جست‌وجوی استاد و پژوهشگر

Filippo Piermaria Ferroniمشاهده پروفایل

استاد

• Empirical Macroeconomics
• Applied Time Series Analysis
• Monetary Economics
• +۳ مورد دیگر

Filippo Piermaria Ferroni is a Full Professor in the Department of Economic Sciences at the University of Bologna. He specializes in Empirical Macroeconomics, Applied Time Series Analysis, and Monetary Economics. Before joining the University of Bologna in 2024, he held positions at the Federal Reserve Bank of Chicago (currently on leave), Banque de France, and the European Commission. His research focuses on structural vector autoregression (SVAR) models, DSGE modeling, and the impacts of monetary policy on labor markets and inequality. Dr. Ferroni holds a B.A. from Bocconi University, Milan, and an M.Sc. and Ph.D. in Economics from Pompeu Fabra University, Barcelona. His work emphasizes bridging theoretical models with empirical evidence, particularly in analyzing policy shocks, labor supply dynamics, and financial market interdependencies. Recent research explores how unusual economic shocks challenge conventional models and the distributional consequences of monetary policy. His articles span topics like inflation dynamics, central bank policy frameworks, and cryptocurrency risk measurement. Notable contributions include developing the Chicago Fed DSGE Model and analyzing the labor share response to monetary policy. Despite no explicit mentions of awards, his extensive publication record reflects recognition in academic and policy circles. Dr. Ferroni’s advising and grant activities are not detailed here, but his professional trajectory underscores sustained engagement with central banks and academic institutions. His research often involves collaborative projects, though specific teams or labs are not explicitly mentioned.

• Condensed Matter Physics
• Materials Science
• Ferroic Materials
• +۵ مورد دیگر

Martin Sarott is a Postdoctoral Researcher in Complex Oxides at the University of Groningen, affiliated with the Zernike Institute for Advanced Materials and the Faculty of Science and Engineering. His research focuses on epitaxial ferroic thin films, domain configurations, optical control of ferroic order, and complex oxides for unconventional computing. He specializes in experimental techniques including pulsed laser deposition, nonlinear optics, X-ray diffraction, and advanced microscopy methods. His work addresses challenges in material synthesis, domain engineering, and device fabrication, with applications in next-generation electronics and energy-efficient systems. Notably, he explores strain effects on polar phases in WO3 thin films and optical manipulation of ferroelectric polarization states. Recent publications highlight contributions to understanding structure-property relationships in epitaxial systems, polar skyrmions, and novel diffraction strategies for hidden order detection. Sarott collaborates on projects involving 2D materials, plasmonic membranes, and quantum sensing for domain imaging. His experimental toolkit includes advanced lithography (photolithography, electron beam lithography) and in-situ characterization methods.

Gerhard Jakob Kläuiمشاهده پروفایل

استاد

• Spintronics
• Magnetism
• Condensed Matter Physics
• +۷ مورد دیگر

Gerhard Jakob Kläui is an active faculty member at the Institute of Physics, University of Mainz, where he holds the academic rank of Außerplanmäßiger Professor (Extraordinary Professor). He is deeply involved in research and teaching activities, focusing on advanced topics in spintronics, magnetism, and quantum materials. His work is conducted within the broader framework of the Faculty of Physics, Mathematics and Computer Science. His research interests span Spintronics , Magnetism , Heusler compounds , Skyrmion dynamics , Spin-orbit torques , and Topological materials . These areas reflect a strong emphasis on next-generation magnetic technologies, including energy-efficient memory, neuromorphic computing, and quantum sensing. His group investigates both fundamental phenomena and device-level applications, particularly in antiferromagnetic and topological systems. The most recent publications (2024–2025) highlight a strong trend in ultrafast spin dynamics , magneto-ionic control , altermagnetism , and nonreciprocal transport . These works appear in high-impact journals such as Science Advances , Nature Communications , and Physical Review Applied , indicating significant contributions to the field. The research combines experimental techniques with theoretical insights, often involving collaborations across institutions. Email: jakob@uni-mainz.de Dr. Kläui has co-authored numerous papers with Prof. Mathias Kläui, suggesting a close collaborative relationship, possibly within the same research group. While no formal students or grants are listed, his extensive publication record implies advisory and leadership roles. His involvement in teaching is confirmed by the university's course listings. The research environment includes access to advanced facilities such as UHV deposition systems, scanning probe microscopy, and high-frequency magnetotransport setups, supporting a wide range of experimental investigations in nanomagnetism and spintronics.

