جست‌وجوی استاد و پژوهشگر

Christian Skalkaمشاهده پروفایل

استاد

• Programming Languages
• Cybersecurity
• Computational Methods in Environmental Science
• +۴ مورد دیگر

Christian Skalka is a Professor and Chair in the Department of Computer Science at the University of Vermont (UVM), part of the College of Engineering and Mathematical Sciences. He joined UVM in 2002 and holds a Ph.D. from Johns Hopkins University, an M.S. from Carnegie Mellon University, and a B.S. from Saint John's College. His research focuses on programming languages, cybersecurity, and computational methods in environmental science. He teaches courses such as Programming Languages, Computer Networks, and Mobile Apps & Embedded Devices. Skalka’s work spans secure multi-party computation, intrusion detection using deep learning, and healthcare systems integration. Notable contributions include developing SMT-boosted security types for MPC, frameworks for secure network code verification, and cyber-physical systems for rehabilitation monitoring. His research often bridges theory and practice, with applications in healthcare, environmental science, and distributed systems. Publications highlight interdisciplinary collaboration, including work on federated learning privacy, snowpack modeling via machine learning, and audit logging formalization. His projects emphasize practical security solutions for real-world challenges in healthcare, environmental monitoring, and networked systems.

Tomasz Związekمشاهده پروفایل

پژوهشگر

• Environmental History
• Historical Geography (HGIS)
• Settlement History of Central and Eastern Europe
• +۳ مورد دیگر

Tomasz Związek is a Researcher at the Institute of Geography and Spatial Organization of the Polish Academy of Sciences (PAN). He holds a PhD in History (2020) from PAN’s Tadeusz Manteuffel Institute of History, where he previously served as an Assistant and later a technical-scientific staff member. His research focuses on historical settlement patterns, environmental history, and historical geography, particularly in Central and Eastern Europe during the medieval and early modern periods. He specializes in integrating historical records with GIS technologies to analyze landscape transformations, tax systems, and ecological changes. **Education**: 2008–2013: BA and MA in History, University of Warsaw 2013–2017: PhD in History at Tadeusz Manteuffel Institute of History PAN **Research Interests**: Medieval and early modern settlement patterns Historical environmental interactions Digitization of historical court records Pre-industrial forest management **Key Projects**: Development of spatial databases for early modern settlement studies Analysis of 16th-century tax records in Greater Poland Historical GIS mapping of water mills and riverine economies **Professional Experience**: 2019–present: Researcher at PAN’s Institute of Geography 2011–2019: Technical roles in historical atlas projects and archival work His work bridges historical methodologies with environmental sciences, emphasizing interdisciplinary approaches to understanding long-term human-environment interactions.

Michael A Normanمشاهده پروفایل

دانشیار

• Discovery Systems
• Transaction Log Analysis
• Web-scale Discovery Services
• +۳ مورد دیگر

Michael A Norman is an Associate Professor in the University Library at the University of Illinois Urbana-Champaign, serving as Discovery Services Librarian and Integrated Library System (ILS) Coordinator. His work focuses on advancing discovery systems, transaction log analysis, and web-scale library technologies. He has contributed to innovations like the Bento discovery framework and conducted extensive research on library resource management, user behavior, and digital preservation. Norman has collaborated on projects involving Primo, SFX, and schema.org integration, emphasizing usability and interoperability in library systems. Education: Details not explicitly stated in provided texts. Research Interests: Discovery systems architecture, user-centered design in library technology, web-scale resource management, metadata standards implementation, and historical collection analysis. His work bridges technical innovation with practical library services, addressing challenges in digital resource access and preservation. Publications: Over 13 peer-reviewed articles and datasets, focusing on discovery systems evaluation, copyright renewal studies, and collection management strategies. Recent work emphasizes Bento architecture and user interaction analysis within digital libraries. Awards: No specific awards listed in provided materials. Advising/Grants: No formal advisee list provided; contributions include collaborative research grants related to library technology projects.

