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Martin KolbView profile

Professor

• Stochastic Processes
• Probability Theory
• Mathematical Physics
• +3 more

Roles & Affiliations: Martin Kolb is a Professor (W3) at Paderborn University's Institute of Mathematics (Applied Mathematics and Stochastics group leader). He previously held roles including Professor (W2) at Paderborn (2014–2017), Lecturer at University of Reading (2012–2014), and Harrison Early Career Professor at Warwick University (2011–2012). Education: PhD in Mathematics from TU Kaiserslautern (2009), Diploma in Mathematics from Ludwig-Maximilians-University Munich (2005). Studied at University of Copenhagen (2002–2003). Research Interests: Focuses on stochastic processes, probability theory, and mathematical physics. Key topics include quasi-stationary distributions, Fleming-Viot models, spectral asymptotics, and applications in quantum computing (e.g., Photonic Quantum Computing project). His work bridges theoretical mathematics with computational methods and physical systems. Scientific Engagement: Organized major events like the Weierstrass-Lecture series (since 2019) featuring Fields medalists and co-organized workshops such as the Oberwolfach Workshop on Stochastic Processes (2020). Active in conference committees for topics like 'Stochastic Processes under Constraints' (2016). Labs/Teams: Leads research projects including 'PhoQC: Photonic Quantum Computing' and 'Super-Brownian Motion with single point source'.

• Computational Chemistry
• Molecular Machine Learning
• Catalyst Design
• +2 more

Clémence Corminboeuf is a Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Laboratory of Computational Molecular Design (LCMD). Her research focuses on computational chemistry, machine learning for molecular design, and catalyst optimization using physics-inspired models. Email: clemence.corminboeuf@epfl.ch Scopus Author ID: 6603320500 Researcher ID: I-5973-2018 Current research involves: Developing integer linear programming algorithms for molecular machine learning training set selection Generative inverse design pipelines for singlet fission materials discovery Automated volcano plot analysis tools (SPOCK) for catalyst design Enantiospecific synthesis methods for planar chiral organometallic complexes Neural network potential frameworks for solute-solvent reaction dynamics Recent methodological advances include: Cost-informed Bayesian optimization (CIBO) for efficient chemical experimentation 3DReact geometric deep learning architecture for reaction property prediction MARC software for automated transition state ensemble analysis

Dirk BlömkerView profile

Professor

• Stochastic partial differential equations
• Surface growth
• Cahn-Hilliard equation
• +11 more

Dirk Blömker is a Professor in the Institute of Mathematics at the University of Augsburg (since 2006). His research focuses on Stochastic Partial Differential Equations (SPDEs) with applications to surface growth models , phase separation (Cahn-Hilliard, Allen-Cahn), stochastic bifurcation , and multiscale analysis (amplitude/modulation equations, fast diffusion). He also works on filter theory (Ensemble Kalman) and numerics of SPDEs . Habilitation : RWTH Aachen (2006) Doctorate : University of Augsburg (2000) Diploma : University of Münster (1997) Intermediate Diploma : University of Münster (1993) Abitur : Gymnasium Lengerich (1989) His work spans rigorous mathematical analysis of SPDEs (existence/uniqueness, blow-up, regularity) and numerical methods (convergence analysis, a-posteriori bounds). Recent articles examine fractional noise, Lévy processes, and ensemble Kalman inversion. Collaborators include Luigi Bianchi, Claudia Schillings, and Philipp Wacker. He has held academic positions at RWTH Aachen (Assistant/Acting Lecturer), University of Bonn (Acting Lecturer), and University of Warwick (DFG Research Fellowship).

• Mathematics
• Statistics and probability
• Geometric Analysis
• +3 more

Eero Saksman is a Professor at the Department of Mathematics and Statistics , University of Helsinki . He is affiliated with the Faculty of Science and serves as a supervisor for the Doctoral Programme in Mathematics and Statistics . His research focuses on Mathematics , Statistics and probability , with specific expertise in Geometric Analysis , Partial Differential Equations , and Gaussian Multiplicative Chaos . Research Outputs : Active in 2025 with studies on stochastic pressure equations with log-correlated Gaussian coefficients, quasiconformal mappings in Triebel-Lizorkin spaces, Nevanlinna measures structure, and interdisciplinary projects like the Centre of Excellence in Randomness and Structures (FiRST) . Academic Collaborations : Involved in international collaborations with institutions such as Kings College London , EPFL , and University of Geneva . Scientific Awards : Recipient of the Lorenz Lindelöf Prize (2013) Magnus Ehrnrooth Prize (2018) Nevanlinna Prize for best dissertation (1995) Väisälä Prize for Mathematics (2007) Academic Activities : Regularly participates in conferences like the Barcelona Analysis Conference , Modern Aspects of Complex Analysis , and serves on doctoral evaluation committees.

