جست‌وجوی استاد و پژوهشگر

• Time Series Econometrics
• Environmental Economics
• Energy Economics
• +۳ مورد دیگر

Andrea Bastianin is an Associate Professor of Economic Policy at the Department of Economics, Management, and Quantitative Methods (DEMM) at the University of Milan. He also serves as Director of the RAMET program at Fondazione Eni Enrico Mattei and organizes the Milan Time Series Seminars. His research focuses on Time Series Econometrics, Environmental Economics, and Energy Economics, with a particular emphasis on structural economic models, policy evaluation, and climate change impacts. Bastianin explores macroeconomic spillovers, energy transition metals, and the socio-economic effects of large-scale research infrastructures like CERN projects. His work bridges theoretical econometric advancements with applied analyses of energy markets, climate policies, and technological innovation. His academic contributions span over two decades, with recent studies analyzing carbon market drivers, weather shock effects on regional economies, and procurement-driven innovation in big science. He has conducted extensive research on oil and gas market dynamics, regulatory reforms in network industries, and the interplay between environmental policies and economic growth. Bastianin’s methodologies often involve advanced structural VAR models, mixed-frequency data analysis, and survival analysis techniques to assess innovation gestation lags. Notable research highlights include evaluating the socio-economic impact of particle accelerator projects, quantifying the connectedness of energy transition metals, and forecasting carbon prices using macroeconomic factors. His work frequently addresses policy-relevant questions, such as the effectiveness of climate mitigation policies and the role of public procurement in fostering technological advancement. Bastianin maintains active collaborations with international institutions like CERN and contributes to interdisciplinary studies on infrastructure impacts, environmental governance, and energy policy. His research has been disseminated through leading journals and platforms like arXiv, Google Scholar, and RePEc, showcasing a commitment to both academic rigor and real-world applicability.

Raquel Criadoمشاهده پروفایل

دانشیار

• Applied Linguistics
• Second Language Acquisition
• Foreign Language Teaching Methodology
• +۱۳ مورد دیگر

Raquel Criado is an Associate Professor in the Department of English Philology at the Faculty of Arts and Humanities, University of Murcia, Spain. She earned her PhD from the same university in 2008 with a thesis on activity sequencing in English as a Foreign Language teaching. Research Interests: Her primary research areas include Applied Linguistics, Second Language Acquisition (SLA), and Foreign Language Teaching Methodology. She has a particular focus on form-focused and meaning-focused instruction, L2 writing processes, written corrective feedback, textbook analysis, and the application of cognitive theories such as Skill Acquisition Theory to language learning. Her work often investigates the cognitive and pedagogical aspects of language teaching materials and classroom practices. Recent Publication Trends: Her most recent publications (2022-2023) demonstrate a continued and deepening engagement with empirical research on textbook use, adaptation, and the balance between form and meaning in instruction. She has also published significant work on the use of first language in non-formal education, collaborative writing in secondary education, and methodological considerations in studying young learners' writing processes using tools like Inputlog. Her research consistently employs robust methodologies, including mixed-methods and quasi-experimental designs. Scientific Contributions: Developed and validated a scale for measuring the form-focus and meaning-focus load of textbook activities. Conducted extensive research on activity sequencing models (e.g., PPP vs. CPM) and their cognitive underpinnings. Explored the effects of written corrective feedback and models on young learners' writing fluency. Investigated the role of depth of processing in L2 writing and feedback. Advising and Collaboration: While specific students are not listed, her role as a PhD supervisor (as mentioned in her thesis acknowledgment) and her frequent co-authorship with researchers like Aitor Garcés-Manzanera, Luke Plonsky, and Joaquín Gris-Roca indicate an active advising and collaborative research role. She has been involved in research groups such as La_L2 (Applied linguistics. Second Language Learning in Educational Texts) and was previously part of LACELL. Labs and Teams: She is a key member of the research group La_L2: Applied linguistics. Second Language Learning in Educational Texts . This group is central to her research on language teaching materials, textbooks, and L2 learning processes.