Elisabeth GASSIATمشاهده پروفایل

استاد

• Statistics
• Probability
• Hidden Markov Models
• +۷ مورد دیگر

Elisabeth GASSIAT is a Professor at the University of Paris-Saclay, affiliated with the Orsay Mathematics Institute and the Probability and Statistics Team. She is a Senior member of the Institut Universitaire de France (IUF) for the period 2020-2025, recognizing her significant contributions to academia. Her primary research interests include: Hidden Markov Models and their theoretical properties Mixture models and nonparametric statistics Deconvolution and inverse problems with unknown noise Machine learning theory with focus on privacy and security Order identification and model selection Professor GASSIAT's recent work shows a strong evolution from classical statistical theory toward modern machine learning challenges, particularly in understanding theoretical limits of learning in complex models and addressing privacy concerns in machine learning systems. Her research spans both theoretical foundations and practical applications across various domains, with a remarkable consistency in methodological rigor throughout her career. She has received notable recognition as a Senior member of the Institut Universitaire de France (2020-2025). Professor GASSIAT is actively involved in teaching, offering courses such as M1 "Statistique" and Mathématiques 2 (Polytech). She has also co-organized the MHC2021 International conference on Mixtures, Hidden Markov models and Clustering with H. Holzmann and P. Vandekerkhove, demonstrating her leadership in the international statistics community.

Pubali Dattaمشاهده پروفایل

استادیار

• Security and Privacy
• Networking and Distributed Systems
• Serverless Cloud Security
• +۵ مورد دیگر

Pubali Datta is an Assistant Professor at the University of Massachusetts Amherst , affiliated with the Manning College of Information & Computer Sciences. She specializes in system security and privacy research, focusing on emerging technologies like serverless computing, IoT, and cloud architectures. Her work bridges technical security challenges with human factors through user studies. PhD in Computer Science (2023) from University of Illinois Urbana-Champaign ME in Computer Science (2013) from Jadavpur University BTech in Computer Science (2011) from West Bengal University of Technology Her research explores core system vulnerabilities obscured by modern abstractions, developing mechanisms for access control , information flow analysis , and auditing . She combines technical analysis with human-centric studies to address security misconfigurations in smart homes and cloud environments. Recent publications focus on serverless security , endpoint protection , and IoT threat modeling , appearing at top venues like IEEE S&P and USENIX Security. Her work has been recognized with a IEEE S&P Distinguished Paper Award (2023) . IEEE S&P Distinguished Paper Award (2023) She collaborates with the Secure & Transparent Systems Laboratory and works on practical solutions for real-world security challenges.

Natalia Sidorovaمشاهده پروفایل

استادیار

• Process Mining
• Petri Nets
• Workflow Analysis
• +۳ مورد دیگر

Natalia Sidorova is an Assistant Professor at the Eindhoven University of Technology , affiliated with the Mathematics and Computer Science department and the Process Analytics research group. She also contributes to the EAISI Health initiative. Her work focuses on process mining, Petri nets, workflow analysis, and healthcare informatics. Research Interests: Dr. Sidorova specializes in formal process modeling, conformance checking, resource-constrained systems, and applications in healthcare and smart environments. Her research bridges theoretical foundations (e.g., soundness verification of workflow nets) with practical tools like log alignment techniques and adaptive workflow systems. She explores data-driven methods for refining process models and analyzing complex event logs. Key Contributions: Her publications cover topics such as log alignment algorithms, resource-aware Petri nets, and stress management systems using sensor data. She co-authored the 2018 Best Paper Award-winning work on intelligent environments and contributed to foundational studies on workflow net soundness undecidability (2008). Teaching: She teaches Foundations of Data Analytics (2023–present). Labs/Teams: Active in Process Analytics and EAISI Health, collaborating on artifact-centric processes and health informatics.

