebrandt is a faculty member at Leibniz University Hannover, affiliated with the Ludwig-Franzius-Institute within the Faculty of Civil Engineering and Geodesy. His research focuses on offshore engineering, hydraulic engineering, and structural dynamics, with a strong emphasis on wave-structure interactions, scour development, and renewable energy systems. He leads projects on offshore wind turbine foundations, wave energy converters, and environmental impact assessments. His work integrates experimental and numerical methods, often collaborating with industry partners like Cawthron Institute and Fraunhofer IWES. Key areas of research include hydrodynamic coefficients of marine structures, floating aquaculture systems, and probabilistic safety assessments for offshore megastructures. Projects such as 'BioSchWelle' explore ecological solutions for wave damping and biodiversity enhancement. He has extensive experience in coastal and hydraulic engineering, including studies on munitions drift in marine environments and scour patterns around offshore foundations. Prof. Hildebrandt contributes to collaborative initiatives like the DFG-funded SFB 1463, which develops integrated design methods for offshore megastructures. His publications span over two decades, addressing topics from wave energy converters to large-scale experimental modeling. Despite no listed awards, his contributions to offshore and environmental engineering are significant, with ongoing work on floating wind turbine operations and metocean data analysis in the German Bight.

Tobias MeggendorferView profile

Professor

• Formal Methods
• Probabilistic Verification
• Risk-aware Verification
• +5 more

Prof. Tobias Meggendorfer is an academic leader in formal methods and probabilistic systems verification. He currently holds an interim professorship at the Technical University Munich (TUM) within the Computational Mathematics department under the TUM School of Computation, Information and Technology. His career includes a postdoc at the Institute of Science and Technology Austria (IST Austria) and a PhD at TUM under Prof. Jan Křetínský. His research focuses on formal verification techniques for probabilistic systems, including risk-aware verification frameworks, stochastic games, and integration of machine learning into verification processes. Notable contributions include the Owl tool library for ω-automata and LTL translations, and the PET partial exploration tool for probabilistic verification. Prof. Meggendorfer has published extensively on topics such as value iteration stopping criteria for stochastic games, entropic risk measures, and semantic learning in LTL synthesis. He actively serves on program committees for AAAI, CAV, and other top conferences, and has reviewed for journals like JACM and IEEE TSE. His work bridges theoretical foundations with practical tool development, evidenced by contributions to open-source projects like the JBDD library and DOMtutor educational framework. He has advised multiple MSc and BSc theses, translating research into impactful educational and industrial applications.

Daniel GrecoView profile

Professor

• Epistemology
• Philosophy of Science
• Metaethics
• +3 more

Daniel Greco is a Professor of Philosophy at Yale University and Director of Undergraduate Studies in the Department of Philosophy. He holds adjunct roles in Yale Law School, specializing in Law and Epistemology. His academic lineage includes a Ph.D. from MIT (2012), an M.Phil. from Cambridge University (2007), and an A.B. from Princeton University (2006). Greco’s research focuses on epistemology, particularly skepticism, epistemic norms, and formal models drawn from economics and decision theory. His 2023 monograph, Idealization in Epistemology: A Modest Modeling Approach , argues for the centrality of formal methods in epistemology. His work appears in journals like Mind , Philosophical Review , and Journal of Philosophy . Awarded the Sir Karl Popper Prize for Significance Testing in Theory and Practice and the Young Epistemologist Prize for Iteration and Fragmentation , Greco also serves as Epistemology Section Editor for Philosophy Compass and Ergo . His teaching spans epistemology, philosophy of science, AI ethics, and directed studies at Yale. Greco’s research and teaching emphasize formal methods, interdisciplinary engagement, and resolving epistemic dilemmas through rigorous modeling.

Mengqi WangView profile

Associate Professor

• Power Electronics
• Electric Vehicles
• Renewable Energy Systems
• +5 more

Mengqi Wang is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Michigan-Dearborn . With a PhD from North Carolina State University and a BS from Xi'an Jiaotong University, her research focuses on power electronics , electric vehicles , and renewable energy systems . Advisor: Dr. Alex Q. Huang (IEEE Fellow) Current role: Director, Academic Program Research themes: high-efficiency power supplies , converter reliability , wide-bandgap devices Grants: 2025-2030 State of Michigan energy storage project, multiple DOE and NSF grants Publication trends emphasize power converter design for automotive applications , thermal management innovations, and microgrid stability analysis. Her recent 2025 Energies paper addresses battery energy storage optimization , while APEC 2025 contributions focus on magnetic-less converter architectures and high-density PCB designs . Teaching portfolio includes Advanced Electric Drive Transportation (ECE 646), Advanced Power Electronics (ECE 615), and foundational courses like Power Electronics (ECE 415) and Renewable Electric Power Systems (ECE 4432).

• Trustworthy AI
• Deep Learning Theory
• Decentralised Learning
• +10 more

Fengxiang He is a Lecturer in AI Driven Business Informatics and Financial Computing at the School of Informatics, University of Edinburgh, where he is affiliated with the Artificial Intelligence and its Applications Institute. He also holds fellowships and affiliations with the Generative AI Laboratory, Institute for Adaptive and Neural Computation, Edinburgh Future Institute, and the Edinburgh Centre for Financial Innovations. Prior to Edinburgh, he was the Founding Lead of the Trustworthy AI project at JD.com, Inc. His research centers on trustworthy AI, with core interests in deep learning theory, decentralised learning, privacy and fairness in machine learning, symmetry in machine learning, and algorithmic game theory, with applications in economics and finance. He leads the Edinburgh Ventures for Intelligence and Economics and is actively involved in shaping ethical and robust AI systems. The recent publications highlight a strong trend in theoretical and applied machine learning, particularly in decentralised learning, trustworthy AI, game theory, and financial modeling. His work bridges deep learning theory with real-world applications, focusing on safety, privacy, fairness, and robustness. The articles span top venues including ICML, NeurIPS, ICLR, IEEE TPAMI, and ACM, reflecting high impact and interdisciplinary reach. Area Chair, ICML Area Chair, NeurIPS Area Chair, UAI Area Chair, AISTATS Area Chair, ECAI Area Chair, ACML Associate Editor, IEEE Transactions on Technology and Society Fengxiang He supervises multiple PhD and MScR students and is currently recruiting for postdoctoral researchers and fully funded PhD studentships through Centres for Doctoral Training in Machine Learning Systems, Designing Responsible NLP, and AI for Biomedical Innovation. He welcomes visitors and collaborators. His team includes PhD students Johnny MyungWon Lee, Kejiang Brian Qian, Saimun Habib, Qian Zuo, and MScR student Ye Wang, along with several interns and visiting researchers. He leads the Edinburgh Ventures for Intelligence and Economics and is a key member of the Generative AI Laboratory and other interdisciplinary research centers, fostering collaboration across AI, finance, and societal impact domains.