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Qianru GuoView profile

Adjunct Professor

• Structural Fire Engineering
• Performance-Based Fire Protection Design
• Reliability Analysis
• +3 more

Qianru Guo serves as an Adjunct Professor in the Department of Civil and Urban Engineering at NYU's Tandon School of Engineering, while maintaining her primary role as Senior Consulting Engineer at Simpson Gumpertz & Heger Inc. (SGH) with over 10 years of specialized experience in performance-based fire protection design and structural fire engineering. Her academic credentials include: Ph.D. in Structural Engineering from University of Michigan, Ann Arbor M.S. in Civil Engineering and Mechanical Engineering from University of Michigan, Ann Arbor B.S. in Civil Engineering from Tongji University, Shanghai, China Dr. Guo's research pioneers the integration of probabilistic methodologies with structural fire engineering, focusing on reliability analysis of fire-exposed structures through advanced computational techniques. Her work develops inverse modeling for heat release rate determination, stochastic finite element methods for fire resistance evaluation, and performance-based design frameworks that redefine safety standards. She addresses critical gaps in predicting structural behavior under fire by quantifying uncertainties in material properties, fire scenarios, and structural responses. Analysis of her publication trajectory reveals consistent methodological evolution from fundamental reliability theory (2011-2013) toward practical performance-based design applications (2014-2018). Her research consistently bridges computational mechanics with fire safety engineering, emphasizing probabilistic safety assessment and code-compliant reliability calibration across residential and commercial structures. Based at 6 MetroTech Center, 4th Floor, Brooklyn, NY 11201, her professional activities integrate academic research with real-world engineering practice to advance fire-resilient infrastructure design.

• Knowledge Engineering
• Ontology-based Systems
• Human-Centric Systems
• +6 more

Dr Flavio Pileggi is a Senior Lecturer in Computer Science at the University of Technology Sydney (UTS), Australia, since July 2023. Previously, he served as Lecturer in Computer Science (2021–2023) and Information Systems (2017–2021) at the same institution. Education: Ph.D. in Computer Networks (Cum Laude), Universitat Politècnica de València, Spain (2011) M.Sc. in Computer Engineering, Università della Calabria, Italy (2005) As a specialist in Knowledge Engineering and Ontology-based Systems, his research focuses on human-centric socially sustainable solutions, particularly socio-technical systems, sustainable development, and climate change. His work bridges technical innovation with socio-economic and environmental value creation. Recent publications explore uncertainty in clustering algorithms, AI ethics, digital twins with ontologies, and socio-technical impacts of AI in education. His 15 most recent articles (2021–2025) span topics like self-reported data risks, smart technology adoption in rural regions, and pandemic resilience metrics. Scientific Awards: Best Paper Award at ICWMC 2006 Teaching Awards from AEMG Education (2022, 2024) He actively supervises PhD/Masters students and participates in 10+ funded collaborative research projects, including grants from UTS MCR Research Capabilities Development Initiative (2025) and CSIRO Next Generation Graduates Program (2024–2027). Currently, he serves on the editorial board of Sustainability (MDPI).

Gustavo Benitez AlvarezView profile

Professor

• Numerical Analysis
• Computational Mathematics
• Finite Difference Methods
• +6 more

Gustavo Benitez Alvarez is a Full Professor in the Department of Exact Sciences at Federal Fluminense University (Universidade Federal Fluminense) in Brazil. His academic profile demonstrates an active research career with numerous publications spanning computational mathematics, numerical analysis, and biomedical applications. His research interests focus primarily on Numerical Analysis , Finite Difference Methods , and Computational Mathematics , with significant applications in Mathematical Oncology and Thermal Physics . He has developed innovative approaches to solving partial differential equations, particularly the Helmholtz equation, where he has created methods that eliminate pollution error in one dimension and minimize dispersion in two dimensions. His publication record shows a strong trend toward interdisciplinary research, with approximately half of his recent work applying advanced numerical methods to biomedical problems, particularly glioma (brain tumor) modeling and treatment optimization. The other half focuses on fundamental numerical methods for PDEs, computational fluid dynamics, and thermal analysis. Gustavo Benitez Alvarez's work demonstrates a consistent pattern of high-quality publications in reputable journals such as Annals of the Brazilian Academy of Sciences , Mathematical Notes , and Semina: Ciências Exatas e Tecnológicas . His research has practical applications in medical treatment planning, engineering design, and computational physics. He collaborates with researchers across multiple institutions, including co-authorship with colleagues from UFF (Universidade Federal Fluminense) and other Brazilian universities. His work on glioma modeling shows particular promise for improving radiotherapy treatment planning through more accurate tumor growth prediction.