Eric Verbeekمشاهده پروفایل

پژوهشگر

• Process Mining
• Process Models
• Process Discovery
• +۳ مورد دیگر

H.M.W. (Eric) Verbeek is an ICT Developer in the Department of Mathematics and Computer Science at Eindhoven University of Technology, specializing in Process Analytics. His work focuses on advancing process mining techniques, standardization of event data (e.g., IEEE XES), and the development of tools like ProM and CPN IDE. He has contributed to over 160 publications since 1986, with recent research emphasizing process discovery algorithms, workflow net synthesis, and context-rich process modeling. Research Interests include Process Mining, Process Models, Process Discovery, Petri Net Analysis, and Workflow Management. He has actively participated in projects like the GDPR compliance toolkit and has been involved in defining standards for event data interoperability. Notable contributions include the IEEE XES standard for process mining, tools for event log generation and analysis, and methods for decomposed conformance checking. His work bridges theoretical advancements in process modeling with practical tool development, supporting both academia and industry applications. Collaborations span international academia, focusing on process mining workflows and compliance monitoring. His research trends highlight a balance between algorithmic innovation and real-world applicability in areas like healthcare, logistics, and service management.

• Artificial Intelligence
• Explainable AI
• Recurrent Neural Networks
• +۵ مورد دیگر

Isel Grau Garcia is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Information Systems group within the Department of Industrial Engineering and Innovation Sciences. Her research focuses on explainable AI, recurrent neural networks, time-series analysis, and bias mitigation in healthcare and business applications. She holds a Ph.D. in Computer Science from Vrije Universiteit Brussel (VUB) and has co-authored over 75 peer-reviewed publications. Key roles include co-promotor for three PhD students and involvement in the ENFIELD project on trustworthy AI. Education: Ph.D. in Artificial Intelligence, Vrije Universiteit Brussel (2020) M.Sc. in Artificial Intelligence, Central University of Las Villas (2014) B.Sc. in Computer Science, Central University of Las Villas (2011) Research Interests: Her work emphasizes interpretability of machine learning models, semi-supervised classification, and interdisciplinary applications in healthcare. Recent projects include developing Fuzzy Cognitive Map models without training data and quantifying trust in clinical decision support systems. Key Contributions: Her articles explore topics like bias detection via SHAP and FCMs, sparsity-optimized feature importance, and recurrent neural networks for temporal dynamics. These contributions align with UN SDG goals for innovation and health. Awards: Best Demo Award at BNAIC/BENELearn 2021 Industria Students Award – Best Talent (2024) Advising & Grants: Supervises three PhD students and collaborates on the ENFIELD project (€ funding). Active in organizing workshops and reviewing for top venues like ECAI and IEEE Transactions on Fuzzy Systems. Labs/Teams: Engaged in interdisciplinary collaborations with institutions like Warsaw University of Technology and the University of Lisbon, focusing on AI ethics and trustworthy decision systems.

Rupert Kiddleمشاهده پروفایل

عضو هیئت علمی

• Computational Social Science
• Recommender Systems
• Social Media Analysis
• +۳ مورد دیگر

Rupert Kiddle is a Research Associate at the Vrije Universiteit Amsterdam , affiliated with the Faculty of Social Sciences and Humanities and the Network Institute . His work bridges computational methods with communication science, focusing on digital media dynamics. Research Associate, Faculty of Social Sciences and Humanities Communication Science, Network Institute Collaborator with University of Amsterdam and GESIS (Mannheim) His research explores computational approaches to understanding social media systems, including: Modeling online toxicity dynamics using information theory Designing ethical recommender systems for news discovery Analyzing temporal patterns in media consumption and popularity Developing structural analysis frameworks for digital networks Recent publications demonstrate expertise in combining machine learning with communication theory to address societal challenges in digital environments. Collaborations span multiple institutions, emphasizing interdisciplinary computational social science. As an educator, he contributes to: Computational Communication Science I course (University of Amsterdam, 2023) Introduction to Machine Learning for Text Analysis with Python (GESIS, 2024) His work appears in Journalism Studies and Journal of Computational Social Science , with particular emphasis on: Structural network analysis Algorithmic accountability Temporal modeling of online behavior

Bram Vanroyمشاهده پروفایل

پژوهشگر

• Machine Translation
• Computational Linguistics
• Translation Technology
• +۲ مورد دیگر