• Particle Astrophysics
• Neutrino Astronomy
• Cosmology
• +۵ مورد دیگر

Francis Halzen is the Gregory Breit Professor and Vilas Research Professor at the University of Wisconsin-Madison, where he has been a key figure in the Department of Physics since 1987. As principal investigator of the IceCube Neutrino Observatory, he has pioneered neutrino astronomy through projects like AMANDA and IceCube, transforming Antarctic ice into a Cherenkov detector for high-energy subatomic particles. Academic Appointments : Vilas Research Professor, Gregory Breit Professor Key Collaborations : IceCube Collaboration, AMANDA Collaboration Research Focus : Dr. Halzen's work spans particle astrophysics, neutrino astronomy, and cosmology. He investigates cosmic ray acceleration mechanisms, dark matter, and neutrino oscillations, with recent studies on extragalactic neutrino sources like NGC 1068 and TXS 0506+056. His team uses machine learning for low-energy neutrino reconstruction and explores theories involving extra dimensions. Scientific Contributions : IceCube's 2023 discovery of Galactic plane neutrinos and 2022 NGC 1068 neutrino emission stand out among his 1,000+ publications. The 2013 observation of PeV-energy neutrinos marked a breakthrough in high-energy astrophysics. Major Awards : Balzan Prize (2015), European Physical Society Prize (2015), Julius Wess Award (2017), Smithsonian American Ingenuity Award (2014) Honorary Doctorates : Ghent University (2013), Southern Methodist University (2017), Ruhr University Bochum (2022) Technical Leadership : Dr. Halzen contributes to detector design, including IceCube-Gen2 upgrades and KM3NeT projects. He advises on major experiments like SNO, Auger-Upgrade, and Fermilab PAC, while advancing open-source analysis frameworks like GollumFit.

Hans-Jörg Schulzمشاهده پروفایل

دانشیار

• Visual Analytics
• Information Visualization
• Graph Visualization
• +۴ مورد دیگر

Hans-Jörg Schulz serves as an Associate Professor in the Department of Computer Science at Aarhus University, Denmark, specializing in visual analytics and information visualization. His research bridges computer science with interdisciplinary applications in food science, neuroscience, and material engineering. He earned his Doctorate in Computer Science (2010) and Diplom (Master's equivalent, 2004) from the University of Rostock, focusing on explorative graph visualization and visual data mining for complex structures. His academic trajectory reflects deep expertise in transforming complex data into actionable visual insights through innovative methodological frameworks. Schulz's research centers on advancing visual analytics methodologies, particularly progressive visual analytics where data processing and user interaction occur simultaneously. His work establishes foundational techniques for visual guidance systems, explainable AI interfaces, and specialized visualization tools for domains like EEG analysis and food rheology. Recent publications demonstrate a strategic expansion into haptic feedback integration, agent-based visualization design, and cross-disciplinary applications requiring novel visual fingerprinting techniques. Analysis of his 2023-2025 publications reveals three dominant trends: (1) Human-centered progressive analytics with focus on trust calibration and cognitive load management, (2) Domain-specific visualization frameworks for complex data like anisotropic food structures and EEG artifacts, and (3) Novel interaction paradigms incorporating force feedback and malleable interfaces. His work consistently emphasizes practical usability while pushing technical boundaries in visual representation. Schulz's contributions have been recognized with significant awards including IEEE InfoVis Best Poster (2010), EuroVis 3rd Best Paper (2012), VDA Best Paper (2015), Cybercartography Competition 1st Place (2022), and IEEE SciVis Contest 2nd Place & Most Innovative Work (2023). These accolades highlight both theoretical innovation and practical impact in the visualization community. As an educator, he supervises PhD candidates and teaches core visualization courses including Data Visualization, Information Visualization, and Visual Analytics. His current ArtiPlex project (2024-present) develops multiplex analytics for EEG artifact detection, securing active research funding while demonstrating his commitment to translating visualization research into domain-specific solutions. Collaborative patterns across his 79 publications indicate strong partnerships with food scientists, neuroscientists, and HCI researchers.