Benjamin TrabingView profile

Researcher

• tropical meteorology
• tropical cyclones
• lightning applications
• +4 more

Benjamin Trabing serves as an Associate Scientist at the University Corporation for Atmospheric Research (UCAR) working remotely with the National Hurricane Center (NHC), focusing on advancing tropical cyclone forecasting capabilities and hazard communication strategies. His research directly impacts operational forecasting practices at one of the world's leading hurricane prediction centers. His academic foundation includes: B.S. in Meteorology from the University of Oklahoma M.S. and Ph.D. in Atmospheric Science from Colorado State University Dr. Trabing's research program centers on critical challenges in tropical cyclone prediction with particular emphasis on: Improving intensity and structure forecasting through advanced modeling techniques Developing and validating probabilistic forecast products for public communication Applying lightning observations to tropical cyclone monitoring Investigating radiation effects on tropical cyclone dynamics Enhancing understanding of diurnal cycles in tropical systems Translating research findings into operational forecasting improvements Analysis of his publication record reveals a strategic research trajectory that increasingly bridges meteorological science with social science considerations in risk communication. His recent work demonstrates growing emphasis on probabilistic forecasting methods and effective communication of forecast uncertainty to diverse stakeholders. Trabing's research consistently addresses operational forecasting challenges identified by the National Hurricane Center, ensuring practical applications for improving public safety during tropical cyclone events. Through his GitLab contributions and collaborative projects like the Landfall-Distribution-Product (LDP) and Intensity Change Bias and Uncertainty Scheme (IBUS), Dr. Trabing actively develops tools that directly inform operational forecasting practices at the National Hurricane Center, strengthening the research-to-operations pipeline in tropical meteorology.

Jingyi WangView profile

Assistant Professor

• Software Engineering for AI
• Formal Methods
• Security
• +4 more

Jingyi Wang is an Assistant Professor at Zhejiang University (ZJU), China, leading the IS2 (Intelligent System Security) Lab. He holds a US-equivalent tenure-track position and has established himself as a prominent researcher in software engineering for AI and formal methods applied to security. His educational background includes: Bachelor's degree from Xi'an Jiaotong University (2013) PhD from Singapore University of Technology and Design (2018) Research Fellow at National University of Singapore (2019-2020) Wang's research focuses on developing principled methodologies for building trustworthy AI models and secure systems. His work spans testing, verification and repair of AI models/systems, AI safety and security, and formal reasoning of security. He has developed innovative approaches for neural network testing, verification, and repair, with particular emphasis on explainability, fairness, and robustness. His publication record shows a clear progression from foundational work on concolic testing (2018) to increasingly sophisticated methods for evaluating and improving AI systems, with recent focus on large language models and decentralized identity systems. His research bridges formal methods with practical software engineering challenges in AI security. His notable achievements include: Two ACM SIGSOFT Distinguished Paper Awards (ICSE 2018 and 2020) ACM SIGSOFT Research Highlights Best Paper Award Runner-up at IEEE TDSC 2024 Wang actively contributes to the research community as Program Committee member for top conferences including CCS, ICSE, ISSTA, ASE, WWW, and AAAI. He serves as PC Co-chair for ICFEM 2025, Large Model Safety Workshop 2025, and SAC-SVT 2026. His leadership in the IS2 Lab demonstrates his commitment to advancing intelligent system security through rigorous research.

