جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Econometrics
• Financial Econometrics
• +۷ مورد دیگر

Anastasija Tetereva is an Assistant Professor in the Department of Econometrics at the Erasmus School of Economics, Erasmus University Rotterdam. Her research lies at the intersection of machine learning and econometrics, with applications in financial markets, real estate pricing, and risk modeling. Her research interests include Machine Learning , Econometrics , Volatility Forecasting , Hedonic Pricing , Financial Sentiment Analysis , and Granger Causality . She applies advanced computational methods to economic and financial data to improve forecasting and decision-making. The recent articles reflect a strong trend in combining machine learning techniques—particularly random forests and hybrid models—with traditional econometric frameworks to analyze volatility, real estate valuations, and financial news causality. Her work demonstrates a consistent focus on improving predictive accuracy in financial and housing markets through data-driven modeling. A Forest Full of Risk Forecasts for Managing Volatility (2022) How much is the view from the window worth? Machine learning-driven hedonic pricing model of the real estate market (2022) Forecasting realized correlations: a MIDAS approach (2019) Sentiment spillover effects for US and European companies (2019) Application of Multivariate Hawkes Graphs to Uncover Granger Causality of Financial News (2018) Anastasija Tetereva has actively contributed to the academic community, including co-organizing the event Financial Econometrics meets Machine Learning (FinEML) in 2023, indicating strong engagement in interdisciplinary research networks. While no direct mention of student advising or grant funding is made in the text, her publication record and collaborative work suggest involvement in research teams and potential supervision of junior researchers. She is affiliated with the research network in financial econometrics and machine learning, contributing to a growing interdisciplinary community that bridges computer science and economic modeling. Her work is disseminated through working papers, peer-reviewed journals, and conference presentations, reflecting a robust and current research agenda.

• Machine Learning
• Medical Data Analysis
• Time-Series Data Analysis
• +۳ مورد دیگر

Dr. Weitong Chen is a Senior Lecturer at the School of Computer and Mathematical Sciences, University of Adelaide, and an ARC EC Industry Fellow at the Australian Institute for Machine Learning (AIML). He holds a PhD from the University of Queensland (2020), with prior roles as a Post-Doc Research Fellow and Associate Lecturer there. His research focuses on machine learning applications in medical data, particularly time-series analysis, semi-supervised learning, and IoT. He collaborates widely across academia, industry, and government, supported by multiple grants. His work emphasizes healthcare applications, adversarial robustness, federated learning, and unlearning mechanisms. Education: PhD in Machine Learning, University of Queensland (2020) Master's Degree, University of Queensland Bachelor's Degree, Griffith University Research Interests: Medical Data Analysis (e.g., EHRs, radiology) Time-Series Modeling (healthcare IoT, irregular data) Adversarial Machine Learning (backdoor attacks, robustness) Federated Learning (modality incompleteness, clustered frameworks) Data Privacy (unlearning, compliance) Grants & Collaborations: ARC EC Industry Fellowship Industry partnerships in healthcare and IoT Labs/Teams: Australian Institute for Machine Learning (AIML) Cross-disciplinary health tech collaborations

Bikramjit Dasمشاهده پروفایل

دانشیار

• Data Science
• Machine Learning
• Operations Research
• +۷ مورد دیگر

Bikramjit Das is an Associate Professor and Associate Head of Pillar (Graduate Programme) at Singapore University of Technology and Design (SUTD). He holds a PhD in Operations Research from Cornell University and prior to SUTD, was a postdoctoral researcher at ETH Zurich’s RiskLab. His research focuses on extreme events analysis using applied probability, optimization, and statistical learning, with applications in finance, telecommunications, federated learning, and climate modeling. He teaches courses in Probability, Stochastic Modeling, and Analytics, and directs the Master of Science in Technology and Design (Data Science) program. Education: PhD in Operations Research (Cornell University), B.Stat & M.Stat (Indian Statistical Institute). Research emphasizes heavy-tailed distributions, risk contagion, and network modeling. Key areas include risk analysis in financial networks, robust optimization under uncertainty, and extreme value theory. His work bridges theoretical probability and real-world applications in data science and public policy. Notable contributions include studies on asymptotic independence in high dimensions, robust newsvendor models, and inference techniques for heavy-tailed data. His articles explore topics ranging from federated learning under noise to climate modeling and congestion phenomena in sparse networks. Collaborations include visiting positions at MIT and the Karlsruhe Institute of Technology. Active in academic leadership, he has contributed to technical reports on healthcare provider choice analysis and probabilistic flood risk assessments for nuclear power plants.

