Rima Alaifari is currently an Assistant Professor for Applied Mathematics at ETH Zürich , where she works on applied analysis, inverse problems, and scientific machine learning. Her research emphasizes stability analysis and regularization of inverse problems, applied harmonic analysis, phase retrieval, and operator learning. She is an associated member of the ETH AI Center and will transition to a full professorship at RWTH Aachen University in 2025 as Chair of Analysis and its Applications. Education : PhD in Mathematics (2010–2014, Vrije Universiteit Brussel); MSc in Applied and Industrial Mathematics (2005–2010, Johannes Kepler University) Research Focus : Stability estimates for inverse problems, phase retrieval in wavelet/Gabor transforms, operator learning with neural networks, and deep learning robustness. Article Trends : Her recent work bridges harmonic analysis with machine learning, focusing on phase retrieval stability, adversarial perturbations in imaging, and mathematically grounded neural operator frameworks like ReNO and CNO. Advising : She has supervised PhD students like Tandri Gauksson and Matthias Wellershoff. Former postdoctoral researchers include Francesca Bartolucci (now at TU Delft) and Jesse Railo (Finnish Inverse Prize winner).
Stefano Grivet Talocia is a Full Professor in the Department of Electronics and Telecommunications at Polytechnic University of Turin. He serves as Director of the Doctoral School, is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, and holds positions on the University Committee for Research and the Commission for the Promotion of Library, Archive and Museum Heritage. He is also President of the Doctoral School Council. His educational background includes a Laurea degree (summa cum laude) in Electronic Engineering (1994) and a Ph.D. in Electronic and Communication Engineering (1998), both from Polytechnic University of Torino. From 1994 to 1996, he worked at NASA/Goddard Space Flight Center in Greenbelt, MD, USA. Professor Grivet Talocia's research focuses on passive macro-modeling of concentrated and distributed interconnect structures for Signal/Power Integrity, order reduction techniques, and modeling and simulation of fields, circuits, and their interactions. His work spans several key areas including fast simulation of transmission lines (TOPLine technique), macromodeling and model order reduction, simulation methods for fields and circuits, passivity enforcement of lumped macromodels, waveform relaxation techniques, and wavelet applications. His research has significant applications in electromagnetic compatibility and signal integrity verification of complex electronic systems. His recent publications demonstrate strong trends in model order reduction techniques applied to power integrity verification, advanced macromodeling for electromagnetic compatibility, nonlinear circuit analysis, uncertainty quantification in PCB design, and power electronics modeling. These works consistently address practical engineering challenges in high-speed electronic design with emphasis on computational efficiency and accuracy. URSI Young Scientist Award (1999) Best symposium paper (2006) Three IBM Shared University Research Awards (2007-2009) IEEE Transactions on Advanced Packaging Best Paper Award (2007) Best EPEP conference paper awards (2007, 2008) Best Associate Editor Award - IEEE Transactions (2020) Best Conference Paper Award (2020) Three Intel SRS Grants (2022-2024) IEEE Fellow (2018) Professor Grivet Talocia actively supervises PhD students working on cutting-edge topics including machine learning applications in signal integrity, model reduction techniques, and electromagnetic compatibility. He has secured significant research funding through competitive grants including PRIN projects and multiple industry-sponsored research contracts with major technology companies such as IBM, Intel, Nokia, Hitachi, and Infineon. His technology transfer activities include co-founding the spin-off IdemWorks (acquired by CST in 2016) and maintaining active collaborations with industry partners. He leads the EMC Group (Electromagnetic Compatibility) within the Department of Electronics and Telecommunications and has developed the autoCircuits web service for automated generation of circuit theory problems. His research has been recognized by inclusion in the top 2% worldwide researcher catalog (Stanford) since 2019.
