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Marco CorneliView profile

Professor

• Static and dynamic network analysis
• Spectral clustering
• Stochastic block models
• +16 more

Marco Corneli is a Professor at the University of Côte d'Azur , where he focuses on developing AI models for archaeology and history. He collaborates with the CEPAM laboratory , is a member of the INRIA research team MAASAI , and part of the LJAD laboratory. His research spans network analysis, optimal transport, and 3D shape analysis. Research Interests : Static/Dynamic Network Analysis (Spectral Clustering, SBMs, HRGs) Optimal Transport for Machine Learning Image and 3D Shape Analysis Probabilistic and Deep Learning Methods Clustering in High-dimensional and Sparse Data Advising : Davide Adamo (2023 - ongoing, co-advised with E. Vila and M. Vuillien) Seydina Ousmane Niang (2023 - ongoing, co-advised with C. Bouveyron and P. Latouche) Giulia Marchello (2020-2023, co-advised with C. Bouveyron) Cédric Vincent-Cuaz (2020-2023, co-advised with R. Flamary) Dingge Liang (2019-2022, co-advised with C. Bouveyron and P. Latouche) Software Development : Co-developed CpDyna (R/C++ package for change point detection in dynamic graphs) Contributed to ordinalLBM (R package for co-clustering ordinal data) Teaching : Delivered courses in statistical learning, graph theory, and data science at University of Côte d'Azur (M2 MA IM), Université Paris 1 (Licence MIASHS), and University of Côte d'Azur (MSc Data Science).

Santosh NagarakatteView profile

Professor

• Compilers
• Programming Languages
• Verification
• +4 more

Santosh Nagarakatte is a Professor and Undergraduate Program Director at the Department of Computer Science, Rutgers University, New Brunswick. He joined Rutgers in January 2013 after obtaining his Ph.D. in Computer Science from the University of Pennsylvania. His research spans hardware-software interfaces including compilers, programming languages, verification, and computer architecture, with a focus on building efficient, robust, and secure software systems. Education: Ph.D. in Computer Science, University of Pennsylvania His research interests include compilers, programming languages, verification, computer architecture, numerical methods, and formal methods. He leads projects on correctly rounded math libraries (RLIBM), verified eBPF ecosystems, lightweight formal methods for LLVM, and tools for parallel programming and debugging. His publications demonstrate a consistent focus on numerical accuracy, verification, and systems building, with recent work emphasizing correctly rounded math functions, eBPF verification in the Linux kernel, and debugging tools for numerical errors. This research bridges theoretical foundations with practical implementations across compilers, architectures, and security domains. Scientific Awards: ACM Distinguished Member (2023) eBPF Foundation Research Gift (2024) Intel Corporation Research Gift (2024) ACM SIGPLAN Dissertation Awards (2018, 2022 as advisor) Distinguished Paper Awards at PLDI, POPL, CGO, ICSE NSF CAREER Award (2015) Google Research Award (2014) IEEE Micro Top Picks (2010, 2013) He leads the Research on Abstractions for Programming and Learning (RAPL) group, advising PhD and Master's students. His research is supported by grants from NSF, Intel, Google, Facebook, and the eBPF Foundation, focusing on verified systems, numerical accuracy, and compiler technologies.

Marilena MüllerView profile

Researcher

• Nonparametric Statistics
• Survival Analysis
• Point Processes
• +5 more

Dr. Marilena Müller is a researcher in the Department of Mathematics at Heidelberg University, specializing in advanced statistical theory and methodology. Her work centers on nonparametric and asymptotic statistics, with applications to survival analysis, point processes, and empirical processes. Research Interests: Survival Analysis / Point Processes Nonparametric Statistics Asymptotic Statistics Linear Mixed Models Empirical Processes Her recent research, published in Bernoulli (2023), focuses on nonparametric estimation of locally stationary Hawkes processes, reflecting her expertise in modeling complex temporal event data using rigorous statistical frameworks. This work contributes to the broader fields of stochastic processes and statistical learning for dependent data. Scientific Awards: No awards listed. Dr. Müller has not advised any named students in the provided information, and there is no mention of grants or funding. She is actively contributing to theoretical statistics and is likely involved in collaborative research within the mathematics and statistics community at Heidelberg University.