Romain Koszulمشاهده پروفایل

پژوهشگر

• Genome Organization
• Chromosome Architecture
• 3D Genomics
• +۱۳ مورد دیگر

Romain Koszul is a Permanent Researcher and group leader at the Institut Pasteur in Paris, France, affiliated with the Department of Genomes and Genetics. He leads the research group focused on viral genome organization and dynamics, where he supervises PhD students, postdoctoral researchers, and engineers. His research is supported by prestigious European Research Council (ERC) grants, including the ERC Starting Grant (DICIG) and ERC Consolidator Grant (Synarchic), reflecting his leadership in the field of genome architecture. His research focuses on the 3D organization of genomes, chromosome dynamics, and the development of computational tools for analyzing chromosome conformation data (Hi-C). He has pioneered the development of several widely used bioinformatics software packages such as Chromosight, instaGRAAL, Serpentine, and metaTOR, which enable high-resolution analysis of chromatin interactions, genome assembly, and metagenomic studies. His work bridges molecular biology, genetics, and computational genomics, with model systems including yeast ( Saccharomyces cerevisiae ) and bacteria. The recent publication trend shows a strong emphasis on chromosome architecture, plasmid-chromosome interactions, epigenetic regulation, and the development of computational methods for genomics. His articles in top journals like Science , Nature , Cell Reports , and EMBO Journal reflect the interdisciplinary and high-impact nature of his research. Scientific Awards: ERC Starting Grant (2010) – Project DICIG ERC Consolidator Grant (2017) – Project Synarchic He actively mentors the next generation of scientists, with former PhD students and postdocs moving on to positions at institutions such as CNRS, EPFL, Oxford Biomedica, and NYU. His lab fosters a collaborative environment integrating experimental and computational approaches to understand fundamental aspects of genome biology. Laboratories and Teams: He leads the 'Organisation et dynamiques des génomes viraux' research group within the Department of Genomes and Genetics at the Institut Pasteur. The team includes permanent researchers, postdoctoral fellows, PhD students, and engineers working on projects related to chromosome conformation, genome assembly, and microbial genomics.

• Numerical Linear Algebra
• Model Order Reduction
• Matrix Equations
• +۵ مورد دیگر

Dr. Jens Saak is a leading researcher and team leader at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. He heads the Computational Methods in Systems and Control Theory research group and has held several leadership positions at the institute since 2010, including Head of the Scientific Computing Team (2010-2020), Head of the Matrix Equations Team (2013-2020), and currently serves as Head of the Data, Infrastructure, Software & Computing Team since 2021. Dr. Saak earned his undergraduate degree in Technomathematik from the University of Bremen (1996-2003), completed his PhD at TU Chemnitz (2003-2009), and held a postdoctoral position there (2009-2010) before joining the Max Planck Institute. His research spans computational mathematics with a focus on large-scale sparse matrix equations , model order reduction , and optimal control of complex systems. He specializes in developing efficient numerical algorithms for Lyapunov and Riccati equations, with applications in mechanical engineering (particularly machine tools), fluid dynamics, and thermal systems. His work bridges theoretical numerical analysis with practical high-performance computing implementations. Analysis of his recent publications reveals a clear trajectory toward energy-efficient computing solutions, sustainable research software practices, and increasingly sophisticated model reduction techniques for complex coupled systems. His work demonstrates a strong emphasis on practical implementations with industrial applications, particularly in mechanical engineering contexts. Dr. Saak has been instrumental in developing and maintaining research software infrastructure, with a growing focus on sustainability and reproducibility in computational science. His leadership extends to collaborative projects across the German mathematical community, particularly in research data management initiatives. His team actively contributes to open-source scientific computing tools, with significant work on the M-M.E.S.S. (Matrix Equations, Sparse Solvers) toolbox, which provides implementations of modern algorithms for matrix equations and model order reduction.

Elon Terrellمشاهده پروفایل

مدرس ارشد

• Fluid Mechanics
• Computational Fluid Dynamics (CFD)
• Thermodynamics
• +۹ مورد دیگر

Elon Terrell is a Senior Lecturer in the Department of Mechanical Engineering at Baylor University, within the College of Engineering. He holds a PhD in Mechanical Engineering from Carnegie Mellon University and both an MS and BS from The University of Texas at Austin. PhD, Mechanical Engineering, Carnegie Mellon University MS, Mechanical Engineering, The University of Texas at Austin BS, Mechanical Engineering, The University of Texas at Austin Dr. Terrell's research focuses on fluid mechanics, computational fluid dynamics (CFD), thermodynamics, heat transfer, energy systems, and tribology , particularly in the areas of friction, lubrication, wear, and nanoscale surface interactions. His work bridges fundamental mechanics with practical applications in energy and manufacturing systems. His 15 most recent publications, spanning from 2006 to 2017, reveal a strong focus on tribology , especially in chemical mechanical polishing (CMP), nanolubricants, graphene, and biodiesel applications. Keywords across these works include mechanical engineering, materials science, nanotechnology, and fluid dynamics, with subfields such as nanotribology, wear mechanisms, lubricant additives, and hydrodynamic modeling. His research demonstrates a consistent emphasis on experimental validation and computational modeling of surface and fluid interactions. Dr. Terrell has made significant contributions to green tribology and sustainable engineering, including studies on wind turbine systems and bio-based lubricants. His collaborative work with researchers like C. F. Higgs III and J. P. Kyle highlights interdisciplinary engagement in semiconductor manufacturing and nanomaterial applications. He teaches foundational and advanced courses such as Introduction to Engineering, Statics, Thermodynamics, Heat Transfer, and Tribology. While no formal advisees or awards are listed, his publication record and teaching load reflect an active academic role. He is based in Rogers 200K at Baylor and can be contacted at Elon_Terrell@baylor.edu .