Bent Nielsenمشاهده پروفایل

استاد

• Econometrics
• Statistical Theory
• Time Series Analysis
• +۴ مورد دیگر

Bent Nielsen is a Professor of Econometrics at the Department of Economics, University of Oxford, and a Professorial Fellow at Nuffield College. His academic career is centered around econometric theory and its applications. Nielsen has established himself as a leading expert in time series analysis, particularly in the areas of cointegration, outlier detection, and age-period-cohort modeling. His research has significant implications for both theoretical econometrics and practical applications in fields such as actuarial science and demographic analysis. Based at Nuffield College in Oxford, he contributes to the vibrant academic community through research, teaching, and software development for statistical analysis. Research Interests: Econometric Theory: Nielsen's work focuses on the theoretical foundations of econometric modeling, particularly in time series analysis. His research on cointegration, unit root testing, and co-explosive processes has advanced our understanding of non-stationary economic time series. Outlier Detection: He has made significant contributions to robust regression methods, particularly through his work on the Forward Search algorithm and Least Trimmed Squares estimation. His theoretical work provides the foundation for reliable outlier detection in economic and financial data. Age-Period-Cohort Analysis: Nielsen has developed innovative methods for age-period-cohort modeling, with applications in demography, epidemiology, and actuarial science. His R package "apc" has become a standard tool in these fields. Chain Ladder Methods: His work on the statistical foundations of chain ladder methods has improved forecasting techniques in general insurance for projecting future liabilities. Research Trends: Nielsen's recent publications (2019-2025) demonstrate a continued focus on robust statistical methods, particularly Least Trimmed Squares estimation and its applications. His work bridges theoretical econometrics with practical applications, especially in demographic modeling and actuarial science. The development of R packages like "apc" and "ForwardSearch" shows his commitment to making advanced statistical methods accessible to practitioners. His collaborations with researchers across institutions highlight the interdisciplinary nature of his work. Scientific Recognition: Best paper award (2016) for "A Joint Chow Test for Structural Instability" Development of widely used R packages: "apc" for age-period-cohort modeling and "ForwardSearch" for outlier detection Teaching and Mentorship: Professor Nielsen teaches graduate-level econometrics courses at Oxford, including MPhil Core Econometrics, MPhil Advanced Econometrics, and MPhil Topics in Probability. He supervises research students working on econometric theory and applications. His textbook "Econometric Modeling: A Likelihood Approach" (co-authored with David Hendry, 2007) is widely used in graduate econometrics courses. Software Development: Nielsen has made significant contributions to statistical computing through the development of several R packages. His "apc" package implements age-period-cohort modeling techniques, while "ForwardSearch" provides tools for robust regression using the Forward Search algorithm. These packages have been downloaded thousands of times and are cited in numerous research papers across multiple disciplines.

Alessio De Magisمشاهده پروفایل

پژوهشگر

• Molecular Biology
• Cellular Biology
• Oncology
• +۶ مورد دیگر

Alessio De Magis is a fixed-term researcher at the Department of Molecular Biology , Faculty of Medicine and Surgery , Università Vita Salute San Raffaele in Milan. His research focuses on G-quadruplex structures, genome instability, and iron homeostasis in cancer models. PhD in Molecular Medicine from Vita-Salute San Raffaele University Specializes in ferroptosis mechanisms across multiple myeloma, prostate cancer, and chronic lymphocytic leukemia (CLL) Developed novel flow cytometry tools for G-quadruplex quantification Recipient of AIRC's TRIDEO prize for oncological research Research Trends show consistent engagement with G-quadruplex biology (10/14 articles), iron-cancer interactions (7/14 articles), and DNA damage pathways (9/14 articles). His work bridges structural biology with clinical applications, particularly in hematological malignancies. Scientific Awards AIRC TRIDEO prize (2016) for iron-cancer research Teaching includes advanced molecular biology courses for Medicine and Surgery students (6-year program) and Physiotherapy degree courses, covering cellular biology, experimental techniques, and bioimaging.