Sukesh AgharaView profile

Professor

• Nuclear Security
• Nuclear Safeguards
• Nuclear Nonproliferation
• +4 more

Sukesh Aghara is a Professor and Associate Dean of Graduate Studies and Research at the Francis College of Engineering, University of Massachusetts Lowell, where he directs the Nuclear and Chemical Engineering program and leads the Integrated Nuclear Security and Safeguards Laboratory (INSSL). He also serves as a Nuclear Security Fellow with the Center for Terrorism and Security Studies (CTSS) and co-director of the IAEA-funded Intercontinental Nuclear Institute (INI) for international nuclear training. His educational background includes a Ph.D. in Mechanical Engineering (2003) and M.S. in Mechanical Engineering (2001) with Nuclear and Radiation Engineering focus from the University of Texas at Austin, plus M.S. (1999) and B.A. (1996) in Environmental Engineering from Vanderbilt University. His academic journey reflects a transition from environmental to nuclear engineering specialization. Dr. Aghara's research centers on nuclear nonproliferation, security, and safeguards using experimental and computational approaches. His work characterizes radiation fields, evaluates reactor fuel inventories, and models cyber-physical nuclear systems. Current projects address cybersecurity vulnerabilities in nuclear plants, safeguards inspection effectiveness, and nuclear reactor applications for decarbonization. His laboratory INSSL focuses on practical solutions for global nuclear security challenges through radiation transport modeling and safeguards verification technologies. Analysis of his recent publications reveals a strong trajectory toward securing next-generation nuclear systems, particularly small modular reactors (SMRs). His work integrates cybersecurity with physical safeguards, develops robotic inspection platforms, and applies stochastic modeling for verification. Key themes include optimizing security architectures for advanced reactors while reducing operational costs and enhancing decarbonization potential through nuclear energy. Best Presentation and Paper Award (American Nuclear Society/European Nuclear Society) Outstanding Achievement in Teaching Award (UT Austin) NASA Administrator's Fellow (Cohort 11, 2014) Visiting Scientist at NASA Langley and Oak Ridge National Laboratory President of American Nuclear Society UT Chapter Outstanding Research Award (Roy G. Perry College of Engineering) As Principal Investigator, Dr. Aghara manages multiple federally funded projects including robotic security platforms for nuclear facilities (NNSA/BNL, 2020-present), advanced reactor operational considerations (INL/UTK, 2019-2023), and cybersecurity incident response planning (INL, 2017-2021). His grants consistently address national priorities in nuclear security infrastructure and next-generation reactor safeguards, with a focus on practical implementation and international capacity building through the INI program. The Integrated Nuclear Security and Safeguards Laboratory (INSSL) operates as a multidisciplinary hub collaborating with national laboratories (ORNL, INL), the IAEA, and international partners. It develops radiation detection systems, cybersecurity frameworks, and training protocols while maintaining close ties with the Center for Terrorism and Security Studies (CTSS) for cross-disciplinary threat analysis. Current projects deploy robotic platforms like the Spot robot for automated inspections and model advanced reactor security architectures.

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.

Suelynn ChoyView profile

Professor

• Satellite Navigation
• Precise Positioning
• Geodesy
• +5 more

Professor Suelynn Choy is a Professor of Satellite Navigation and Director of the RMIT SPACE Research Centre at RMIT University. Her roles include Deputy Dean, Industry, International & Engagement and Interim Head of Department in the School of Science. She holds a PhD in precise Global Navigation Satellite Systems (GNSS) from RMIT and specializes in satellite positioning, atmospheric remote sensing, and disaster management. Her research focuses on GNSS applications, satellite remote sensing for climate studies, and geospatial technologies for disaster resilience. Education: PhD in precise GNSS (RMIT University). Research Interests: Precise satellite positioning, GNSS remote sensing of the atmosphere, disaster risk management, and climate studies. Key areas include GNSS-based weather monitoring, multi-hazard early warning systems, and blending satellite and ground-based data for improved environmental analysis. Awards: Recipient of the 2016 Australian National Measurement Institute Prize, 2018 RMIT School of Science Leadership Award, and others recognizing her contributions to satellite navigation and climate research. Advising & Grants: Leads projects on drought monitoring, tropical cyclone risk assessment, and smartphone positioning. Collaborates with national/international committees on satellite navigation and disaster resilience. Supervises PhD and Master's students in geospatial sciences and related fields. Labs & Teams: Director of RMIT SPACE Research Centre, affiliated with the RMIT Sir Lawrence Wackett Centre and Urban Futures initiatives.