• Stochastic Analysis
• Optimal Transport
• Ricci Flows
• +۳ مورد دیگر

Eva Kopfer is an Associate Professor at the University of Bonn's Institute for Applied Mathematics, Department of Stochastic Analysis. Her research focuses on stochastic analysis, optimal transport theory, Ricci flows, and geometric analysis. She has led courses on stochastic analysis, financial mathematics, and advanced calculus. Notable contributions include work on exponential ergodicity for kinetic SDEs, stochastic homogenization of transport problems, and quantum gravity measures on manifolds. Her research interests bridge probability theory, differential geometry, and functional analysis, with applications to geometric flows and metric measure spaces. Recent work explores conformally invariant random fields, polyharmonic structures in quantum gravity, and generalized Ricci flow dynamics. She has collaborated extensively on projects involving discrete-to-continuous limits in optimal transport and Liouville quantum gravity measures. Publications highlight advancements in stochastic differential equations, ergodic theory, and geometric PDEs. Her work often integrates probabilistic methods with geometric analysis, yielding insights into transport phenomena and manifold structures. Current research themes include non-equilibrium systems, functional inequalities, and random geometric structures. Eva Kopfer's academic contributions are evident in high-impact journals like Journal of Functional Analysis and Communications in Pure and Applied Mathematics. She actively engages in seminar series and lecture series at the University of Bonn, covering topics from stochastic calculus to frontiers in economics and mathematics.

Jarosław Swaczynaمشاهده پروفایل

استادیار

• Functional Analysis
• Set Theory
• Fractal Geometry
• +۳ مورد دیگر

Jarosław Swaczyna is an Assistant Professor at the Division of Applications of Contemporary Mathematical Analysis , Lodz University of Technology. His research spans functional analysis, set theory, fractal geometry, and mathematical logic. Research Interests: His work focuses on the intersection of infinite-dimensional spaces, probabilistic combinatorics, and topological properties of Polish groups. He investigates generalized iterated function systems, density ideals, and continuity of functionals in Banach spaces. Publications Trends: Recent contributions include probabilistic models for random graphs, structural properties of Hamel bases, and topological dynamics of fractal attractors. Earlier works explore density ideals, Cantor sets, and generalized IFSs. Contact: Email: jaroslaw.swaczyna@p.lodz.pl Phone: (+48) 42 631-38-51 Room: 154

Thomas Haettelمشاهده پروفایل

مدرس ارشد

• Geometric Group Theory
• Nonpositive Curvature Geometry
• Metric Spaces
• +۳ مورد دیگر

Thomas Haettel is a Senior Lecturer at the University of Montpellier and IUT Montpellier-Sète since 2013. He is a Junior member of the Institut Universitaire de France (IUF) for 2025-2030. His academic affiliation spans the Faculty of Science's Department of Mathematics, where he contributes to research and teaching. His research focuses on geometric group theory, specifically group actions on nonpositively curved spaces. Key areas include: Combinatorial nonpositive curvature (CAT(0) cube complexes, Helly graphs, Garside categories) Coarse nonpositive curvature (hierarchically hyperbolic spaces, coarse median spaces) Continuous nonpositive curvature (symmetric spaces, Teichmüller spaces, injective metric spaces) Braid groups and Artin-Tits groups Mapping class groups and surface fundamental groups His article analysis reveals: Geometric rigidity of higher rank lattices Injective metric structures in buildings and symmetric spaces New Garside theory applications Proper affine actions on Lp spaces Horofunction compactifications Automorphism classifications in Helly graphs Awards: IUF Junior membership HDR (Habilitation à Diriger les Recherches) 2021 He supervises PhD students in geometric group theory and has held visiting positions at CRM Montréal, EPFL, and Princeton University.