Laurent FERRARAمشاهده پروفایل

استاد

• International Economics
• Macroeconomics
• Forecasting
• +۶ مورد دیگر

Laurent FERRARA is a full Professor of International Economics at SKEMA Business School (since 2019). Previously, he led the International Macroeconomics Division at the Banque de France, focusing on global economic outlook and policy issues. He holds a PhD in Econometrics (2001) and a Habilitation (HDR) in Economics (2007). His research spans international economics, macroeconomic forecasting, and business cycle analysis, with over 50 peer-reviewed papers and 3 books. He serves on the Board of Directors of the International Institute of Forecasters (heading the Macro Forecasting section) and chairs the French business cycle dating committee. He is an associate editor for the International Journal of Forecasting and International Economics . Notable contributions include developing the World Trade Leading Index (WTLI), analyzing uncertainty shocks' global impacts, and high-frequency monitoring of macroeconomic risks. Key affiliations include the Paris School of Economics, University of Paris Nanterre (EconomiX), and the SKEMA Centre for Global Risks. His work emphasizes real-time data integration (e.g., Google data for GDP nowcasting) and policy-relevant forecasting methodologies. Recent focus areas include commodity currencies' behavior under uncertainty, inflation dynamics, and fiscal policy effects on exchange rates. He organizes major conferences like the International Macro workshop (Banque de France) and has been honored for his editorial roles and contributions to macroeconomic theory-practice bridges. His research often addresses practical policy challenges, such as post-recession employment dynamics and Growth-at-Risk during crises like the 2020 pandemic.

Jonas Striaukasمشاهده پروفایل

استادیار

• Econometrics
• Statistical/Machine Learning Methods
• Mixed Frequency Data Econometrics
• +۳ مورد دیگر

Jonas Striaukas is an Assistant Professor of Statistics and Finance at the Copenhagen Business School (CBS), Department of Finance, and a Marie Skłodowska-Curie Action fellow. His research focuses on econometrics, statistical/machine learning methods, and mixed frequency data modeling. He holds a PhD from Université Catholique de Louvain (2022), supervised by Professors Andrii Babii and Eric Ghysels. Prior to CBS, he was a research fellow at Fonds de la Recherche Scientifique—FNRS in Belgium. Research Interests: High-dimensional econometric modeling, nowcasting techniques, factor-augmented regressions, and applications of machine learning to financial and macroeconomic data. His work emphasizes robust statistical methods for time series analysis and sparse/dense model comparisons. Key Projects: Developed the 'midasml' and 'FAS' R packages for econometric analysis. Conducted a 2022 project at the National Bank of Belgium on machine learning-based nowcasting models. Affiliations: Department of Finance, Center for Statistics, CBS. Member of the Marie Skłodowska-Curie fellowship program.

Liwei Wangمشاهده پروفایل

استادیار

• Machine Learning
• Computational Engineering
• Data-Driven Design
• +۱۹ مورد دیگر