Jean-Michel ZAKOIANمشاهده پروفایل

استاد

• Financial Econometrics
• Time Series
• Dynamic Risk Measures
• +۷ مورد دیگر

Jean-Michel ZAKOIAN is a Full Professor at ENSAE Paris-Saclay and Professor of Applied Mathematics at Université de Lille, currently on secondment at ENSAE since 2008. He leads the Finance-Insurance lab at CREST and serves as Associate Editor for Econometric Theory , Scandinavian Journal of Statistics , and Journal of Time Series Analysis . Co-author of GARCH models. Structure, Statistical Inference and Financial Applications (2nd ed., 2019) Published over 80 peer-reviewed works in top journals like Econometrica and Journal of the American Statistical Association Research Interests : Specializing in Financial Econometrics and Time Series , ZAKOIAN's work focuses on GARCH-type models, dynamic risk measures, estimation risk, unit roots, explosive processes, and quasi-maximum likelihood (QML) estimation. His recent publications address challenges in volatility modeling without moment constraints and systemic risk inference. Academic Contributions : As a supervisor of 13 PhD theses in Applied Mathematics and Economics, he has significantly shaped emerging researchers in econometric theory. His editorial roles across four journals reflect his standing in advancing methodological rigor in time series analysis.

Xiang Jianمشاهده پروفایل

مدرس

• Fluid Dynamics
• Computational Fluid Dynamics (CFD)
• Stratified Flows
• +۳ مورد دیگر

Xiang Jian is a Lecturer at the University of Southern California (USC) Dornsife College , focusing on fluid dynamics and stratified flows. His research examines wake structures in stratified environments through both experimental and computational approaches. Academic Rank: Lecturer Institution: University of Southern California Contact: xjian@usc.edu His work centers on fluid dynamics with an emphasis on stratified flows , vortex dynamics , and internal waves . He employs techniques like Dynamic Mode Decomposition (DMD) to analyze wake structures and quantify turbulence in stratified environments. Recent publications highlight his expertise in stratified wake analysis , CFD validation , and buoyancy-induced flow patterns . Studies span from low Reynolds number sphere wakes to turbulent grid-generated flows, with applications in environmental and geophysical fluid dynamics.

Murat Inalpolatمشاهده پروفایل

استاد

• Structural Health Monitoring
• Diagnostics and Prognostics
• Structural Dynamics
• +۸ مورد دیگر

Murat Inalpolat is a Professor and Associate Chair for Doctoral Studies in the Department of Mechanical and Industrial Engineering at the University of Massachusetts Lowell's Francis College of Engineering. He leads research projects related to wind energy, aerospace and automotive technologies and serves as an Associate Editor for the ASME Letters in Dynamic Systems and Controls Journal. In 2022, he was awarded the prestigious NASA Faculty Fellowship for work on aircraft electrification. His research focuses on structural health monitoring, diagnostics and prognostics of rotating machinery, damage detection and identification, probabilistic design and analysis, uncertainty quantification, reliability and sustainability, electric propulsion, and wind energy. His work combines theoretical modeling with experimental validation to address complex structural dynamics and acoustic challenges. His recent publications demonstrate a strong focus on wind turbine blade monitoring using acoustic sensing techniques, with several papers addressing damage detection methods, vibration analysis, and structural health monitoring systems for operational wind turbines. His research bridges theoretical modeling with practical engineering applications. NASA Fellow – Electrification of Aircrafts (2022) UML ME Department's Teaching Excellence Award (2020) Chancellor's recognition for Research Excellence and Productivity (2020) ASME Engineering Design Engineering Division Service Recognition Award (2019) CTI's Young Drive Experts award (2009) Professor Inalpolat has successfully advised numerous graduate students in mechanical engineering, with recent thesis topics focusing on structural health monitoring, wind turbine analysis, and dynamic systems. He leads significant research projects including a $1.4 million DOE grant for monitoring wind turbine blades and an NSF IUCRC WindStar project. His work has resulted in multiple patents related to turbomachinery design and clearance optimization. He is a core faculty member of the Structural Dynamics and Acoustic Systems Laboratory (SDASL), which he joined in 2014, significantly expanding the lab's capabilities in structural health monitoring, rotating machinery, and nonlinear dynamics. The lab is well-equipped for structural dynamic, acoustical, modal analysis, and control system experiments and modeling.

