Find Academic Supervisors

• Mathematics Education
• Educational Sciences
• Technology in Education
• +3 more

António Augusto Gaspar Ribeiro serves as Professor at the Polytechnic Institute of Viseu's School of Education, where he has maintained continuous academic service since 1986, progressing from Assistant to current Professor Coordenador rank. His scholarly identity centers on Mathematics Education with specialized expertise in geometry pedagogy and educational technology integration across primary and teacher education contexts. His academic formation includes: Doctorate in Didactics of Mathematics (2005, University of Aveiro) Doctorate in Didactics (2005, University of Aveiro) Master's in Mathematics Teaching Methodology (1995, University of Lisbon) Licenciatura in Mathematics Teaching/Nature Sciences (1987, Polytechnic Institute of Viseu) Ribeiro's research program demonstrates sustained innovation in mathematics education, particularly through dynamic geometry environments like Cabri-Géomètre for primary classrooms. His work bridges theoretical frameworks with practical applications, examining teacher cognition, curriculum implementation challenges, and technology-enhanced learning. Recent scholarship shows expanded focus on interdisciplinary approaches in higher education and the pedagogical potential of humor in mathematics instruction. Analysis of his 25+ publications reveals evolving thematic trajectories: early work (2002-2006) established foundations in geometry education and technology integration; mid-career research (2009-2014) addressed curriculum reform and teacher development; current output (2017-2021) emphasizes interdisciplinary collaboration, early childhood mathematics, and socio-emotional dimensions of learning. His scientific recognition includes: 1º Concurso Nacional de Software Educacional - PUZZLE (1993) 1º Concurso Nacional de Software Educacional - JornalHist (1993) Ribeiro has supervised eight master's theses in Educational Sciences, primarily focusing on geometry education, technology integration, and teacher cognition. He leads the SmartCityKidsLab initiative (2019-present) and previously coordinated the PRINT interdisciplinary project (2017-2018) with Brazilian partners. His research portfolio includes four funded projects through the Center for Research in Education, Technologies and Health. As an institutional leader, he serves on the School of Education's Scientific Council and has held multiple governance roles including Pedagogical Council Secretary (1996-1998) and Vice-President of the School Board (2000-2002). His professional engagement extends to the Association of Mathematics Teachers and international collaborations with Brazilian institutions.

Juan Antonio Corrales RamónView profile

Research Fellow

• Robotics
• Human-Robot Interaction
• Robotic Manipulation
• +3 more

Juan Antonio Corrales Ramón is a Research Fellow at the University of Santiago de Compostela's CiTIUS laboratory under the prestigious Beatriz Galindo program. Previously, he served as an Associate Professor at Sigma Clermont Engineering School (2014-2020) and conducted research at UPMC/ISIR in France. His academic credentials include a PhD in Automatic Control and Robotics (2011) and a Computer Engineering degree (2005) from the University of Alicante. Dr. Corrales' research centers on robotics with specialized interests in: Human-robot physical interaction for industrial applications Deformable object manipulation and control theory Tactile sensing technologies and sensor fusion Robotic grasping and in-hand manipulation Multi-robot coordination systems His recent publications demonstrate strong focus on adaptive control systems for deformable objects, tactile sensor development, and human-robot collaboration frameworks. He has participated in significant EU projects including: H2020 SoftManBot (soft material handling) H2020 Bots2Rec (recycling robotics) FUI Aerostrip (aerospace applications) Sudoe Commandia (service robotics) Honors include competitive fellowships: FPU Program Fellowship (Spanish Ministry of Education) Beatriz Galindo Program Fellowship (research excellence) At CiTIUS laboratory, he contributes to advanced robotics research with applications in industrial automation, healthcare, and agricultural robotics.

