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Jens JanuschkeView profile

Professor

• Cell Polarity
• Asymmetric Cell Division
• Stem Cell Biology
• +5 more

Professor Jens Januschke is Chair of Development Cell Biology in the Molecular Cell and Developmental Biology department within the School of Life Sciences at the University of Dundee. He was promoted to Personal Chair (Professor) as part of the 2023-24 Annual Review process and serves as joint divisional lead of the Molecular, Cell and Developmental Biology division alongside Tom Owen-Hughes, having assumed this leadership role in May 2023 following Kate Storey stepping down. His research focuses on the establishment of cell polarity in rapidly dividing Drosophila stem cells, particularly investigating asymmetric cell division (ACD) in neural stem cells (neuroblasts) of the developing central nervous system. He examines how cycling stem cells robustly control ACD to prevent errors in cell fate generation, with special attention to the subcellular localization of mRNAs and their contribution to cell polarity and fate specification. His recent publications demonstrate a strong focus on cell polarity mechanisms, actomyosin dynamics, centrosome asymmetry, and epithelial morphogenesis in Drosophila models, revealing fundamental principles of stem cell biology and developmental processes. Sir Henry Dale Research Fellowship (2013) Sir Henry Dale Research Fellowship (renewal) (2017) Science For All Badge (2023) Professor Januschke actively supervises PhD students through unfunded opportunities with deadlines extending to 2026, focusing on cell polarity and mechanical regulation in epithelia, as well as the role of shape and form in biological systems. His research involves imaging living neuroblasts in primary cell culture and whole mount brain explants to document and measure cell polarization dynamics during consecutive neuroblast cycles. He collaborates on multiple research projects, including those funded by the Biotechnology and Biological Sciences Research Council. His work contributes to understanding fundamental biological processes with implications for tissue homeostasis, developmental disorders, and potential applications in regenerative medicine.

• Mathematical Biology
• Disease Modeling
• Physiological Modeling
• +2 more

Erica Rutter is a faculty member in the Department of Applied Mathematics at the University of California, Merced, where she joined in July 2019. Prior to this position, she was a postdoctoral scholar at North Carolina State University. Dr. Rutter received her B.Sc. from the University of Michigan in 2009 in Applied Mathematics and Japanese. After her undergraduate studies, she worked for several years as a Research Technician at the Center for Radiative Shock Hydrodynamics in the Atmospheric Oceanic and Space Sciences Department at the University of Michigan. She then pursued her doctoral studies, obtaining a PhD in Applied Mathematics from Arizona State University in 2016. Her research interests span mathematical biology with a focus on disease modeling, physiological modeling, and ecotoxicology. She employs techniques from various mathematical disciplines, including machine learning, to address complex biological problems. Her work often involves developing mathematical models to understand biological processes and applying computational methods to analyze biological data. Dr. Rutter's publication record demonstrates an interdisciplinary approach across mathematical oncology, ecotoxicology, and biomedical image analysis. Her research shows a progression toward increasingly sophisticated methods for modeling biological systems, with emphasis on tumor heterogeneity, glioblastoma growth, and equation learning from limited biological data. She maintains an active research program at UC Merced, contributing to the academic community through her teaching and research mentorship in the Department of Applied Mathematics.

• Civil Engineering
• Climate Science
• Machine Learning
• +6 more

Brennan Bean is an Assistant Professor in the Mathematics and Statistics Department at Utah State University's College of Arts & Sciences. His work focuses on geospatial modeling, statistical methods for extreme weather analysis, and machine learning applications in structural and environmental engineering. Recent publications highlight expertise in snow load prediction, Bayesian entropy, and interdisciplinary data science. Notable contributions include optimizing design methods for insulated concrete wall panels and addressing deployment challenges for ML models in engineering contexts. Research trends span geospatial data integration, climate change impact assessments, and educational interventions in STEM. Key subfields include ground snow load mapping, extreme value statistics, climate downscaling, and high-dimensional ecological modeling.

