جست‌وجوی استاد و پژوهشگر

Amael Poulainمشاهده پروفایل

پژوهشگر

• Vadose Zone Hydrology
• Groundwater Recharge
• Dye Tracer Techniques
• +۴ مورد دیگر

Amael Poulain is a hydrogeology researcher at the University of Namur specializing in karst systems and groundwater dynamics. With a PhD completed in 2017 under supervisor Hallet V., Poulain has established a strong research profile focusing on vadose zone processes, tracer testing, and hydrological monitoring in karst environments. Their work combines field measurements with computational modeling to understand complex groundwater systems in Belgian karst regions. PhD in Geology (2017), University of Namur Principal Investigator on 6 research projects (2017-2023) 16 research outputs including journal articles and conference contributions Recipient of Young Karst Researcher Prize (2015) Active contributor to international karst research conferences Poulain's research focuses on understanding groundwater recharge processes in karst systems through innovative monitoring techniques. Their work examines solute transport, breakthrough curve analysis, and the impact of flash flood events on groundwater systems. Key contributions include developing ultra-portable fluorometry for dye tracing in remote karst environments and investigating the relationship between surface water features and underground flow paths in Belgian karst regions. Analysis of Poulain's 16 research outputs reveals consistent focus on experimental hydrogeology in karst environments, particularly using tracer tests and novel monitoring technologies. The research spans vadose zone processes, breakthrough curve analysis, and the development of field instrumentation for hydrological monitoring. Recent work (2020-2023) shows increasing emphasis on technological applications including fluorimeter industrialization and gravity monitoring techniques. Young Karst Researcher Prize (2015) awarded at conference in Birmingham, UK Recognition for contributions to groundwater research and karst hydrology Active participation in international karst research community Poulain leads multiple research projects including STREAM fluorimeter industrialization and eco-village construction expertise. Their work bridges academic research and practical applications through spin-off development for hydrological monitoring equipment. As Principal Investigator on projects like 'Expertise of tracing test at the ponds of Villeneuve,' Poulain demonstrates strong grant acquisition and project management capabilities. The research involves collaboration with multiple institutions across Belgium's karst regions. Poulain's laboratory work focuses on hydrological monitoring in karst environments, particularly through the STREAM project which develops fluorometric solutions for groundwater monitoring. Their research utilizes cave percolation monitoring, gravity measurements, and electrical resistivity tomography to study water movement through vadose zones. The work is conducted primarily in Belgian karst regions including Rochefort and Furfooz, with emphasis on practical applications for water resource management.

Roie Levinمشاهده پروفایل

استادیار

• Approximation Algorithms
• Online Algorithms
• Submodular Functions
• +۵ مورد دیگر

Roie Levin is an Assistant Professor at Rutgers University's Department of Computer Science. He received his PhD in Algorithms, Combinatorics and Optimization from Carnegie Mellon University in 2022, advised by Anupam Gupta. Prior to that, he worked at the Allen Institute for Artificial Intelligence (2015-2017) and earned dual BSc degrees in Computer Science/Applied Mathematics and Mathematics from Brown University (2015). Before joining Rutgers, he was a Fulbright Postdoctoral Fellow at Tel Aviv University under Niv Buchbinder. Current Role: Assistant Professor in Computer Science Academic Training: PhD (2022) CMU, BSc (2015) Brown University Postdoctoral: Fulbright Fellow at Tel Aviv University Levin's research focuses on approximation algorithms for uncertain environments (online/dynamic/streaming models) and submodular function optimization. His work spans theoretical foundations and practical implementations across distributed systems, geometric constraints, and reinforcement learning paradigms. Teaching includes graduate and undergraduate algorithms courses (CS 344, CS 513) with emphasis on problem-solving techniques, computational complexity, and modern algorithmic trends. His publications showcase expertise in online algorithms, submodular optimization, and approximation theory with applications in clustering, caching, and machine learning. The 2025 articles demonstrate continued exploration of online consistency and contention resolution, while 2023-2024 works focus on submodular optimization under uncertainty and dynamic environments. Earlier publications (2015-2017) cover semantic parsing, geometric approximation, and planar graph optimization. Fulbright Postdoctoral Fellow Levin's research connects theoretical guarantees with practical implementations, bridging classical algorithm design with modern machine learning applications. His recent work explores primal-dual methods in online settings and robust subspace approximation techniques for streaming data environments.

