Find Academic Supervisors

• Temperature Distribution
• Himalayas
• Last Glacial
• +9 more

Jean Braun is affiliated with the Institut des Sciences de la Terre (ISTerre) in Grenoble, France. Their research spans multiple domains in Earth sciences, including thermochronology , landscape evolution modeling , and glacial-isostatic adjustment . Key contributions involve inverse modeling techniques for reconstructing topographic history, quantifying exhumation rates, and analyzing the interplay between climate and tectonic processes. Research Interests: Temperature distribution, Himalayan tectonics, passive margin escarpments, and climate-driven geomorphic processes. Jean Braun’s recent work emphasizes the impact of glacial isostatic adjustment on river dynamics, as seen in studies of the U.S. mid-Atlantic coast and the Yellow River Delta. Their methodological innovations include the CLICHE and FastScape models, which integrate climate variability and efficient algorithms for solving stream power equations. Key findings highlight the longevity of passive margin escarpments due to flexural rebound and the role of rock thermal conductivity in refining exhumation rate estimates. Notable publications span journals like Earth and Planetary Science Letters , Geomorphology , and Tectonophysics , with a focus on inverse modeling, thermochronological data analysis, and the tectonic evolution of regions such as Scandinavia, the Himalayas, and the Namibian margin. Collaborative efforts with researchers like T. Pico, K. Mezger, and P.A. Van Der Beek underscore interdisciplinary approaches to understanding Earth’s surface processes.

Dirk ScherlerView profile

Professor

• geomorphology
• tectonics
• Cosmogenic nuclides
• +5 more

Prof. Dr. Dirk Scherler is a Professor for Cosmogenic Nuclides jointly appointed at the Free University of Berlin and the German Research Centre for Geosciences (GFZ) in Potsdam. His research focuses on Earth's surface processes, landscape development, and Himalayan glaciers. He leads the research group in the Department of Geosciences within the Faculty of Mathematics and Natural Sciences, specializing in geomorphology, tectonics, and glaciology using cosmogenic nuclide techniques and remote sensing methodologies. Dr. Scherler received his Doctorate in Geology from the University of Potsdam in 2010 and completed his Diploma in Geography at Humboldt University of Berlin in 2005. His academic career includes a postdoctoral position at the California Institute of Technology (2012-2014) as an Alexander von Humboldt fellow, followed by another postdoc at the University of Potsdam (2010-2012). From 2014 to 2022, he served as Junior Professor for Cosmogenic Nuclides before being promoted to full Professor in April 2022. His research interests encompass geomorphology, tectonics, cosmogenic nuclides, glaciology, and remote sensing. Dr. Scherler specializes in understanding the dynamics of Earth's surface processes, particularly focusing on rivers, slopes, and glaciers. His work examines landscape development processes across various spatial and temporal scales, with significant emphasis on Himalayan glaciers where he investigates erosion processes, sediment transport, and climate-glacier interactions using advanced analytical techniques. Dr. Scherler's extensive publication record demonstrates expertise across geomorphology, glaciology, and tectonics, with research spanning diverse geographical regions including the Himalayas, Swiss Alps, Andes, Albanides, and Chilean Coastal Cordillera. His methodological approach combines field measurements, cosmogenic nuclide dating, remote sensing analysis, and numerical modeling to address fundamental questions about landscape evolution and surface processes. Dr. Scherler currently leads several significant research projects including 'TopoToolbox 3' (improving terrain analysis software, funded by DFG), 'Dynamics of erosion and vegetation in the Western Mediterranean Region' (funded by Freie Universität Berlin), and 'Glacial and erosional contributions to Late Quaternary uplift of the European Alps' (funded by DFG). His previous projects include the ERC Starting Grant 'Climate sensitivity of glacial landscape dynamics (COLD)' and various DFG-funded research initiatives investigating erosion processes and landscape evolution. At GFZ Potsdam, Dr. Scherler is affiliated with the Department of Geochemistry (formerly Earth Surface Geochemistry) where he conducts laboratory work focused on cosmogenic nuclide analysis. His research group collaborates extensively with international partners and contributes significantly to advancing our understanding of landscape evolution processes across different geological timescales.