Xiao ShouView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Data Science
• +6 more

Dr. Xiao Shou is an Assistant Professor in the Department of Computer Science at Baylor University, within the College of Arts and Sciences. He holds a PhD in Applied Mathematics and an MS in Computer Science, both from Rensselaer Polytechnic Institute (RPI), where he also served as a visiting researcher prior to joining Baylor. His academic journey includes an MS from Ohio State University and a BA from Wittenberg University. His research centers on machine learning for sequential data , with a focus on temporal point processes and time series. He develops deep learning models that incorporate causality, uncertainty, and attention mechanisms to better understand complex event sequences. His work bridges theoretical advances with real-world applications in data-intensive domains. The selected publications highlight a strong trend in modeling event sequences using advanced neural architectures. Key themes include causal learning, probabilistic attention, and multi-label prediction in streaming environments. These works have been published in top-tier conferences such as NeurIPS, ICML, and AAAI, reflecting high impact and technical rigor. Scientific Awards: 2023 IBM Research Accomplishment Award 2021 ACM BCB Best Student Paper Award Dr. Shou has served as a Program Committee (PC) member for premier conferences including NeurIPS, ICML, ICLR, AISTATS, and AMIA, indicating active engagement in the research community. While specific grant details are not mentioned, his collaborative work with IBM suggests industry-academic partnerships. He is likely involved in mentoring students, though no advisees are currently listed. His research group likely focuses on developing next-generation machine learning models for dynamic systems, with potential applications in healthcare, finance, and network analysis. Future work may expand into interpretable AI, real-time decision systems, and causal discovery in high-dimensional event streams.

• Planetary Geology
• Remote Sensing
• Artificial Intelligence
• +6 more

Philippa Mason is an Associate Professor in Planetary Remote Sensing at the Department of Earth Science & Engineering, Imperial College London (Faculty of Engineering). She specializes in satellite imagery analysis for geological studies on Earth and other planets, particularly Venus and Mars. Her work integrates InSAR and imaging spectroscopy to understand planetary surfaces and processes. Education: BSc (Hons) Geology from University of Southampton (1984–1987), MSc Remote Sensing from University College London (1992–1993), and PhD from Imperial College London (1994–1999). She has been at Imperial College since 2000. Research focuses on planetary geology, Earth Observation techniques, and computational modeling. Key affiliations include the EnVision Venus mission (NASA VenSAR Science Team and ESA SST), Earth Observation Network, and Imperial Centre for Geohazards. Her recent work includes analyzing Venusian tectonics, climate change impacts on small islands, and developing AI-driven environmental monitoring tools. Labs/Teams: Involved with the AVENGERS Initiative (terrestrial analog studies for Venus exploration), EnVision mission planning, and collaborations with the Civil & Environmental Engineering department at Imperial. Advising: Supervises multiple PhD students funded by agencies like STFC and ESA, focusing on topics ranging from planetary geology to geohazard monitoring. Former students hold roles in academia and industry (e.g., Technical Director at SkyGeo).

• Sea level rise
• Cryosphere
• Earth Observation
• +3 more

Professor Jonathan Bamber is a leading glaciologist and Professor in the School of Geographical Sciences at the University of Bristol, specializing in sea level rise, cryospheric dynamics, and Earth Observation technologies. His research leverages big data analytics and satellite remote sensing to address critical questions about polar ice sheet stability and climate change impacts. With a B.Sc. from the University of Bristol and a Ph.D. from the University of Cambridge, he has established himself as a key figure in polar science through extensive fieldwork and computational modeling. Education: B.Sc. in Geographical Sciences, University of Bristol Ph.D. in Earth Sciences, University of Cambridge Research Focus: Bamber's work centers on quantifying ice sheet contributions to sea level rise using satellite altimetry, gravimetry, and novel machine learning approaches. He investigates Antarctic and Arctic ice dynamics, glacier retreat patterns, and freshwater flux impacts on ocean circulation. His research integrates physics-based modeling with data-driven techniques to improve predictions of cryospheric responses to global warming, with particular emphasis on the vulnerability of polar regions to temperature increases beyond 1.5°C. Publication Trends: Analysis of his recent publications reveals a strategic shift toward AI-enhanced cryospheric monitoring, including physics-aware machine learning frameworks for ice thickness estimation and glacier mapping. His work increasingly focuses on high-resolution datasets (e.g., Bedmap3 for Antarctica), spatio-temporal modeling of ice sheet changes, and quantifying uncertainties in climate projections. The research demonstrates strong interdisciplinary connections between glaciology, oceanography, and climate policy, with growing emphasis on actionable insights for climate adaptation. Awards and Recognition: While specific awards aren't detailed in source materials, his leadership in major projects like ESA's Sea Level Budget Closure (SLBC_cci+) initiative underscores significant professional recognition. Collaborative Frameworks: Bamber actively participates in international research consortia, including contributions to IPCC assessments and CMIP6 climate modeling efforts. His work on the MAGIC Mission Science team demonstrates engagement with space-based Earth observation systems. Current projects like the SLBC_cci+ (2023-2026) exemplify his role in bridging satellite data validation with fundamental sea level science questions.