• Nuclear Engineering
• Thermal-Hydraulics
• Severe Accident Analysis
• +5 more

Pavel Kudinov is an Associate Professor at the Department of Nuclear Science & Engineering, KTH Royal Institute of Technology. He specializes in nuclear thermal-hydraulics, severe accident analysis, and computational fluid dynamics (CFD). His research focuses on phenomena such as steam explosions, containment venting, thermal stratification in suppression pools, and debris coolability during severe accidents. He actively contributes to experimental validation efforts using facilities like PPOOLEX and PANDA. His work integrates advanced modeling techniques with experimental data to improve safety assessments for boiling water reactors (BWRs) and lead-cooled reactors. He has collaborated on projects like SAFEST and SESAME, addressing core degradation, melt relocation, and ex-vessel accident management strategies. Key areas of expertise include: CFD modeling of multiphase flows, uncertainty quantification in severe accident codes (e.g., MELCOR), and risk-oriented accident analysis methodologies (ROAAM+). He teaches courses on nuclear reactor technology and applied modern physics at KTH.

Rosh RanasingheView profile

Professor

• Coastal Engineering
• Climate Change Adaptation
• Oceanography
• +5 more

Rosh Ranasinghe is an academic with extensive contributions to coastal engineering, climate change adaptation, and oceanography. He has held academic roles across Australia, the USA, and the Netherlands, focusing on coastal processes, climate impacts, and risk assessment. His research emphasizes probabilistic modeling, long-term coastal dynamics, and the strategic application of numerical models to address climate change challenges. Education: He earned a Bachelor of Science Engineering (1st Class Honours) from the University of Peradeniya, Sri Lanka (1993), and a PhD in Coastal Engineering and Oceanography from the University of Western Australia (1998). Research Interests: Key areas include coastal vulnerability assessment, morphodynamic modeling, reduced-complexity models, and the strategic management of coastal systems. His work integrates field data, numerical simulations, and policy-driven analyses to address global challenges like sea-level rise and extreme weather impacts. Advising & Grants: Rosh has supervised/co-supervised 10 post-doctoral fellows, 20+ PhD candidates, and numerous MSc students. He has led capacity development projects in Sri Lanka, India, Vietnam, Thailand, and São Tomé and Príncipe. His contributions to the IPCC include roles as Coordinating Lead Author, Lead Author of the Summary for Policymakers, and Contributing Author to multiple assessment reports, including AR6 and the SROCC. Labs/Teams: His work aligns with international collaborations, particularly through the IPCC, where he contributes to climate risk analysis and coastal adaptation strategies. Projects focus on global datasets (e.g., GCC, Global Sandy Beach Areas) and probabilistic assessments of shoreline changes.