Zhenman FangView profile

Associate Professor

• Hardware Acceleration
• Computer Architecture
• Big Data Computing
• +7 more

Dr. Zhenman Fang is an Associate Professor in the School of Engineering Science (Computer Engineering Option) and Associate Member in the School of Computing Science at Simon Fraser University, Canada. He founded and directs the HiAccel Lab, focusing on accelerator-rich architectures. His PhD (2014) is from Fudan University, China, with 15 months spent at the University of Minnesota. Prior to SFU, he was a Staff Software Engineer at Xilinx (2017-2019) and a postdoc at UCLA (2014-2017). His research spans: Hardware acceleration for ML, big data, genomics, and HPC FPGA-based customizable computing and near-data processing Compiler/runtime systems for heterogeneous platforms Performance/reliability optimization of accelerator-rich systems His recent publications (2024-2025) focus on FPGA acceleration for machine learning (e.g., on-device training, quantization), computational chemistry, image/video compression, database systems, and reconfigurable computing, demonstrating cross-domain applications of specialized hardware. Awards & Honors: Best Paper Awards: FPL 2024, MEMSYS 2017, TCAD 2019 Best Paper Nominations: ICCAD 2025, FCCM 2025, HPCA 2017, ISPASS 2018 SFU Research Excellence Horizon Award (2025) NSERC Alliance, CFI JELF, and Xilinx University Awards He advises 20+ PhD/Master's students in HiAccel Lab, focusing on accelerator design. Major grants include NSERC Alliance (2020) and CFI JELF (2019). The lab operates a 10-node cluster with FPGA/GPU infrastructure.

Kambis VeschginiView profile

Professor

• Stream Processing
• Algorithms
• Data Streams
• +3 more

Professor Kambis Veschgini serves in the Faculty of Industrial Engineering and Healthcare at Amberg-Weiden University of Applied Sciences, based at the Weidener Technologie-Campus (WTC) in Room 1.26, Weiden. His responsibilities span Mathematics and Informatics instruction with flexible office hours available by appointment. His research specializes in efficient continuous data stream processing , focusing on algorithmic optimization and system architectures for real-time analytics. He leads the development of JSONJet , a high-performance stream processing engine designed to handle complex data workflows in industrial and healthcare contexts. This work bridges theoretical computer science with practical engineering applications in resource-constrained environments. Contact is maintained via phone (+49 (961) 382-1635), fax (+49 (961) 382-2635), and email for academic collaboration and student advisement.

• Artificial Intelligence
• Computer Vision
• Data Science
• +4 more

Dr. Hongda Tian is a Senior Lecturer at the University of Technology Sydney's Data Science Institute within the Faculty of Engineering and Information Technology. With a strong background in AI and data science, he focuses on translating research into practical solutions for real-world problems across multiple sectors including water, transport, energy, and retail. BSc and MEn from Beijing University of Posts and Telecommunications (2006, 2009) PhD from University of Wollongong, Australia (2015) Postdoctoral Fellow with DATA61 | CSIRO Computer Vision Scientist with Kandao Australia Pty Ltd Associate Research Fellow with University of Wollongong Dr. Tian's research spans artificial intelligence, computer vision, data science, and machine learning with a focus on practical applications. His work combines theoretical advancements with industry implementation, particularly in environmental sustainability, water management, and infrastructure monitoring. He excels at transforming real-world issues into data science problems and developing appropriate solutions that demonstrate industrial applicability to stakeholders. His publication record shows a clear trend toward applying AI to critical infrastructure and environmental challenges, with recent work focusing on water quality prediction, electricity price forecasting, carbon intensity modeling, and bushfire smoke detection. These publications appear in top journals including International Journal of Computer Vision, IEEE Transactions on Image Processing, and IEEE Transactions on Multimedia. 2025 The Australian Financial Review AI Awards (Sustainability category) 2024 NSW iAwards Winner for Sustainability & Environmental Solution 2024 NSW Merit iAwards for Government & Public Sector Solution 2024 Distilling Research Impact 2023 NSW Merit iAwards for Sustainability & Environmental Solution Chinese Government Award for Outstanding Self-financed Students Abroad Dr. Tian has secured approximately $1.07 million in external research funding as Chief Investigator since 2020 and has led or delivered over 15 research innovation projects with government and industry partners. His projects span multiple sectors including water (Dynamic Prediction of Raw Water Quality, Water Quality Prediction for Drinking Water Delivery Systems), transport (Structural Health Monitoring for Sydney Harbor Bridge, Computer Vision-Based Track Defect Detection), energy (Electrical Network-Related Incidents Prediction), and retail (Woolworths Endcap Compliance). He serves on thesis examination committees and as an editorial board member and reviewer for over 20 peer-reviewed journals and conferences.

