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Weiran WangView profile

Assistant Professor

• Machine Learning
• Speech Processing
• Artificial Intelligence
• +7 more

Weiran Wang is an Assistant Professor in the Department of Computer Science at the University of Iowa. Previously, he worked as a Staff Research Scientist at Google (2021-2024), Senior Research Scientist at Salesforce Research (2019-2020), and Amazon Alexa (2017-2019). He completed his PhD in 2013 at UC Merced under Miguel A. Carreira-Perpinan and postdoctoral research at Toyota Technological Institute at Chicago (2014-2017) with Karen Livescu and Nathan Srebro. PhD: EECS Department, UC Merced (2013) MS: Computer Science, Chinese Academy of Sciences (2008) BS: Computer Science, Huazhong University of Science and Technology (2005) His research focuses on machine learning algorithms for speech processing , multi-view representation learning , and optimization . Key contributions include advancements in end-to-end speech recognition, stochastic canonical correlation analysis, and deep variational methods for multi-modal data. Notable work includes improving WER metrics for telephony speech and developing GPU/TPU implementations for ASR biasing. The 15 most recent publications span 2024-2018, emphasizing speech recognition (2024 NAACL/Interspeech), multi-view learning (2022 ICLR), self-training (2020 Interspeech), and acoustic modeling (2018). Trends include deep learning, attention mechanisms, and distributed optimization techniques. No scientific awards are explicitly mentioned in the provided texts. Current teaching includes CS4980: Deep Learning (Spring 2025) and CS4420: Artificial Intelligence (Fall 2024).

• Artificial Intelligence
• Explainable AI (XAI)
• Green AI
• +12 more

Dr Andrew Starkey is a Reader in the School of Engineering at the University of Aberdeen, where he also completed his PhD in 2001. He holds an Honours degree in Applied Mathematics from the University of St Andrews. He is actively involved in research and currently accepting PhD students in Engineering. His work bridges academia and industry, with a focus on AI applications in engineering, bioinformatics, and geosciences. University: University of Aberdeen School: School of Engineering Academic Rank: Reader Email: a.starkey@abdn.ac.uk Phone: +44 (0)1224 272801 Dr Starkey's research centers on Explainable AI (XAI) , Green AI , and Autonomous AI , with applications in robotics, econometrics, bioinformatics, seismic data analysis, and virtual reality. He has developed novel methods for feature selection, autonomous learning, and knowledge abstraction from agent-environment interactions. His work emphasizes low computational cost and transparency in AI systems. The most recent publications reflect a strong trend in applying AI to complex real-world problems, including digital rock technology, robotic grasping, real-time event detection, and medical data analysis. His interdisciplinary research combines machine learning with domain-specific knowledge in engineering and life sciences, often resulting in practical, industry-ready solutions. Millennium Product Award John Logie Baird Award for Innovation Enterprise Fellowship from Royal Society of Edinburgh and Scottish Enterprise Dr Starkey has supervised multiple research projects and secured funding from major bodies including EPSRC, BBSRC, and industry partners. His past work on the GRANIT project led to the development of AI-based condition monitoring for ground anchorages, resulting in commercialization through BlueFlow Ltd. He has collaborated with researchers across disciplines, including Dr Alasdair MacKenzie (bioinformatics), Dr Anne Schwab (seismic analysis), and Dr David Hazlerigg (genomics). He leads research in AI-driven engineering solutions and is the CEO of BlueFlow Ltd, a spinout company commercializing AI technologies developed at the University of Aberdeen. His lab focuses on developing autonomous, explainable, and environmentally sustainable AI systems for real-world deployment.

Magdalini EirinakiView profile

Professor

• Recommender Systems
• Machine Learning
• Data Mining
• +5 more

Magdalini Eirinaki is a Professor and Academic Program Coordinator for the MS in Artificial Intelligence at San José State University's Charles W. Davidson College of Engineering. With a career spanning two decades, her work bridges recommender systems , machine learning , and smart city applications . PhD in Computer Science (2006), Athens University of Economics and Business MSc in Advanced Computing (2000), Imperial College London BSc in Computer Science (1998), University of Piraeus Her research focuses on machine learning and recommender systems with extensions to generative AI , privacy-sensitive algorithms , and social network analysis . Recent publications explore federated learning , multi-resolution diffusion models , and autonomous network defense using reinforcement learning. Current projects include NSF-funded CollaborAIte (2024) EU Horizon/Marie Sklodowska-Curie's MUSIT (2024) IBM SkillsBuild Cloud Credits for Sustainability (2024) She has received multiple teaching and mentorship awards including: Newnan Brothers Award (2019) Applied Materials Award (2017) 5-time SJSU Distinguished Faculty Mentor Award Dr. Eirinaki advises students in AI , ML , and smart city projects, with recent graduates presenting at IEEE CAI (2025) and CSU Conference (2025).