Liwei Wang is an Assistant Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering, where he leads the Computational and Physical Intelligence Laboratory (CPhI Lab). His research integrates computational modeling, machine learning, and mechanics to design advanced materials and smart systems. Education: Ph.D., Mechanical Engineering, Shanghai Jiao Tong University (2022, Honors) B.S., Mechanical Engineering, Shanghai Jiao Tong University (2017, Honors) Liwei Wang's research interests lie at the intersection of machine learning, computational engineering, and advanced materials . He develops data-driven and physics-informed frameworks for topology optimization, metamaterials, 3D/4D printing, soft robotics, and programmable materials systems . His work emphasizes physical intelligence —designing materials that can sense, adapt, and respond to their environments. Applications span mechanical protective cloaks, flexible electronics, minimally invasive surgery, and mechanical computing . His recent publications reveal a strong trend toward multi-scale, data-efficient design of metamaterials using advanced machine learning techniques such as Gaussian processes, latent variable models, and neural networks. He focuses on scalable, differentiable, and task-aware optimization methods that enable rapid discovery and deployment of functional material systems. Scientific Awards: ASME Design Automation Dissertation Award ASME Design Automation Conference Best Paper Award Institution-level Outstanding Ph.D. Dissertation Award Dr. Wang advises a growing research group at CMU and leads the CPhI Lab, where his team develops next-generation computational tools for materials innovation. While specific grants are not listed, his research is likely supported by federal and private funding given the scope and impact of his work. His lab emphasizes interdisciplinary collaboration and translation of computational designs into physical prototypes via additive manufacturing. Laboratory & Team: The Computational and Physical Intelligence Laboratory (CPhI Lab) focuses on co-designing materials and structures with embedded intelligence, combining simulation, data science, and experimental validation to push the boundaries of what engineered materials can achieve.

Eric Y. Maمشاهده پروفایل

استادیار

• Physical Electronics
• Quantum Materials
• Photonics
• +۹ مورد دیگر

Eric Y. Ma is an Assistant Professor in the Department of Physics and by courtesy in Electrical Engineering and Computer Science (EECS) at UC Berkeley, where he holds the Georgia Lee Chair in Physics. He leads the Ma Lab, focusing on wave-matter interactions in quantum regimes, sub-wavelength scales, and unconventional frequencies (THz to UV). His research combines simulation, optimization, and experimental techniques across microwave to ultraviolet domains. Education 2016: Ph.D. in Applied Physics, Stanford University 2010: B.S. in Physics, Peking University Research Focus Dr. Ma's research explores electromagnetic interactions with condensed matter systems, developing novel tools for nanoscale characterization. His work spans quantum materials, inverse design methodologies, microwave microscopy innovations, and applications in computing, sensing, and mixed reality. The lab maintains a balance between fundamental physics and practical implementation. Publication Trends Recent publications demonstrate strong focus on advancing microwave impedance microscopy techniques for quantum materials characterization, developing physics-agnostic inverse design frameworks, and exploring spin wave dynamics. Significant contributions appear in nanophotonics, quantum measurement, and interdisciplinary applications bridging physics with machine learning. Awards & Honors Hellman Fellow (2024) Google Research Scholar (2024) Amazon Physical Science Fellowship (2022) Georgia Lee Chair in Physics Research Support Moore Foundation: Quantum-Limited Atomic Force Microscope development (2024) NSF: Advanced Inverse Design of Microwave Devices (2024) Google: Parsing Physics Literature with LLMs (2024) Laboratory The Ma Lab specializes in developing experimental platforms spanning microwave to ultraviolet frequencies, emphasizing DIY instrumentation and collaborative research. The team includes postdoctoral researchers, graduate students, and undergraduates working on quantum materials characterization, inverse design algorithms, and novel microscopy techniques.

Marilyn Lightstoneمشاهده پروفایل

استاد

• Computational Fluid Dynamics (CFD)
• Heat Transfer
• Turbulent Flows
• +۲ مورد دیگر

Marilyn Lightstone is a Professor in the Mechanical Engineering department at McMaster University , specializing in Computational Fluid Dynamics (CFD) , Heat Transfer , and Turbulent Flows . Her research spans Thermo Fluid Sciences with applications to Nuclear Reactor Cooling Systems , Solar Energy Thermal Storage , and Atria Geometries . She collaborates with institutions like Queen’s University and University of Waterloo alumni. Education: B.Sc., Queen’s University (1985) M.A.Sc., University of Waterloo (1987) Ph.D., University of Waterloo (1992) Research Interests focus on advanced turbulence and particle dispersion models for industrial and nuclear applications. Key areas include: Development of stochastic and DNS-based particle dispersion models Thermal mixing prediction in nuclear subchannel geometries Validation of CFD models for atria flow dynamics Optimization of thermal storage systems for solar energy Investigation of turbulence modulation by particulates Her work on Detached Eddy Simulation (DES) achieved 68.4 Hz pulsation frequency prediction (vs experimental 68 Hz) in nuclear subchannels, while exploring cost-effective Unsteady RANS methods. Recent projects involve collaborations with Solar Buildings Research Network and funding from UNENE and NSERC CRD . Scientific Contributions : McMaster Students Union Teaching Award recipient Advising Legacy includes guiding Eleanor Hennick, Cathy Strutt, Fusheng Yan, and current students Matthew Cernick, Stephen Murray, and Alan Chettle. Her lab addresses fundamental fluid mechanics challenges with practical applications in energy and nuclear safety.