• Medical Imaging
• Radiation Therapy
• Machine Learning
• +3 more

Jie Wei is a Professor of Computer Science at the City College of New York (CCNY), with an affiliation to the Computer Engineering department. His work bridges computer vision, medical imaging, and machine learning with applications in radiation therapy, infrastructure monitoring, and disaster mitigation. Dr. Wei's research focuses on developing intelligent systems for medical diagnostics, autonomous object detection, and environmental safety assessment. His team pioneers techniques like SAM-based auto-segmentation for clinical applications and uses transfer learning for non-invasive motion tracking in oncology. Education details are listed on CCNY's site but not explicitly provided here. His research spans over 15 years, with notable contributions to 4D medical imaging analysis, lung tumor tracking, and lidar-based infrastructure monitoring. Recent work emphasizes AI-driven solutions for radiation therapy complications, autonomous vehicle classification, and efficient damage assessment systems. Publications reflect a strong trend towards interdisciplinary applications of machine learning, particularly in healthcare and civil engineering. His work often integrates deep learning models with domain-specific challenges, such as real-time CCTV threat detection and volumetric respiratory motion simulation. Collaborations likely span biomedical engineering, computer science, and civil infrastructure sectors.

• Point Process Models
• Spatial Statistics
• Temporal Analysis
• +2 more

Frederic R. Schoenberg serves as an Associate Professor in the Department of Statistics at the University of California, Los Angeles (UCLA). He earned his Ph.D. in Statistics from the University of California, Berkeley in 1997, completing his dissertation titled "Assessment of Multi-dimensional Point Process Models" under the supervision of David Brillinger. His research expertise centers on point process modeling with applications in spatial and temporal data analysis. His methodological work focuses on developing assessment techniques for complex multi-dimensional statistical models. As a tenured faculty member at UCLA, he contributes to the department's teaching mission and research endeavors in statistical methodology.

• Medical Imaging
• Radiology
• Oncology
• +4 more

Tommaso Vincenzo Bartolotta is a Full Professor in the Department of Biomedicine, Neuroscience and Advanced Diagnostics at the University of Palermo's School of Medicine and Surgery. He specializes in advanced diagnostic imaging techniques, with office hours held at the Institute of Radiology within Palermo University Hospital. His research focuses on: Innovations in medical imaging (MRI, ultrasound, contrast-enhanced techniques) AI-driven diagnostics for oncology (breast, liver, prostate, brain tumors) Radiomics and quantitative imaging biomarker development Clinical applications of elastography and microvascular ultrasound Recent publications demonstrate a strong emphasis on artificial intelligence integration into radiological workflows, particularly for breast cancer characterization, liver disease assessment, and therapeutic response prediction. No scientific awards or student advising relationships were documented in the provided materials. Contact is available via institutional email.

Oliver GeißendörferView profile

Researcher

• LiDAR
• Remote Sensing
• Surveying
• +6 more

Oliver Geißendörfer is a Research Associate at the Chair of Engineering Geodesy within the Technical University of Munich . His work focuses on LiDAR technology, point cloud processing, and geodetic monitoring systems, with applications in structural analysis and environmental engineering. Education: Master of Science in Geodesy and Geoinformation (2018-2021) Bachelor of Science in Geodesy and Geoinformation (2014-2018) His research interests include spatio-temporal analysis of vibration responses, sensor fusion (LiDAR, GNSS, IMU), and efficient point cloud processing algorithms. Recent work emphasizes combining LiDAR with time-domain frequency analysis for enhanced structural monitoring. He has supervised multiple theses on topics like steel component detection in point clouds, MEMS LiDAR comparisons, GNSS accuracy studies, and boundary point setting techniques.