Vitaveska Lanfranchiمشاهده پروفایل

پژوهشگر ارشد

• Human-Computer Interaction
• Social Media
• Visual Analytics
• +۳ مورد دیگر

Vitaveska Lanfranchi is a Senior Research Fellow at the University of Sheffield , School of Computer Science, focusing on Human-Computer Interaction , Social Media , and Visual Analytics . Her work addresses real-time data analysis for emergency response and knowledge management, with projects funded by EPSRC, UK/SBRI, and EU Framework 6. Research Interests : Human-Computer Interaction Social Media for Emergency Response Visual Analytics Knowledge Management Mobile Interaction Recent publications (2008–2012) explore semantic user networks, knowledge dashboards, hybrid search techniques, and AI usability, particularly in aerospace engineering and emergency response. Projects like Randms and WeKnowIt emphasize collective intelligence and web-scale data analysis. Contact : v.lanfranchi@dcs.shef.ac.uk

Larry P Heckمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۷ مورد دیگر

Larry P. Heck is a Professor with a joint appointment in the School of Electrical and Computer Engineering and School of Interactive Computing at the Georgia Institute of Technology. He holds the Rhesa S. Farmer Advanced Computing Concepts Chair and is a Georgia Research Alliance Eminent Scholar . Education: BSEE, Texas Tech University (1986) MSEE, Georgia Institute of Technology (1989) PhD EE, Georgia Institute of Technology (1991) His research focuses on conversational AI , dialogue systems , and machine learning applied to natural language processing and speech recognition . He pioneered early industrial applications of deep learning in speech processing and has contributed to advancements in multimodal interaction, knowledge distillation, and real-time question answering systems. Recent publications emphasize moral reasoning in AI , multimodal dialogue , and large-scale dataset creation for conversational systems. His work bridges language modeling , sensor fusion , and ethical AI through innovations in contextual reasoning and interface masking. Scientific Distinctions: IEEE Fellow (2020) IEEE Signal Processing Society Best Paper Award Academy of Distinguished Engineering Alumni, Georgia Tech (2017) Distinguished Engineer Award, Texas Tech University (2017) Fellow, National Academy of Inventors (2025) He has secured significant funding from DARPA and NSA for speaker recognition systems and has led cutting-edge research at institutions including Microsoft, Google, and Samsung. His lab focuses on conversational systems and deep learning for speech and multimodal data.

• Marine Ecology
• Fisheries Science
• Oceanography
• +۷ مورد دیگر

Professor Iain Suthers is a Professor at the University of New South Wales in the School of Biological, Earth & Environmental Sciences . He is based at the Sydney Institute of Marine Science , a collaborative hub involving Sydney-based universities and government agencies. His career spans 34 years at UNSW since 1991, following a PhD in Canada and postdoctoral work in Norway and Australia. Key Research Themes Marine ecosystem dynamics influenced by the East Australian Current Larval fish ecology and connectivity Zooplankton size spectra and carbon transport Artificial reef ecology and fish passage solutions Climate change impacts on marine and estuarine systems Leadership Inaugural Chair of NSW-IMOS (2007–2012) Currently leads the IMOS Larval Fish Monitoring subfacility Methodology Oceanographic field campaigns Long-term biodiversity databases Ecological modelling Collaborative research with 6 PhD students and colleagues Recent Publications emphasize biophysical interactions in western boundary currents, larval fish recruitment, and innovative fish passage designs like the Tube Fishway. His team contributes to global databases (e.g., BioTIME 2.0) and conservation-focused studies on fish migration fragmentation and crab ecology.