Christoph Lenzenمشاهده پروفایل

استاد

• Distributed Computing
• Clock Synchronization
• Randomized Load Balancing
• +۵ مورد دیگر

Christoph Lenzen is a Professor at CISPA Helmholtz Center for Information Security since July 2021. Previously, he served as a Group Leader at the Max Planck Institute for Informatics (2014-2021), and held postdoctoral positions at MIT CSAIL (2013-2014), Weizmann Institute (2012), and Hebrew University (2011). Research Interests Design and analysis of distributed algorithms Clock synchronization and adversarial fault tolerance Randomized and self-stabilizing systems Hardware algorithms for fault-tolerant systems Grants and Awards ERC Proof-of-Concept Grant (2021) ERC Starting Grant (2017-2022) ETH Zurich Medal for Outstanding Thesis (2011) Education Ph.D. in Computer Science (2011), ETH Zurich Diploma in Mathematics (2007), University of Bonn Teaching Clock Synchronization and Adversarial Fault Tolerance (2022 winter) How To Clock Your Computer (2022 summer) Theory of Distributed Systems (2019 winter, 2020 winter, 2018 winter, 2017 winter, 2016 winter, 2015 winter)

Lele Wangمشاهده پروفایل

استادیار

• Information Theory
• Machine Learning
• Data Science
• +۷ مورد دیگر

Lele Wang is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) at the University of British Columbia (UBC), Vancouver. He is affiliated with the Mathematics of Information, Learning and Data (MILD) research cluster and the Institute of Applied Mathematics. His research focuses on information-theoretic approaches to data science and machine learning, with contributions to universal compression, coding theory, statistical inference on graphs, and noisy computing. Before UBC, he held postdoctoral positions at Stanford University, Tel Aviv University, and as an NSF Center for Science of Information fellow. Education: PhD in Electrical Engineering from the University of California San Diego (2015), B.E. in Academic Talent Program from Tsinghua University (2009). Research Interests: Developing tools in information theory, coding theory, high-dimensional statistics, combinatorics, random graphs, and deep generative models for data-driven challenges. Notable works include universal graph compression for stochastic block models and coding-theoretic approaches to distributed machine learning. Awards: Recipient of the 2017 IEEE Information Theory Society Thomas M. Cover Dissertation Award and NSF CSoI Postdoctoral Fellowship. His students have won multiple awards including GSI Awards and NSERC grants. Mentoring: Prioritizes student development in critical and creative thinking, offering courses in real analysis, probability theory, and theoretical frameworks. Hosts round-robin group meetings inspired by Thomas Cover to foster concise communication and problem-solving skills.

Michael Raskinمشاهده پروفایل

مدرس

• Theoretical Computer Science
• Distributed Systems
• Algorithms
• +۶ مورد دیگر

Michael Raskin is a Lecturer (maître de conferences) at LaBRI, University of Bordeaux, where he conducts research in theoretical computer science with a focus on distributed systems and formal methods. His work bridges theoretical foundations with practical implementations, particularly in population protocols, temporal graphs, and Petri nets. His research interests span Theoretical Computer Science , Distributed Systems , Algorithms , and Formal Methods , with specific expertise in population protocols, temporal graph analysis, vector addition systems, and verification techniques for parameterized systems. His publications demonstrate a consistent focus on computational complexity, state complexity, and threshold phenomena in distributed models. Recent publication trends show a strong emphasis on temporal graph theory (with multiple papers on random temporal graphs and giant components), population protocol analysis (including modular protocols and leaderless rendez-vous systems), and verification techniques for parameterized systems. His work often combines mathematical rigor with practical implementation insights. Raskin has developed significant software projects including: Agnostic Lizard - A portable code walker for Common Lisp QueryFS - A filesystem defined through compiled queries His technical contributions span both theoretical advances and practical systems implementation, with a recurring theme of applying programming language techniques to systems problems. His work on Common Lisp tools demonstrates a commitment to practical language implementation alongside theoretical research.