Ann SteinerView profile

Professor

• Book History
• Publishing Studies
• Sociology of Literature
• +6 more

Ann Steiner is Professor of Sociology of Literature at Uppsala University's Department of Literature and Rhetoric within the Faculty of Languages. She serves as head of the Sociology of Literature section and maintains active research in publishing systems and literary culture. Her research examines literature's societal role through studies of publishing economics, literary value systems, and digital transformations. Key interests include: Global book market structures and digital disruption Audiobook evolution and children's digital literature Bestseller dynamics and cultural valuation processes Historical publishing trade development Recent publications reveal a strong focus on digital publishing transitions , particularly audiobook markets and streaming services' impact on literary consumption. Her work combines historical analysis with contemporary market research, emphasizing Nordic publishing contexts while addressing global industry trends. Professor Steiner actively contributes to academic discourse through editorial work, including the 2024 collection Litteratursociologi i nytt ljus , and maintains significant industry engagement through projects like the annual Bokbarometern market analysis. Her research methodology blends Large-scale data analysis of consumption patterns Qualitative industry discourse examination Historical publishing trade documentation Cross-media production studies with particular attention to children's literature and gender dynamics in contemporary fiction.

Simone OrcioniView profile

Associate Professor

• Sensor Systems
• Biomedical Engineering
• Signal Processing
• +5 more

Prof. Simone Orcioni is an Associate Professor at the Department of Information Engineering, Università Politecnica delle Marche. His research focuses on sensor systems, biomedical engineering, nonlinear signal processing, and embedded systems. He leads projects in structural health monitoring, battery management, and telemedicine solutions. Orcioni's work integrates hardware design, algorithm development, and data analytics to address challenges in infrastructure maintenance, healthcare, and sustainable energy. Education: Not explicitly listed, but his role as an associate professor implies advanced academic credentials in electrical or biomedical engineering. Research interests include wearable sensors for healthcare monitoring, IoT-enabled environmental systems, and stochastic modeling of nonlinear processes. He collaborates on interdisciplinary projects involving robotics, circular economy, and smart infrastructure. His publications span journals and conferences in signal processing, biomedical engineering, and electronics. Notable areas include electrochemical impedance spectroscopy, sleep monitoring systems, and low-cost sensor networks. Orcioni's work emphasizes practical applications, such as non-invasive vital sign detection and sustainable battery recycling processes. Awards: None explicitly mentioned. Grants and collaborations are implied through his research projects but not detailed in the text. He contributes to academic governance via his teaching and administrative roles at the university. Labs/Teams: Affiliated with the Department of Information Engineering's research groups focused on embedded systems, biomedical instrumentation, and nonlinear signal analysis. Collaborates with industry partners on projects like smart EV charging systems and telemedicine platforms.

Dirk Van den PoelView profile

Professor

• Data Analytics
• Streaming Processing Frameworks & Analytics
• Internet of Things (IoT) & Industry 4.0
• +2 more

Dirk Van den Poel is a Professor of Data Analytics/Big Data at Ghent University, Belgium, specializing in statistical computing, social media analytics, and predictive modeling. He has co-founded pioneering academic programs including the first predictive analytics master’s program globally, the Master of Science in Statistical Data Analysis, and the Master of Science in Business Engineering/Data Analytics. Teaches courses: Statistical Computing, Big Data, Social Media Analytics Research focus: Analytical CRM, ensemble classification methods, big data 110+ ISI-indexed publications in journals like IEEE Transactions on Power Systems and Decision Support Systems His recent work explores topics such as rumor detection in social media, predictive maintenance using sensor data, and crisis response through service adaptation. He leverages machine learning and deep learning for applications in agriculture, healthcare, and energy sectors. Current projects involve topic modeling (BERTopic), credit scoring with interpretable models, and spatiotemporal crime prediction using mobile phone data. Dirk’s methodology integrates stream processing frameworks , reinforcement learning , and ensemble techniques .

