جست‌وجوی استاد و پژوهشگر

Katy Craigمشاهده پروفایل

دانشیار

• Optimal Transport
• Nonlinear Partial Differential Equations
• Calculus of Variations
• +۳ مورد دیگر

Katy Craig is an Associate Professor in the Department of Mathematics at the University of California, Santa Barbara (UCSB), within the College of Letters and Science. Her research focuses on nonlinear partial differential equations, optimal transport theory, calculus of variations, and numerical analysis. She leads the NSF-funded CAREER project on Optimal Transport and Dynamics in Machine Learning and co-organized the Optimal Transport Graduate Summer School at UCSB in 2025. Dr. Craig's academic career includes roles as an Assistant Professor (2016-2023) and postdoctoral fellowships at UCSB and UCLA. She has been recognized with awards such as the 2023 Mochizuki Memorial Fund Award and the 2020 Hellman Faculty Fellowship. Her teaching spans courses in real analysis, optimal transport, and graduate-level topics in mathematics. Her research has been supported by grants from the NSF and UC system, and she actively mentors graduate and undergraduate students in interdisciplinary projects. Craig is also involved in conference organization, including leading workshops on optimal transport and machine learning, and contributes to academic service roles such as NSF panel participation and editorial work for journals. Key research themes include the development of numerical methods for optimal transport, applications to machine learning and physics, and the analysis of gradient flows in metric spaces. Her work bridges theoretical mathematics with computational and applied challenges, fostering collaborations across disciplines.

• Material Science
• Mathematical Modeling
• Fractal Networks
• +۵ مورد دیگر

Professor Tony Roberts is the Head of School in the School of Mathematical Sciences at Queensland University of Technology (QUT). He holds a PhD from the Australian National University and is a Fellow of the Australian Mathematics Society. His research focuses on the interplay between material microstructure and macroscopic properties, with emphasis on topology optimization, random structure modeling (e.g., Gaussian fields, percolation models), and material property analysis such as conductivity, diffusion, and fluid flow. He develops computational methods for analyzing experimental techniques like 3D statistical reconstruction and small-angle scattering. His recent work includes optimizing piezoelectric materials for robotics, studying diffusion dynamics in fractal networks, and modeling material failure mechanisms. Key contributions span multi-functional piezoelectric components, anisotropic elastic properties of additively manufactured alloys, and fracture mechanics in perforated materials. Awards include his fellowship in the Australian Mathematics Society. Supervision interests include structural optimization, diffusion in random media, and porous material failure modeling. Education: PhD (Australian National University) Affiliations: Faculty of Science, School of Mathematical Sciences Research Themes: Material science, computational modeling, fracture mechanics, stochastic systems

Augusto Gerolinمشاهده پروفایل

استادیار

• Optimal Transport Theory
• Mathematical Physics
• Theoretical and Computational Chemistry
• +۴ مورد دیگر

Augusto Gerolin is an Assistant Professor jointly appointed in the Departments of Mathematics and Statistics and Chemistry and Biomolecular Sciences at the University of Ottawa. He holds a Tier II Canada Research Chair in Artificial Intelligence at the Interface of Chemistry and Mathematics. His research focuses on Optimal Transport Theory, Mathematical Physics, Theoretical and Computational Chemistry, and Machine Learning. Gerolin’s work bridges quantum chemistry, mathematical analysis, and computational methods, with applications in Density Functional Theory and quantum information science. He obtained his PhD from the University of Pisa and was a Marie Skłodowska-Curie fellow at Vrije Universiteit Amsterdam. He is a member of the European Laboratory for Learning and Intelligent Systems (ELLIS) and leads a research group exploring AI-driven solutions in chemistry and mathematics. His current projects include developing optimal transport frameworks for quantum systems, advancing machine learning algorithms in scientific computing, and fostering collaborations across disciplines through initiatives like the OQMG Network. Gerolin’s research has led to advancements in multi-marginal optimal transport, entropy-regularized methods, and the strong-interaction limit of density functional theory. His group actively collaborates with institutions worldwide, including the Fields Institute, IPAM, and the University of Genoa. He has supervised numerous PhD, Master’s, and undergraduate students, many of whom have contributed to cutting-edge studies in computational chemistry, quantum algorithms, and mathematical analysis. Key awards include the Canada Research Chair designation, and his work has been supported by grants from NSERC, MITACS, and the University of Ottawa. Gerolin is also committed to diversity in science, endorsing principles such as the Diversity Axioms, and advocates for academic solidarity with researchers affected by global conflicts.