David Bevanمشاهده پروفایل

مدرس

• Enumerative Combinatorics
• Asymptotic Combinatorics
• Probabilistic Combinatorics
• +۷ مورد دیگر

David Bevan is a Lecturer in the Department of Mathematics and Statistics at the University of Strathclyde, Faculty of Science. His research is centered on combinatorics, particularly in the areas of enumerative, asymptotic, probabilistic, and extremal combinatorics, with a focus on permutations and permutation classes. His research interests include: Permutation patterns and avoidance Limit shapes and permutons Monotone grid classes and structural combinatorics Random integer compositions and probabilistic models Graph theory and network structures Analytic combinatorics and asymptotic enumeration The trends in his recent publications (2025–2017) reflect a deep and consistent focus on the structural and asymptotic behavior of discrete combinatorial objects, especially permutations. His work often combines probabilistic reasoning, analytic techniques, and extremal analysis to derive enumeration results and characterise limiting behaviors. He frequently collaborates with researchers such as Dan Threlfall, Robert Brignall, and Sergey Kitaev. David Bevan has served in key professional roles, including: Guest Editor, Discrete Mathematics and Theoretical Computer Science (2023) Guest Editor, Enumerative Combinatorics and Applications (2021) Organiser, Permutation Patterns 2021 Virtual Workshop Organiser, Scottish Combinatorics Meeting 2023 Peer Reviewer, Combinatorial Theory (2023) He is the Principal Investigator on the Maths DTP 2020 project at the University of Strathclyde (2020–2025), focusing on combinatorial research with PhD student Daniel Threlfall. He has delivered numerous invited talks at international workshops and conferences, including at Oberwolfach, Dagstuhl, and the Permutation Patterns series. David Bevan leads research within the Strathclyde Combinatorics Group and maintains an active research blog. He teaches courses in graph theory, networks in finance, logic, computation, and computer systems, reflecting his interdisciplinary expertise bridging mathematics and computer science.

Melanie Schmidtمشاهده پروفایل

استاد

• Geometric Data Analysis
• k-means clustering
• Combinatorial Optimization
• +۳ مورد دیگر

Professor Melanie Schmidt is a faculty member in the Department of Computer Science at Heinrich-Heine-Universität Düsseldorf, where she leads the Algorithms and Data Structures research group. Previously, she was affiliated with the University of Bonn's Institute of Computer Science, where she completed her PhD under Prof. Dr. Heiko Röglin and headed a subgroup on "clustering for big data" within his research group. Current Position: Professor at Heinrich-Heine-Universität Düsseldorf Previous Position: Researcher and lecturer at University of Bonn PhD Advisor: Prof. Dr. Heiko Röglin Her research focuses on geometric data analysis, particularly k-means clustering in data streams, combinatorial optimization, and approximation algorithms. Her work bridges theoretical computer science with practical applications in big data processing. She has made significant contributions to understanding the theoretical foundations of clustering algorithms while developing efficient implementations for real-world applications. Professor Schmidt's publication record shows a consistent evolution from theoretical analysis of k-means to practical implementations for big data environments. Her recent work explores fairness in clustering, privacy-preserving techniques, and efficient algorithms for high-dimensional data. She has published in top-tier conferences including SODA, ICALP, ESA, and ITCS, demonstrating both theoretical rigor and practical relevance. Best Student Paper Award at ESA 2012 (joint work with Martin Groß, Jan-Philipp W. Kappmeier, and Daniel Schmidt) She actively supervises numerous Master's and Bachelor's students, with current advisees including Lena Carta, Lukas Drexler, and Anna Arutyunova. Her research group includes members such as Anja Rey, Julian Wargalla, and Annika Hennes. She teaches advanced courses in algorithms and data structures, with a focus on randomized algorithms and efficient algorithm design for big data problems. Professor Schmidt leads the Algorithms and Data Structures research group at Heinrich-Heine-Universität Düsseldorf, which focuses on developing and analyzing efficient algorithms for fundamental computational problems, with particular emphasis on clustering, geometric data analysis, and big data applications.