Daniel TwardView profile

Assistant Professor

• Neuroimaging
• Computational Neuroscience
• Machine Learning
• +4 more

Dr. Daniel Tward is an Assistant Professor at the University of California, Los Angeles (UCLA), holding joint appointments in the Department of Neurology and the Department of Computational Medicine. He earned his Ph.D. in Biomedical Engineering from Johns Hopkins University and completed postdoctoral training at the Kavli Neuroscience Discovery Institute. His research integrates neuroimaging, machine learning, and differential geometry to analyze brain structure changes in neurodegenerative diseases like Alzheimer's, with a focus on bridging molecular pathology and clinical imaging. Research Interests: Dr. Tward's work addresses challenges in neuroimaging data complexity, developing computational tools to map brain anatomy across scales (from centimeters to microns). Key areas include neurodegeneration in the medial temporal lobe, multi-modal image registration, and spatial transcriptomics. His lab emphasizes high-dimensional statistics and geometry-driven analysis to improve diagnostic accuracy and clinical trial design. Grants & Projects: Secured NIH funding for: A 3D multimodal human brain atlas integrating MRI and histology. CloudReg—a distributed framework for massive neuroimage registration. Contributions to the BRAIN Initiative Cell Census Network (BICCN) for mouse/rat brain atlases. Students & Training: Mentors undergraduate researchers via the BIG Summer program, with projects on neural networks, spatial transcriptomics, and MRI analysis. No PhD/Master's advisees listed.

• Spatial data analysis
• Time series data
• Machine learning
• +4 more

Maria Sinziiana Astefanoaei is an Assistant Professor at the IT University of Copenhagen , affiliated with the Data, Systems, and Robotics department. Her research focuses on spatiotemporal data analysis, urban computing, and graph algorithms using machine learning techniques. Research Interests: Spatial data analysis, Time series data processing, Large-scale visualizations, Machine learning, Human mobility modeling, and Embeddings. Projects: Principal Investigator for CCAI: Towards greener last-mile operations (2022-2023), contributing to cargo-bike logistics optimization, and Co-Investigator for the Pilot Hub project (2020-2022) funded by the Danish Agency for Research and Education. Publications: 2021 conference paper at CIKM '21 on spatiotemporal signal processing frameworks with neural machine learning models. Her work intersects computer science , urban logistics , and environmental sustainability , with applications in smart city technologies and multi-modal transportation systems.

• Diophantine Approximation
• Geometry of Numbers
• Parametric Approximation
• +4 more

Leonhard Summerer is an Associate Professor at the Faculty of Mathematics, Department of Mathematics . His research primarily focuses on Diophantine Approximation , Geometry of Numbers , and Parametric Approximation . His work explores the Approximation Property in parametric settings, Lattice Theory , and Linear Dependence in number theory. Recent publications include studies on Jarník’s identity, simultaneous approximation to multiple reals, and geometric interpretations of number-theoretic problems. He has authored numerous peer-reviewed articles and contributed chapters to educational books such as 77-mal Mathematik für Zwischendurch , emphasizing mathematical outreach and pedagogical innovation . Active in academic discourse, he has delivered talks on topics like Packings and Tilings in Z and Simultane Approximation m reeller Zahlen since 2006.

Andrew ZimmerView profile

Associate Professor

• Several Complex Variables
• Discrete Subgroups of Lie Groups
• Geometric Structures on Manifolds
• +3 more

Andrew Zimmer is an Associate Professor and Director of Undergraduate Studies in the Department of Mathematics at the University of Wisconsin-Madison. His office is located in 611 Van Vleck Hall. He is actively engaged in research and mentoring graduate and undergraduate students in the field of mathematics. Dr. Zimmer's research focuses on geometric problems in several complex variables and discrete subgroups of Lie groups. His work spans multiple areas including: Geometric structures on manifolds Several complex variables Riemannian geometry Discrete subgroups of Lie groups Geometric group theory Hyperbolic dynamics Ergodic theory His recent publications demonstrate a strong focus on the intersection of complex analysis, geometry, and group theory. Zimmer's work frequently examines geometric structures, particularly focusing on convex domains, Anosov representations, and rigidity phenomena. A significant portion of his research explores how intrinsic metrics relate to geometric properties of domains in complex spaces, with applications to understanding the behavior of automorphism groups and boundary structures. Dr. Zimmer actively mentors students through the Madison Experimental Mathematics Lab, providing undergraduate research opportunities. He currently advises two graduate students: Rou (Vicky) Wen (since 2022) and Aleksander Skenderi (since 2022). His mentoring philosophy emphasizes seeking advice from multiple sources and learning from the experiences of others who have completed PhD programs in mathematics. Dr. Zimmer maintains an active research program with numerous collaborations, particularly with researchers such as R. Canary, T. Zhang, M. Islam, and others. His work appears in top mathematics journals including Geometry and Topology, Journal of Differential Geometry, and Advances in Mathematics.