Xiaodong ChengView profile

Assistant Professor

• Control Systems
• Optimization
• Machine Learning
• +5 more

Xiaodong Cheng is an Assistant Professor at the Mathematical and Statistical Methods (Biometris) group in the Department of Plant Science at Wageningen University & Research. His research focuses on control systems, optimization, and machine learning, with applications in agricultural and energy systems. He holds a Ph.D. (cum laude) from the University of Groningen, under Prof. Jacquelien Scherpen, and prior roles include Research Associate at the University of Cambridge and Postdoctoral Researcher at Eindhoven University of Technology. Education: B.S. and M.E. from Northwestern Polytechnical University, China (2011, 2014) Ph.D. (cum laude) in Engineering from the University of Groningen, Netherlands (2018) Research Interests: Data-driven modeling, dimensionality reduction, learning-based control, system identification, and applications in agriculture and energy. He emphasizes practical implementations through tools like SYSDYNET and Bayesian neural ODEs for greenhouse systems. Key Contributions: His work spans model reduction for network systems, fault-tolerant control, and stochastic MPC for greenhouse production. Recent trends in his publications highlight advancements in resilient microgrid control, precision agriculture via drone-based sensing, and Bayesian methods for dynamic systems. Awards: Automatica Paper Prize Award (2017–2019) IEEE Transactions on Control Systems Technology Outstanding Paper Award (2020) Labs/Teams: Leads the Biometris group's efforts in integrating control theory and machine learning for sustainable systems. His work often collaborates with agricultural and energy sector stakeholders for real-world impact.

• Induced Seismicity
• Geothermal Energy
• Hydro-Geomechanical Modeling
• +3 more

Dr. Vanille Ritz is a postdoctoral researcher at the Swiss Seismological Service (SED), ETH Zurich, with a split position between the Modelling Group and the GeoBest team. Her expertise lies in developing hydro-geomechanical and statistical models to understand and forecast induced seismicity in geothermal systems. She focuses on projects like GeoBest, which provides seismological support for Swiss geothermal energy initiatives, and has been a key contact for cantons Vaud, Jura, and Geneva. She holds a Ph.D. from ETH Zurich (2023) and a M.Sc. in Seismology from Université de Strasbourg (2017). Research interests include induced seismicity mitigation, geothermal reservoir engineering, and real-time seismic monitoring. Her work integrates data-driven approaches with physics-based models to optimize energy production while minimizing seismic risks. Notable contributions include the 'Transient Evolution of Earthquake Size Distributions' study (2022), recognized with the SSA Student Award. Education: Ph.D., ETH Zurich (2018–2023): Modelling Induced Seismicity in Deep Geothermal Systems M.Sc., Université de Strasbourg (2015–2017): Seismology Geophysical Engineering, Ecole et Observatoire des Sciences de la Terre (2014–2017) Professional activities include leadership in projects like DEEP (2021–2024), COSEISMIQ (2018–2021), and DESTRESS (2017–2018), advancing de-risking strategies for geothermal energy. Awards include the 2023 SSA Student Presentation Award for seismic risk indicator research. Her work bridges geoscience and engineering, emphasizing collaboration with cantonal governments and energy stakeholders to ensure sustainable geothermal development. Current efforts focus on synthetic benchmark datasets and real-time forecasting tools for induced seismicity management.

Felice ArenaView profile

Professor

• Nonlinear Ocean Waves
• Wave Statistics
• Coastal Engineering
• +5 more

Felice Arena is a Full Professor of Ocean Engineering at the Mediterranea University of Reggio Calabria and serves as Director of the Natural Ocean Engineering Laboratory (NOEL). His research focuses on nonlinear sea waves, wave statistics, hydrodynamic forces on coastal/offshore structures, and innovative wave energy devices. He has authored over 200 publications and led significant research projects funded by the Italian Ministry of Environment, EU Marie Curie actions, and others. Research Interests Nonlinear Ocean Waves Wave Energy Conversion Hydrodynamic Modeling Marine Renewable Energy Stochastic Analysis Coastal/Offshore Structures Scientific Leadership includes chairing the Ph.D. program in Ocean Engineering, serving as Associate Editor for the Journal of Offshore Mechanics and Arctic Engineering (ASME), and participating in international committees like PIANC and EERA. He has advised over 90 undergraduate/MS students, mentored 17 PhD graduates, and supervised 3 current PhD candidates. Notable Achievements : OMAE 2011 SSR Best Paper Award Scientific Coordinator for Marie Curie FP7-PEOPLE-2013-IRSES Key contributor to EU TEN-T and Italian Ministry projects Editorial Board Member: Probabilistic Engineering Mechanics, Mathematical Problems in Engineering