Laurent Doyenمشاهده پروفایل

پژوهشگر

• Game Theory
• Automata Theory
• Formal Verification
• +۷ مورد دیگر

Laurent Doyen is a CNRS Researcher at the Laboratoire Méthodes Formelles (LMF), ENS Paris-Saclay. He holds a PhD from the Université Libre de Bruxelles (2006) and an HDR from ENS Cachan (2012). His research focuses on formal methods, game theory, automata, and verification of quantitative and probabilistic systems. PhD: Université Libre de Bruxelles, 2006 HDR: ENS Cachan, 2012 Research Interests: Laurent's work centers on algorithms and tools for the verification and synthesis of reliable software, hardware, and embedded systems. His primary areas include game and automata theory, with a focus on discrete quantitative and probabilistic systems, timed and hybrid systems. He investigates synchronization, mean-payoff objectives, and imperfect information in games, contributing significantly to theoretical foundations and practical tools. The recent publications highlight a strong trend in stochastic games, synchronization in Markov decision processes, and quantitative verification. His work spans theoretical computer science, formal methods, and practical applications in system design, often appearing in top venues like LICS, ICALP, and CONCUR. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: Laurent has advised several PhD students including Mahsa Shirmohammadi, Julien Reichert, Thomas Soullard, and Pranshu Gaba. He leads and participates in multiple research projects such as QuaVerif (PI), IFCPAR SMILeS (coPI), Cassting, ARiSE, and Quasimodo. He has been a Rutherford Visiting Fellow at the University of Warwick and is involved in various academic communities like GAMES, CFV, and GDR-IM. Labs and Teams: He is a key member of the Laboratoire Méthodes Formelles (LMF) at ENS Paris-Saclay and has been associated with the LSV (Laboratoire Spécification et Vérification) in the past. He contributes to the development of tools like Alaska and Alpaga for automata analysis and model checking.

• Signal Processing in Wireless Communications
• Multi-antenna systems (MIMO)
• Wireless Power Transfer
• +۱۴ مورد دیگر

Francisco A. Monteiro is an Associate Professor in the Department of Information Science and Technology at ISCTE – University Institute of Lisbon, and an Integrated Researcher at Instituto de Telecomunicações (IT), Lisbon, Portugal. He holds a PhD in Engineering from the University of Cambridge and prior degrees from Instituto Superior Técnico, University of Lisbon. His academic journey includes progressive roles at ISCTE-IUL, culminating in his promotion to Associate Professor in May 2024. Education: PhD in Engineering, University of Cambridge, UK (2012) MSc in Electrical and Computer Engineering, Instituto Superior Técnico, University of Lisbon (2003) Licenciatura in Electrical and Computer Engineering, Instituto Superior Técnico, University of Lisbon (1998) His research focuses on signal processing in wireless communications, particularly in MIMO systems, network coding, quantum communications, and advanced decoding techniques like GRAND. He has made significant contributions to quantum error correction, entanglement purification, and low-latency decoding for 5G/6G systems. His recent work bridges classical wireless theory with emerging quantum networking paradigms, as evidenced by publications in Quantum , IEEE Access , and IEEE Transactions . The trend in his recent publications (2023–2024) shows a strong shift toward quantum information processing—especially quantum error correction via noise-guessing decoding (QGRAND), entanglement routing, and purification—while maintaining a foundation in classical MIMO, full-duplex communications, and network coding. His work is highly interdisciplinary, combining information theory, signal processing, and quantum computing. Scientific Awards: Best Paper Award, ECWT 2007 Best Student Paper Award, ECWT 2004 Exemplary Reviewer Award (2014, 2015), IEEE Wireless Communications Letters Innovation Young Engineer Prize (3rd place), Ordem dos Engenheiros, 2002 College Senior Scholarship, Fitzwilliam College, Cambridge, 2007 Fellowship from Fundación Endesa & Fundación Carolina, 2019 Francisco Monteiro has advised several MSc students, including Francisco Rosário and João Sande Lemos, both winners of the Luís Vidigal Prize. He has co-supervised PhD-level research and served on multiple thesis committees. His research has been funded through competitive grants and collaborations with institutions in Canada, Finland, Spain, and the UK. He has served as General Chair of ISWCS 2018, Lead Guest Editor for EURASIP Journal, and organizer of the Quantum Internet Alliance Workshop. He is actively involved in research groups such as the Radio Systems Group at IT and has led projects on MIMO, wireless energy transfer, and quantum networking. His lab work emphasizes algorithmic innovation for practical implementations in next-generation wireless and quantum systems.