Nikos GiatrakosView profile

Assistant Professor

• Big Data Management
• Internet of Things (IoT)
• Distributed Systems
• +3 more

Nikos Giatrakos is an Assistant Professor at the School of Electronic & Computer Engineering, Technical University of Crete, and a core member of the Software Technology and Network Applications Lab (SoftNet) . His work bridges Big Data systems, IoT, and advanced analytics, with a focus on real-time processing and scalable architectures. Previously, he served as a postdoctoral researcher at the same laboratory. Education PhD in Computer Science, University of Piraeus (2012) Postgraduate Diploma in Information Systems, Athens University of Economics and Business (2008) BSc in Computer Science, University of Piraeus (2006) Research Focus : Nikos specializes in software architectures for Big Data streaming, including Distributed Big Data Processing , Federated Machine Learning , Cloud-to-Edge Data Management , and Approximate Query Processing . His work has also advanced Complex Event Processing and Outlier Detection in decentralized environments. Scientific Contributions : His research has led to the DAG* workflow optimizer for IoT, the SuBiTO framework for real-time neural learning, and the INFORE approach for cross-platform analytics. He received the Best System Demonstration Award at ACM CIKM 2020 for INforE. Academic Leadership : Nikos teaches Object-Oriented Programming, Data Science, and Distributed Systems. He has supervised numerous European and national grants as Principal Investigator and served on program committees for top-tier conferences like SIGMOD, VLDB, and DEBS.

Rebecca RoweView profile

Professor

• Community Ecology
• Historical Ecology
• Landscape Ecology
• +5 more

Rebecca Rowe is a Professor and Chair in the Department of Natural Resources and the Environment at the University of New Hampshire. Her research bridges community ecology, historical ecology, and landscape ecology to investigate anthropogenic environmental change impacts on species distribution and biodiversity, particularly in small mammals of mountainous regions. She employs field surveys, historical records, and geospatial data to model ecological dynamics. Education: Ph.D. in Evolutionary Biology from the University of Chicago B.A. in Biology and Anthropology from Bowdoin College Research Focus: Rebecca’s work addresses spatial and temporal scale synthesis to forecast chronic environmental change effects. She emphasizes natural history collections for ecological conservation and explores disturbance gradients, habitat tolerance, and computational ecology tools like BirdNET for biodiversity monitoring. Publication Trends: Recent (2024–2025) articles integrate computational methods with field studies, examining Arctic greening, breeding strategies, and BirdNET validation. Earlier work (2009–2011) analyzes elevational diversity gradients, Great Basin resource decline, and Philippine endemic fauna resilience. All highlight interdisciplinary approaches to climate change and land use impacts. Courses: INCO 790: Advanced Research Experience NR 603: Landscape Ecology NR 642: Introduction to Biogeography NR 712/812: Mammalogy NR 903: Approach to Research

Jan RatajView profile

Professor

• Integral Geometry
• Geometric Measure Theory
• Stochastic Geometry
• +2 more

Jan Rataj is a Professor at the Faculty of Mathematics and Physics , Charles University , specializing in Integral Geometry , Geometric Measure Theory , and Stochastic Geometry . He has been affiliated with Charles University since 1991 and has held visiting positions at universities in Jena, Karlsruhe, Ulm, and Aarhus. Research interests focus on geometric probability, convex geometry, and measure theory Teaches courses on Measure and Integration Theory , Geometry , and advanced topics in geometric measure theory Scientific contributions include coauthoring 2 monographs and ~50 research papers. His work explores: Random measurable sets and perimeter analysis Stochastic modeling of microstructures Geometric functionals and invariants Applications to spatial statistics and probability theory He supervises PhD students and leads the Seminar of Stochastic Geometry , which examines topics like: Max-stable random fields Convex body reconstruction Point process analysis

• Dynamical systems and gradient flows
• Analysis of partial differential equations
• Stochastic analysis
• +2 more

