جست‌وجوی استاد و پژوهشگر

• Automated reasoning
• Formal methods
• Autonomous systems
• +۵ مورد دیگر

Zhe Hou is a Senior Lecturer at the School of Information and Communication Technology , Griffith University, Australia. His academic journey includes a PhD in automated reasoning for separation logic from the Australian National University (2015) and prior research roles at Nanyang Technological University, Singapore (2015-2017). He joined Griffith University in 2017 and became permanent faculty in late 2019. Research Interests : Formal methods for software verification Automated reasoning with logical frameworks Blockchain technology and security Quantum computing verification Integration of LLMs with rigorous reasoning Sports analytics via model checking Recent Publications demonstrate expertise in neural-symbolic reasoning, blockchain security, quantum SAT solvers, and runtime verification frameworks. His work combines formal logic with machine learning for applications in cybersecurity and AI trustworthiness. Scientific Awards : ACM SIGSOFT Distinguished Paper Award (2025) Supervision Roles : Principal/Associate Supervisor for 6+ doctoral projects in blockchain security, AI verification, and network security. Professional Activities : Editor for Springer-Nature and Formal Aspects of Computing special issues, conference chair for ICFEM, ICECCS, and ISACE symposia.

• Environmental Engineering
• Water Resource Management
• Urban Hydrology
• +۳ مورد دیگر

Thomas Ertl is a Professor at the University of Natural Resources and Life Sciences, Vienna (BOKU), leading the Institute of Sanitary Engineering and Water Pollution Control. His research focuses on urban water systems, emphasizing sustainable drainage, climate change adaptation, and nature-based solutions. He has coordinated 55 projects, including international initiatives in Albania and Austria, addressing stormwater management, wastewater heat recovery, and sewer infrastructure resilience. Key Projects : Analysing re-activated watercourses for urban resilience (2024–2028) Resilient rainwater management in Austria (2024–2028) Assessing pollutant emissions in Albania (2024–2027) Research Themes : Building Information Modeling (BIM) in wastewater systems Uncertainty analysis under climate change Stormwater pollution and heat island mitigation Awards : 2018: Goldene Ehrennadel des ÖWAV 1993: Best student award at AGIT 93 His recent publications highlight multidisciplinary approaches to wastewater energy recovery, spatial compatibility of nature-based solutions, and prioritization of stormwater management sites. He has supervised numerous theses and contributed to media discussions on urban climate resilience, including articles in Austrian outlets like futurezone.at and Der Kurier.

• Agricultural Sustainability
• Environmental Impact Assessment
• Animal Behavior and Welfare
• +۳ مورد دیگر

Maurizio Ramanzin is a Full Professor at the University of Padova , affiliated with the School of Animal Science and Department of Agronomy, Animals, Food and Natural Resources (DAFNAE) . His research focuses on Agricultural Sustainability , Environmental Impact Assessment , and Precision Livestock Farming . Academic Field : AGR/19 Email : maurizio.ramanzin@unipd.it Address : Agripolis - Viale dell'università, 16 - Legnaro (Padova) – ITALY His work explores the interactions between livestock systems and ecosystem services in mountainous regions, with emphasis on: Grazing Management and biodiversity conservation Life Cycle Assessment (LCA) of dairy and beef systems Climate Change Adaptation in Alpine ungulates Animal Welfare in small-scale farms Technological Tools (GPS, NIRS) for monitoring grazing behavior Key trends in his recent publications include: Quantifying environmental drivers of wolf predation on livestock Developing low-cost biologging systems for dairy cows Analyzing social-ecological trade-offs in mountain agriculture Assessing microbial dynamics in alpine soils

• Hydrology
• Climate Change Impact Assessment
• Drought Monitoring
• +۲ مورد دیگر

Ehsan Modiri is a researcher at the Department of Hydrosystem Modelling , Helmholtz Centre for Environmental Research (UFZ), Germany. His work focuses on climate change impacts on hydrological systems, drought monitoring, and environmental modeling using advanced computational frameworks. Affiliation: UFZ - Helmholtz Centre for Environmental Research Department: Hydrosystem Modelling Research Themes: Climate Change, Droughts, Hydrological Forecasting, Water Resource Management Research Interests: Modiri specializes in understanding hydrological responses to climate change, particularly in drought dynamics and soil moisture variability. His work bridges observational data with sophisticated modeling frameworks to improve predictability of water balance components under warming scenarios. Scientific Contributions: Recent publications highlight his role in developing high-resolution drought simulations, evaluating hydrological model performance, and analyzing groundwater responses to global warming. He participates in large-scale European hydrological projects and collaborates on climate-hydrology integration initiatives.