• Changepoint Detection
• High-dimensional Time Series
• Statistical Inference
• +۳ مورد دیگر

Haeran Cho is Professor of Statistical Science in the School of Mathematics at the University of Bristol, holding a BSc and PhD in Statistics. Her research focuses on developing foundational methodologies for detecting structural changes in complex high-dimensional data streams, with applications spanning finance, environmental monitoring, and biomedical engineering. Her primary research interests include changepoint detection in non-sparse regression frameworks, factor model diagnostics for tensor time series, and robust nonparametric segmentation techniques. She pioneers approaches that handle heavy-tailed distributions, temporal dependence, and high-dimensional scaling—addressing critical limitations in classical change point theory through adaptive covariance scanning and multiscale inference frameworks. Professor Cho received the Research Prize in 2013 for her contributions to statistical theory. She currently leads the £1.2M EPSRC-funded project Statistical Foundations for Detecting Anomalous Structure in Stream Settings (DASS, 2024-2029), developing real-time anomaly detection systems for industrial applications. Previous projects include quantile factor modeling for high-dimensional time series (2019). Her software implementations ( CptNonPar , mosum , fnets ) have become standard tools in statistical computing, with over 500 citations. She actively collaborates through Horizon Europe initiatives and supervises postgraduate researchers in statistical methodology development.

• financial econometrics
• functional analysis
• probability theory
• +۴ مورد دیگر

Borzykh Dmitry Alexandrovich is an Associate Professor at the National Research University Higher School of Economics , affiliated with the Department of Applied Economics under the Faculty of Economic Sciences . He also serves as a Research Fellow at the International Laboratory of Stochastic Analysis and its Applications . With 18 years of scientific and teaching experience since joining HSE in 2006, he teaches advanced courses in probability theory, mathematical statistics, and econometrics at both undergraduate and graduate levels. Education: Candidate of Physical and Mathematical Sciences (2022, HSE), Master's in Economics (2006, HSE), Bachelor's in Economics (2004, HSE) Key research areas: financial econometrics , stochastic analysis , structural breaks in time series , and stochastic volatility models His recent publications focus on stochastic processes , structural break detection , and quantile function applications across financial and economic modeling. He has received multiple institutional awards, including Best Teacher (2014, 2016–2025) and formal gratitudes from HSE departments (2019, 2022). He provides consultations via email and holds office hours at Pokrovsky Boulevard campus (room S517).

Monica Colaiacovoمشاهده پروفایل

استاد

• Meiosis
• Germline Maintenance
• Chromosomal Inheritance
• +۵ مورد دیگر

Dr. Monica Colaiacovo is a Tenured Full Professor at Harvard Medical School's Department of Genetics within the Blavatnik Institute. Her research focuses on meiotic processes, germline maintenance, and environmental impacts on reproductive health. Harvard Medical School Department of Genetics Blavatnik Institute affiliation Established C. elegans as key model organism Research interests include: Meiotic chromosome dynamics Synaptonemal complex assembly Histone modification roles in DNA repair Environmental toxicant screening Key article trends show environmental chemical effects on meiosis, epigenetic regulation of DNA damage repair, and molecular mechanisms in synaptonemal complex formation using C. elegans models. Scientific contributions include: PLOS Genetics editorial board member (2016) High-throughput toxicant screening platform Lab members span from research associates to visiting students, with recent focus on DEET and phthalate effects on reproduction. Collaborations extend to Harvard's Yankner Lab and Church's team for interdisciplinary research.