Nejc Geržiničمشاهده پروفایل

پژوهشگر

• Travel behaviour
• Public transport
• Micromobility
• +۷ مورد دیگر

Nejc Geržinič is a PostDoc researcher at Delft University of Technology in the Transport, Mobility and Logistics department within the Civil Engineering & Geosciences school. His research focuses on travel behavior, particularly examining the integration of new mobility services with traditional public transport systems and investigating sustainable mobility solutions through innovative approaches like tradeable mobility credits. Dr. Geržinič completed his PhD at TU Delft between 2019 and 2023, with research focusing on the impact of on-demand mobility and its interaction with public transport, as well as long-distance international travel behaviour. His doctoral work contributed significantly to the CriticalMaaS project, which developed comprehensive frameworks for Mobility as a Service systems. Geržinič's research spans several critical areas in contemporary transportation studies. His work primarily investigates travel behaviour with a focus on how new mobility options like on-demand services and micromobility integrate with traditional public transport systems. He employs advanced choice modelling techniques to understand passenger decisions, particularly regarding access/egress trips in train travel chains and the potential of tradeable mobility credits systems. His research contributes significantly to the fields of sustainable transport and modal shift , aiming to develop more efficient and environmentally friendly urban mobility solutions. His expertise aligns with UN Sustainable Development Goals related to sustainable cities and communities. Analysis of Geržinič's publication record reveals a strong progression from foundational work on on-demand mobility services toward more sophisticated modeling of travel behavior and innovative mobility concepts. His recent 2025 publications indicate an expanding research agenda that now includes examining barriers to micromobility adoption through latent class analysis and exploring the potential of microtransit for regional commuting patterns. His work consistently examines how new mobility services can serve as effective first/last mile connectors for train stations, potentially increasing overall public transport usage while reducing private vehicle dependency. Dr. Geržinič has been actively engaged in disseminating his research through multiple media channels, with documented media contributions each year from 2019 to 2023. His presentations at workshops and conferences have addressed critical topics including the role of on-demand services in urban mobility mixes and the market potential of pooled mobility services. As part of the CriticalMaaS project (2019-2023), Geržinič collaborated with a multidisciplinary team including O. Cats, N. van Oort, and S.P. Hoogendoorn. His current postdoctoral research continues to explore innovative mobility concepts, particularly focusing on access/egress trips in train travel chains and travel behaviour in systems with tradeable mobility credits. His research outputs include 16 publications, 7 datasets, and numerous academic activities, demonstrating consistent productivity and contribution to the transportation research community.

Davide La Vecchiaمشاهده پروفایل

استاد

• Time series analysis
• Robust and semiparametric inference
• Financial econometrics
• +۳ مورد دیگر

Davide La Vecchia is a Full Professor at the Research Institute for Statistics and Information Science (RISIS) at the University of Geneva, Switzerland. He holds dual PhDs in Statistics from Bocconi University (2007) and Economics from Università della Svizzera italiana (2011). His academic journey includes roles as an Assistant Professor at the University of St. Gallen and Monash University, and he has held visiting positions at Princeton University, the University of Copenhagen, and CREST (Paris). His expertise spans time series analysis, robust and semiparametric inference, financial econometrics, and spatial statistics. Key research contributions include work on saddlepoint approximations, optimal transportation methods, and latent variable models. Davide has led multiple grants, including from the Swiss National Science Foundation and the Australian Research Council. He serves as a referee for top-tier journals in statistics and econometrics. Teaching focuses on advanced statistical methods, including probability theory, time series analysis, and multivariate inference. His recent work emphasizes high-dimensional data modeling, spatio-temporal factor models, and applications to commodities trading networks. He has been honored with editorial roles and speaker invitations at global conferences, reflecting his leadership in statistical methodology.