Jes Frellsenمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Generative AI
• Deep Learning
• +۵ مورد دیگر

Jes Frellsen is an Associate Professor at the Department of Applied Mathematics and Computer Science (DTU) since 2016. Previously, he held academic positions at the IT University of Copenhagen (2016-2019), postdoctoral roles at University of Cambridge (2013-2016) and University of Copenhagen (2011-2013). Education: PhD in Bioinformatics (2011), University of Copenhagen MSc in Bioinformatics (2007), University of Copenhagen BSc in Mathematics and Computer Science (2005), University of Copenhagen EAP Exchange at University of California, Santa Cruz (2004-2005) Research Focus Jes Frellsen specializes in statistical machine learning , particularly generative AI and deep generative models with applications in bioinformatics . His work integrates Bayesian inference , directional statistics , and Markov chain Monte Carlo methods to address challenges in macromolecular structure prediction and missing data imputation . Recent efforts explore uncertainty quantification in image segmentation and generative modeling for materials science. Advising & Collaborations He actively supervises PhD students and postdoctoral researchers in projects spanning news recommendation systems , medical imaging , and 3D structure generation . Collaborations include work with Zoubin Ghahramani (Cambridge) and Thomas Hamelryck (Copenhagen), with contributions to protein structure prediction and statistical methods in structural bioinformatics .

• Big data analytics
• Data analysis
• Data management
• +۸ مورد دیگر

Daniele Apiletti is an Associate Professor at the Polytechnic University of Turin , affiliated with the Department of Control and Computer Engineering (DAUIN). He serves as a member of the Interdepartmental Center SmartData@PoliTO and acts as Academic Advisor for the Master's degree program in Data Science and Engineering. Research Groups: DBDM - Database and Data Mining Group (DAUIN) ERC Sectors: Algorithms, Artificial Intelligence, Machine Learning, Web and Information Systems Research Interests span Big Data Analytics, Data Science, Machine Learning, Computer Vision, and Quantum Computing. His work focuses on integrating data-driven and theory-guided approaches for heterogeneous data querying, cloud continuum machine learning, and spatio-temporal models for crisis management. Recent Publications highlight trends in medical image segmentation, predictive industrial modeling, and fault-tolerant data systems. Key subfields include AI in healthcare, scalable manufacturing analytics, and vision-language models for game tutorials. Teaching roles include course ownership of Big Data: Architectures and Data Analytics and Internships across multiple academic years. He has collaborated on courses in Data Science, Database Technologies, and Data Management. PhD Students Supervised: Etibar Vazirov (Cloud Continuum Machine Learning) Gabriele Scaffidi Militone (Cloud Storage Microservices) Daniele Rege Cambrin (Spatio-Temporal Ecology Models) Simone Monaco (Theory-Guided Data Science) Research Projects include commercial contracts on: - Natural language querying of corporate research archives - National tourism ecosystem platforms - AI for thermotechnical system design - Machine Learning in clinical trials and supply chains

• Control Systems
• Optimization
• Market Design
• +۴ مورد دیگر

Junjie Qin is an Assistant Professor of Electrical and Computer Engineering at Purdue University’s Elmore Family School of Electrical and Computer Engineering. His research focuses on control systems, optimization, market design, and data analytics applied to power systems and the energy-transportation nexus. He explores challenges in distributed energy resource management, smart grid technologies, and the integration of renewable energy sources. His work addresses issues such as scheduling under limited observability, neural risk-limiting dispatch, and joint optimization of transportation-energy systems through electric vehicle charging strategies. Key research areas include power system stability, inverter-dominated grid dynamics, and machine learning applications in energy systems. He investigates topics like real-time charging control for electric roadways, loss function selection in learning-based optimal power flow, and pricing mechanisms for workplace EV charging. His contributions span theoretical frameworks and practical algorithms, emphasizing data-driven solutions and system-level optimization. While no awards or grants are explicitly listed, his publications reflect a strong focus on advancing smart grid technologies and sustainable energy systems. His advising activities are not detailed here, but his research group likely engages in cutting-edge projects at the intersection of control theory and energy infrastructure.