Vikram KrishnamurthyView profile

Professor

• Statistical Signal Processing
• Stochastic Control (POMDPs)
• Stochastic Optimization
• +10 more

Professor Vikram Krishnamurthy is a distinguished academic at Cornell University , affiliated with the School of Electrical & Computer Engineering , Center for Applied Mathematics , and Mechanical & Aerospace Engineering . He leads the Cornell Statistical Signal Processing Lab and is part of the Foundations of Information, Network, and Decision Systems (FIND) collaborative. His career spans institutions like the University of British Columbia (Canada Research Chair, 2002-2016) and University of Melbourne (1994-2002). His research bridges statistical signal processing , stochastic control (POMDPs), and network science , with applications in social networks , cognitive radar , and ion channel biosensors . Key themes include human-machine interfacing , inverse reinforcement learning , and behavioral economics . Recent publications focus on adaptive inverse reinforcement learning , LLM agent interactions , and quantum decision systems , reflecting his work in machine learning , stochastic optimization , and social network analytics . His book Partially Observed Markov Decision Processes (2nd ed., 2025) is a seminal text in the field. Scientific Awards: Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Distinguished Lecturer, IEEE Signal Processing Society Editor-in-Chief, IEEE Journal on Selected Topics in Signal Processing Honorary Doctorate from KTH Royal Institute of Technology, Sweden Jean Pierre LeCadre Award (2019) He currently advises PhD students including Shashwat Jain, Luke Snow, Adit Jain, and Yiming Zhang. His teaching includes advanced courses like Bayesian Estimation and Stochastic Optimization (ECE 7230) and Data Science for Engineers (ECE 2720).

Noemi MauroView profile

Assistant Professor

• Recommender Systems
• Personalization
• User Modeling
• +9 more

Noemi Mauro is a Tenure-track Assistant Professor in the Department of Computer Science at the University of Torino, Italy. She holds a PhD in Computer Science with honors from the same institution, supervised by Prof. Liliana Ardissono. Her research focuses on personalization, recommender systems, user modeling, and information retrieval, with a strong emphasis on inclusive technologies for people with Autism Spectrum Disorders (ASD). PhD in Computer Science with Honors, University of Turin (2016–2019) Master’s in Computer Science, University of Turin (2014–2016), 110/110 summa cum laude Bachelor’s in Computer Science, University of Turin (2011–2014), 110/110 Visiting PhD at Alpen-Adria-Universität Klagenfurt (2018–2019) Visiting PhD at University College Dublin (2017–2018) Her research interests include Recommender Systems , Personalization , User Modeling , Information Retrieval , Accessibility , and Cultural Heritage . She has made significant contributions to inclusive technologies, particularly through systems that support people with autism in tourism and urban navigation. Her work integrates ethical and sustainable dimensions into recommendation algorithms. Her recent publications (2023–2025) reflect a strong trend toward explainable AI , sustainable consumption , cultural heritage exploration , and inclusive design . She frequently publishes in top venues such as ACM UMAP, RecSys, IEEE Access, and Springer journals. Her research often involves multimodal interfaces, service-oriented architectures, and user-centered evaluations. Best Paper Award, UMAP 2020 Young Researcher Award, University of Turin (2022) Women in RecSys Journal Paper of the Year (2022) Outstanding PC Member, HT 2020 Best Master’s Thesis Award, University of Turin (2017) Noemi Mauro actively supervises PhD and master’s students and has advised numerous bachelor’s theses on topics ranging from accessibility to gamification and ethical recommendation. She leads major research projects such as SPACES (Fondazione CRT) and ACCESS (PRIN), focusing on social inclusion and anxiety management for people with ASD. She also plays key roles in national and European initiatives like RevIs and NODES . In teaching, she delivers courses on databases and recommender systems. She is deeply embedded in the academic community as a workshop co-chair (PATCH series), editorial board member of UMUAI, and co-editor of special issues on AI for social good and inclusive systems. She leads and contributes to labs and research teams focused on intelligent user interfaces , personalized access to cultural heritage , and inclusive recommender systems . Her work is conducted within collaborative environments such as the co3project (H2020) and the OnToMap platform, emphasizing semantic data interoperability and public engagement.