Marta Molinas is a Professor at the Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). Her research spans multiple interdisciplinary domains with a focus on EEG technology and brain-computer interfaces. She actively supervises numerous Master's projects and maintains extensive international collaborations with institutions including Kavli Institute for Systems Neuroscience, RIKEN Center for Brain Science, University of Tsukuba, Juntendo University, and several European universities. Professor Molinas' research interests center on developing innovative EEG technologies, particularly her FlexEEG concept for reduced-channel EEG systems with brain imaging capabilities. Her work integrates signal processing, artificial intelligence, and neuroscience to create practical applications in mental health, sleep research, neurorehabilitation, and human-computer interaction. She specializes in EEG source imaging, machine learning for brain signal analysis, and the development of brain-computer interfaces for various applications including locked-in syndrome communication, ADHD treatment, and driver monitoring systems. Her publication portfolio demonstrates strong trends in interdisciplinary research combining neuroscience with electrical engineering and artificial intelligence. The work shows particular emphasis on developing practical EEG-based systems that minimize invasiveness while maintaining analytical power, with applications spanning healthcare, rehabilitation, and human augmentation. Her research bridges theoretical signal processing with real-world implementations through numerous student projects and international collaborations. Professor Molinas actively supervises a large team of Master's and PhD students across multiple projects, with each project typically requiring two students working collaboratively. Her research is supported through numerous international collaborations with institutions in Japan, India, and Europe, indicating substantial research funding and project leadership. She has developed a pipeline of student projects that build upon previous work, creating a cumulative knowledge base within her research group. She leads the EEG ITK research team at NTNU, which focuses on developing the FlexEEG headset prototype featuring flexible, wireless, dry electrodes designed to move across the scalp. This team works at the intersection of neuroscience, electrical engineering, and computer science, developing applications for sleep research, mental health monitoring, neurorehabilitation, and brain-computer interfaces. The team collaborates extensively with international partners including the Kavli Institute for Systems Neuroscience, the International Institute of Integrative Sleep Medicine at University of Tsukuba, and several engineering departments across Europe and Asia.
Shen Wei is the KoGuan Distinguished Professor of Law at the Shanghai Jiao Tong University Law School, with a concurrent role as Visiting Professor (2025). His academic career spans legal practice and academia, focusing on international investment law, corporate governance, financial regulation, and international commercial arbitration. Concurrently, his research extends into computational and mathematical domains, including machine learning, deep neural networks, and approximation theory. He teaches international investment law, international financial regulation, company law, and international economic law. His interdisciplinary work bridges legal scholarship with advanced mathematical modeling and algorithmic analysis. Recent research emphasizes neural network architecture, optimization techniques, and approximation theory applied to complex systems. Notable contributions include studies on deep network expressivity, gradient methods, and wavelet-based image restoration. Awards and grants are not explicitly mentioned, but his work reflects significant contributions to both legal and computational fields.
Daniel Sage is a Lecturer and Scientific Advisor at École polytechnique fédérale de Lausanne (EPFL) , affiliated with the Biomedical Imaging Laboratory (LIB) under the College of Engineering (STI) and School of Life Sciences (SV) . He specializes in bioimage informatics , structured-illumination microscopy , and deep learning applications for biomedical imaging. His work spans algorithm development for single-molecule localization microscopy (SMLM) , fluorescence imaging , and 3D reconstruction . His research group has developed open-source tools like FlexSIM for light inhomogeneity correction, DeepImageJ for integrating deep learning in ImageJ, and Steer'n'Detect for orientation-accurate template detection. His publications focus on correcting multiple-blinking artifacts in PALM, optimal transport metrics for SMLM evaluation, and contextual feature analysis for xenograft cell classification. He mentors PhD students and contributes to interdisciplinary education through courses such as Bioimage Informatics and Fundamentals of Image Analysis , emphasizing practical software solutions and Java programming for bioimage processing. His collaborations include institutions like Howard Hughes Medical Institute and Centre National de la Recherche Scientifique (CNRS) .
Hamid Krim is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He leads the Vision, Information and Statistical Signal Theories and Applications (VISSTA) group, focusing on statistical signal/image analysis, data science, and machine learning. His prior roles include Research Scientist at MIT’s Laboratory for Information and Decision Systems and Member of Technical Staff at AT&T Bell Labs. He holds a Ph.D. in Electrical Engineering from Northeastern University, and degrees from the University of Washington and University of Southern California. Education: Ph.D., Electrical Engineering, Northeastern University (MA), 1990s Master's, Electrical Engineering, University of Washington Bachelor's, Electrical Engineering, University of Southern California and University of Washington Research Interests: Machine Learning, AI, Signal Processing, Communications, and Control Systems . His work bridges formal mathematical frameworks with applied problems, emphasizing generative AI, adversarial robustness, and subspace-driven data analysis. Recent innovations include Volterra neural networks and expansive synthesis techniques for data generation. Awards & Recognition: 2000 NSF CAREER Award 2008 IEEE Fellow 2019 IEEE SPS Sustained Impact Paper Award Multiple extended research invitations at top institutions globally Grants & Advising: Leads the VISSTA Lab, collaborating on projects like medical algorithm development (e.g., lung wheeze analysis) and hurricane activity prediction. His work spans interdisciplinary applications in healthcare, robotics, and defense systems. Labs & Teams: Director of the VISSTA Lab, fostering research in signal theory and machine intelligence. Collaborates with academia and industry on cutting-edge AI and sensor fusion technologies.