Anand Padmanabhanمشاهده پروفایل

عضو هیئت علمی

• Advanced Cyberinfrastructure
• CyberGIS
• Geospatial Data Science
• +۵ مورد دیگر

Anand Padmanabhan is a Research Associate Professor in the Department of Geography & Geographic Information Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the School of Earth, Society & Environment within the College of Liberal Arts & Sciences. He holds a Ph.D. in Computer Science from the University of Iowa, alongside an MS in Computer Science (University of Iowa) and a BE in Computer Engineering (University of Mumbai). His research focuses on advanced cyberinfrastructure, cyberGIS, geospatial data science, and high-performance computing. He leads the spatial algorithms and systems team at the CyberGIS Center for Advanced Digital and Spatial Studies, developing cyberGIS capabilities to leverage advanced computing for geospatial innovation. His work emphasizes scalable geocomputation, cloud-based frameworks, and reproducible research environments, with contributions to tools like CyberGIS-Compute and EasyScienceGateway. Recent publications highlight advancements in science gateway frameworks, middleware systems, and geospatial education platforms. He serves as Online MS Program Adviser and has secured NSF and EPA grants for interdisciplinary projects. His work spans transdisciplinary training programs, urban sensing analytics, and integration of social media with geospatial data.

Kaize Dingمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Large Foundation Models
• +۷ مورد دیگر

Kaize Ding is an Assistant Professor in Statistics and Data Science at Northwestern University, leading the REAL Lab and affiliated with the IDEAL Institute. He holds a Ph.D. in Computer Science from Arizona State University (2023) under Prof. Huan Liu, with prior degrees from Beijing University of Posts and Telecommunications. His research focuses on reliable AI systems for autonomous decision-making, knowledge-guided algorithms using GNNs/LLMs, and applications in healthcare, environmental science, and cybersecurity. Collaborations include Google Brain, Microsoft Research, and Amazon Alexa AI. Education: Ph.D. in Computer Science, Arizona State University (2023) M.S. and B.S., Beijing University of Posts and Telecommunications Research Interests: Developing robust AI for decision-making under uncertainty Graph-based machine learning for anomaly detection and network analysis Large language models (LLMs) integrated with domain-specific knowledge Cross-domain applications in healthcare diagnostics, environmental monitoring, and cybersecurity Recent Activities: Received Amazon Research Award and Google Research Grant NeurIPS 2025 Area Chair and ARR Area Chair Postdoc opening in AI4Health for swallowing disorder research Recent publications at AAAI, NeurIPS, EMNLP, and KDD Lab & Team: The REAL Lab focuses on advancing AI through interdisciplinary projects. Current students include Ruiyao Xu (PhD), Qingcheng Zeng (co-advised), and over 10 master's/undergraduate researchers. Alumni have moved to top PhD programs at UVa, UIC, and JHU.

• Survival Data Analysis
• Frailty Models
• Cure Models
• +۳ مورد دیگر

Catherine Legrand serves as Professor of Biostatistics at UCLouvain (Louvain-la-Neuve, Belgium), affiliated with the Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA) and the Louvain School of Statistics, Biostatistics and Actuarial Sciences (LSBA) within the Faculty of Science. She chaired ISBA from 2019-2022 and currently presides over the Louvain Institute for Data Analysis and Modeling (LIDAM). Her academic foundation includes: Master Degree in Mathematics from Université Libre de Bruxelles (1998) PhD in Statistics from Hasselt University (2005) supervised by Prof. Paul Janssen and Prof. Luc Duchateau, specializing in survival analysis and frailty models Her research program centers on Survival Data Analysis with emphasis on frailty models, cure models, and Joint Models for Longitudinal and Survival Data. She develops methodologies for clinical trial design and analysis with direct applications in oncology data, bridging theoretical statistics and clinical practice through collaborations with medical researchers. Analysis of her recent publications (2023-2025) reveals three dominant trends: 1) Advanced survival modeling techniques (semi-Markov, cure rate models) for disease progression; 2) Validation frameworks for surrogate endpoints in clinical trials using joint modeling; 3) Emerging applications in health insurance analytics and public health systems. Her work increasingly addresses real-world healthcare challenges while maintaining methodological rigor. No scientific awards were documented in the available sources. Information regarding student supervision and research grant funding was not provided in the current materials. She leads research within LIDAM and ISBA, building on her prior role as primary statistician for the EORTC Lung Cancer Group where she contributed to multiple Phase II/III trials in lung cancer and mesothelioma. Her current work integrates biostatistical methodology development with practical applications across oncology, public health, and actuarial science domains.