Lea Petrellaمشاهده پروفایل

استاد

• Quantile regression
• Graphical models
• Random Forest
• +۳ مورد دیگر

Lea Petrella is a Full Professor at the Department of Methods and Models for Economics, Territory, and Finance, Sapienza University of Rome. She teaches courses in Time Series Analysis and Advanced Statistical Methods , focusing on practical applications using R software. Research Interests: Quantile regression, Graphical models, Hidden Markov Models, Risk measures, and Time Series analysis Key Projects: Generalized Dynamic Graphical Models for pandemic impacts, Penalized quantile regression for risk assessment, Multivariate quantile regression frameworks Her recent publications include: 2025: Mid-quantile mixed graphical models for public shootings 2025: Spatial quantile random forests for economic mobility 2024: Expectile hidden Markov models for cryptocurrency returns 2024: Mixed-frequency quantile regressions for risk forecasting She supervises postdocs and PhD students including Maria Saiz, Beatrice Foroni, and Valentina Raponi. Her work spans financial risk modeling, environmental statistics, and biomedical applications. Email: Lea.Petrella@uniroma1.it or lea.petrella@uniroma1.it

Hans Julius Skaugمشاهده پروفایل

استاد

• Biostatistics
• Marine Ecology
• Computational Statistics
• +۴ مورد دیگر

Hans Julius Skaug is a Professor in the Department of Mathematics at the University of Bergen (UiB), Norway, with a distinguished career spanning several decades focused on statistical methodology and its applications to biological and ecological problems. Dr. Skaug's research expertise encompasses several interconnected domains: Biostatistics, particularly applications of statistics and probability to marine ecology Computational statistics, with pioneering work on Automatic Differentiation and Laplace approximation for complex model fitting Artificial Intelligence, where he has recently focused on variational autoencoders and diffusion models Development of innovative methods for line transect surveys and close-kin mark recapture (CKMR) His publication record shows a clear progression from foundational statistical methodology to increasingly sophisticated applications. The 2016 paper with Bravington and Anderson on 'Close-kin mark-recapture' in Statistical Science established him as a leader in population estimation methods. Recent publications (2023-2025) demonstrate continued innovation in refining CKMR techniques, applying TMB to diverse fields like insurance claims analysis, exploring sparse Bayesian learning, and addressing measurement errors in marine mammal surveys. His work consistently bridges theoretical statistical development with practical applications to real-world conservation and management problems. Dr. Skaug served as co-Editor in chief of the Scandinavian Journal of Statistics from 2018 to 2021, demonstrating his significant standing in the international statistical community. He teaches STAT110 Basic course in statistics at UiB during both spring and fall semesters and has conducted specialized workshops on CKMR and TMB at institutions including Dalhousie University in Halifax and the Institute of Marine Research in Bergen. He is actively involved in software development for statistical modeling through the TMB project (https://github.com/kaskr/adcomp) and ADMB (http://admb-project.org/), which implement his theoretical work on combining Automatic Differentiation with statistical modeling. His current research trajectory shows increasing integration of AI techniques with traditional statistical approaches, reflecting his observation that backpropagation in deep learning is fundamentally the same computational technique as Automatic Differentiation, which he has worked with for over 20 years.

• Quantitative Linguistics
• Psycholinguistics
• Morphological Productivity
• +۷ مورد دیگر