• Analytical Chemistry
• Bioinformatics
• Environmental Sciences
• +۷ مورد دیگر

Dr. Beatriz Galindo-Prieto is a Researcher at Imperial College London's School of Public Health (Faculty of Medicine), part of the Environmental Toxicology Group within the Environmental Research Group. She specializes in developing multiblock/multi-omics and multivariate data analysis methods, with a focus on big data interpretation, variable selection, and applications in environmental health and bioinformatics. Her current projects include analyzing air pollution effects on health via RNA-seq datasets (UKRI NERC-funded) and policy studies on urban air quality. Affiliations include the MRC Centre for Environment and Health, and NIHR Health Protection Research Units in Environmental Exposures and Chemical/Radiation Hazards. Education: MSc in Chemistry (University of the Balearic Islands) and PhD in Chemometrics (Umeå University, Sweden). Her PhD involved novel variable selection algorithms for multivariate data (VIPOPLS, VIPO2PLS, MB-VIOP). Post-PhD roles include postdoctoral fellowships at NTNU (Norway) and Weill Cornell Medical College (USA), focusing on machine learning for big data in neuroscience and epidemiology. She has taught undergraduate/graduate courses and co-supervised students. Research Interests: Combining chemometrics, bioinformatics, and machine learning to address societal challenges. Key areas include multivariate data analysis for environmental systems, neurodegenerative diseases (Alzheimer’s), and real-time epidemic modeling (Ebola, COVID-19). She emphasizes interdisciplinary collaboration across analytical chemistry, systems biology, and public health policy. Grants & Funding: UKRI NERC, ERCIM (Big Data Cybernetics project), and institutional support from Imperial College London. Her work bridges academia and real-world applications, aiming to translate computational methods into actionable insights for health and environmental policy. Labs/Teams: Active in the Environmental Toxicology Group at Imperial, collaborating with the MRC Centre and NIHR units. Her lab develops novel methodologies for data fusion, visualization, and predictive modeling in complex systems.

Pau Batlle Franchمشاهده پروفایل

پژوهشگر ارشد

• Statistics
• Applied Mathematics
• Uncertainty Quantification
• +۴ مورد دیگر

Pau Batlle Franch is a Research Fellow in the Computing and Mathematical Sciences Department at California Institute of Technology (Caltech), working with Professor Houman Owhadi. He holds a PhD from Caltech (June 2025) and was a research affiliate at NASA Jet Propulsion Laboratory (JPL). His research focuses on the intersection of statistics and applied mathematics, including frequentist confidence intervals in inverse problems, game-theoretical uncertainty quantification, and Gaussian processes. He has applied his work to domains like remote sensing, biology, earthquake prediction, and telecommunications engineering. Education : PhD in Computing and Mathematical Sciences (Caltech, 2025); Double undergraduate degree in Mathematics and Engineering Physics from Universitat Politècnica de Catalunya (CFIS program); Research visitor at NYU's Center for Data Science. His research interests include optimization-based statistical methods, Gaussian process frameworks for scientific computing, and uncertainty quantification in physical systems. Notable contributions include resolving the Burrus conjecture and developing computational hypergraph discovery techniques applied to NASA JPL projects. His work bridges theory and application, addressing challenges in ill-posed inverse problems and robust statistical inference. Recent activities include presenting at SIAM conferences and workshops on inverse problems in Earth science. His Gaussian process methods have been published in journals like PNAS and SIMODS, with applications ranging from PDE solving to RNA classification. Collaborations include JPL and the Groningen seismic study. Grants & Collaborations : Ongoing work with NASA JPL on lunar rover control and computational graph discovery; Seismic modeling in the Groningen gas field with epistemic/aleatoric uncertainty frameworks. He maintains an active GitHub profile showcasing projects in machine learning and scientific computing, including repositories like DarwinProjectAnalytics and emb4class .