Maks Ovsjanikov is a Professor in the Computer Science Department at École Polytechnique, France , and a Visiting Research Scientist at Google DeepMind. His research focuses on mathematically principled approaches for geometric data analysis and synthesis, including learning on surface meshes, 3D point clouds, and graphs. Key Collaborations: Google DeepMind, Sanofi, Dassault Systèmes Research Themes: Non-rigid shape matching, 3D reconstruction, transfer learning, learning on geometric data, functional maps, deep learning for scientific discovery Recent Article Trends emphasize geometric deep learning, with publications at top venues like SIGGRAPH Asia, ICCV, and CVPR. Topics include surface reconstruction, functional maps, 3D keypoint detection, and diffusion models for shape matching. Scientific Honors include: ERC Consolidator Grant (VEGA Project, 2023) ERC Starting Grant (2017) ACM SIGGRAPH 2023 Test-of-Time Award Best Paper Awards at 3DV 2021 and 3DV 2022 Student Advisees have received prestigious awards, such as the IP Paris Best PhD Thesis Award (Souhaib Attaiki, 2023) and GdR IG-RV Runner-Up (Nicolas Donati, 2024). The GeomeriX Team at École Polytechnique drives his group's research, supported by the VEGA and AIGRETTE projects.
Olufemi A. Omitaomu is an Adjunct Professor at the Department of Industrial and Systems Engineering within the Tickle College of Engineering at the University of Tennessee, Knoxville. He serves as a Group Leader and Distinguished R&D Staff at Oak Ridge National Laboratory (ORNL), leading the Computational Urban Sciences Group in the Computational Sciences and Engineering Division. Ph.D., Industrial Engineering (Information Engineering concentration), University of Tennessee, Knoxville M.S., Mechanical Engineering, University of Lagos, Nigeria B.S., Mechanical Engineering, Lagos State University, Nigeria Dr. Omitaomu’s research focuses on artificial intelligence in energy systems , cognitive coupling of human-machine systems , anomaly detection in complex systems , energy infrastructure siting and analysis , and disaster risk analysis with urban systems resilience . His work integrates computational models, optimization techniques, and geospatial frameworks to address challenges in critical infrastructure systems. The 15 most recent publications highlight trends in renewable energy integration , climate adaptation strategies , and emergency resource allocation . Key methodologies include agent-based modeling , multicriteria decision analysis , and wavelet shrinkage , applied to domains like energy systems , disaster management , and urban sustainability . Scientific recognition includes: Distinguished R&D Staff, Oak Ridge National Laboratory Senior Member, Institute of Industrial and Systems Engineers (IISE) Senior Member, Institute of Electrical and Electronics Engineers (IEEE) He actively mentors MS and PhD students with expertise in Python programming , game theory , and human-machine systems . His research is supported by collaborations with ORNL and interdisciplinary grants.
