Find Academic Supervisors

Peter FelferView profile

Professor

• Atom Probe Tomography
• Materials Science
• Nanomaterials Analysis
• +7 more

Professor Peter Felfer is a leading expert in atom probe tomography and materials science at Friedrich Alexander University Erlangen-Nuremberg. He holds a professorship in Atom Probe Tomography within the Department of Materials Science in the Faculty of Engineering. His research focuses on advanced materials characterization techniques, particularly for analyzing nanoscale structures and hydrogen behavior in metals. Professor Felfer's research interests span multiple areas of materials science. He specializes in atom probe tomography, which allows for three-dimensional chemical mapping at the atomic scale. His work has significant applications in understanding hydrogen embrittlement in metals, superalloy development, additive manufacturing of metals, and corrosion science. He has made substantial contributions to improving atom probe instrumentation, data analysis techniques, and specimen preparation methods, particularly for challenging materials systems. His recent publications demonstrate a strong focus on advancing atom probe tomography techniques while applying them to solve critical materials challenges. A significant portion of his recent work addresses hydrogen analysis in metals, reflecting growing interest in hydrogen as an energy carrier and the need to understand its effects on structural materials. His research also shows increasing integration of computational approaches for data analysis and instrument control. Professor Felfer leads a research group focused on atom probe tomography at FAU Erlangen. His laboratory develops and applies advanced characterization techniques to study materials at the atomic scale, with particular emphasis on metals and alloys for demanding applications.

Kazimierz ChorośView profile

Professor

• digital image and video processing
• content-based video indexing and retrieval
• computer animations
• +4 more

Kazimierz Choroś is a Professor at the Department of Applied Informatics , Faculty of Information and Communication Technology , Wrocław University of Science and Technology. He held leadership roles as Deputy Director of the Institute of Informatics (2008-2014) and Deputy Dean of the Faculty of Computer Science and Management (2016-2020). Research interests: digital image/video processing, content-based video indexing, computer animations, multimedia systems, web systems analysis, and information systems design. Organizer and Chair of the International Conference on Multimedia & Network Information Systems (MISSI 2022) Chair of Special Session WebSys 2020 at ICCCI 2020 Other activities: Since 1993, member and former President (1993-2002) of the SAGE Association (Polish Graduates of French Grandes Ecoles). Since 1982, member of the Polish Numismatic Society, author of dozens of numismatic publications, and Editor-in-Chief of Wrocławskie Zapiski Numizmatyczne (2003-present). Contact: Email: kazimierz.choros@pwr.edu.pl Phone: +48-71.320.3799 Office: Building D2, Room 201/1 Address: Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland

Craig DouglasView profile

Professor

• Dynamic Data-Driven Application Systems
• Big Data
• High Performance Computing
• +6 more

Dr. Craig Douglas is a Professor of Mathematics and Statistics at the University of Wyoming's College of Engineering and Physical Sciences, with an Adjunct Professor appointment in Computer Science. He earned his Ph.D. from Yale University and is globally recognized for pioneering research in Dynamic Data-Driven Application Systems (DDDAS) for Big Data, high-performance computing, and multigrid algorithms for partial differential equations. He co-developed the first commercial DDDAS for oil and gas pipeline monitoring, deployed across 100+ countries. His work bridges theoretical mathematics and practical engineering challenges. Research Interests Dynamic Data-Driven Application Systems (DDDAS) High-Performance Computing (HPC) Multigrid Algorithms Big Data Analytics for Energy and Environment Computational Science Applications Recent Publications Advances in DDDAS frameworks Computational wildfire modeling CO 2 sequestration simulation Finite-difference and finite-element methods Cache optimization for multigrid algorithms Email: cdougla6@uwyo.edu