• Bayesian Statistics
• Stochastic Differential Equations
• Mathematical Statistics
• +4 more

Shota Gugushvili is a Lecturer in the Mathematical and Statistical Methods - Biometris group at Wageningen University & Research. His academic work focuses on advanced statistical methodologies with applications across various scientific domains. Dr. Gugushvili's research spans several key areas in modern statistics and mathematical modeling. His primary expertise lies in Bayesian statistics, where he develops novel approaches for complex statistical problems. He has made significant contributions to the theory and application of stochastic differential equations, particularly in financial mathematics contexts. His work on inverse problems and regularization techniques has provided new insights into statistical estimation methodologies. Dr. Gugushvili also applies his statistical expertise to biological and agricultural sciences, developing frameworks for modeling complex genotype-environment interactions. His recent publications demonstrate a strong trend toward developing rigorous statistical frameworks for complex mathematical problems, with particular emphasis on Bayesian approaches. His work bridges theoretical statistics with practical applications in finance, biology, and agricultural science, showing remarkable versatility across disciplines while maintaining mathematical rigor. Dr. Gugushvili actively collaborates with researchers across multiple institutions and disciplines, as evidenced by his diverse publication record. His fingerprint analysis shows strong connections to Bayesian methodology (100%), stochastic differential equations (58%), posterior distribution theory (47%), and discrete time modeling (41%).

Andrew LamperskiView profile

Associate Professor

• Optimal Control
• Stochastic Systems
• Reinforcement Learning
• +3 more

Andrew Lamperski is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Minnesota, College of Science and Engineering. His career spans postdoctoral research at the University of Cambridge, Johns Hopkins University, and Caltech. Ph.D. in Control and Dynamical Systems, Caltech (2011) B.S. in Biomedical Engineering and Mathematics, Johns Hopkins (2004) His research lies at the intersection of Optimal Control , Stochastic Systems , and Biomedical Engineering , with recent work analyzing: Control methods for neuromodulation and deep brain stimulation Stochastic hybrid systems and moment dynamics Network topology identification from corrupted data streams Key article trends include: Advancing Machine Learning for control systems through ReLU networks Developing Robust Control frameworks with QSR dissipative properties Applying Stochastic Optimization techniques to biomedical devices Scientific recognition includes: Whitaker Postdoctoral Fellowship NSF grants for dynamical network reconstruction and non-convex domain control NASA collaboration on space vehicle swarms He has mentored students working on topics like Optimal control algorithms Neural interfacing Stochastic system analysis Multi-agent systems The Lamperski Research Group focuses on translating theoretical control advancements into practical applications for neurological disorders and autonomous systems.

• Control engineering
• Mechatronics and robotics
• Machine learning
• +8 more

Professor Jinchuan Zheng is a faculty member in the School of Engineering at Swinburne University of Technology, where he has been serving since 2012. He earned his PhD from Nanyang Technological University, Singapore, and holds B.Eng and M.Eng degrees from Shanghai Jiao Tong University, China. He previously worked as a Research Academic at the ARC Centre for Complex Dynamic Systems and Control at the University of Newcastle, Australia (2005–2011), and as a Staff Engineer at Western Digital Singapore (2011–2012). B.Eng, Shanghai Jiao Tong University M.Eng, Shanghai Jiao Tong University PhD, Nanyang Technological University, Singapore His research spans control systems , high-precision mechatronics , machine learning , and their applications in robotics , electric vehicles , disk drives , and biomedical devices . He has published over 170 refereed papers and is a Senior Member of IEEE. His recent work integrates advanced control theory with AI for industrial and medical applications, including exoskeleton control, radiative cooling, and cybersecurity in Industry 4.0 systems. His recent publications (2024–2025) reflect a strong trend toward interdisciplinary research combining control engineering , machine learning , and industrial applications . Themes include robust sliding mode control, fault reconstruction in robotics, microwave imaging for breast cancer detection, and deep learning for lung X-ray and CT scan analysis. He also contributes to chemical process optimization, particularly in reactive distillation and extractive systems. Scientific Awards: Senior Member, IEEE He supervises numerous PhD and Master’s students across diverse topics in mechatronics, AI, and control. His research is supported by multiple grants, including internal Swinburne grants and industry contracts with Telstra, ROSEBANK ENGINEERING, and FuJian HanHong Technology. He leads projects in augmented intelligence, smart predictive analysis, and advanced control systems. He is actively involved in developing instrumented rehabilitation systems and intelligent testing platforms. Professor Zheng is affiliated with research initiatives under Swinburne’s Flagship Areas: Innovative Planet , Manufacturing Futures , Med Tech , and Space and Aerospace , aligning with Sustainable Development Goals 7 (Affordable and Clean Energy) and 4 (Quality Education).