Cesar Ruizمشاهده پروفایل

استادیار

• Metal Additive Manufacturing
• Process-Informed Machine Learning
• Quality Control
• +۳ مورد دیگر

Cesar Ruiz is an Assistant Professor in the Department of Industrial & Systems Engineering at The University of Oklahoma. His research focuses on integrating domain knowledge with machine learning and stochastic modeling for engineering applications, particularly in metal additive manufacturing, predictive maintenance, and quality control. He holds a Ph.D. and M.S. in Industrial Engineering from the University of Arkansas and a B.A. in Business Engineering from the Higher School of Economics and Business in El Salvador. His research domains include metal additive manufacturing processes, process-informed machine learning frameworks, and reliability evaluation in complex systems. Notable works address challenges in layer segmentation for quality assessment, predictive maintenance strategies, and degradation-based reliability analysis. Ruiz has received prestigious awards such as the RAMS Golomski Award (2020, 2022), ASQ Best Reliability Paper (2021), and IEEE CASE Best Conference Paper (2021). His affiliations include SRE, INFORMS, and ASME. His research spans aerospace and defense systems, with recent publications emphasizing advanced manufacturing techniques, functional data analysis, and Bayesian modeling for reliability growth. He collaborates on projects involving biosensor optimization and large-scale system maintenance frameworks.

• Reliability Engineering
• Maintenance Optimization
• Risk and Safety Analysis
• +۸ مورد دیگر

Jørn Vatn is a Professor in reliability, availability, maintainability, and safety at the Norwegian University of Science and Technology (NTNU) , specifically within the Faculty of Engineering and the Department of Mechanical and Industrial Engineering . His work focuses on integrating risk assessment and maintenance strategies for critical infrastructure. MSc courses: TPK4120 Safety and Reliability Analysis , RAMS optimization PhD course: PK8200 Risk Influence Modelling and Risk Indicators Continuing Education: Maintenance Optimization , Safety and Reliability in Railway Applications , Project Risk Management , Risk Reducing Measures Research Interests span maintenance management, reliability modeling, condition-based maintenance, risk influence analysis, and digital twin applications in infrastructure. His methods emphasize statistical analysis and Markov processes to model degradation and optimize maintenance scheduling. The 15 most recent articles (2025-2022) demonstrate trends in applying Markov models , digital twins , and data-driven optimization to offshore wind turbines, railways, hydrogen pipelines, and cyber-physical systems. Subfields include predictive maintenance, stochastic degradation modeling, and infrastructure resilience. He leads the ROSS Gemini Centre and the RAMS group , advising on risk modeling and maintenance strategies. Collaborations involve SINTEF, industry partners, and international researchers.

• Natural Hazards
• Debris Flows
• Rockfall
• +۶ مورد دیگر

Johannes Hübl is a Full Professor at the University of Natural Resources and Life Sciences, Vienna (BOKU), leading the Institute for Alpine Natural Hazards within the Department of Landscape, Water and Infrastructure. His research focuses on alpine mass movement dynamics—particularly debris flows, rockfalls, and floods—with emphasis on risk management, mitigation engineering, and climate change adaptation. He maintains an active project portfolio including EU-funded initiatives and Austrian federal collaborations. His primary research interests include the physical processes of debris flows (surge dynamics, run-up behavior, sediment transport), rockfall hazard assessment, flood protection engineering, and climate change impacts on alpine hazards. Recent work pioneers advanced monitoring techniques like pulse-Doppler radar and LiDAR for real-time measurement of flow velocity and height, directly informing protective dam design and early warning systems. He also develops GIS-integrated simulation models for granular flows and risk assessment. Hübl's publications trend toward high-resolution field studies of debris flow dynamics, with significant focus on spatial impact distribution, statistical discharge modeling, and wildfire-hazard linkages. This reflects growing priorities in climate-resilient infrastructure and quantifiable risk assessment methodologies across European alpine regions. Scientific awards: No awards, fellowships, or medals are documented in the provided materials. Advising and grants: Professor Hübl has supervised numerous university theses as indicated by his profile. His grant leadership spans over 30 projects since 1994, including: EU projects: Nature-Based Solutions for Climate-Resilient Infrastructure (2024-2028), THARMIT (2000-2003) Austrian federal funding: 20+ projects with BMLFUW/BMF (e.g., rockfall detection, sediment management) International commissions: Bhutan flood mitigation (2000), Melamchi River reviews (2023) Industry collaborations: Check dam failure analysis, Cougar Creek optimization Labs and teams: He directs field monitoring operations including the Illgraben (Switzerland) and Gadria Creek (Italy) test sites. His institute employs physical modeling (hydraulic experiments, laser scanning) and develops real-time warning systems for alpine catchments, working closely with Austrian watershed management agencies and European consortia on event documentation and structural countermeasures.