Fabio VivianoView profile

Research Fellow

• Financial Mathematics
• Actuarial Science
• Risk Management
• +3 more

Dr. Fabio Viviano is a Research Fellow in Mathematical Methods for Economics, Actuarial and Financial Sciences at the Department of Economics, Statistics and Finance 'Giovanni Anania' (DESF) within the University of Calabria . He serves as Professor of Pension Funds and Social Security in the Master's degree course in Finance and Insurance. PhD in Managerial and Actuarial Sciences (University of Udine/University of Trieste, 2022) Research periods at Université Catholique de Louvain (Belgium) and Bayes Business School (UK) Research Focus : The primary research areas of Dr. Viviano revolve around advanced numerical methods for complex derivative securities valuation, longevity forecasting for dependent lives, and morbidity/disability risk management . His work emphasizes computational finance techniques and stochastic modeling for insurance applications. Publication Trends : His peer-reviewed articles focus on Monte Carlo simulation techniques, biometric risk modeling, stochastic valuation of insurance portfolios, and multi-state analysis of health-related risks. Recent works (2024-2025) address semi-Markov modeling of disability, subordinated hypercube mortality models, and iterative simulation approaches for biometric indices.

Botond SzaboView profile

Associate Professor

• Bayesian Statistics
• High-dimensional Statistics
• Nonparametric Methods
• +3 more

Botond Szabo is an Associate Professor in the Department of Decision Sciences at Bocconi University. He serves as a Fellow of the Bocconi Institute for Data Science and Analytics (BIDSA) and is faculty at the Bayesian Learning Laboratory (BayesLab). Additionally, he leads the ERC Starting Grant project "BigBayesUQ" (2022-2027). His research primarily focuses on the theoretical understanding of Bayesian high-dimensional and nonparametric methods, along with scalable machine learning techniques. His work bridges theoretical statistics with practical machine learning applications, emphasizing mathematical foundations and computational efficiency. As an academic leader, Szabo serves as Associate Editor for Annals of Statistics (since 2022) and Bayesian Analysis (since 2016), contributing significantly to the statistical research community. Fellow of Bocconi Institute for Data Science and Analytics (BIDSA) Project leader of ERC Starting Grant "BigBayesUQ" (2022-2027) Szabo maintains active editorial roles in leading statistical journals and conducts research at the intersection of theoretical statistics and machine learning. His office is located at 3-e2-12, via Roentgen 1, 20136, Milano, Italy.

Mijin KimView profile

Assistant Professor

• Nanotechnology
• Cancer Diagnostics
• Chemical Biology
• +4 more

Mijin Kim is an Assistant Professor in the School of Chemistry and Biochemistry at Georgia Institute of Technology. Her research focuses on advancing nanosensor technology for cancer diagnostics and biomedical applications through interdisciplinary approaches combining nanoscale engineering, fluorescence spectroscopy, and machine learning. Research interests include: Exciton photophysics in low-dimensional nanomaterials Diagnostic/nano-omics sensor technology for early disease detection Lysosome biology and autophagy investigation Integration of machine learning with nanosensors Carbon nanotube-based optical sensors Scientific innovations include: Recipient of 2023 STAT Wunderkinds award Named in MIT Technology Review Innovators Under 35 (2022) Development of autophagy-monitoring nanosensors Quantum defect engineering for biomedical applications Advances in SWIR/NIR fluorescence imaging The Kim Lab actively develops nanosensor platforms that combine chemical engineering, data analytics, and biological interrogation to address critical challenges in cancer research and cellular biology.

• Missing Data Imputation
• Machine Learning
• Healthcare Informatics
• +3 more

Thu Nguyen is a Research Fellow in the Department of Holistic Systems at Simula Metropolitan, a leading Norwegian research institute specializing in data science and computing. Her work focuses on developing robust methodologies for handling missing data across critical domains including healthcare diagnostics and environmental monitoring systems. Her primary research interests include: Advanced missing data imputation techniques Machine learning explainability under incomplete data Healthcare informatics and medical data analysis Environmental data processing for air quality prediction Multimodal data integration challenges Analysis of her publication record reveals a strong trajectory in developing computationally efficient imputation algorithms with real-world applications. Key trends include the application of principal component analysis and generative models to healthcare datasets (sperm tracking, medical imaging), environmental monitoring (air quality prediction), and multimodal systems. Her work consistently bridges theoretical statistical foundations with practical implementation needs, emphasizing model transparency and performance validation. Dr. Nguyen actively collaborates within Simula Metropolitan's data science ecosystem, contributing to interdisciplinary projects that address fundamental challenges in data completeness and usability across scientific domains.