Arnulf Jentzen is a distinguished mathematician holding dual positions as Presidential Chair Professor at the School of Data Science and Shenzhen Research Institute of Big Data at The Chinese University of Hong Kong, Shenzhen, and as Full Professor at the Faculty of Mathematics and Computer Science at the University of Münster, Germany. His research spans multiple institutions with significant contributions across mathematical disciplines. His primary research interests include dynamical systems and gradient flows (particularly geometric properties, domains of attractions, blow-up phenomena), analysis of partial differential equations, stochastic analysis (including stochastic calculus and well-posedness analysis), machine learning (with focus on mathematics for deep learning and stochastic gradient descent methods), and numerical analysis (particularly computational stochastics and computational finance). His work demonstrates a strong interdisciplinary approach bridging pure mathematics with practical computational applications. Jentzen's publication record shows a clear trend toward machine learning applications in solving complex mathematical problems, particularly in overcoming the curse of dimensionality in high-dimensional PDEs through deep neural networks. His research group actively publishes on optimization methods like Adam, convergence analysis, and applications of deep learning to partial differential equations and optimal control problems. ICBS Frontier of Science Award in Mathematics (2024) Fellow, Lamarr Institute (2023) ERC Consolidator Grant (2022) Joseph F. Traub Prize for Achievement in Information-Based Complexity (2022) Felix Klein Prize, European Mathematical Society (EMS) (2020) Professor Jentzen advises numerous PhD students across both institutions and serves on multiple editorial boards including SIAM Journal on Numerical Analysis, Journal of Complexity, and Communications in Computational Physics. His research group at Münster and CUHK-Shenzhen focuses on developing mathematical foundations for machine learning with applications to scientific computing problems. He has received significant research funding including an ERC Consolidator Grant, supporting his interdisciplinary work at the intersection of mathematics and artificial intelligence.

• Engineering Education
• Harmonic Analysis
• Functional Analysis
• +5 more

Anita Maria Tabacco is a Full Professor at the Department of Mathematical Sciences (DISMA) of Politecnico di Torino, with a focus on Engineering Education and Harmonic Analysis. She holds multiple administrative roles, including Rector's Delegate for Transparency and Internal Communication, Director of the University Observatory for academic dynamics, and Head of the INDAM local unit. Education: PhD in Mathematics, Washington University in St. Louis (1986) Research: Harmonic and Functional Analysis, Applications to PDEs, Gender Equality in STEM Her recent publications highlight intersections of mathematical analysis, educational technology, and gender diversity initiatives. She has led Erasmus+ projects like HerTechVenture and W-STEM to empower women in tech. Tabacco supervises PhD students including Maria Giulia Ballatore and contributes to textbooks such as Palestra di Analisi Matematica I . She co-leads the TEACH research group at DISMA, integrating pedagogical innovation with advanced mathematics.

Roie LevinView profile

Assistant Professor

• Approximation Algorithms
• Online Algorithms
• Submodular Functions
• +5 more

Roie Levin is an Assistant Professor at Rutgers University's Department of Computer Science. He received his PhD in Algorithms, Combinatorics and Optimization from Carnegie Mellon University in 2022, advised by Anupam Gupta. Prior to that, he worked at the Allen Institute for Artificial Intelligence (2015-2017) and earned dual BSc degrees in Computer Science/Applied Mathematics and Mathematics from Brown University (2015). Before joining Rutgers, he was a Fulbright Postdoctoral Fellow at Tel Aviv University under Niv Buchbinder. Current Role: Assistant Professor in Computer Science Academic Training: PhD (2022) CMU, BSc (2015) Brown University Postdoctoral: Fulbright Fellow at Tel Aviv University Levin's research focuses on approximation algorithms for uncertain environments (online/dynamic/streaming models) and submodular function optimization. His work spans theoretical foundations and practical implementations across distributed systems, geometric constraints, and reinforcement learning paradigms. Teaching includes graduate and undergraduate algorithms courses (CS 344, CS 513) with emphasis on problem-solving techniques, computational complexity, and modern algorithmic trends. His publications showcase expertise in online algorithms, submodular optimization, and approximation theory with applications in clustering, caching, and machine learning. The 2025 articles demonstrate continued exploration of online consistency and contention resolution, while 2023-2024 works focus on submodular optimization under uncertainty and dynamic environments. Earlier publications (2015-2017) cover semantic parsing, geometric approximation, and planar graph optimization. Fulbright Postdoctoral Fellow Levin's research connects theoretical guarantees with practical implementations, bridging classical algorithm design with modern machine learning applications. His recent work explores primal-dual methods in online settings and robust subspace approximation techniques for streaming data environments.