• Mathematics Education
• Teacher Education
• Cognitive Psychology
• +5 more

Danyal Farsani is an Associate Professor at the Department of Teacher Education , Norwegian University of Science and Technology (NTNU), specializing in mathematics education through the lens of embodied cognition and cultural responsiveness . His work bridges teacher training , gesture-based learning , and cross-linguistic analysis in educational contexts. Key Research Themes: Embodied mathematical concepts, cognitive load in arithmetic, cultural/ethnomathematical practices, and multimodal communication in classrooms. Recent Collaborations: Focus on global North-South educational dynamics, robotics integration in teacher training, and metaphorical processing in language learning. Methodological Focus: Empirical studies using mental arithmetic tasks, gesture analysis, and cross-cultural curriculum evaluations. Publication Trends (2022–2025): Analysis of 15 recent articles reveals recurring emphasis on embodied cognition , mathematical metaphors , visual attention mechanisms , and culturally adaptive teaching tools like the Ethiopian Gebeta game. Subfields span gesture studies , cross-linguistic pedagogy , and innovative teacher education frameworks . No scientific awards or student advisories are explicitly mentioned in the provided data.

Aleksey ZaytsevView profile

Senior Lecturer

• Artificial Intelligence
• Machine Learning
• Predictive Modeling
• +4 more

Aleksey Zaytsev is a Senior Lecturer at Skoltech, where he leads the Applied Research Laboratory «Skoltech-Sberbank» at the Center for Applied Artificial Intelligence. He holds a PhD in Physics and Mathematics and is a laureate of the Moscow Government Prize for Young Scientists. Education: PhD in Physics and Mathematics His research focuses on artificial intelligence, particularly on developing predictive models for geospatial analysis, drilling safety, and aerospace engineering. He has created practical AI solutions for drought prediction, accident prevention, and aircraft quality monitoring. His work also emphasizes model reliability and safe deployment in industrial contexts. The recent studies highlighted his contributions to neural network accuracy in banking applications, algorithmic hesitation for improved decision-making, and geospatial data optimization. These works demonstrate interdisciplinary applications of AI in environmental, financial, and engineering domains. Scientific Awards: Moscow Government Prize for Young Scientists As head of the Artificial Intelligence Center's applied research lab, Zaytsev bridges academic research with industry needs, particularly in AI safety and real-world implementations. He also engages in public education through lectures like 'How to Deceive Artificial Intelligence' at the Yeltsin Center, addressing AI vulnerabilities and security implications.

• 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.

• 5G/6G Systems
• Next-Generation Wi-Fi
• Wireless Internet of Things
• +4 more

Evgeny Khorov is a Full Professor and Deputy Chair at Moscow Institute of Physics and Technology (MIPT) and Head of the Wireless Networks Lab at the Institute for Information Transmission Problems of the Russian Academy of Sciences (IITP RAS) and the Telecommunication Systems Lab at Higher School of Economics (HSE). His research focuses on 5G/6G systems , next-generation Wi-Fi , Wireless IoT , and QoS-aware optimization. Ph.D. (2012) and D.Sc. (2022) in Telecommunications from IITP RAS and MIPT Visiting Research Fellow at King's College London (2015) His work includes mathematical modeling of networking protocols, Wi-Fi standardization (IEEE 802.11), and contributions to Wi-Fi 6 (802.11ax) and Wi-Fi 7 (802.11be) . He supervises students and has co-authored over 200 papers. Recent articles highlight advancements in Wi-Fi 7/8 , URLLC , 5G multi-connectivity , and machine learning for traffic classification . Scientific Awards: Best Demo Award, ACM Mobihoc (2022) Best Paper Awards: IEEE ISWCS (2012), Elsevier Computer Communications (2018), IEEE PIMRC (2019) Moscow & Russian Government Prizes for Young Scientists (2013, 2016) Scopus Award Russia (2018) Best Cooperation Project Leader (multiple times) He serves as Editor-in-Chief of Problems of Information Transmission (since 2024) and chairs major IEEE conferences (Globecom 2018 Workshop, BlackSeaCom 2019).