• Hydrology
• Climate Change Adaptation
• Flood Hydrology
• +11 more

Pierluigi Claps is a Full Professor in the Department of Environmental, Land and Infrastructure Engineering (DIATI) at the Polytechnic University of Turin. He serves as a member of the Coordinating Committee of the Politecnico University Centre for Social Affairs and the Interdepartmental Center SISCON for Safety of Infrastructures and Constructions. He is also the Spoke Leader for research and innovation activities in the RETURN Foundation and leads major research initiatives including LIFE IP CLIMAX PO and the RETURN project under PNRR. He has held visiting researcher positions at the University of Melbourne and the University of Virginia. His research interests include climate change adaptation , dams safety , extreme events , flood hydrology , probabilistic risk assessment , and water resources . His work spans both theoretical and applied hydrology, with a strong emphasis on regional flood frequency analysis, climate-hydrology interactions, and the development of tools for risk assessment and infrastructure resilience. He has led numerous national (PRIN) and international (Interreg, LIFE) research projects and has contributed to major initiatives such as FLORA, RENERFOR, and CUBIST. The recent publications (2024–2025) highlight a strong focus on rainfall extremes , flood modeling , citizen science (SIREN project) , and the development of national databases such as FOCA (Flood and Catchment Atlas) and TILDE (Italian Large Dams dataset) . These works employ advanced statistical methods, GIS, and data integration techniques to address climate change impacts on hydrological extremes and infrastructure vulnerability. He has supervised multiple PhD students and serves on the doctoral college committees of both Politecnico di Torino and Università degli Studi di Catania across programs in Civil and Environmental Engineering and Defense Against Natural Risks. His teaching portfolio includes courses such as Climatology and Hydrology , Civil Protection , Water Resources Planning , and Evidence, Models, and Perceptions of Climate Change . He has also taught advanced statistical methods for climate variability assessment. He has held leadership roles in professional organizations, including President of the Italian Hydraulics Group (2012–2016), and has organized major conferences such as the EGU Leonardo Conference on Hydrology and Society (2012) and the Plinius Conference on Mediterranean Storms. He has also served on scientific committees of EGU, IAHS, and STAHY workshops.

• Applied Probability
• Stochastic Processes
• Queueing Theory
• +7 more

Bert Zwart is a Professor and Scientific Staff Member at Centrum Wiskunde & Informatica (CWI) in Amsterdam, where he leads the Stochastics group. He also holds a professorship by special appointment at Technische Universiteit Eindhoven (TU/e). His research spans applied probability, stochastic processes, queueing theory, and operations research, with applications in power systems, supply chains, and risk modeling. His research interests focus on extreme value theory , large deviations , asymptotic analysis of stochastic systems , and random graphs . He develops theoretical tools to analyze rare events and performance limits in complex systems such as fork-join queues, power grids, and stochastic networks. His work combines deep probabilistic insights with practical applications in engineering and operations management. Recent publications (2023–2025) reveal a strong focus on tail behavior in queueing systems, large deviations in heavy-tailed processes, and optimization in power networks. The articles span high-impact journals like Mathematics of Operations Research , Queueing Systems , and Random Structures & Algorithms , indicating sustained leadership in applied probability and stochastic modeling. His scientific awards include: Erlang Prize (2008) Dantzig Prize (2015) Best publication award, INFORMS Revenue Management & Pricing (2016) IBM Faculty Award (2008) ASML Prize (2002) Gijs de Leve Prize (2002) Applied Probability Trust Award (2001) Best paper, Performance conferentie, Namen, Belgium (2010) Zwart has secured significant research funding, including a Vici grant from NWO on rare events, multiple NWO TOP and STAR cluster grants, and funding for PhD positions and postdocs. He has advised numerous students (though not explicitly listed here) and collaborates widely with researchers such as Maria Vlasiou, Remco van der Hofstad, and Onno Boxma. He is actively involved in teaching, offering advanced courses like Stochastic Decision Theory and Asymptotic Methods in Queueing Theory . He leads the Stochastics group at CWI, a leading research unit in probability and operations research.