Bram Vanroy is a Senior Researcher at Ghent University's Language and Translation Technology Team (LT³), specializing in machine translation evaluation and human-translation interaction. His work bridges computational linguistics and practical translation workflows with a focus on real-world applications. His educational background includes: Master's degree in Computational and Formal Linguistics from KU Leuven Advanced Master's degree in Artificial Intelligence from KU Leuven PhD in Language and Translation Technology from Ghent University (2021) Vanroy's research centers on machine translation evaluation methodologies, translation difficulty prediction, and cognitive aspects of human translation processes. He investigates neural machine translation integration in professional workflows, educational adaptations for the neural era, and cross-modal translation challenges including sign language. His work combines corpus linguistics, syntactic analysis, and human-subject studies to develop practical evaluation frameworks. Analysis of his 15 most recent publications reveals strong trends in human-centered MT evaluation, with increasing focus on real-world professional scenarios (2022-2024), sign language translation (SignON project), and zero-shot NLP applications. Key themes include syntactic equivalence metrics, educational adaptation to neural MT, and resource development like the LeConTra learner corpus. He actively contributes to major research initiatives: MATEO (Machine Translation Evaluation Online) - developing open evaluation platforms PreDicT (Predicting Difficulty in Translation) - foundational PhD project SignON - EU-funded sign language translation system development As a core member of LT³, Vanroy collaborates on advancing translation technology through empirical studies, tool development, and interdisciplinary research at the intersection of computational linguistics and translation practice.

• Stochastic Modeling
• Model Checking
• Hybrid Systems
• +۴ مورد دیگر

Dr. Anne K.I. Remke is a Full Professor in the Department of Mathematics of Operations Research at the University of Twente's College of Engineering. Her research focuses on stochastic modeling, hybrid systems, and formal verification techniques, with applications in energy systems, cybersecurity, and healthcare. She contributes to UN Sustainable Development Goals related to affordable and clean energy (SDG7) and industry innovation (SDG9). Her recent work includes developing stochastic hybrid system specifications, intrusion detection systems for smart grids, and parameter estimation methods for battery models. She has collaborated internationally on projects involving energy distribution networks and cybersecurity infrastructure. Remke has supervised 3 PhD students and maintains an active publication record with over 76 research outputs since 2004. Her technical expertise spans model checking algorithms, probabilistic verification, and algorithm design for infinite-state systems. She frequently publishes in peer-reviewed journals like Energy Informatics and conferences such as E-Energy and IEEE CSR, focusing on real-world applications like smart grid communication and energy management optimization.

JEFFREY SEVERINGHAUSمشاهده پروفایل

استاد

• Climate Science
• Geochemistry
• Paleoclimatology
• +۴ مورد دیگر

Jeffrey Severinghaus is a Professor in the Geosciences Research Division, specializing in marine chemistry, geochemistry, and climate sciences. His research focuses on reconstructing past climate changes using ice core analyses, with particular emphasis on isotopic measurements (nitrogen, argon, oxygen) and noble gas proxies to study temperature fluctuations, atmospheric composition, and carbon cycle dynamics. Key areas include abrupt climate shifts, millennial-scale oscillations, and Pleistocene CO2 variations. He has contributed to projects like the Center for Oldest Ice Exploration (COLDEX) and the Rapid Access Ice Drill (RAID), advancing drilling and dating techniques for ancient ice cores. Education : B.A., Oberlin College, Ohio M.A., University of California, Santa Barbara PhD, Columbia University, Lamont-Doherty Earth Observatory Research Interests : Quantifying abrupt temperature changes via ice core isotopes Mechanisms of millennial climate variability Reconstructing atmospheric noble gas composition over millennia Testing methane clathrate degassing hypotheses Ice core chronology using argon and krypton dating Collaborations : Active in international ice core initiatives, including COLDEX for oldest ice exploration and RAID for deep drilling. His work integrates geophysics, geochemistry, and climate modeling. Labs/Teams : Lead researcher in COLDEX, advancing Antarctic ice core site selection and drilling technologies. Collaborates with institutions like Oregon State University on methodological advancements.