• MCMC Theory
• MCMC Methodology
• Bayesian Inference
• +7 more

Professor Chris Sherlock is a Professor of Statistics at Lancaster University , affiliated with the School of Mathematical Sciences and the Department of Mathematics and Statistics . His research spans MCMC theory , methodology , and applications in epidemiology, ecology, and environmental science, with a focus on stochastic processes (SDEs, hidden Markov models) and spatial statistics . Key research areas : Non-reversible MCMC algorithms (e.g., Bouncy Particle Sampler), pseudo-marginal methods, and inference for reaction networks. Recent publications address flood risk estimation, scalable Bayesian learning, and clinical trial recruitment prediction using novel MCMC frameworks. He leads the Computational Statistics group (2019-present) and the MARS UG program (2024-present). Supervised PhD students have explored topics like extreme value theory, particle filters, and disease modeling. Collaborations include researchers such as Paul Fearnhead , Chris Nemeth , and Andrew Golightly , with EPSRC funding (EP/P033075/1) supporting non-reversible MCMC innovations.

• Mathematical Foundations of Complex Systems
• Polynomial Interpolation
• Numerical Integration
• +4 more

Dr. Michael Hecht leads the Mathematical Foundations of Complex System Science group at the CASUS - Center for Advanced Systems Understanding , part of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) . His research focuses on computational mathematics, numerical methods, and their applications in physics-informed machine learning and partial differential equations. Research Interests: Polynomial interpolation in high-dimensional spaces Topological data structure preservation in neural networks Hybrid surrogate models for complex systems Uncertainty quantification and numerical integration Recent Trends in Publications highlight advancements in polynomial-based methods for machine learning, numerical solutions to PDEs, and open-source tools like UQTestFuns and Minterpy , emphasizing efficiency and approximation accuracy across disciplines. Scientific Awards & Recognition: HZDR Innovation Contest participant ORCID: 0000-0001-9214-8253 Labs & Teams: Affiliated with the CASUS - Center for Advanced Systems Understanding , contributing to complex system science through interdisciplinary research in mathematics, computer science, and physics.

• Multi-Label Learning
• Probabilistic Inference
• Explainable AI
• +7 more

Jesse Read is a Professor at École Polytechnique (Institut Polytechnique de Paris) affiliated with the ORAILIX team within the Data Analytics and Machine Learning pôle of the Computer Science Laboratory (LIX). His work focuses on multi-label and probabilistic inference , explainable and robust methods , and learning from data streams and sequential data with applications in medicine, energy, and transportation. He obtained his PhD from the University of Waikato (2010) and held postdoc positions at INFRES Télécom Paris, Aalto University, and Universidad Carlos III de Madrid before becoming Assistant Professor (2017) and Professor (2019) at École Polytechnique. Research Trends His recent publications emphasize multi-label learning (Classifier Chains, Regressor Chains) and data stream analysis with applications in degradation modeling and medical diagnostics. Key methodological contributions include probabilistic inference in chained models, scalable ensemble learning , and Monte Carlo optimization for sequential tasks. Application domains span healthcare (ECG analysis, insomnia detection), energy systems (wind turbine control), and transportation (route prediction). Scientific Awards Test of Time Award at ECML-PKDD 2019 for foundational work on Classifier Chains Habilitation à Diriger des Recherches (HDR) in Computer Science (2017) Advising & Grants Read has supervised PhD theses from Ekaterina Antonenko (Multi-Target Learning) and Olivier Pallanca (Paradoxical Insomnia Analysis). He secured an ANR grant for Dynamic Graph Signal Processing with Dr. Johannes Lutzeyer and Dr. Luca Martino.