• Hydrology
• Soil Erosion and Conservation
• Sediment and Water Interaction
• +۷ مورد دیگر

Dr. Fahimeh Mirchooli is a postdoctoral researcher in the Department of Geography at the University of Bonn, Germany, working within the AG Klaus research group. Her work focuses on hydrology, soil erosion, and sustainability of catchment systems. She holds a PhD in Natural Resource Engineering from Tarbiat Modares University, Iran, and has held academic research roles at Sari Agricultural Sciences and Natural Resources University and Hakim Sabzevari University. PhD, Natural Resource Engineering (Watershed Science and Engineering), Tarbiat Modares University, Iran, 2020 Visiting Researcher, University of Salzburg, Austria, 2019 MSc, Natural Resource Engineering (Watershed Management), Isfahan University of Technology, Iran, 2013 BSc, Natural Resource Engineering (Rangeland and Watershed Management), University of Tehran, Iran, 2011 Dr. Mirchooli's research centers on soil erosion and conservation , sediment-water interactions , spatial modeling using machine learning , and catchment health and sustainability . She integrates geospatial analysis, environmental modeling, and data science to assess land degradation, ecosystem services, and environmental risk. Her work often involves remote sensing, GIS, and hybrid modeling approaches combining empirical and machine learning techniques. Her recent publications (2018–2024) reflect a strong trend in environmental risk modeling , land degradation assessment , and machine learning applications in hydrology and soil science . Topics include gully erosion susceptibility, dust emission risk, flood modeling, and watershed sustainability. The interdisciplinary nature of her research spans ecology, geoscience, agricultural science, and public health . Scientific awards and honors include: Ranked 1st among PhD students in Watershed Management Sciences and Engineering (2020) 6th rank in national PhD entrance exam for Watershed Management (2015) George Forster Fellowship (2025) Sabbatical Scholarship from Ministry of Science, Research and Technology, Iran (2019–2020) Dr. Mirchooli has received research grants including the George Forster Fellowship and a Sabbatical Scholarship. While no formal advisees are listed, her collaborative publication record indicates active mentorship and team-based research. She is involved in hydrology and sustainability research within the AG Klaus group, contributing to projects on catchment health, erosion modeling, and environmental monitoring.

Ecaterina Sava-Hussمشاهده پروفایل

استاد

• Stochastic Processes
• Probability Theory
• Random Walks
• +۵ مورد دیگر

Ecaterina Sava-Huss is a Full Professor in Mathematics at the University of Innsbruck, Austria, affiliated with the Department of Mathematics within the Faculty of Mathematics, Computer Science and Physics. She holds a PhD from Graz University of Technology (2010) and a Habilitation in Mathematics (2019). Previously, she served as an associate professor and tenure-track assistant professor at the University of Innsbruck, and as an assistant professor and visiting scholar at institutions including TU Graz and Cornell University. Her research focuses on stochastic processes, particularly random walks and rotor walks on graphs, fractals, and aggregation models. Key interests include the interplay between structural properties of infinite state spaces and stochastic processes. She has organized conferences such as the Austrian Stochastics Days and is the outreach coordinator for the Institute of Mathematics, engaging in public science initiatives like the MIP Day and Girls' Day. Her funding includes an FWF Grant P34129 (2021–2025) on growth models and quasi-random walks, and a prior Erwin Schrödinger Fellowship (2015–2016). She has supervised numerous PhD and Master’s students, with research topics ranging from branching processes to quantum probability. Her work has been published in journals such as Advances in Applied Probability , Bernoulli , and Journal of Fractal Geometry .