Fabian Leal-VillasecaView profile

Researcher

• Geostatistics
• Deep Neural Networks
• Spatial Interpolation
• +3 more

Fabian Leal-Villaseca is a researcher affiliated with the School of Physics, Mathematics and Computing, focusing on geostatistics and machine learning applications. His work bridges computational methods with spatial data analysis. Research Output: Published 1 peer-reviewed article in Computers and Geosciences (2025) on Deepkriging for geostatistical interpolation. Current research trends involve integrating deep neural networks with traditional geostatistical techniques like kriging to enhance spatial modeling accuracy. His work has garnered 4 citations and is linked to mineral resource assessment challenges. Collaborations: Collaborates on interdisciplinary projects involving geostatistics, resource modeling, and computational decision-making systems.

Jasmine FooView profile

Professor

• Cancer Evolution
• Mathematical Oncology
• Stochastic Modeling
• +4 more

Jasmine Foo serves as Associate Head and Distinguished McKnight University Professor at the University of Minnesota-Twin Cities' School of Mathematics, holding the Northrop Professorship and co-directing the Therapy Modeling and Design Center. Her leadership spans academic administration and interdisciplinary research initiatives in mathematical oncology. Foo's research pioneers stochastic evolutionary modeling of cancer dynamics, integrating mathematical theory with clinical data to understand tumor initiation, progression, and treatment resistance. Her group focuses on five interconnected themes: plasticity and epigenetics in tumor evolution; drug resistance optimization; data-driven precision oncology; spatial carcinogenesis; and tumor-microenvironment interactions using organoid models. This work bridges probability theory, systems biology, and clinical oncology to develop novel therapeutic strategies. Analysis of her 15 most recent publications reveals a consistent emphasis on quantitative approaches to cancer evolution, with growing integration of machine learning and high-resolution experimental data. Key trends include modeling phenotypic plasticity in resistance development, optimizing dosing schedules using evolutionary principles, and translating spatial tumor dynamics into clinical applications. Scientific recognition includes: Honorable Mention, Feldman Prize for theoretical contributions to tumor evolution modeling Foo actively mentors graduate students and postdoctoral researchers through the School of Mathematics, with research supported by multiple grants (though specific funding sources aren't detailed). Her group maintains strong collaborations with experimental oncology labs and clinical researchers, facilitating data-driven model validation. She co-leads the Therapy Modeling and Design Center and organizes the UMN MathBio Group Meetings, fostering cross-disciplinary collaboration between mathematicians, biologists, and clinicians in cancer research.

Angela DepaceView profile

Assistant Professor

• Gene Regulation
• Cis-Regulatory Elements
• Transcriptional Networks
• +5 more

Angela Depace is an Assistant Professor at Harvard Medical School, specializing in Gene Regulation , Cis-Regulatory Elements , and Quantitative Developmental Biology . Her research integrates experimental and computational approaches to study how transcription factors and enhancer sequences control gene expression in Drosophila embryos, with implications for evolutionary biology and synthetic biology . B.S., Molecular Biophysics and Biochemistry, Yale University Ph.D., Biochemistry, University of California, San Francisco (advisor: Jonathan Weissman) Postdoctoral Research, University of California, Berkeley (advisor: Michael Eisen) Her work focuses on unraveling the kinetic roles of transcriptional activators , the evolution of regulatory sequences , and the computational modeling of gene networks . Recent publications explore mechanistic principles of enhancer function , transcriptional synergy , and developmental precision . Scientific contributions include the NSF CAREER Award and co-authoring Visual Strategies: A Practical Guide to Graphics for Scientists and Engineers . Her lab emphasizes collaborative research , mentoring , and innovative teaching in systems biology graduate courses.