• Data Science
• Network Science
• Computational Social Science
• +5 more

Yu-Ru Lin is an Associate Professor at the School of Computing and Information , University of Pittsburgh , and serves as Research & Academic Director at the Institute for Cyber Law, Policy and Security (Pitt Cyber) . With secondary appointments in Political Science, Computer Science, and the Intelligent Systems Program, their interdisciplinary work bridges computational methods with social science inquiry. Research interests focus on: Social and political network analysis Computational social science Data mining and visualization Digital accountability in political communication Policy diffusion analytics Trust-distrust dynamics in networked societies Key research themes include: Developing network theories for misinformation and extremism Designing visual analytics for policy diffusion and group bias detection Studying collective behavior during emergencies Creating computational tools for social data analysis Major grants include NSF awards for: Public policy diffusion analytics COVID-19 misinformation mitigation Urban mobility and smart city initiatives Understanding extremism in cyber-social spaces Scientific leadership roles: Editor-in-Chief of ICWSM Editorial board member at Frontiers in Big Data, EPJ Data Science, PLOS ONE, and Nature Scientific Reports Co-chair roles at KDD Humanitarian Mapping Workshop, WWW Web & Society Track, and multiple SBP-BRiMS graduate programs As founder of the Pitt Computational Social Dynamics Lab (PICSO LAB) , they lead research on social media analysis, network modeling, and urban informatics through projects like PittSmartLiving – a $1.4M NSF-funded initiative exploring real-time transit incentives and community engagement.

• Biostatistics
• Genetics
• Mechanisms of Cancer
• +5 more

Eric Lock is an Associate Professor in the Department of Biostatistics at the University of Minnesota. His research spans biostatistics, genetics, proteomics, computational biology, and neuroimaging, with a focus on neurodegenerative diseases like ALS and Friedreich's ataxia, as well as pulmonary sarcoidosis and microbiome research. Current affiliations: University of Minnesota (2020-present), NIH-funded projects Key collaborators: NIH (NHGRI, NHLBI), Ohio State University, University of Chicago, FDA Research Interests : Eric develops advanced statistical and computational methods for high-dimensional biological data. His work includes tensor array techniques for RNA-Seq analysis, proteomic classifiers for pulmonary sarcoidosis, and neuroimaging biomarkers for tracking neurodegenerative disease progression. He also contributes to microbiome dataset curation frameworks. Article Trends : Recent publications emphasize Bayesian statistical models for missing data Neuroimaging in ALS and Friedreich's ataxia Proteomic and microbiome data integration Computational immunity models for vaccination response prediction Grants & Collaborations : Actively leads NIH-funded projects on tensor array methods and human microbiome compendium. Collaborates on multimodal imaging studies with institutions across the US, focusing on longitudinal disease tracking and molecular diagnostics.

Francesco RavazzoloView profile

Professor

• Bayesian econometrics
• Energy economics
• Financial econometrics
• +6 more

Francesco Ravazzolo is Full Professor of Econometrics at the Free University of Bozen-Bolzano (Faculty of Economics and Management) and Head of the Department of Data Science & Analytics at BI Norwegian Business School. He also acts as visiting professor at BI’s Center for Applied Macro and Commodity Prices and is president of the Society of Nonlinear Dynamics and Econometrics. Education: Ph.D. in Econometrics, Tinbergen Institute & Erasmus University Rotterdam, 2007 Research domains: Ravazzolo’s work sits at the intersection of Bayesian econometrics, energy economics, financial econometrics and macroeconometrics. He develops forecasting methods for electricity prices, commodity markets, cryptocurrencies and macro aggregates, with particular emphasis on regime-switching, mixed-frequency and high-dimensional techniques. His recent projects exploit textual data and semantic networks to predict consumer confidence and financial volatility. Publication trends: Since 2022 he has published extensively on (i) hourly/daily electricity-price forecasting with renewables and macro drivers, (ii) robust or Markov-switching time-series methodologies, (iii) big-data sentiment indicators for bond and stock markets, and (iv) energy-commodity and crypto-asset econometrics. Applications span European gas-price caps, Italian zonal imbalances, Nordic and German power markets, and global CDS indices. Editorial & professional service: Editorial boards: Annals of Applied Statistics, International Journal of Forecasting, Journal of Applied Econometrics, Spatial Economic Analysis, Studies in Nonlinear Dynamics & Econometrics President, Society of Nonlinear Dynamics & Econometrics Grants & enterprise: He is co-founder of three academic spin-offs: AIAQUA (AI for water and energy management), COMMODIA (commodity-data analytics) and STAIRS (forecasting platforms), securing continuous grant and industry funding for applied research. Labs & teams: At BI he leads the Department of Data Science & Analytics (ca. 40 faculty) and coordinates the Commodity Prices Research Hub; at unibz he supervises the Econometrics & Forecasting group involved in regional energy-market projects.