• Smart Cities
• Geovisual Analysis
• Urban Dynamics
• +۵ مورد دیگر

Dr. Gavin McArdle is an Associate Professor at the University College Dublin (UCD) School of Computer Science, specializing in spatial data analysis and smart cities. He holds academic affiliations with the National Centre for Geocomputation (Maynooth University) and CeADAR (Data Analytics Centre). His research focuses on urban dynamics, geovisual analytics, smart transportation, and remote sensing applications. He has received a College of Science Teaching Excellence Award for his contributions to education. McArdle earned his BSc, PhD, and a Prof Dip in University Teaching & Learning from UCD. His work bridges academia and industry through collaborative grants, including those from Science Foundation Ireland and EU funding. Notable projects include the Dublin Dashboard (urban analytics platform) and DubSim (traffic simulation using digital footprints). His research outputs span over 147 publications, with recent work addressing Airbnb's impact on urban gentrification, sustainable mobility, and environmental monitoring via satellite data. He actively contributes to professional committees, including roles in the UCD Data Protection Impact Assessment Committee and international conferences like Web and Wireless GIS. McArdle coordinates courses such as Research Practicum and Computer Programming II, emphasizing practical research and technical skills. His interdisciplinary approach integrates machine learning, spatial statistics, and urban informatics to address real-world challenges in smart cities and environmental sustainability.

Andreas Maierمشاهده پروفایل

پژوهشگر

• Drug Repurposing
• Network Medicine
• Bioinformatics
• +۳ مورد دیگر

Andreas Maier is a Researcher at the University of Hamburg's Faculty of Mathematics, Informatics and Natural Sciences, affiliated with the Computational Systems Biology department. He began his PhD in May 2021 with Cosy.Bio (Center for Systems Biology) at UHH, focusing on drug repurposing projects such as REPO-TRIAL. Previously, he completed a Bioinformatics master's thesis at TUM (Technical University of Munich), developing a web application for analyzing molecular disease networks. His research interests emphasize network medicine, drug repurposing, and computational tools for biomedical discovery. He has contributed to platforms like NeDRex-Web, Drugst.One, and BioCypher, which democratize access to systems medicine workflows. His work bridges heterogeneous data integration, federated learning for rare diseases, and quantum computing applications in genetics. Maier's publications highlight innovations in knowledge graph-based drug discovery, privacy-preserving federated learning, and single-cell network analysis. He actively develops open-source bioinformatics tools to address challenges in disease module identification and patient stratification. His projects align with the REPO4EU consortium and other collaborative initiatives in translational bioinformatics.

Wei Dingمشاهده پروفایل

استاد

• Knowledge Discovery
• Data Mining
• Machine Learning
• +۷ مورد دیگر

Wei Ding is a Professor in the Department of Computer Science at the University of Massachusetts Boston (UMass Boston). She earned her Ph.D. in Computer Science from the University of Houston in 2008. From 2019 to 2023, she served as a Program Director at the National Science Foundation's Division of Information and Intelligent Systems (IIS), overseeing programs in Information Integration, Smart Health, Deep Learning Foundations, and Scalable Systems. Her research integrates knowledge discovery, data mining, and machine learning with applications spanning health sciences, astronomy, geosciences, and environmental sciences. She employs advanced techniques like spatio-temporal modeling, deep neural networks, and semantic analysis to address complex real-world problems such as disease subtyping, physical activity prediction, and environmental forecasting. Her work emphasizes interdisciplinary collaboration and societal impact. Analysis of her recent publications reveals a focus on AI-driven healthcare solutions (e.g., neuroimaging biomarkers, disorder diagnosis), fundamental ML advancements (e.g., generalization, GAN stability), and cross-domain applications (e.g., climate forecasting, animal behavior analysis). Recurring themes include low-data learning, interpretability, and scalable algorithms. Awards & Honors: IEEE Fellow (2023) NSF Director's Award (2022) WISAY Distinguished Woman in Science Award, Yale University (2019) AI for Earth Award (2018) Best Paper Awards (ICTAI 2011, ICCI 2010) Advising & Grants: She mentors PhD and Master’s students in the Knowledge Discovery Lab (KDLab), with alumni at institutions like Facebook, Google, and McKinsey. Her research is funded by NSF, NIH, NASA, and DOE, including: NIH R01: Predicting youth physical activity (2016) NSF EAGER: Machine learning for cancer subtyping (2017) NIH R01: Accelerometer/gyroscope data for activity estimation (2022) Leadership: She directs the KDLab and co-founded the Women in Sciences Club (WINS). She serves as Associate Editor for ACM TKDD, TIST, and KAIS journals.