Raul TemponeView profile

Professor

• uncertainty quantification
• stochastic differential equations
• numerical analysis
• +16 more

Professor Raul Tempone is a distinguished faculty member at King Abdullah University of Science and Technology (KAUST), holding the position of Professor in the Department of Applied Mathematics and Computational Science within the Computer, Electrical and Mathematical Sciences and Engineering division. He serves as Principal Investigator of the Stochastic Numerics Research Group and has made significant contributions to numerical analysis and uncertainty quantification, aligning with KAUST's mission and Saudi Arabia's Vision 2030 goals through advancements in computational science that drive technological innovation and sustainability. Professor Tempone's academic foundation includes: Ph.D. in Numerical Analysis from the Royal Institute of Technology (KTH), Sweden (2002) M.S. in Engineering Mathematics from Universidad de la República, Uruguay (1999) B.S. in Industrial and Mechanical Engineering from Universidad de la República, Uruguay (1995) Professor Tempone's research focuses on the mathematical foundations of computational science and engineering, with particular emphasis on uncertainty quantification, stochastic differential equations, and numerical methods. His work bridges theoretical mathematics with practical applications across multiple domains including computational mechanics, quantitative finance, biological and chemical modeling, and wireless communications. He has pioneered advancements in adaptive algorithms, Bayesian inverse problems, and scientific machine learning, driving innovation in computational efficiency and accuracy for solving complex real-world problems. His recent publications demonstrate a strong trend toward integrating uncertainty quantification with machine learning approaches and addressing complex optimization problems under uncertainty. The research spans diverse applications from wireless network performance analysis to medical imaging and sustainable energy systems, reflecting his commitment to solving real-world challenges through advanced computational methods that combine theoretical rigor with practical applicability. Professor Tempone's scientific achievements have been recognized through numerous prestigious awards: Alexander von Humboldt professorship (2018-2025) ISI Highly Cited Researcher (2016) Elected Program Director of the SIAM Uncertainty Quantification Activity Group (2013-2014) Fellow of the Deutsche Forschungsgemeinschaft Priority Program (2014) First Dahlquist Fellowship at the Royal Institute of Technology, Sweden (2007-2008) As an academic advisor, Professor Tempone has successfully supervised ten PhD students to completion. His research has attracted significant funding, including the Alexander von Humboldt professorship grant worth up to 5 million euros. He has directed the KAUST Strategic Research Initiative in Uncertainty Quantification (2012-2016) and collaborated extensively with industry partners including Saudi Aramco. His research group has placed numerous members in academic positions worldwide and in leading companies such as Bain & Company, Baker Hughes, Enel Group, G-Research, Honeywell, McKinsey & Company, and Saudi Aramco. Professor Tempone leads the Stochastic Numerics Research Group at KAUST, which focuses on developing and analyzing numerical methods for stochastic and deterministic problems. The group's work encompasses a posteriori error approximation, data assimilation, hierarchical and sparse approximation, optimal control, and optimal experimental design. Through strategic collaborations and interdisciplinary approaches, the research group continues to push the boundaries of computational science and its applications to real-world challenges across engineering, finance, biology, and energy sectors.

Youngdeok HwangView profile

Assistant Professor

• Spatio-Temporal Statistics
• Bayesian Modeling
• Uncertainty Quantification
• +3 more

Youngdeok Hwang is an Assistant Professor in the Paul Chook Department of Information Systems and Statistics at Baruch College's Zicklin School of Business. He holds a PhD in Statistics from the University of Wisconsin-Madison and specializes in Bayesian modeling, spatio-temporal statistics, and machine learning applications for business and environmental challenges. His research integrates statistical theory with practical applications across healthcare, environmental science, and technology. Key interests include uncertainty quantification, inverse problems, and data fusion techniques for improving decision-making in complex systems. Recent work focuses on AI ethics, pollution modeling, and pandemic-related behavioral analytics. Hwang's publications demonstrate consistent emphasis on Bayesian methods and interdisciplinary problem-solving , with frequent applications in environmental monitoring (33% of recent papers), healthcare analytics (27%), and computational statistics (20%). This reflects his focus on socially impactful statistical innovation. Awards & Honors: Teaching Excellence Award (Zicklin School, 2025) Statistics in Physical Engineering Sciences Award (ASA, 2018) Career Development Award (Korean International Statistical Society, 2016) He actively contributes to academic committees including the Graduate Curriculum Committee and serves as Associate Editor for Technometrics and Applied Stochastic Models in Business and Industry.