Rolf Harald Baayen is a Professor of Quantitative Linguistics at the University of Tübingen, where he holds the Alexander von Humboldt Professorship and leads a research group on discriminative learning in language processing. He has held academic positions at Radboud University Nijmegen, the University of Alberta, and the Max Planck Institute for Psycholinguistics. Baayen has had a long-standing collaboration with the University of Tartu, where he was awarded an honorary doctorate in linguistics in 2023 for his contributions to quantitative methods in linguistics and mentorship of Estonian scholars. His research interests center on quantitative linguistics , psycholinguistics , and statistical modeling of language . He is particularly known for his work on morphological productivity , language processing , and discriminative learning models . His methodological contributions, especially through the textbook Analyzing Linguistic Data , have revolutionized how linguists apply statistical techniques using R. His recent projects, such as WIDE and SUBLIMINAL, funded by the European Research Council, explore word production and comprehension in spontaneous speech and cross-linguistic processing, especially in Asian languages. Baayen's recent publications reflect a strong focus on computational and statistical approaches to linguistic theory, integrating corpus data, cognitive modeling, and machine learning. His work consistently bridges theoretical linguistics with empirical, data-driven analysis, influencing both cognitive science and computational linguistics. His scientific awards include: Alexander von Humboldt Professorship Two ERC Advanced Grants (WIDE and SUBLIMINAL) Pionier Career Advancement Award (Netherlands Research Council) Membership in the Academia Europaea Honorary Doctorate from the University of Tartu Muller Chair from the Royal Netherlands Academy of Arts and Sciences Baayen has supervised 32 doctoral dissertations (25 completed), significantly contributing to the training of next-generation linguists in quantitative methods. He has organized numerous workshops at institutions like the University of Tartu and has been instrumental in advancing research infrastructure in quantitative linguistics. He is currently the editor-in-chief of The Mental Lexicon and continues to lead cutting-edge research in language modeling and cognitive processing. He is affiliated with research groups at the University of Tübingen and maintains active collaborations with linguists across North America, Europe, and Asia, particularly in Estonia and the Netherlands. His work with Estonian language data has helped position Estonia as a hub for innovative linguistic research.

• Gender Studies
• Queer Theory
• Television Fiction
• +۳ مورد دیگر

Beatriz González de Garay Domínguez is a Senior Lecturer in Audiovisual Communication at the University of Salamanca and member of the Observatory for Audiovisual Contents. She holds a BA with an Extraordinary Final Degree Award from Universidad Carlos III de Madrid and a European PhD from Universidad Complutense Madrid. Research focuses on gender and sexual diversity studies in television fiction and audiovisual heritage Former teaching experience at Universidad Complutense under an Education Ministry grant Research stay at Humboldt University of Berlin (2011) Professional background includes non-academic roles as script analyst for Telecinco and assistant director for the series Hospital Central . Her scholarly work examines: Queer childhood representations in Spanish cinema LGBTQ+ portrayal across traditional and streaming platforms Gender dynamics in director-led film production Visual media's role in historical education Narrative persuasion strategies for health communication

Bogdan M. Wilamowskiمشاهده پروفایل

استاد

• Semiconductor Devices and Sensors
• Mixed Signal and Analog Signal Processing
• Computational Intelligence
• +۷ مورد دیگر

Bogdan M. Wilamowski is a Professor and Director of the Alabama Nano/Micro Science and Technology Center (ANMSTC) at Auburn University's College of Engineering, Department of Electrical and Computer Engineering. He holds the Alumna Professor title and leads cutting-edge research in microelectronics and intelligent systems. MS in Computer Engineering, 1966 PhD in Neural Computing, 1970 Dr. Habil. in Integrated Circuit Design, 1977 Full Professor title awarded by the President of Poland, 1987 His research spans semiconductor devices and sensors, analog and mixed-signal processing, and computational intelligence. He has pioneered work in neural networks, intelligent control, and microelectronic fabrication, with applications in industrial electronics and embedded systems. His academic leadership includes editorial roles in top IEEE journals and co-editing the CRC Book Series in Industrial Electronics. The recent publications highlight a sustained focus on analog signal processing, neural networks, microelectronic fabrication, and computational intelligence. These works reflect a strong integration of theoretical development with practical engineering applications, particularly in circuit design, sensor systems, and intelligent automation. The trend shows continued innovation in both analog electronics and AI-driven microsystems. IEEE Fellow Honorary Member of the Hungarian Academy of Sciences Commander Cross of the Order of Merit of the Republic of Poland (2008) Dr. Wilamowski has advised approximately 150 graduate students and has authored 9 books and over 300 refereed publications. He has held editorial leadership as Editor-in-Chief of IEEE Transactions on Industrial Electronics (2008–2010) and IEEE Transactions on Industrial Informatics (2011–2013). His research has been supported by extensive collaborations and institutional leadership, including directing research centers at Auburn and Idaho. He has also led major professional initiatives within IEEE, contributing significantly to the global engineering community. He leads the Alabama Nano/Micro Science and Technology Center (ANMSTC), a multidisciplinary research hub focused on advancing microelectronics, nanotechnology, and intelligent systems. The center fosters collaboration across academia and industry, supporting innovation in semiconductor devices, analog circuits, and computational intelligence applications.