Richelle Allen-KingView profile

Professor

• Contaminant Transport in Groundwater
• Hydrogeochemistry
• Organic Contaminants
• +5 more

Richelle Allen-King serves as Professor and Director of Graduate Studies in the Department of Earth Sciences within the College of Arts and Sciences at the University at Buffalo. Her academic career centers on hydrogeochemistry and environmental geochemistry, with specialized expertise in contaminant transport processes in groundwater systems. Her educational background includes: PhD in Earth Sciences (Hydrogeology) from the University of Waterloo (1991) Dr. Allen-King's research focuses on understanding the fate and transport of contaminants in groundwater , particularly organic pollutants like chlorinated solvents and nutrients. She integrates field measurements, laboratory experiments, and numerical modeling to investigate critical processes including sorption, diffusion, and biotic/abiotic degradation in heterogeneous aquifers. Her work addresses fundamental limitations in predicting natural attenuation and designing effective remediation strategies for contaminated sites, with significant contributions to understanding nonlinear sorption in low-organic-carbon sediments and aquifer heterogeneity effects. Analysis of her publication record reveals three dominant research trajectories: (1) Advanced characterization of chlorinated solvent behavior in sedimentary rock aquifers , particularly diffusion and degradation processes in low-permeability media; (2) Investigation of nutrient transport dynamics in watersheds with applications to Lake Erie eutrophication; and (3) Development of educational frameworks for early-career geoscientists. Her recent work increasingly incorporates high-fidelity modeling of heterogeneous systems and field validation at research sites like the Borden Aquifer. Her research is supported by major external funding including: SERDP Project ER-2533: Developing field methods for quantifying chlorinated solvent diffusion and degradation in low-permeability media NSF IGERT ERIE grant: Fostering interdisciplinary training in ecosystem restoration Dr. Allen-King has mentored 14 graduate students to completion, with alumni now working as hydrogeologists at firms including Geosyntec Consultants, Leggette Brashears & Graham, and Intera Inc. She teaches advanced courses in environmental geochemistry and supervises thesis research focused on contaminant hydrology. Her laboratory investigations frequently involve collaborative field work at the Borden Aquifer research site in Ontario, Canada, where she examines lithofacies controls on contaminant transport properties.

Leonard J. SchulmanView profile

Professor

• Algorithms
• Communication Protocols
• Combinatorics
• +6 more

Leonard J. Schulman is a Professor of Computer Science at the California Institute of Technology (Caltech), where he has been on the faculty since 2000. He is affiliated with the Caltech Center for the Mathematics of Information (which he directed from 2003 to 2017) and the Institute for Quantum Information and Matter. His academic appointments have included positions at UC Berkeley, the Weizmann Institute of Science, the Georgia Institute of Technology, and the Mathematical Sciences Research Institute. Schulman received his BSc in Mathematics in 1988 and his PhD in Applied Mathematics in 1992, both from the Massachusetts Institute of Technology (MIT). Schulman's research spans several overlapping areas in theoretical computer science and applied mathematics. His work focuses on algorithms and communication protocols , combinatorics and probability , coding and information theory , and quantum computation . More recently, his research has expanded into causal inference and machine learning , particularly in the areas of mixture models, causal discovery, and structure learning. His approach combines deep theoretical insights with practical applications across multiple domains. An analysis of Schulman's recent publications (2019-2025) reveals a strong focus on causal inference and machine learning, particularly in the areas of mixture models, causal discovery, and structure learning. His work bridges theoretical computer science with statistical learning, often developing novel algorithms with provable guarantees. He has also maintained his foundational work in coding theory, algorithms, and quantum computation, demonstrating remarkable breadth across theoretical computer science. IEEE Schelkunoff Prize (2004) ACM Notable Paper (2012) UAI Best Paper Award (2016) FOCS Test of Time Award (2022) S. A. Schelkunoff Transactions Prize Paper Award (2004) SIAM Fellow NSF CAREER award NSF mathematical sciences postdoctoral fellowship MIT Bucsela prize in mathematics Schulman has advised numerous PhD students and postdoctoral researchers who have gone on to successful careers in academia and industry. His former students and postdocs include notable researchers such as Ashwin Nayak, Yaoyun Shi, Sean Hallgren, Jie Gao, and Michael Langberg. He served as Editor-in-Chief of the SIAM Journal on Computing from 2013 through 2018 and has been on the editorial boards of several prestigious journals including the Journal of the ACM, ACM Transactions on Algorithms, and SIAM Journal on Discrete Mathematics. Schulman directs the Caltech Center for the Mathematics of Information, a research center focused on the mathematical foundations of information processing, communication, and computation. His work often involves interdisciplinary collaborations across computer science, mathematics, physics, and economics.