Santiago FigueiraView profile

Professor

• Theoretical computer science
• Computability theory
• Algorithmic randomness
• +8 more

Santiago Figueira is a Professor at the Department of Computer Science, Faculty of Exact and Natural Sciences, University of Buenos Aires, and a Researcher at the Institute of Computer Science (ICC), CONICET (Argentine National Council for Scientific and Technical Research) in Buenos Aires, Argentina. He leads the Logic, Language and Computability Research Group (GLyC) and co-directs the Argentinian-French Laboratory SINFIN, a collaborative initiative between Université Paris-CNRS and Universidad de Buenos Aires-CONICET. Additionally, he is a researcher in the Quantum Information, Computation, and Communication team (QuICC). Dr. Figueira's research spans theoretical computer science with a strong emphasis on computability theory, algorithmic randomness, Kolmogorov complexity, and mathematical logic. His work extends to applications in database theory, quantum information, and cognitive modeling. He has made significant contributions to modal logics, model theory, and the theory of databases, particularly in the context of data trees and XPath query languages. His interdisciplinary approach bridges formal methods with practical applications in quantum computing and human cognition. His publication record demonstrates a consistent trajectory of high-impact research, with recent work focusing on modal logic extensions, quantum information theory, cognitive modeling of concept learning, and database query languages. His collaborations span international institutions, particularly with French researchers through the SINFIN laboratory, and with cognitive scientists studying human memory and concept formation. Dr. Figueira has supervised numerous PhD students and postdoctoral researchers, including Gabriel Goren-Roig, Santiago Cifuentes, Edwin Pin Baque, Sergio Romano, Gabriel Senno, and Sergio Abriola. His former postdocs and CONICET researchers include Guido Bellomo, María Emilia Descotte, and Ariel Bendersky, indicating an active and productive research group. He has developed lecture notes in Spanish on Logic and Computability (2020) and Computability Theory (2021), contributing to academic education in his field. His research group GLyC serves as a hub for theoretical computer science research in Argentina, fostering collaborations between Argentine and international researchers.

Irène MarcoviciView profile

Professor

• Probability Theory
• Dynamical Systems
• Cellular Automata
• +4 more

Irène Marcovici is a Professor in the Department of Mathematics at the University of Rouen Normandy, affiliated with the Raphaël Salem Mathematics Laboratory (LMRS). She leads the Probability and Dynamical Systems team within LMRS and participates in the ALEA and SDA2 working groups of the GDR Informatique Fondamentale et ses Mathématiques. PhD in Mathematics (2013, University of Paris Diderot) Habilitation à Diriger des Recherches (2021, University of Lorraine) Her research focuses on probability theory , dynamical systems , and cellular automata , with significant contributions to percolation theory, self-organization phenomena, and combinatorics on words. She investigates how randomness influences complex systems, particularly through probabilistic cellular automata and their ergodic properties. Analysis of her recent publications (2021-2025) reveals a strong emphasis on percolation dynamics (e.g., corner percolation with directional bias), self-stabilization mechanisms in cellular systems, and combinatorial structures like Kolakoski sequences. Her work bridges theoretical computer science and pure mathematics, often employing stochastic methods to solve problems in symbolic dynamics and discrete geometry. Supervision & Collaborations: Currently supervising Maxence Poutrel (with Jérôme Casse) Previously supervised Pierrick Siest (2021-2024), Jocelyn Begeot (now Associate Professor), and Pierre-Adrien Tahay (now PRAG at Telecom Nancy) Regular collaborations with Régine Marchand, Nazim Fatès, and Pascal Moyal Laboratory Context: As leader of the Probability and Dynamical Systems team at LMRS, she contributes to France's national research infrastructure in fundamental mathematics, with connections to CNRS and international working groups focused on automata theory and discrete probability.