• Biomedical Engineering
• Medical Informatics
• Machine Learning
• +4 more

Bobak Jack Mortazavi is an Associate Professor in the Department of Cardiovascular Medicine at Yale School of Medicine, affiliated with the Center for Outcomes Research & Evaluation (CORE). He holds a PhD in Computer Science from UCLA and focuses on applying machine learning and embedded systems to clinical data analysis. His research spans wearable sensor development, predictive modeling in cardiology, and AI-driven healthcare solutions. Education: B.A. Applied Mathematics & B.S. Electrical Engineering/Computer Science (UC Berkeley, 2007); M.S. Electrical Engineering (UC Irvine, 2009); Ph.D. Computer Science (UCLA, 2014). Research interests include medical data analytics, cardiovascular AI applications, and wearable sensor frameworks for clinical monitoring. He has published widely on topics like ECG analysis, glucose prediction algorithms, and telehealth systems. Recent work highlights include developing the Pulse2AI framework for clinical sensor data standardization and studies on hypertension management using AI. Awards include the 2012 IEEE Best Demonstration Award for BSN research. Labs/Teams: Active member of Yale's CORE and collaborates with teams in cardiovascular informatics. Engages in peer review for journals like IEEE Transactions and serves on conference review committees.

Hillol KarguptaView profile

Professor

• Distributed Data Mining
• Ubiquitous Data Mining
• Privacy-Preserving Data Mining
• +7 more

Professor Hillol Kargupta is a faculty member in the Department of Computer Science and Electrical Engineering at the University of Maryland Baltimore County. He received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 1996 and co-founded AGNIK LLC, a ubiquitous data analytics company. His research spans distributed and ubiquitous data mining, sensor networks, privacy-preserving techniques, sustainable computing, and security applications. Research Interests : Mobile/distributed data mining, vehicular sensor networks, peer-to-peer systems, privacy-preserving algorithms, data stream analysis, and societal impacts of knowledge discovery Education : Ph.D. in Computer Science (1996), University of Illinois at Urbana-Champaign His work addresses privacy-preserving data mining with random perturbation techniques, distributed eigen-monitoring for astronomy data streams, and asynchronous P2P algorithms for clustering/classification. Recent publications focus on greenhouse emissions analysis , scalable outlier detection , and secure distributed inferencing . Scientific awards include: 2013 Frost & Sullivan Award (Agnik LLC) 2010 IEEE Fellow Top-10 Data Mining Case Study (ICDM'2010) 2008 IBM Innovation Award 2007 NSF Next Generation Data Mining Symposium 2003 IEEE Data Mining Best Paper Award

• Decision Agriculture
• Sustainable Agricultural Systems
• Data Analytics in Agriculture
• +3 more

Dr. Matthew Knowling is a Senior Lecturer and Research Program Lead in Decision Agriculture at the University of Adelaide's School of Agriculture, Food and Wine, within the Faculty of Sciences, Engineering and Technology. His work focuses on developing decision support tools that integrate data from agronomy, hydrology, economics, and engineering to address agricultural sustainability challenges. He leads the $7.6M GRDC Farming Systems South program, evaluating profitability and sustainability in southern Australian farming systems, and previously directed the VitiVisor project, enhancing vineyard management through data assimilation and forecasting. He is eligible to supervise Master's and PhD students in agricultural systems analysis. Research Interests: Decision Agriculture, sustainable resource management, process-based modeling, data assimilation, climate risk assessment, and interdisciplinary approaches to agricultural decision-making. His work emphasizes translating complex data streams into actionable strategies for farmers, businesses, and policymakers. Grants & Programs: GRDC Farming Systems South ($7.6M): Evaluates strategic and tactical decisions in water-limited farming systems, focusing on profitability, sustainability, and emerging practices. VitiVisor ($5M): Developed decision support tools for Riverland vineyards, integrating sensors, financial benchmarks, and grapevine dynamics models. Labs & Teams: Part of the Decision Agriculture team at the Waite Campus, collaborating with multidisciplinary groups to advance precision agriculture and sustainable practices.

Giri Kumar TayiView profile

Professor

• Operations Management
• Information Systems
• Operations Research
• +10 more

Giri Kumar Tayi is a Professor of Management Science and Information Systems at the State University of New York at Albany. His expertise spans interdisciplinary research areas including Operations Management, Information Systems, and Operations Research with a focus on E-Commerce, Supply Chain Optimization, Data Quality, Digital Government, and Software Process Diversity. He holds a Ph.D. from Carnegie Mellon University. Dr. Tayi's research focuses on strategic challenges in digital ecosystems, such as omni-channel retailing dynamics, information security governance, and sustainable consumption practices. He has pioneered studies in consumer behavior in online platforms, supply chain transparency through certification systems, and the application of ontologies in data architecture design for ethical consumption. With over 60 refereed journal articles and 20+ conference proceedings, his work appears in top-tier journals like Operations Research , Management Science , and Information Systems Research . He has held editorial roles at 10+ journals including Information Systems Research and IEEE Intelligent Systems . His research has been supported by NSF and industry collaborations. Key contributions include frameworks for data quality management in clinical trials, ontological modeling for certification systems, and strategic analyses of digital platforms. His current projects explore blockchain applications in supply chain governance and AI-driven decision support systems for sustainable operations.