Jaime B. PalterView profile

Associate Professor

• Physical Oceanography
• Climate Change
• Biogeochemical Cycles
• +3 more

Jaime B. Palter is an Associate Professor of Oceanography at the University of Rhode Island's Graduate School of Oceanography. Her research explores large-scale ocean circulation dynamics and their impact on biogeochemical cycles and global climate. Affiliation: URI Graduate School of Oceanography Academic Rank: Associate Professor (Oceanography) Using Lagrangian floats, ocean gliders, and climate models, Palter investigates carbon and oxygen transport in western boundary currents like the Gulf Stream, with recent projects focusing on Marine Carbon Dioxide Removal strategies and ocean alkalinity enhancement verification protocols. Current research trends include: North Atlantic circulation variability Gulf Stream biogeochemical interactions Labrador Sea water transport mechanisms Autonomous vehicle-based ocean observations Climate change mitigation through ocean interventions Historical hydrographic analysis for AMOC reconstruction Palter's lab trains students and postdocs in oceanographic research, with alumni now working at institutions like the University of Washington, Bandung Institute of Technology, and Woods Hole Group.

Peter McintyreView profile

Associate Professor

• Aquatic Conservation Ecology
• Freshwater Ecosystems
• Biodiversity Conservation
• +7 more

Peter Mcintyre serves as an Associate Professor in the Department of Ecology and Evolutionary Biology at Cornell University's College of Arts and Sciences. He holds the Dwight Webster Sesquicentennial Faculty Fellowship and is affiliated with the Institute for African Development as a Core Faculty member and the Southeast Asia Program as a Faculty Associate. His educational background includes a B.A. in Biology from Harvard University (1998) and a Ph.D. in Ecology & Evolutionary Biology from Cornell University (2006). Mcintyre is an aquatic conservation ecologist whose research focuses on developing management approaches that balance human interests with protecting biodiversity. His work spans multiple continents, with field studies conducted in New York, the Great Lakes, East Africa, Southeast Asia, and Hawaii. His academic interests encompass Biogeochemistry and Ecosystem Science, Community Ecology and Population Biology, Evolutionary Patterns and Processes, Organismal Biology, and Sustainability, Environment and Conservation. Key research themes include fisheries, food webs, climate change, ecological stoichiometry, global biodiversity, conservation planning, river networks, and life history and migration. His recent publications reveal a consistent focus on freshwater ecosystem conservation, with particular attention to global patterns of biodiversity, climate change impacts on aquatic systems, fisheries management, and conservation planning. His work increasingly integrates molecular techniques like eDNA with traditional ecological methods, while maintaining a strong emphasis on practical conservation applications across diverse global contexts from the Amazon to Hawaiian streams. Dwight Webster Sesquicentennial Faculty Fellow Mcintyre leads the McIntyre Lab, which addresses ecology and conservation in rivers and lakes worldwide, with special focus on the organism-ecosystem interface. His lab studies how animals affect ecosystem productivity and nutrient dynamics, with particular emphasis on freshwater fisheries in the Adirondack Mountains and developing nations. His students and postdocs have received significant recognition including NSERC graduate research fellowships and Doctoral Dissertation Improvement Grants. The lab's work spans multiple major research projects including the Adirondack Fisheries Research Program, Lake Tanganyika Ecosystem Project, Great Lakes connectivity initiatives, and conservation of Native Stream Gobies in the Hawaiian Archipelago. The McIntyre Lab maintains active research sites across the globe, from the Adirondacks to Lake Tanganyika in East Africa, with particular strength in studying the connections between organismal biology and ecosystem processes in freshwater environments. Their work consistently bridges fundamental ecological questions with practical conservation applications, making significant contributions to both scientific understanding and on-the-ground management of freshwater resources.