Davide Gabrielliمشاهده پروفایل

استاد

• Statistical Mechanics
• Probability Theory
• Non-equilibrium Thermodynamics
• +۲ مورد دیگر

Davide Gabrielli is a Full Professor in the Department of Information Engineering, Computer Science and Mathematics (DISIM) at the University of L'Aquila, Italy. His academic position focuses on Mathematical Physics with extensive contributions to statistical mechanics and probability theory. His primary research interests encompass Statistical Mechanics, Probability Theory, Non-equilibrium Thermodynamics, and Stochastic Processes. Gabrielli's work bridges theoretical mathematics with physical applications, particularly in understanding non-equilibrium systems, large deviations theory, and transport phenomena. His research demonstrates strong interdisciplinary connections between probability theory, statistical physics, and mathematical analysis. Analysis of his recent publications reveals consistent focus on non-equilibrium statistical mechanics, with particular emphasis on large deviation principles, stochastic processes on graphs, and thermodynamic uncertainty relations. His work frequently appears in leading journals including Journal of Statistical Physics, Physical Review Letters, and Communications in Mathematical Physics. Gabrielli maintains active collaborations with prominent researchers including L. Bertini, A. De Sole, G. Jona-Lasinio, and C. Landim, forming part of an influential research group in non-equilibrium statistical mechanics. His teaching responsibilities include advanced courses in Stochastic Processes, Probability Calculus, and Equations of Physics-Mathematics.

• Geometric Data Processing
• Computational Geometry
• Optimal Transport
• +۷ مورد دیگر

Justin Solomon is an Associate Professor in the Department of Electrical Engineering & Computer Science at Massachusetts Institute of Technology, where he serves as Principal Investigator of the Geometric Data Processing Group. He maintains dual affiliations with the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the MIT Center for Computational Science and Engineering (CCSE), reflecting his interdisciplinary research bridging theoretical mathematics with practical applications in graphics and machine learning. His research interests center around geometric data processing, computational geometry, and optimal transport theory, with significant contributions to computer graphics, machine learning, and computer vision. Solomon's work spans fundamental mathematical theory to practical implementations, particularly in shape analysis, 3D reconstruction, and geometric deep learning. His research demonstrates consistent innovation in developing algorithms that bridge discrete and continuous geometry with applications in graphics, vision, and AI. The publication trends reveal Solomon's evolving research trajectory from foundational work in geometry processing toward increasing integration with modern machine learning techniques. His recent work shows strong emphasis on diffusion models, geometric deep learning, and applications of optimal transport in AI, with significant contributions to SIGGRAPH, NeurIPS, and ICML proceedings. The research demonstrates both mathematical rigor and practical impact, with applications spanning character animation, 3D reconstruction, and generative AI. Amazon Research Award (2017) for Large-Scale Geometrically-Structured Sampling Amazon Research Award (2023) for Lightweight Algorithms for Generative AI Ben Wegbreit Prize for Best Undergraduate Honors Thesis Firestone Medal for Excellence in Undergraduate Research Boothe Prize for Excellence in Writing 2nd place, SGP best paper awards (2010) Solomon has secured substantial research funding through awards like the Amazon Research Awards and maintains active collaborations across academia and industry. His group has produced numerous influential publications with students and collaborators, contributing significantly to both theoretical foundations and practical implementations in geometric data analysis. His textbook "Numerical Algorithms" demonstrates his commitment to education alongside research. As Principal Investigator of the Geometric Data Processing Group, Solomon leads a research team focused on developing mathematical foundations for analyzing and processing geometric data. The group maintains strong connections with both theoretical mathematics and practical applications, working at the intersection of computer graphics, machine learning, and computational geometry. Their work has significant implications for fields ranging from computer animation to medical imaging and scientific computing.