• Applied Statistics
• Biostatistics
• Time Series Analysis
• +3 more

Dr. Carolina Euan is a Lecturer in Statistics at the School of Mathematical Sciences, Lancaster University, with affiliations to the Data Science Institute and STOR-i Centre for Doctoral Training. Her research focuses on time series analysis, spatio-temporal modeling, and their applications in environmental data science and brain data analysis. School of Mathematical Sciences Data Science Institute STOR-i Centre for Doctoral Training Biostatistics Research Group Centre of Excellence in Environmental Data Science Her work spans multiple disciplines: Environmental Data Science: Marine heatwaves, solar irradiance modeling, particle number size distribution Brain Data Analysis: Neural connectivity, brain signal clustering, coherence-based inference Statistical Methodology: Spatio-temporal extremes, spectral estimation, functional data analysis Recent publications demonstrate expertise in: 2025: Neural connectivity modeling, functional data analysis, particle distribution 2024: Virtual collaboration frameworks, precipitation regime modeling, spectral estimation 2023: Brain signal clustering, source apportionment, directional wave spectra Supervision includes: Kajal Dodhia: STOR-i (extreme sea temperatures) Jordan Hood: Bayesian modeling (COVID prevalence) Carla Pinkney: STOR-i

• Energy Systems Modeling
• Geothermal Energy
• Optimization Algorithms
• +3 more

Smajil Halilovic is a Researcher at the Chair of Renewable and Sustainable Energy Systems at the Technical University of Munich . His work focuses on energy systems modeling and optimization, particularly for geothermal and renewable energy integration. Current projects include Geo.KW , a coupled hydrothermal and infrastructure model for urban-scale geothermal use Research emphasizes optimization techniques for groundwater heat pump systems Key contributions in PDE-constrained optimization and thermal resource assessment Halilovic has published extensively in Renewable Energy and Energy Conversion and Management , with recent work on spatial optimization of geothermal systems and urban heat planning. His collaborations with Prof. Thomas Hamacher and Kai Zosseder highlight his role in advancing sustainable energy infrastructure. He teaches Mathematical Modeling of Complex Systems in the Energy Field and contributes to interdisciplinary projects like the Interdisciplinary project internship: Concept development of a renewable energy system in a developing country . His publications demonstrate expertise in geothermal integration, optimization algorithms, and urban energy modeling.

• Bayesian Statistics
• Bayesian Machine Learning
• Environmental Science
• +8 more

Julyan Arbel is an Associate Researcher (chargé de recherche) at Inria Grenoble in the Statify team and a member of Laboratoire Jean Kuntzmann at Université Grenoble Alpes. He holds a PhD in Applied Mathematics from Université Paris-Dauphine and graduated from École Polytechnique and ENSAE. He defended his Habilitation à Diriger des Recherches (HDR) in 2019. Research Interests His work spans Bayesian Statistics (approximations, computation, nonparametrics, extreme value theory, objective Bayes) and Bayesian Machine Learning (Bayesian neural networks, variational inference), with applications in environmental science and neuroscience. Key focus areas include: Statistical methods for ecological modeling and species distributions Integration of biotic interactions in predictive frameworks Bayesian approaches to machine learning challenges Publications Overview Recent articles (2022-2025) demonstrate a strong focus on statistical ecology, including species distribution modeling, biotic interactions, community ecology, and biodiversity conservation. Methodological innovations center on joint species distribution models and Bayesian computational techniques applied to environmental datasets. Academic Service Associate Editor: Statistics and Computing (2025–present) Associate Editor: Bayesian Analysis (2019–present) Associate Editor: Australian and New Zealand Journal of Statistics (2019–present) Associate Editor: Statistics & Probability Letters (2019–present) Associate Editor: Statistical Methods & Applications (2024–present) Former Associate Editor: Computational Statistics & Data Analysis (2020–2023) Students Supervised PhD students: Giovanni Poggiato Daria Bystrova Minh Tri Lê Théo Moins Teams Statify Research Team, Inria Grenoble Laboratoire Jean Kuntzmann, Université Grenoble Alpes