Serona EltonView profile

Professor

• Music Business
• Copyright Law
• Digital Licensing
• +3 more

Serona Elton is a Professor at the University of Miami within its Frost School of Music , specifically the Music Industry Department . She also holds administrative roles as Interim Vice Dean and Director of the Music Industry Program . Her career bridges academia, music industry practice, and legal expertise, with past senior roles at Warner Music Group and EMI Recorded Music , as well as consulting for Sony Music , Universal Music Group , and Zumba Fitness . Education : B.S.B.A. in Finance, University of Florida M.M. in Music Media and Industry, University of Miami J.D., Brooklyn Law School Her research and professional focus lies in music business , copyright law , digital licensing , mechanical royalties , and artist management . Through her publications and Mechanical Licensing Collective (MLC) role as Head of Educational Partnerships, she has significantly shaped understanding of music licensing reforms like the Music Modernization Act . Her work addresses both foundational and emerging challenges, including AI’s impact on copyright and international anti-piracy strategies . The trends in her 10 publications (2009–2023) span music industry history , digital distribution , copyright enforcement , and artist revenue models . These works emphasize mechanical licensing , graduated response programs to piracy, and contemporary music business practices . Scientific Awards and Recognitions : Recognized as Yamaha Master Educator Served as President of MEIEA (Music and Entertainment Industry Educators Association) Trustee of the Copyright Society of the U.S.A. Active in the Florida Bar Entertainment, Arts, and Sports Law Section Media Appearances include NPR Marketplace , CNN , The New York Times , and Wired , where she comments on concert ticket pricing , music catalog sales , streaming platform negotiations , and copyright issues . She also co-authored the widely cited textbook Music Business Handbook and Career Guide , now in its 13th edition, which serves as a core resource for students and professionals.

• French Literature
• Bulgarian Literature
• Literary Theory
• +3 more

Krasimir Hristakiev is a Senior Lecturer at the Department of Romance Languages within the University of Veliko Tarnovo. His academic career spans over two decades, focusing on literature and theater studies with a particular emphasis on French and Bulgarian cultural intersections. Primary Affiliations: University of Veliko Tarnovo (Department of Romance Languages) Research Themes: French literature (Molière, Rabelais), Bulgarian literary theory, theater and visual studies, intertextuality, and philosophical discourse. His publications analyze the dialogue between classical and modern literary forms, with recurring themes of madness, labor, and cultural identity. Notable works include studies on Giuseppe Tornatore's cinema, Bakhtinian theory in Bulgarian contexts, and the adaptation of Molière's plays in regional festivals. Recent articles explore the blurring lines between theater and digital media , focusing on structural differences and audience engagement in virtual performances. His bibliography reflects a deep engagement with philological rigor and comparative cultural analysis. Key trends in his work include the reception of French classics in Bulgaria, stylistic evolution in literature, and the metaphorical representation of labor and death in contemporary narratives. He contributes regularly to journals like Visual Studies and Proglas .

Lorenzo De StefaniView profile

Lecturer

• Algorithms
• Big Data
• Statistical Learning
• +3 more

Lorenzo De Stefani serves as an Assistant Teaching Professor (formally titled Lecturer) in Brown University's Department of Computer Science, teaching core courses including Theory of Computation (CSCI1010), Design and Analysis of Algorithms (CSCI1570), and Data Science (CSCI1951A). His academic credentials feature dual doctoral degrees: a PhD in Computer Science from Brown University (2020) under Eli Upfal, and a PhD in Computer Engineering from the University of Padova (2016) under Gianfranco Bilardi. Additional qualifications include a Master of Science (2012) and Bachelor of Science (2009) in Computer Engineering from the University of Padova. PhD, Brown University, 2020 PhD, University of Padova, 2016 MSc, University of Padova, 2012 BSc, University of Padova, 2009 De Stefani's research centers on algorithmic innovation for large-scale data challenges, with emphasis on memory-efficient graph stream processing, statistical learning for multiple hypothesis testing, and Byzantine-resilient computation. His work bridges theoretical computer science and practical data analysis through rigorous probabilistic frameworks. Publication trends reveal consistent contributions to graph algorithm optimization, particularly in single-pass triangle counting for dynamic networks and I/O complexity bounds for fundamental operations like matrix multiplication. His methodologies prioritize computational efficiency while maintaining statistical validity in massive datasets. Scientific recognition includes: KDD Best Student Paper Award (2016) for Tríest streaming algorithms While advising PhD candidates during his doctoral studies at Brown and Padova, current student mentorship details remain unspecified. No major grant awards are documented in available materials. He actively participates in Brown's computer science research ecosystem through collaborative networks focused on algorithmic foundations and data science applications.