J. Daniel Gezelterمشاهده پروفایل

استاد

• Theoretical Chemistry
• Computational Chemistry
• Molecular Dynamics
• +۱۲ مورد دیگر

J. Daniel Gezelter is a Professor and Chair of the Department of Chemistry & Biochemistry at the University of Notre Dame within the College of Science. His research focuses on theoretical and computational studies of complex condensed-matter systems, particularly using molecular dynamics simulations to understand emergent behavior at interfaces. Ph.D. in Chemistry, University of California, Berkeley (1995) CPS in Chemistry, University of Cambridge, UK (1990) B.S. in Chemistry & Philosophy, Duke University (1989) Gezelter's research interests lie at the intersection of physical chemistry, statistical mechanics, and computational science. His group develops novel algorithms for molecular dynamics simulations to study energy and mass transport across interfaces. Key areas include thermal transport in nanoparticles, enantiomeric separation via shear flow, electrostatic interactions in condensed phases, and dynamics at ice-water interfaces. The lab combines analytical theory with state-of-the-art simulations, often developing open-source software like OpenMD to advance the field. The most recent publications demonstrate a strong trend toward understanding interfacial transport phenomena—particularly thermal and momentum conductance—using reverse non-equilibrium molecular dynamics (RNEMD) methods. The work spans applications from gold nanoparticle heat dissipation to chiral molecule separation and ice surface physics, reflecting a unifying theme of emergent dynamics in complex systems. Methodological innovations in electrostatics (e.g., damped shifted force) and implicit solvent modeling (e.g., Langevin Hull) underpin these investigations. His scientific awards include: Provost's Award for Teaching Excellence in the Core Curriculum (2023) Shilts/Leonard Award for Outstanding Teaching (2020) Rev. Edmund P. Joyce Award (2013, 2020) National Science Foundation CAREER Award (2002) Camille and Henry Dreyfus New Faculty Award (1999) National Science Foundation Graduate Research Fellowship (1990–1993) Churchill Scholar (1989–1990) Gezelter advises graduate students and leads an active research group supported by the National Science Foundation, the Camille & Henry Dreyfus Foundation, the Alfred P. Sloan Foundation, and the University of Notre Dame. His lab emphasizes open science, making software and data freely available. He has held leadership roles including Associate Dean for Undergraduate Studies (2020–2023) and Senior Associate Dean for Education & Undergraduate Programs (2023–2025), underscoring his commitment to academic administration and education. The Gezelter Laboratory develops and maintains OpenMD , an open-source molecular dynamics engine, and contributed to the early development of Jmol , a widely used computational chemistry viewer. The lab collaborates with both experimental and theoretical groups and promotes inclusivity and diversity in science.