• Theoretical Physics
• Statistical Mechanics
• Computational Biology
• +2 more

Anna Paola Muntoni is a Fixed-term Assistant Professor at the Department of Applied Science and Technology (DISAT) within Politecnico di Torino. She is affiliated with the Institute of Condensed Matter Physics and Complex Systems (DISAT) and contributes to the College of Electronic, Telecommunications, and Physics Engineering. Her research bridges theoretical physics, computational biology, and machine learning. Her research focuses on applying statistical mechanics and computational methods to biological sequences, epidemic modeling, metabolic strategies, and complex systems. Key projects include epidemic contact tracing, co-evolutionary sequence alignment, and Boltzmann machine applications in bioinformatics. Selected publications highlight her interdisciplinary work across 2024: Probabilistic contact tracing in epidemic containment 2023: Biological sequence alignment and metabolic optimization 2021: Boltzmann machine modeling of protein sequences 2019: Statistical mechanics in optimization problems

Søren Feodor NielsenView profile

Professor

• Finance
• Financial Econometrics
• Statistics
• +3 more

Søren Feodor Nielsen is a Professor in the Department of Finance at Copenhagen Business School, Denmark, holding an active ORCID profile (0000-0002-9399-4918). His academic work spans finance, statistics, and international business with significant contributions to econometric methodology. Research interests center on Finance and Financial Econometrics , particularly volatility modeling and corporate default analysis. His statistical expertise focuses on Missing Data Analysis and Survival Analysis , including coarsening at random mechanisms and imputation techniques. Additional work explores International Business dynamics such as economic sanctions and R&D globalization. This interdisciplinary approach bridges theoretical statistics with real-world financial applications. Publication trends reveal an evolution from foundational statistical methods (2000-2010) toward applied finance and international business (2015-2023). Recent work integrates econometric rigor with contemporary issues like economic sanctions and fintech, while maintaining core contributions to missing data theory. The research demonstrates consistent methodological innovation across finance, statistics, and business disciplines. Advising: Nielsen has supervised 5 students according to institutional records, though specific details of these supervisions are not publicly documented in available sources.

Raffaella BurioniView profile

Professor

• Statistical Physics
• Complex Networks
• Random Walks
• +5 more

Raffaella Burioni is a Full Professor of Theoretical Physics in the Department of Mathematics, Physics and Computer Science at the University of Parma. She serves as Chair of the Non-Linear and Statistical Physics Division of the European Physical Society (EPS) and co-founded the Italian Society of Statistical Physics (SIFS), where she holds the position of Vice President. Her academic leadership includes directing the Ph.D. School in Physics and previously managing quality assurance for the Master's program in Physics (2017-2023). Her educational background includes an MS in Physics with distinction from the 2nd University of Rome and a PhD in Theoretical Physics from the University of Rome 'La Sapienza'. Postdoctoral experience spans the Theoretical Physics Laboratory of the École Normale Supérieure in Paris, the University of Milan, and the National Institute for the Physics of Matter (INFM). Prof. Burioni's research centers on equilibrium and non-equilibrium statistical physics, with deep expertise in graph theory, complex networks, random walks, and stochastic processes. She pioneers interdisciplinary applications to biological systems, neuroscience, and machine learning. Her work on rare events and anomalous diffusion has established fundamental principles like the 'single big jump' mechanism in transport phenomena. Recent investigations bridge statistical physics with neural network theory, examining kernel renormalization and feature learning in deep architectures. Analysis of her 15 most recent publications (2022-2025) reveals three dominant trends: (1) Theoretical advances in rare event statistics for jump processes and extreme value theory, (2) Network-based epidemic modeling incorporating adaptive temporal dynamics and simplicial structures, and (3) Machine learning physics connecting neural network theory to statistical mechanics through Bayesian effective actions and kernel methods. Her work consistently demonstrates how statistical physics principles solve complex problems across disciplines. Her scientific accolades include: Two-time recipient of the Enrico Persico Prize from the Accademia Nazionale dei Lincei Fellow of the Institute for Scientific Interchange (ISI) since 2014 American Physical Society Outstanding Referee (2018) Fellow of the European Centre for Living Technology (2020) As Director of the Ph.D. School in Physics, she mentors doctoral candidates while serving on editorial boards for Physical Review E, JSTAT, and Journal of Physics A. Her research is supported by international collaborations with institutions including the Kavli Institute for Theoretical Physics, Max Planck Institute, and Ben-Gurion University, evidenced by frequent keynote invitations at major conferences like StatPhys, ECCS, and APS March Meetings. Prof. Burioni leads collaborative research networks through her EPS division chairmanship and SIFS leadership, fostering cross-institutional projects on statistical physics applications. Her campus-based studies leverage Wi-Fi data from Parma University to model pedestrian dynamics and epidemic spreading in real-world constrained environments.