Professor David Taubman is a distinguished academic serving as Professor and Deputy Head of School (Research) at the School of Electrical Engineering and Telecommunications (EE&T) at the University of New South Wales (UNSW) in Sydney, Australia. He is also co-director of Kakadu Software Pty. Ltd. and its affiliates Kakadu R&D and Kakadu GPU. With a career spanning over three decades, Professor Taubman has made significant contributions to the field of image and video compression, most notably as the author of the EBCOT coding algorithm adopted in the JPEG2000 international standard. Professor Taubman earned his B.Sc. in Mathematics and Computer Science (1986) and B.E. (Medal) in Electrical Engineering (1988) from the University of Sydney, followed by an M.Sc. (1992) and Ph.D. (1994) in Electrical Engineering from the University of California at Berkeley. His professional journey includes engineering work at the Electricity Commission of N.S.W. (1988-1990), research positions at Hewlett-Packard Laboratories in Palo Alto (1994-1998), and an academic career at UNSW where he progressed from Senior Lecturer (1998-2003) to Associate Professor (2004-2009) and finally to Professor (2009-present). He has held various leadership roles including Head of the EE&T Telecommunications Research Group (2003-2014), Head of the EE&T Signal Processing Research Group (2014-present), Director of Research for the School of EE&T (2011-2016), and Deputy Head of School (Research) since 2017. Professor Taubman's research interests center on image and video compression, with particular expertise in JPEG2000 standards and implementations. His work spans signal processing, wavelet transforms, scalable video coding, motion modeling, and multimedia systems. He has pioneered numerous compression algorithms and frameworks, including the EBCOT coding algorithm that became central to the JPEG2000 standard. His recent research focuses on efficient motion modeling with cuboidal partitioning, learned lifting-based transform structures, and high-throughput implementations of JPEG2000 for video applications. His work bridges theoretical foundations with practical implementations, as evidenced by the commercially successful Kakadu Software tools that have garnered around 500 commercial licensees. Analysis of Professor Taubman's recent publications reveals a consistent focus on advancing compression technologies with particular emphasis on scalability, efficiency, and adaptability. His work spans traditional image compression (JPEG2000 extensions), video coding (cuboid-based partitioning for UHD/360-degree video), and emerging applications (nanopore sequencing data compression). A notable trend is the integration of machine learning techniques with traditional compression frameworks, as seen in his work on learned lifting-based transform structures. His research maintains strong connections to real-world applications across diverse domains including medical imaging, astronomical data processing, and genomic sequencing. IEEE Fellow Engineers Australia Fellow (by invitation) Professor Taubman has served as Associate Editor for the IEEE Transactions on Image Processing for two four-year appointments (2003-2005 and 2010-2013). He has been actively involved in numerous research grants focused on image and video compression technologies, particularly those related to the JPEG2000 standard and its extensions. His work has received significant industry support, reflected in his consultancy with various U.S., Japanese, and Australian corporations. He has also contributed to international standards development as a member of Standards Australia Technical Committee MS-065 (mirroring ISO TC42 on Digital Photography) and as a constitutional member of Standards Australia Technical Committee IT-029 (Coded Representation of Picture, Audio and Multimedia/Hypermedia Information). Professor Taubman co-directs Kakadu Software Pty. Ltd. and its research affiliates Kakadu R&D and Kakadu GPU, which have developed the commercially successful Kakadu Software tools for JPEG2000. His research group at UNSW focuses on advanced image and video compression techniques, with particular expertise in wavelet-based methods, scalable coding, and motion modeling. The group maintains strong industry connections and has contributed significantly to the development and standardization of image compression technologies worldwide.
Jennifer Ryan is a Professor of Numerical Analysis and Division Head of Numerical Analysis, Optimization, and Systems Theory at the Department of Mathematics, KTH Royal Institute of Technology. Her research focuses on designing and developing numerical schemes to extract accuracy from simulations, particularly through superconvergence properties and computational efficiency improvements. She applies these techniques to applications such as imaging, fluid visualization, and plasma dynamics. Education: PhD in Applied Mathematics, Brown University; MS in Mathematics, Courant Institute; BA in Applied Mathematics, Rutgers University. Professional Activities: Member of editorial boards for BIT Numerical Mathematics, ESAIM:M2AN, and Communications on Applied Mathematics and Computation; Steering committee member of AWM's Women in Numerical Analysis and Scientific Computing (WINASc). Her publications emphasize discontinuous Galerkin methods, SIAC filtering, and applications in fluid dynamics. She has served on multiple grant review panels and received awards for diversity and inclusion initiatives. Grants: Principal Investigator for projects funded by the Swedish Research Council, NSF, and US Air Force Office of Scientific Research. Awards: Fellow of UK Higher Education Academy, DAAD Fellowship, and Householder Fellowship.