• Policy Entrepreneurship
• Crisis Management
• Risk Management
• +4 more

Evangelia Petridou serves as Associate Professor in the Department of Humanities and Social Sciences at Mid Sweden University, where she is core faculty at the Risk and Crisis Research Centre (RCR). Her academic work bridges political science and practical crisis management, with emphasis on European policy processes and Swedish governance structures. Her research focuses on policy entrepreneurship dynamics during crises, particularly examining how actors navigate institutional constraints in emergency contexts. Key areas include reactive policy entrepreneurship, risk communication across EU nations, and the operationalization of national policy styles during pandemics. She integrates theoretical frameworks like Kingdon's Multiple Streams with empirical analyses of Swedish municipal responses to disasters. Recent publications (2022-2025) reveal three dominant trends: conceptual expansion of policy entrepreneurship to street-level bureaucrats, methodological innovation through immersive simulations in crisis training, and comparative analyses of European pandemic responses. Her work consistently addresses the tension between centralized policy directives and local implementation challenges, especially in decentralized systems like Sweden. Dr. Petridou leads multiple externally funded projects including FUTURESILIENCE, LEGITIPREP, and RISE (Resilience in Sweden: Governance, Social Networks and Learning). She directs the RCR Lab at Mid Sweden University, which pioneers experimental design in crisis management education through virtual/physical immersion classrooms. Her collaborative network spans 15+ European institutions with frequent co-authorship on cross-national policy studies.

Pierre Lafaye de MicheauxView profile

Associate Professor

• Applied Statistics
• Biostatistics
• Statistical Theory
• +11 more

Associate Professor Pierre Lafaye de Micheaux is a statistician based at the School of Mathematics and Statistics, University of New South Wales , where he has worked since 2020. He previously held academic roles at Université Paul Valéry (2020, Associate Professor), ENSAI (2015–2017, Professor), Université de Montréal (2011–2016, Associate Professor), and Grenoble Alps University (2003–present, Assistant Professor). His research spans theoretical and applied statistics , focusing on complex random vectors , neuroimaging genetics , and data science for IoT . Education: PhD in Statistics (2003, Université de Montréal & Montpellier) MSc in Biostatistics (1998, Montpellier) BSc in Mathematics and Physics (1996, Montpellier) MSc in Cognitive Neuroscience (2007, Grenoble Institute of Technology) Research interests include: Dependence Measures : Leveraging complex analysis for big data dependence testing under 3V's (Volume, Variety, Velocity). Neuroimaging Genetics : Developing statistical tools for fMRI/EEG/DTI phenotyping of genetic variation with institutions like CHeBA and INSERM. IoT Data Science : Creating Raspberry Pi-based statistical computing tools for real-time sensor data streams. Complex-Valued Inference : Building a unified framework for complex random vectors in neuroimaging and nuclear engineering. Recent publications demonstrate expertise in circular data analysis , nonparametric testing , and central limit theorem counterexamples , with applications in medical imaging and finance. He has supervised numerous PhD, MSc, and honors students on topics ranging from deep learning to stochastic processes. Scientific achievements include: Université de Montréal Provost Honor List (2003) Editor of the Journal of Statistical Software (2017–present) Co-leader of three research groups: Dependence Measures , Neuroimaging Genetics , and Data Science & IoT He has secured grants from UNSW Research Infrastructure Scheme and NSERC , with industry collaborations including BNP Paribas (credit risk) and Olea Medical (stroke treatment analytics). Current teaching includes Statistical Inference (ZZSC5905) and Data Science (DATA3001) at UNSW.