Sarah Leggeمشاهده پروفایل

استاد

• Conservation Biology
• Wildlife Ecology
• Fire Ecology
• +۳ مورد دیگر

Sarah Legge is a Professor at the Fenner School of Environment & Society, Australian National University, specializing in conservation biology and wildlife ecology. Her research focuses on understanding threats to biodiversity, particularly the impacts of invasive species, fire regimes, and habitat fragmentation on threatened fauna. Her work integrates field ecology with conservation management, emphasizing solutions for species recovery. Key research areas include predator-prey dynamics, fire ecology, and Indigenous land management practices. Recent projects examine the ecological impacts of feral cats and foxes, and the role of cultural burning in ecosystem health. Legge's publications consistently demonstrate multidisciplinary approaches to conservation, combining empirical data with policy-relevant outcomes. Her research supports evidence-based strategies for mitigating extinction risks across Australian ecosystems. She contributes significantly to large-scale initiatives like the Arid Zone Monitoring Project, leveraging Indigenous ecological knowledge alongside scientific methods. This work advances collaborative conservation models in complex landscapes.

• Mineralogy
• Geometallurgy
• Hyperspectral Imaging
• +۳ مورد دیگر

Cassady Harraden is a postdoctoral research fellow at the Mineral Deposit Research Unit (MDRU) within the Department of Earth, Ocean and Atmospheric Sciences at the University of British Columbia. She holds a PhD in geometallurgy from the University of Tasmania (2018), where she developed methods for geotechnical rock mass and grain size assessment using multi-sensor systems. Her current research focuses on porphyry exploration and vectoring in British Columbia, with emphasis on mineralogy and mineral chemistry derived from LIBS data. Prior to UBC, she spent four years at Corescan advancing hyperspectral data interpretation and geometallurgical/geotechnical applications of automated core logging technology. Her expertise spans analytical techniques like Laser-Induced Breakdown Spectroscopy (LIBS), micro-XRF, and LA-ICP-MS, applied to mineral exploration, orebody characterization, and geotechnical assessments. Key contributions include real-time orebody knowledge via pLIBS, integration of hyperspectral and structural data for robust interpretations, and automated core logging methodologies for deposit characterization. She has published extensively on porphyry systems, geometallurgical domain mapping, and the application of advanced spectroscopic tools in mining geology. Education: PhD in Geometallurgy (University of Tasmania, 2018) Research Themes: LIBS mineralogy, automated core analysis, porphyry exploration, geometallurgical modeling Collaborations: MDRU, Corescan, CODES group (Australia) Her work bridges field-based analytical innovation with practical geotechnical and metallurgical applications, contributing to more efficient mineral resource evaluation strategies.

Chris E. Johnsonمشاهده پروفایل

استاد

• Environmental Chemistry
• Soil Chemistry
• Biogeochemical Processes
• +۳ مورد دیگر

Chris E. Johnson is a Professor at Syracuse University and an Adjunct Professor at Griffith University (Brisbane, Australia). His research focuses on environmental chemistry, particularly soil chemistry and biogeochemical processes in forest ecosystems. He holds a Ph.D. in Geology from the University of Pennsylvania (1989), with additional degrees in Statistics (M.A., 1988) and Civil and Urban Engineering (B.S.E., 1983). His research explores trace metal dynamics in forest soils, effects of logging on soil chemistry, and the role of organic matter in environmental systems. He uses advanced techniques like NMR spectroscopy and chromatography to study natural organic matter. Key projects are based in New York’s Catskills/Adirondacks and New Hampshire’s White Mountains, as well as Queensland’s plantation forests. Notable awards include a Fulbright Scholarship (Czech Republic, 1994) and the Excellence in Graduate Education Award (2012). His work bridges environmental chemistry and ecological sustainability, addressing long-term ecosystem changes and climate impacts. He collaborates internationally and contributes to open-source soil data initiatives like SoDaH.