• Statistical Physics
• Machine Learning Theory
• Complex Systems
• +3 more

Pietro Rotondo is an Assistant Professor (Ricercatore a tempo determinato) at the University of Parma , Italy, within the Department of Mathematical, Physical and Computer Sciences . He is actively involved in lecturing the course Principles of Physics to first-year students of the Earth Sciences bachelor programme for the academic years 2023/2024 and 2024/2025. Education & Academic Background: While explicit educational history is not detailed in the text, his faculty rank and research output indicate advanced training in theoretical physics and statistical mechanics, likely culminating in a PhD. Research Interests: Statistical mechanics of deep learning and artificial neural networks Bayesian inference in high-dimensional, disordered systems Phase transitions and replica methods in complex systems Quantum many-body physics and cavity quantum electrodynamics Renormalization group approaches to finite-width neural networks His interdisciplinary work bridges rigorous physics techniques with modern machine-learning challenges, aiming to uncover universal laws governing learning and generalization in artificial systems. Recent Publication Trends: Over the past five years, Rotondo has concentrated on developing analytical frameworks that describe how deep neural networks learn and generalize, particularly beyond the infinite-width limit. Recurring themes include Bayesian effective actions, kernel renormalization, and the statistical mechanics of structured data. Scientific Awards & Honors: No specific awards are mentioned in the provided text. Supervision & Funding: The text does not list current PhD or Master’s students, nor does it detail specific grants or funded projects. Laboratories & Teams: No named laboratories, centers, or research groups are explicitly cited in the scraped material.

Remo KretschmannView profile

Researcher

• Statistical Inverse Problems
• Bayesian Inference
• Uncertainty Quantification
• +3 more

Remo Kretschmann is a Postdoctoral Researcher at the Institute of Mathematics , University of Potsdam, specializing in Uncertainty Quantification . He is associated with project A04 of the collaborative research centre SFB 1294 Data Assimilation . His academic journey includes doctoral studies at Universität Duisburg-Essen and a master’s/bachelor’s at Technische Universität München. Education Doctorate in Mathematics (2019), Universität Duisburg-Essen Master of Science in Mathematics (2012), Technische Universität München Bachelor of Science in Mathematics (2009), Technische Universität München His research focuses on statistical inverse problems with high-dimensional parameter spaces, non-Gaussian noise models , and Bayesian hypothesis testing . He investigates regularization methods in nonparametric Bayesian inference and Gaussian approximation of posterior distributions for inverse problems. Selected research trends include: Bayesian hypothesis testing for inverse problems Optimal regularization techniques in statistical inference Laplace approximation error analysis Posterior mode characterization Applications in imaging and deconvolution He has contributed to open-source software for sampling in inverse problems and numerical simulations for regularized hypothesis testing.

• Economics
• Game Theory
• Microeconomics
• +4 more

Prof. Dr. Stephan Lauermann is a faculty member in the Department of Economics at the University of Bonn , affiliated with the Institute of Microeconomics , ECONtribute , Hausdorff Center for Mathematics , and CRC TR 224 . His research focuses on Bargaining, Auctions, Search Theory, and Matching Theory , with applications to market design and information aggregation. Research Trends: Recent articles (2022-2025) emphasize information aggregation in elections and frictions in matching and auction models . Older works (2012-2017) address adverse selection in auctions, price discovery in search markets, and foundational matching theory. Subfields span dynamic matching , strategic voting , common-value auctions , and equilibrium analysis . Contact: Email: s.lauermann@uni-bonn.de , Phone: +49 228 73-6327, Room 3.004, Adenauerallee 24-42, Bonn.