• Optical characterization of nanomaterials
• Experimental nonlinear photonics
• Quantum photonics
• +۵ مورد دیگر

Massimo Cazzanelli is a highly experienced researcher at the University of Trento, currently serving as a Tecnologo EP2 (Researcher with High Professionalism) in the Department of Industrial Engineering since February 2023. He has been affiliated with the University of Trento since 1995, previously working in the Department of Physics where he held progressively senior research positions. His academic background includes a Master's Degree in Physics from the University of Trento (1996), a Master of Science in Physics from Trinity College-University of Dublin (1998), and a PhD in Physics (2003). Dr. Cazzanelli's research focuses on optical characterization of functionalized nanomaterials, experimental nonlinear and quantum photonics, and silicon optical and radiation sensors. His work spans from fundamental research on silicon nanocrystals to practical applications in quantum sensing, optical sensors, and flexible electronics. He has published over 70 scientific articles with an h-index of 28 (ISI WEB of Science) and 31 (Google Scholar), accumulating nearly 3000 citations. His recent publications demonstrate a strong focus on nanodiamond synthesis and applications, particularly for quantum sensing, as well as work on silicon-based radiation sensors and hydrothermal carbonization processes. This reflects his transition from pure silicon photonics to a broader range of nanomaterials and sensing applications. Graduate Student Award for best paper at E-MRS symposium (1998) Dr. Cazzanelli has served as a teaching assistant for multiple physics courses across various degree programs from 2005 to 2023, demonstrating his commitment to academic education. He has also been actively involved in the scientific community as a Review Editor for Frontiers in Physics, Editorial Board Member for Micromachines, and Guest Editor for several journals. His extensive peer review activities for numerous prestigious journals highlight his standing in the research community. His current research at the Laboratory of Electronics, Microsystems and Quantum Photonics focuses on advanced optical characterization techniques and development of novel sensing platforms using nanomaterials, particularly nanodiamonds and silicon-based structures.

Jing Qianمشاهده پروفایل

پژوهشگر

• Proton Therapy
• Clinical Therapeutical Physics
• Quantitative Image Analysis
• +۵ مورد دیگر

Dr. Jing Qian, Ph.D., is a medical physicist in the Department of Radiation Oncology at Mayo Clinic Comprehensive Cancer Center in Rochester, Minnesota. He holds an appointment at Mayo Clinic College of Medicine and is board-certified in Therapeutic Medical Physics by the American Board of Radiology. His clinical expertise focuses on proton therapy and improving radiation treatment delivery efficiency, outcomes, and safety. Dr. Qian completed his Doctor of Philosophy in Physics at Yale University in 2009 and finished his residency in Radiation Oncology Medical Physics at Mayo Clinic in 2021. His early academic achievements include Yale University's Henry Fan Family Fellowship and Leigh Page Prize in 2002. His research spans proton therapy optimization, quantitative image analysis, and artificial intelligence applications in radiation oncology. Recent work demonstrates his shift toward developing deep learning models for proton dose calculation, analyzing imaging signatures for treatment response prediction, and optimizing treatment delivery systems. His interdisciplinary approach bridges physics, oncology, and computer science to improve cancer treatment outcomes. Dr. Qian's publication record shows consistent output in high-impact journals with an increasing focus on AI-driven solutions for radiation therapy challenges. His collaborative work spans multiple institutions and clinical specialties, reflecting the interdisciplinary nature of modern medical physics research. 2024 Distinguished Physics Researcher, Mayo Clinic Department of Radiation Oncology 2022 Mayo Scholar, Google Visiting Scholar Multiple beOUTSTANDING awards from Schlumberger Corporation (2014-2017) Outstanding Contributor and Most Valued Reviewer for Radiation Measurements journal Yale University's Henry Fan Family Fellowship and Leigh Page Prize (2002) Dr. Qian actively mentors physics residents and postdoctoral fellows, contributing to the next generation of medical physicists. His work with Mayo Clinic's Comprehensive Cancer Center provides access to advanced proton therapy facilities and cutting-edge research infrastructure. His collaborations extend to Google researchers and multiple clinical departments within Mayo Clinic, creating a rich environment for translational medical physics research. His laboratory work focuses on developing and validating new technologies for radiation treatment delivery and outcome prediction. Current projects involve AI-driven optimization of proton therapy systems, radiomics analysis of treatment response, and improving the precision of radiation dose delivery while minimizing toxicity.