Uri HassonView profile

Professor

• Neuroscience
• Natural Language Processing
• Cognitive Science
• +7 more

Uri Hasson is a Professor at the Princeton Neuroscience Institute, Princeton University, focusing on the neural basis of brain-to-brain human communication, natural language processing, and language acquisition in real-world contexts. His lab challenges traditional experimental models by using deep learning to decode cognition during natural stimuli. Research Highlights: Investigates neural alignment in speaker-listener interactions. Develops computational tools to model language processing in everyday conversations. Explores shared principles between biological and artificial neural networks. Recent Article Trends: 2025–2023 publications emphasize deep language models matching neural responses, event segmentation via Bayesian surprise, and multibrain convergence during storytelling. Key keywords: Neuroscience, Natural Language Processing, Deep Learning, Cognitive Modeling . Awards: NIH Pioneer Award Lab & Collaborations: The Hasson Lab integrates neuroimaging (ECoG, fMRI) with large-scale datasets and AI to study real-life cognition. Collaborations span psychology, computational neuroscience, and machine learning.

• Optical Transmission Systems
• Space Division Multiplexing
• Quantum Cryptography
• +5 more

Chigo Okonkwo is Full Professor and Chair of Secured Ultra High Capacity Transmission at the Department of Electrical Engineering , Eindhoven University of Technology. He leads the high-capacity optical transmission laboratory at the Institute for Photonics Integration and contributes to the Center for Quantum Materials and Technology Eindhoven (QT/e) . Academic Qualifications: MSc in Telecommunications and Information Systems, University of Essex (2002) PhD in Optical Signal Processing, University of Essex (2010) Research Interests: Professor Okonkwo focuses on: Maximizing capacity of single-mode fiber systems through advanced-coded modulation and Probabilistic/Geometrically shaped signals Developing Space Division Multiplexing (SDM) systems for Petabit/s transmission using multi-mode/multi-core fibers Quantum secure communications and cryptographic protocol development Optical vector network analyzer (OVNA) technology for SDM fiber characterization Free-space optical link deployment in urban environments Low-complexity digital signal processing algorithms Recent Publications Trends: His 15 most recent articles (2023-2025) demonstrate active research in: Quantum-classical network integration Extreme capacity fiber transmission (Petabit/s systems) Machine learning for optical diagnostics SDM fiber measurement technologies Hybrid QKD-PQC security frameworks Free-space optical urban communication Scientific Awards: Asia Communications and Photonics Conference (ACP) 2018 Best Paper Award European Conference on Optical Communications (ECOC) 2018 Student Paper Award Optica Student Paper Awards (2022) Corning Outstanding Student Paper Competition Finalist (2025) Advisory & Collaborations: Advisor to 8+ researchers including Menno van den Hout, Vincent van Vliet, and Thomas Bradley Technical Program Committee Member, European Conference on Optical Communications (ECOC) since 2014 Sub Committee Chair for Digital Signal Processing track at ECOC 2018 General Chair for OSA Advanced Photonics Congress on Signal Processing for Photonics Collaborates with EU projects (HOMTech, PhotonDelta) and industrial partners Co-founder and Chief Technology Officer of CUbIQ Technologies Laboratory & Infrastructure: Maintains the world-class High Capacity Optical Transmission Lab at TU/e, featuring: Advanced SDM fiber testing equipment Quantum communication research infrastructure Free-space optical link experimental setups Multi-core fiber amplification systems Coherent transmission testbeds Machine learning-enabled diagnostic tools

Mathieu FontaineView profile

Associate Professor

• Machine Listening
• Speech Enhancement
• Speaker Separation
• +9 more

Mathieu Fontaine is an Associate Professor in Machine Listening at Télécom Paris , affiliated with the LTCI Lab within the IDS Department (Information, Data, Signal). His research focuses on machine listening for speech and audio signal processing. PhD in Informatics (2019), Lorraine University Master in Applied and Fundamental Mathematics (2015), Poitiers University BSc in Fundamental Mathematics (2013), Rennes University Fontaine's research spans speech enhancement , speaker separation , source localization , and music source separation using heavy-tailed probabilistic models and deep Bayesian networks , with applications in augmented reality . He has expertise in Python , signal processing , and machine learning (80% proficiency). His recent publications (2024) include work on diffusion models for speech synthesis , room acoustics estimation from 3D meshes , robust audio scene analysis , and direction-aware speech processing . Earlier publications (2022-2023) explore flow-based NMF , alpha-stable representations , and adaptive beamforming in multiparty environments. Fontaine collaborates with the S2A team and ADASP group at LTCI Lab. His work integrates probabilistic modeling with deep learning to address challenges in real-world audio processing, including reverberation, noise, and complex acoustic environments.