Giorgio ScorzelliView profile

Researcher

• Extreme Data Management
• Scientific Visualization
• Computational Topology
• +6 more

Giorgio Scorzelli is a researcher at the University of Utah, serving as Director of Software Development for the Center for Extreme Data Management, Analysis, and Visualization (CEDMAV) and the National Science Data Fabric (NSDF) . He specializes in extreme data management, scientific visualization, and computational topology, with a focus on scalable solutions for climate science, materials science, and neuroscience datasets. His work emphasizes democratizing data access through platforms like OpenVisus , enabling efficient analysis of petascale and exascale data. Key contributions include orchestrating cyberinfrastructure, optimizing parallel I/O, and developing real-time visualization systems for heterogeneous resources. Notable scientific contributions include the NSF Grant #2127548 for NSDF development . His projects integrate cloud computing, geo-distributed storage, and FAIR digital objects to lower barriers to data democratization. Giorgio's research spans multi-resolution algorithms , computational topology , and 3D geometric modeling , with applications in infrastructure security, archaeological reconstruction, and biomedical imaging. His work bridges abstract mathematical frameworks (e.g., Boolean algebras, chain complexes) with practical software solutions.

Michael M. GoldmanView profile

Professor

• Sport Marketing
• Sport Sponsorship
• Consumer Behavior
• +4 more

Michael M. Goldman is Professor of Sport Management and Associate Dean of Graduate Programs and Strategic Initiatives at the University of San Francisco’s College of Arts & Sciences. He is a full-time faculty member who also serves as adjunct faculty at the Gordon Institute of Business Science (GIBS) in Johannesburg and sits on the board of the Case Research Foundation. Education & Prior Roles DBA, Gordon Institute of Business Science (GIBS), 2014 MBA, Gordon Institute of Business Science (GIBS), 2005 Former Senior Lecturer in Marketing, University of Pretoria (GIBS) Advisor to the 2018 Rugby World Cup Sevens Host Committee Advisor, Rumble Ventures Sports Innovation Fund Research & Applied Expertise Goldman’s scholarship lies at the intersection of sport marketing, sponsorship analytics, and consumer psychology. He studies how fans form identities with teams, how brands leverage sport partnerships, and how organizations can translate these insights into revenue growth. His work spans professional sport franchises (San Francisco Giants, LA Clippers), global sponsors (MTN Group), and governing bodies such as CONCACAF and Cricket South Africa. Across more than fifteen recent publications, Goldman’s research trajectory reveals a consistent focus on three themes: fan engagement and disengagement , sponsorship ROI and brand building , and innovative case-based pedagogy . He employs mixed methods—qualitative case studies, quantitative market analyses, and experimental designs—to tackle questions ranging from esports broadcasting during COVID-19 to the financial impact of team rebranding. Honours & Awards Visiting Professorship, Universidad Peruana de Ciencias Aplicadas (2020) North American Case Research Association – Silver Best Case Award (2019) Sarlo Prize for Excellence in Teaching, USF (2017) NACRA – Bronze Best Case Award (2017) Paul R. Lawrence Fellowship, Case Research Foundation (2015) Consulting, Grants & Labs Goldman maintains an active portfolio of industry partnerships that function as living laboratories for his research and teaching. Recent collaborations include designing sales-skills workshops with the Los Angeles Clippers, advising MTN on FIFA World Cup activation, and assessing brand equity for the San Francisco Giants’ season-ticket retention campaigns. While specific grant amounts are not disclosed, his ongoing work with teams and federations provides continuous funding for case development and experiential-learning initiatives.

• Lean Manufacturing
• Continuous Improvement
• Operations Management
• +4 more

Associate Professor with Habilitation at the School of Engineering, University of Minho , where he also serves as a Senior Researcher at Centro ALGORITMI. Member of both the IEM R&D Group and LPSL R&D Lab. Education: Degree in Production Engineering (1989) - University of Minho MSc in Computer Integrated Manufacturing (1992) - Loughborough University Ph.D. in Scheduling and Process Planning Integration (1997) - University of Nottingham His research spans Lean Approaches across diverse environments including industry, healthcare, construction, offices, and education. Key focus areas include pull flow methods for complex production environments, stable continuous improvement systems , and the integration of Scrum methodology in educational settings. Recent publications show a clear trend toward applying lean principles in retail logistics, healthcare operations, and sustainability frameworks, with increasing emphasis on performance measurement systems and Science-Based Targets. He has authored the book "Continuous Improvement in Organizations" and published over 100 scientific articles with an h-index of 17 and 1131 citations. His current projects include "HOMLean - Hospital Operations Management" and a Smart Retail initiative with Sonae company funded by PRR program. He actively applies his expertise through project-based learning approaches in engineering education, having documented over ten years of experience with PBL at the University of Minho. His work bridges academic research with practical industry applications, particularly in lean manufacturing implementation across various sectors.