Christian BeecksView profile

Professor

• Machine Learning
• Data Mining
• Knowledge Discovery
• +7 more

Univ.-Prof. Dr. Christian Beecks is a full Professor at the Faculty of Mathematics and Computer Science, FernUniversity in Hagen, and heads the Intelligent Data Analysis research group at the Fraunhofer Institute for Applied Information Technology FIT. His work bridges theoretical advancements in machine learning with practical applications across industry and biomedicine. Education: PhD in Computer Science (RWTH Aachen University, 2007-2013) Diploma in Computer Science (RWTH Aachen University, 2001-2007) As a leading figure in data science, Beecks specializes in machine learning and big data analytics , focusing on scalable algorithms for complex data spaces. His research has produced over 100 peer-reviewed publications and notable contributions in time series analysis , clustering methods , and IoT data processing . Recent work explores Gaussian process modeling for anomaly detection and spatiotemporal signal analysis. His publications demonstrate expertise in automated pattern discovery and interpretable AI systems . Key themes include: clustering validation , time series representation , industrial applications , and knowledge ontologies . Technically, his team leverages Ptolemaic geometry , skyline queries , and component mining for real-world data challenges. 2021 SIAM Best Research Paper Award 2018 Warwick Workshop Best Poster Award 2015 & 2011 Best Paper Awards Through leadership roles at FernUniversity and Fraunhofer FIT, Beecks drives initiatives in competency-based education and AI workforce empowerment . His research groups develop frameworks for edge-to-cloud AI orchestration and automated model inference , with applications in manufacturing, biomedicine, and digital humanities. Current projects focus on data pooling, zero-touch orchestration, and educational AI tools like virtual tutors.

Susan LiView profile

Professor

• Supply Chain Management
• Management Information Systems
• Data Envelopment Analysis (DEA)
• +7 more

Professor Susan Li is a full Professor in the Decision Sciences and Marketing Department within Adelphi University’s Robert B. Willumstad School of Business. With a Ph.D. in Management Science and Information Systems from The University of Texas at Austin (1992) and a B.S. in Economics from Renmin University of China (1988), she has built a distinguished career spanning more than three decades in operations research and supply-chain analytics. Education Ph.D. in Management Science and Information Systems, The University of Texas at Austin, 1992 B.S. in Economics, Renmin University of China, 1988 Research Interests Her scholarship focuses on the intersection of information systems, supply-chain coordination, and quantitative decision models. Key themes include information sharing in supply chains, distribution channel design, data envelopment analysis (DEA), chance-constrained programming, franchising strategy, and game-theoretic approaches to multi-criteria decision making. She also maintains active interests in e-commerce, business application development, and database management systems. Publication & Grant Trends Over the last fifteen years her work has appeared in top-tier journals such as Omega , European Journal of Operational Research , International Journal of Production Research , and Journal of Modelling in Management . The research trajectory shows a steady evolution from foundational DEA theory toward applied supply-chain analytics, sustainability-oriented efficiency evaluations, and healthcare operations. Scientific Awards & Honors Bender Research Fellowship Award for Outstanding Corporate Social Responsibility Paper (2024) Excellence in Faculty Scholarship and Creative Work Award, Adelphi University (2016-2017) Multiple Bender Research Fellowship Awards (2005, 2007, 2008, 2016) University Professor Recognition Award, Adelphi University (2014) Competitive Fellowship Award, McCombs School of Business, UT Austin (1989-1990) Grants & Advising She has secured grants from Adelphi’s President’s Faculty Development Grant program and external sponsors such as EnterpriseAsia for projects on IT investment opportunities in Hong Kong and mainland China. While the provided text does not enumerate individual doctoral or master’s students, her extensive conference leadership and keynote invitations indicate active mentoring and collaboration. Laboratory & Professional Service Professor Li serves on the editorial boards of the Journal of Modelling in Management and the International Journal of Data Science , has guest-edited special issues of the International Journal of Information Technology & Decision Making , and has chaired or organized sessions at INFORMS, DSI, EURO, and other premier international conferences.