Stefano Grivet-Talocia is a Full Professor at the Department of Electronics and Telecommunications at the Polytechnic University of Turin, where he also serves as Director of the Doctoral School and President of the Doctoral School Council. He is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, the University Committee for Research, Technology Transfer and Services to the Territory, and the Commission for the Promotion of Library, Archive and Museum Heritage. His academic career spans over two decades at Politecnico di Torino, where he has established himself as a leading researcher in electromagnetic modeling and signal integrity. Grivet-Talocia earned his Laurea degree (summa cum laude) in Electronic Engineering in 1994 and his Ph.D. in Electronic and Communication Engineering in 1998, both from the Polytechnic University of Turin. Between 1994 and 1996, he conducted research at NASA/Goddard Space Flight Center in Greenbelt, Maryland. His educational background laid the foundation for his expertise in electromagnetic modeling, wavelet analysis, and signal processing. His research focuses on behavioral modeling, electromagnetic compatibility, macromodeling, model order reduction, numerical modeling, passivity, power integrity, signal integrity, transmission lines, and wavelets . Grivet-Talocia is particularly renowned for his work on passive macromodeling of interconnect structures, development of the TOPLine technique for transmission line simulation, and pioneering contributions to passivity enforcement algorithms. He has co-authored the first book entirely dedicated to Macromodeling (2016) and developed innovative approaches to waveform relaxation and wavelet-based signal processing. His recent publications (2024-2025) demonstrate continued leadership in model order reduction, with significant contributions to data-driven modeling of linear and nonlinear systems, power integrity analysis, and electromagnetic compatibility. His work spans both theoretical advances in numerical methods and practical applications in circuit design, with strong industry relevance particularly for semiconductor and electronic design automation companies. IEEE Fellow (2018-present) Three Intel SRS Grants (2022-2024) Three IBM SUR Grant Awards (2007-2009) Best Associate Editor Award - IEEE Transactions on Components, Packaging and Manufacturing Technology (2020) Multiple Best Conference Paper Awards (2006-2020) URSI Young Scientist Awards (1999) Ranked among the "top 2% worldwide researchers" (Stanford) since 2019 Grivet-Talocia actively supervises doctoral students including Michele Cusano, Sara Paknezhad Panahi, Antonio Carlucci, and Kun Zhao. He has secured numerous research grants from competitive national calls (PRIN) and commercial contracts with industry partners including Intel, IBM, Nokia, Hitachi, Infineon, and Cadence. His technology transfer activities include co-founding the spin-off IdemWorks (2007-2016), which was acquired by CST in 2016. He also developed the autoCircuits web service for automated circuit problem generation, widely used in electrical engineering education. He leads the EMC Group (Electromagnetic Compatibility) at DET and has been instrumental in establishing the Compact Dynamical Modeling research area. His work has practical applications in high-speed electronics design, with algorithms embedded in commercial tools like IBM PowerSPICE. Grivet-Talocia maintains strong industry connections through his research projects and serves as Associate Editor for IEEE Transactions on Components, Packaging and Manufacturing Technology.
Dustin Scheinost is an Associate Professor at Yale School of Medicine, affiliated with the Department of Radiology & Biomedical Imaging, Yale Child Study Center, Department of Statistics, and Yale Biomedical Imaging Institute. His research focuses on connectomics , machine learning , and neuroinformatics through the Multi-modal Imaging, Neuroinformatics, & Data Science (MINDS) Lab. Radiology & Biomedical Imaging (Primary) Child Study Center (Secondary) Statistics (Secondary) Wu Tsai Institute Yale Stress Center Research Interests include developing novel statistical and machine learning methods for functional connectivity in big neuroscience data, leading the BioImage Suite Web (BISWeb) platform, and advancing early life neuroimaging through the Fetal, Infant, Toddler Neuroimaging Group (FIT’NG). His work is supported by grants from NIMH, NIAA, NIDA, and NHLBI. Selected Scientific Contributions span functional connectivity in laterality preferences, anti-racist AI governance in psychiatry, self-citation trends in neuroscience, and predictive modeling of mood disorders. He collaborates extensively with Todd Constable and others on multimodal neuroimaging studies.
Ina Fiterau Brostean is an Assistant Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst, where she leads the Information Fusion Lab. Previously, she was a Postdoctoral Fellow at Stanford University's Mobilize Center (2015–2018) and earned her PhD in Machine Learning from Carnegie Mellon University (2015). Her research focuses on hybrid systems for multimodal data integration, particularly in healthcare, aiming to develop predictive models for clinical outcomes using time series, text, and images. Key areas include disease trajectory modeling, weakly-supervised transfer learning, and adaptive representation learning. Education: PhD in Machine Learning (Carnegie Mellon, 2015), MSc in Machine Learning (Carnegie Mellon, 2012), BEng in Computer Engineering (Politehnica Timisoara, Romania, 2009). Professional roles include teaching COMPSCI 651 (Optimization in Computer Science) and organizing NeurIPS workshops on Machine Learning in Healthcare. Research interests span machine learning methodologies for healthcare applications, including interpretable models, time series analysis, and dimensionality reduction. Notable achievements include the Marr Prize (ICCV 2015) and Star Research Award (SCCM 2016). Her lab collaborates on projects like predicting Alzheimer's disease progression and surgical outcomes using Bayesian networks and deep learning. Awards and recognitions include Rising Stars Workshop (2016), Manning IALS Research Award (2019), and GE Foundation Scholar Leader Award (2007). She actively contributes to the ML4Health community through leadership roles and workshop organization.