Urs Schreiberمشاهده پروفایل

استاد

• Mathematical Physics
• Quantum Physics
• Algebraic Topology
• +۷ مورد دیگر

Urs Schreiber is a Professor at New York University in Abu Dhabi (NYU AD), working in the Mathematics Division of Science. He leads the Research Center for Quantum and Topological Systems (CQTS) under the group of Prof. Hisham Sati. His work bridges mathematical physics, quantum theory, and topology, with significant contributions to understanding strongly-coupled quantum systems and their applications to topological quantum technology. His research focuses on applying tools from algebraic topology and geometric homotopy theory to fundamental problems in quantum physics. His major projects include Hypothesis H on flux quantization in M-theory, geometric engineering of anyons on M5-branes, and developing quantum language via linear homotopy types. His work represents a unique synthesis of advanced mathematical techniques with cutting-edge theoretical physics, particularly in the areas of M-theory, topological quantum computing, and differential cohomology. Schreiber has established himself as a leading figure in the application of higher categorical and homotopical methods to physics. His research demonstrates a clear trajectory toward developing mathematical frameworks for next-generation quantum technologies, with particular emphasis on topological approaches that could provide fault-tolerant quantum computing platforms. His work shows strong connections between abstract mathematical structures and practical quantum computing applications, particularly through homotopy type theory implementations. Monograph: The Character Map in Non-Abelian Cohomology (with D. Fiorenza & H. Sati, 2023) Monograph: Equivariant Principal ∞-Bundles (with H. Sati, 2025) Contributor: Mathematical Foundations of Quantum Field and Perturbative String Theory (2011) Schreiber has advised numerous graduate students in areas spanning homotopy theory, quantum field theory, and mathematical physics. His teaching portfolio includes courses on Mathematical Quantum Field Theory, Topological K-Theory, and Stable Homotopy Theory. He is also the founder of the nLab research wiki project (started in November 2008), which has become an essential resource for researchers working at the intersection of mathematics and physics. His research group actively develops mathematical foundations for quantum and topological systems with practical implications for quantum technology development.

Evina Katsouمشاهده پروفایل

استاد مهمان

• Sustainable resource recovery from wastewater
• Circular economy assessment
• Data analytics for environmental systems
• +۵ مورد دیگر

Dr Evina Katsou is a Visiting Professor in the Department of Mechanical and Aerospace Engineering within the College of Engineering, Design and Physical Sciences at Brunel University London. She serves as Course Director for the Water Engineering MSc program and leads the Water & Environmental Engineering research group with 20 researchers. Dr Katsou's educational background includes a PhD in 'Wastewater Treatment with the Use of Membranes' (2008-2011), an MSc in Water Resources Science & Technology (2006-2008), and an MEng in Chemical Engineering (2000-2005), all from the National Technical University of Athens (NTUA), Greece. Her research program focuses on three interconnected areas: (1) Sustainable resource recovery from wastewater and safe reuse; (2) Data analytics, knowledge discovery and process modelling; and (3) Circularity & sustainability measurement and assessment. Dr Katsou has developed innovative approaches for biological nutrient removal from wastewater, including partial nitrification/denitrification and complete autotrophic nitrogen removal processes. Her work integrates advanced data analytics methods such as multivariate statistics, clustering techniques, machine learning algorithms, and artificial neural networks to identify complex relationships between process variables, particularly for measuring GHG emissions in water treatment systems. Dr Katsou has authored 98 journal publications (h-index: 27) and 12 book chapters with over 100 presentations, and holds a patent on biopolymers recovery. Her recent publications demonstrate a strong focus on circular economy applications in water systems, nature-based solutions for urban environments, and resource recovery technologies. The research trends show increasing integration of data-driven decision support systems with environmental sustainability metrics, particularly in the water-energy-food nexus domain. Chartered Chemical Engineer (Greece) HEA Fellow (FHEA) of the Higher Education Academy Mentor in the APEX (Academic Practice & Professional Excellence Framework) scheme Member of management group of the Royal Academy of Engineering (RAE) Visiting Professors Scheme Co-leader of Circular Economy VLT in Water Europe Leader of SMART WATER Working Group in ICT4Water Cluster-EC Member of ISO standards drafting team on Circular Economy Dr Katsou has successfully supervised 16 PhD students to completion and currently supervises several ongoing doctoral projects. She has secured substantial research funding as Principal Investigator on 11 national and international projects including RESILEX Horizon Europe, SYMBIOREM Horizon Europe, BORECER Horizon Europe, HYDROUSA H2020, and WATER-MINING H2020. Her industrial consulting portfolio includes significant contracts with leading UK water industry partners such as Affinity Water, Arup, Severn Trent, and Anglian Water, with projects ranging from carbon footprint assessment to corrosion mitigation and process optimization. She leads the Transformation Tools group of the Cost Action on Circular Cities, co-leads the Circular Water VLT of Water Europe, and co-leads the SMART-WATER group of the ICT4Water Cluster-EC. Dr Katsou is also a member of the drafting team for new ISO standards on Circular Economy (Measuring and Assessing Circularity - ISO5902).