• Comparative Neuroanatomy
• Neuroimaging
• Evolutionary Neuroscience
• +4 more

Rogier Mars is a Professor of Neurosciences and MRC Senior Non-clinical Fellow at the University of Oxford, affiliated with the Nuffield Department of Clinical Neurosciences (NDCN) and St Hilda's College. His research focuses on understanding how brain structure and function evolve in response to environmental challenges, combining human neuroimaging techniques (e.g., fMRI, TMS) with comparative analyses of post-mortem animal brains. Key projects include cross-species tractography, cortical surface reconstruction pipelines, and evolutionary studies of language-related neural infrastructure. Research interests emphasize structural connectomics, neuroecology, and primate brain evolution. His work bridges human and non-human primate neuroanatomy, exploring adaptations in brain organization across species. Recent publications highlight innovations in imaging techniques like hybrid MRI-microscopy and computational tools for multi-species brain comparisons. Scientific awards include the prestigious MRC Senior Non-clinical Fellowship. He leads the Neuroecology & Anatomy Lab and the Preclinical Imaging Group, advancing open-access resources such as the Digital Brain Bank for post-mortem datasets. Collaborative efforts prioritize multi-center initiatives to standardize protocols for neuroimaging and comparative neuroscience.

Paul PangView profile

Associate Professor

• Cyber Security
• Digital Health
• Cognitive Cyber Security Intelligence
• +6 more

Dr. Paul Pang is an Associate Professor of Cyber Security at Federation University Australia's Institute of Innovation, Science and Sustainability (IISS). Previously, he held a Professorship in Data Analytics and directed the Center for Computational Intelligence for Cybersecurity at Unitec Institute of Technology, New Zealand. His research focuses on Cognitive Cyber Security Intelligence, Cyber Resilience, and Applied Data Analytics for Digital Health, leveraging over NZD$3.5M in grants from organizations like MBIE and HRC. He has authored over 100 refereed articles and holds 1 patent with 3 pending applications. Education: PhD in Engineering from Shanghai Jiao Tong University (2000). Research Interests: Pang's work spans cybersecurity, digital health, and big data analytics. He develops intelligent systems for cyber threat detection, resilience frameworks, and healthcare applications. His methods include machine learning for data streams, incremental learning algorithms, and multi-agent cooperative systems. Publications Trends: His articles emphasize cybersecurity (e.g., darknet traffic analysis, malware grouping), healthcare analytics (e.g., schizophrenia prediction), and foundational machine learning (e.g., SVMs, LDA algorithms). Collaborations span institutions in Japan, China, and the U.S. Awards: IEEE Best Paper Awards (2003, 2008), Unitec Excellence Awards (2011-2015), ARC Assessor (Australia). Advising & Grants: Supervised 10+ PhD/Masters students; led 13 grants totaling NZD$3.5M. Notable students include Snjezana Soltic (Pest Survival Analysis) and Simon Dacey (Land Use Management). Professional Roles: IEEE Senior Member, Vice President of APNNS, Global AI Summit Judge (2018). Labs/Teams: Active in Federation University's Cybersecurity and Digital Health research groups. Collaborates with industry partners like NICT Japan and Telecom NZ.

• Ontology
• Semantic Web
• IoT
• +7 more

Stijn Verstichel is a Postdoctoral Researcher at Ghent University within the Faculty of Engineering and Architecture and the Department of Information Technology (EA05) . He is affiliated with IMEC , focusing on ontology-driven solutions for context-aware systems in healthcare, IoT, and railway domains. Research Interests: Ontology, semantic web technologies, IoT, eHealth, context-aware systems, and machine learning. Recent Trends: His 15 most recent articles emphasize semantic frameworks for IoT, predictive maintenance dashboards, time-series data on Solid pods, and ontology-enabled healthcare platforms. Scientific Awards: No awards explicitly mentioned in the provided data.