Christopher Goyne is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of Virginia (UVA) and Director of the UVA Aerospace Research Laboratory. He holds a B.Eng. (1991) and Ph.D. (1999) in Mechanical Engineering from the University of Queensland, Australia. His research focuses on hypersonic propulsion, scramjet technology, instrumentation development, and advanced manufacturing. He leads the UVA Hypersonics Research Complex and is a key figure in the University Consortium for Applied Hypersonics. Goyne’s work includes contributions to NASA’s Hyper-X Program and the HyShot scramjet flight test program. He is an Associate Fellow of the AIAA and serves on editorial and advisory boards for journals and organizations such as the Shock Waves journal and Virginia’s Aerospace Advisory Council. Education: B.Eng. (Mechanical Engineering, University of Queensland, 1991); Ph.D. (Mechanical Engineering, University of Queensland, 1999). Research Interests: Hypersonics and scramjet propulsion Diagnostic techniques (e.g., laser-based methods, optical emission spectroscopy) Wind tunnel and flight testing Controls and adaptive systems for hypersonic flow paths Advanced manufacturing for aerospace components Awards: Recipient of the 2023 James C. McDaniel Fellow Award and 2022 Outstanding Researcher Award. Holds leadership roles in AIAA committees, including past Chair of the HyTASP Program Committee. Recognized with the Sigma Gamma Tau Outstanding Aerospace Professor Award (2006) and multiple research fellowships. Grants and Projects: Funded by NASA, the Air Force Office of Scientific Research, and industry partners. Leads UVA’s contributions to hypersonic ground and flight testing, including sensor development and combustion efficiency studies. Labs and Teams: Directs the UVA Aerospace Research Laboratory, collaborating on projects such as the UVA Hypersonics Research Complex and the University Consortium for Applied Hypersonics. Advises student chapters of AIAA and Sigma Gamma Tau.
Dr. Galatia Cleanthous is a Lecturer in the Department of Mathematics and Statistics at Maynooth University, Ireland, affiliated with the Faculty of Science & Engineering and the Hamilton Institute. She joined Maynooth in 2020 after postdoctoral positions at Trinity College Dublin, Newcastle University, and University of Cyprus, and holds a PhD in Pure Mathematics from Aristotle University of Thessaloniki (2014). Education PhD in Mathematics, Aristotle University of Thessaloniki, Greece (2014) MSc in Mathematics, Aristotle University of Thessaloniki, Greece Diploma in Mathematics, Aristotle University of Thessaloniki, Greece Research Interests Her research bridges pure and applied mathematics, focusing on Mathematical Analysis , Probability , and Statistics . Specifically, she explores Geometric Analysis , Geometric Function Theory , and Harmonic Analysis on manifolds and metric spaces. In statistics, she works on Nonparametric , Spatial , and Environmental Statistics , developing adaptive estimation techniques and studying Gaussian random fields on spheres and other domains. Publication Trends From 2025 back to 2013, her work has consistently appeared in top journals such as Annals of Statistics , Bernoulli , Journal of Nonparametric Statistics , and Transactions of the American Mathematical Society . A clear trend emerges: early publications concentrate on pure analytic topics like Fourier multipliers and function spaces, while recent outputs integrate these theoretical tools into modern nonparametric statistics, density estimation on manifolds, and stochastic modeling of environmental and seismological data. Scientific Awards Master’s degree ranked first with grade 9.8/10, Aristotle University of Thessaloniki (2011) Diploma ranked first among ~200 students, grade 9.7/10, Aristotle University of Thessaloniki (2009) Undergraduate merit awards for three consecutive academic years (2005-2008), State Scholarship Foundation of Greece National first place in Cypriot high-school mathematics entrance exams (2005), Ministry of Education, Cyprus Advising & Outreach Dr. Cleanthous has supervised BSc and MSc students, including Ultán Doherty (BSc, 1st Class Honors, 2021) and Anush Harish (MSc, 2022). She serves as Chair of the Department PR Committee, Member of the University STEM Promotions Committee, and Member of the departmental Equality, Diversity & Inclusion committee. Beyond campus, she trains young mathematicians at the North Kildare Maths Problem Solving Club and organizes public engagement events for Science Week. Labs & Teams She is associated with the Hamilton Institute at Maynooth University, a multidisciplinary research institute fostering collaboration between mathematics, computer science, and engineering.