• Cryptography
• Computer Security
• Algorithms
• +۶ مورد دیگر

Rafail Ostrovsky is a Distinguished Professor of Computer Science and Mathematics (by courtesy) at UCLA's Henry Samueli School of Engineering and Applied Science. He holds the Norman E. Friedman Chair in Knowledge Sciences and serves as Director of the Center for Information and Computation Security. With over 350 refereed publications and 15 issued USPTO patents, he is one of the most influential researchers in theoretical computer science and cryptography. Professor Ostrovsky's research spans cryptography, network algorithms, and search and classification of large-scale, high-dimensional data. His work focuses on foundational aspects of secure computation including zero-knowledge proofs, secure multi-party computation, private information retrieval, and privacy-preserving data analysis. His contributions to streaming algorithms, metric embedding, and clustering for high-dimensional data have established important theoretical frameworks with practical applications. He has pioneered techniques for secure computation that maintain privacy while enabling collaborative data analysis. His recent publications demonstrate continued leadership in advancing secure computation protocols, with emphasis on efficiency improvements, practical implementations, and novel applications in blockchain technology and distributed systems. His work consistently addresses fundamental theoretical challenges while maintaining relevance to real-world security problems, bridging the gap between theoretical cryptography and practical security solutions. Selected Honors: 1993 Henry Taub Prize 2017 IEEE Computer Society Edward J. McCluskey Technical Achievement Award 2018 RSA Award for Excellence in Mathematics 2022 W. Wallace McDowell Award (the highest award given by the IEEE Computer Society) Professor Ostrovsky has served in significant leadership roles including chair of the IEEE Technical Committee on Mathematical Foundations of Computing (2015-2018) and chair of the IEEE FOCS 2011 Program Committee. He has served on over 40 international conference program committees and currently serves on the editorial boards of the Journal of ACM and Algorithmica Journal. As a Fellow of the National Academy of Inventors, AAAS, ACM, IEEE, and IACR, and as a foreign member of Academia Europaea, his contributions have been widely recognized across multiple disciplines. He actively teaches advanced courses including Introduction to Cryptography (CS183), Foundations of Cryptography (CS282A/M209A), and Cryptographic Protocols (CS282B/M209B), mentoring the next generation of security researchers while continuing to push the boundaries of secure computation through his research.

• RFID Technology
• Wireless Sensor Networks
• Internet of Things
• +۵ مورد دیگر

Asier Perallos Ruiz is a Professor in the Faculty of Engineering at the University of Deusto, specializing in the Department of Computing, Electronics and Communication Technologies. His research focuses on RFID technology, wireless sensor networks, and computational intelligence applications with significant contributions to intelligent transport systems and antenna design. Dr. Perallos Ruiz's research interests span multiple domains with a focus on RFID technology , Wireless sensor networks , Internet of Things (IoT) , Computational intelligence , Evolutionary algorithms , and Intelligent transport systems . His work bridges theoretical advancements with practical applications, particularly in transportation systems, healthcare, and industrial automation. His research often involves interdisciplinary collaboration across engineering disciplines. His publication portfolio shows a consistent trend toward improving RFID systems, developing efficient anti-collision protocols, and applying computational intelligence to real-world problems. Recent work has focused on polarization-diversity rotation sensing, customizable RFID platforms, and the integration of RFID with IoT applications. His research demonstrates a progression from foundational RFID technology to more complex system integration and application-specific solutions. Dr. Perallos Ruiz has supervised several graduate students including Muralter Florian (2021), Arjona Aguilera Laura (2018), Cmiljanic Nikola (2018), Lopez Garcia Pedro (2016), and Moreno Emborujo Asier (2016). His research has been supported by various projects focusing on RFID technology, intelligent transportation systems, and wireless communication applications. He leads research teams focused on RFID systems development, wireless sensor networks, and computational intelligence applications. Current work appears to be advancing RFID sensing capabilities, energy-efficient protocols, and system integration for practical applications in transportation and industry.

Robb W Lindgrenمشاهده پروفایل

استاد

• Embodied Learning
• Educational Technology
• Mixed Reality Simulations
• +۵ مورد دیگر

Robb W Lindgren is a Professor at the University of Illinois at Urbana-Champaign with appointments in Curriculum and Instruction and Educational Psychology . He serves as Associate Dean for Research in the College of Education and holds affiliations with the National Center for Supercomputing Applications (NCSA) , Beckman Institute for Advanced Science and Technology , and Center for Social & Behavioral Science . His research focuses on Embodied learning through gesture and physical interaction Design of mixed/augmented reality educational systems Collaborative STEM education with immersive technologies Recent publications highlight his work in: Biochemistry simulations using haptic feedback (2024) VR-based spatial reasoning for astronomy education (2023) Metaverse learning environments with theory-driven design (2023) Climate change simulations with full-body tracking (2022) His research group explores how physical movement and gestural interfaces shape scientific understanding and conceptual change. Key collaborations include work with Jee Hyang Park , Jun Kang , and Thomas Kim , focusing on Gesture-mediated collaboration XR learning analytics Agency in embodied design

• Astronomy
• Astrophysics
• Machine Learning
• +۵ مورد دیگر

Robert Brunner serves as Professor of Astronomy at the University of Illinois at Urbana-Champaign, where he bridges astrophysical research with computational innovation. His work focuses on extracting knowledge from massive astronomical datasets through advanced statistical and machine learning techniques, while also extending methodologies to finance and agricultural applications. Research interests center on developing machine learning algorithms (random forests, deep neural networks, Bayesian estimation) for astronomical data analysis, cosmological parameter constraints via n-point clustering measurements, and hardware acceleration using GPUs/cloud systems. His interdisciplinary approach spans source classification, transient phenomena detection in surveys like SDSS and DES, and applications in financial time-series analysis and agricultural remote sensing. Recent publications (2019-2025) reveal strong cross-domain expertise: astronomical catalogs for Rubin Observatory and Spitzer surveys coexist with financial market analysis using community detection methods and agricultural computer vision systems. Key methodological threads include spatio-temporal forecasting, multimodal learning for earnings calls, and anomaly detection via extended isolation forests, demonstrating consistent innovation in handling petascale datasets across scientific boundaries.