• Photogrammetry
• Remote Sensing
• Geomatics
• +3 more

María Luz Gil Docampo is a researcher at the University of Santiago de Compostela's Department of Agroforestry Engineering, focusing on photogrammetry, remote sensing, and 3D modeling applications. She collaborates extensively with colleagues like Juan Ortiz Sanz and Santiago Martínez Rodríguez, contributing to interdisciplinary projects in heritage conservation, structural analysis, and environmental monitoring. Education : PhD in Agroforestry Engineering (2001, University of Santiago de Compostela), with a thesis on satellite imagery applications. Research : Specializes in low-cost photogrammetric systems, 3D documentation of cultural heritage, biomass determination in agriculture, and land-use change detection using LiDAR and satellite data. Publications : 15+ journal articles and book chapters spanning 2001-2024, with recent work on full-scale timber structure monitoring (2024) and ethical considerations in multimedia systems (2021). Supervision : Directed 6 doctoral theses including UAV-based biomass monitoring (2022), agroforest plantation survival analysis (2016), and photogrammetry applications in archaeology (2020). Projects : Key member of GI-2114 Group (USCAN3D), developing cost-effective 3D scanning solutions for cultural assets and rural infrastructure. Her work bridges technical innovation with practical applications in agriculture , forestry , and cultural heritage preservation , often involving multidisciplinary methodologies like UAV integration, open-source software, and LiDAR data analysis.

Ruaridh ClarkView profile

Academic

• Signal Processing
• Biomedical Engineering
• Wearable Sensors
• +5 more

Ruaridh Clark is a Senior Research Fellow in the Department of Electronic and Electrical Engineering at the University of Strathclyde, United Kingdom. He is actively engaged in research, supervising PhD students, and contributing to multiple funded projects in biomedical signal processing and neurotechnology. His research interests lie at the intersection of biomedical engineering, signal processing, and human-computer interaction, with a strong focus on EEG-based systems, wearable sensors, and real-time health monitoring. His work enables advancements in brain-computer interfaces, emotion recognition, seizure detection, and cognitive state assessment. The analysis of his recent publications reveals a consistent trend in developing robust, real-time, and wearable neurophysiological systems. These works emphasize adaptive signal processing, multimodal data fusion, machine learning, and low-power embedded solutions for clinical and real-world applications. 9 prizes or awards (specific names not listed in text) Ruaridh Clark supervises several PhD students, including Agathe Bouis, Joshua Gribben, and Beth Probert. He is involved in 18 research projects, indicating active grant funding and collaborative research. His work is highly interdisciplinary, bridging engineering, neuroscience, and clinical applications. He is affiliated with the research group led by Professor Malcolm Macdonald and collaborates across disciplines, including with researchers in psychology. His lab focuses on developing wearable, real-time, and intelligent systems for neurophysiological monitoring and human performance assessment.

• Climate Statistics
• Extreme Weather Modeling
• Atmospheric Science
• +5 more

T. Economou is affiliated with the Department of Mathematics and Computer Science at the University of Exeter, UK, where he conducts research at the intersection of statistics and meteorology. His work primarily focuses on modeling extreme weather events, particularly the serial clustering of extratropical cyclones, using climate model outputs and statistical methodologies. His research interests lie in climate statistics , spatio-temporal modeling , and extreme event analysis , with applications to atmospheric dynamics and climate change impacts. He applies advanced statistical techniques to understand patterns in storm occurrences and their variability under changing climate conditions. The two available publications show a strong trend in analyzing extreme meteorological phenomena through rigorous statistical frameworks. His work emphasizes quantifying uncertainty, modeling dispersion in storm counts, and assessing the role of large-scale climate drivers such as the North Atlantic Oscillation. These studies contribute to improving risk assessments related to clustered extreme weather events in Europe and the North Atlantic region. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: There is no information available regarding students advised or grants received. However, his active involvement in CMIP5-based research suggests potential participation in collaborative, funded climate science initiatives. Labs and Teams: While no formal lab or team structure is described, T. Economou collaborates with researchers from institutions such as the University of Reading and the University of Cologne, particularly within the context of multi-model climate analysis and extratropical cyclone dynamics.