Michael Gallaugherمشاهده پروفایل

استادیار

• Clustering and Classification
• Machine Learning
• Matrix and Tensor Variate Data Analysis
• +۵ مورد دیگر

Dr. Michael Gallaugher is an Assistant Professor of Statistical Science at Baylor University. He holds a Ph.D., M.S., and B.S. in Statistics from McMaster University. His research focuses on clustering and classification methodologies, particularly in matrix/tensor variate data, mixed-type data, clickstream analysis, and outlier detection. He has been recognized with prestigious awards including the Vanier Canada Graduate Scholarship and the Banting Postdoctoral Fellowship. Education: Ph.D., Statistics, McMaster University (2020) M.S., Statistics, McMaster University (2017) B.S., Statistics, McMaster University (2015) Research Interests: Dr. Gallaugher's work emphasizes advanced clustering techniques for complex data structures, including high-dimensional datasets, clickstream behavior analysis, and skewed distribution modeling. His contributions span statistical methodology development and applications in fields like bioinformatics and sports science. His recent work explores hidden Markov models for time series and robust co-clustering algorithms. Publications Trends: His publications reflect a strong focus on matrix-variate distributions, skewed data modeling, and algorithmic innovation in clustering. Recent work (2022-2025) highlights advancements in contaminated normal mixtures, co-clustering for high-dimensional data, and spatial regression models. Awards: Vanier Canada Graduate Scholarship Banting Postdoctoral Fellowship Advising & Grants: While no advisees are listed, his research has been supported by grants from the Natural Sciences and Engineering Research Council of Canada. He contributes to statistical consulting services at Baylor and collaborates internationally on methodological projects. Labs & Teams: He is affiliated with Baylor's Department of Statistical Science and collaborates with research groups focused on machine learning and statistical computing.

İpek İmamoğluمشاهده پروفایل

استاد

• Environmental Chemistry
• Organic Compound Fate
• Microplastic-Organic Interactions
• +۵ مورد دیگر

Prof. İpek İmamoğlu is a Professor of Environmental Engineering at Middle East Technical University (METU), Ankara, Turkey, specializing in environmental chemistry and pollutant fate. She holds leadership roles, including former Vice Dean of the Faculty of Engineering (2012-2015) and Vice Chair of the Environmental Engineering Department (2009-2012). Her research focuses on organic compound fate, microplastic interactions, contaminated site management, and receptor modeling for pollutants like PCBs and PBDEs. Education: PhD from University of Wisconsin-Milwaukee (2001), M.S. from University of Newcastle upon Tyne (1996), and B.S. from METU (1995). Awards include the METU Ph.D. Dissertation Supervision Award (2017, 2010) and the British Chevening Scholarship (1995/96). She has supervised over 20 PhD and MS students in environmental chemistry and engineering. Research spans contaminated sediment remediation, PCB/PBDE degradation pathways, and policy impacts of microplastics. Her projects include TUBITAK-funded studies on microplastic-organic interactions and POPs management. She advises on national policies, including Turkey’s implementation of the Stockholm Convention on POPs. Key Projects: Molecular Level Microplastic-Organic Interactions (TUBITAK 2021), PCB Fate in Aquatic Sediments (TUBITAK 2015), and Zeolite-Wastewater Treatment (METU Funded 2002). Awards: UWM Dissertator Fellowship, NATO Travel Grant, and ISO 17025 Auditor Certification. Her publications (over 80) cover environmental forensics, receptor modeling, and bioremediation. She actively participates in international conferences and serves as a consultant for UNDP and EU-funded initiatives.