Rebecca Yangمشاهده پروفایل

استاد مهمان

• Building Integrated Photovoltaics
• Solar Energy Applications in Buildings
• Construction Innovation
• +۲ مورد دیگر

Rebecca Yang is a Visiting Professor at RMIT University's School of Property, Construction and Project Management, specializing in building, construction, and distributed renewable energy research. She integrates theoretical knowledge with cutting-edge technologies to advance sustainable urban development. Her research focuses on solar energy applications in buildings, construction innovation, and international energy policy frameworks through her leadership roles in the International Energy Agency's Photovoltaic Power Systems Programme (PVPS) Task 15 and Solar Heating and Cooling Programme (SHC) Task 66. She established RMIT's Solar Energy Application Lab and has 8 years of BIPV expertise. Notable achievements include: Australian representative in international BIPV standardization (IEC 63092) 2019 Facilitator Prize for BIPV Tool development She supervises research projects related to: Solar building envelope optimization Machine learning for energy systems Fire safety in BIPV installations Blockchain-enabled energy trading Circular economy for PV waste

Fei Chiangمشاهده پروفایل

دانشیار

• Data Management
• Data Quality
• Data Privacy
• +۶ مورد دیگر

Dr. Fei Chiang is an Associate Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. Her research focuses on data management , with emphasis on data quality, data privacy, information extraction , and contextual data cleaning . She has collaborated with IBM Global Services and Microsoft Research on improving data quality in enterprise systems. Key research themes include graph databases , temporal data analysis , and privacy-aware data processing Recent publications explore federated learning , SQL understanding in LLMs , and temporal graph constraints Industry collaborations with IBM Toronto Lab and Microsoft Research have led to innovations in data cleaning automation and semantic analysis. Her work bridges database theory with machine learning applications in healthcare inventory optimization and flight reliability prediction.

Lei Buمشاهده پروفایل

استاد

• Model Checking
• Bounded Model Checking
• Formal Methods
• +۷ مورد دیگر

Lei Bu is a Professor and Vice Dean at the Software Institute , Nanjing University . He leads research in formal verification, cyber-physical systems, and software engineering, with a focus on bounded model checking and hybrid system analysis. B.Sc. and Ph.D. in Computer Science from Nanjing University (2004, 2010) Visiting student at Carnegie Mellon University and University of Texas at Dallas His research integrates formal methods and machine learning for verifying complex systems like IoT and software with real-time constraints. Key projects include BACH Toolset and BRICK for reachability analysis. Recent publications demonstrate expertise in hybrid system verification , cache side-channel detection , and parallel code analysis frameworks . His work bridges theoretical advancements with practical applications in safety-critical systems. Zhongchuang Software Talent Award (2023) CCF-IEEE CS Young Computer Scientist Award (2022) High-Tech Software Innovation Awards (2019-2023) As Principal Investigator, he leads major projects funded by National Science Foundation of China and Jiangsu Natural Science Foundation (2020-2027). Current tools include BACH for hybrid systems and MLB for Java symbolic execution.

• Artificial Intelligence
• Natural Language Processing
• Computational Linguistics
• +۶ مورد دیگر

Ayşenur Akyüz Birtürk serves as a Lecturer in the Department of Computer Engineering at Middle East Technical University (METU), Ankara, where she has taught since February 1994. Her academic career spans foundational programming courses to advanced graduate seminars in AI and Computational Linguistics, reflecting 30+ years of institutional commitment. She earned all her degrees from METU, culminating in a 1998 Ph.D. focused on Turkish language computational analysis. Her educational journey includes: B.S. in Computer Engineering (1985) M.S. in Computer Engineering (1988) with thesis on “A Model for Representing Concepts: Conceptual Dependency Theory” Ph.D. in Computer Engineering (1998) with thesis on “A Computational Analysis of Turkish using the Government-Binding Approach” Dr. Birtürk’s research centers on Artificial Intelligence and Natural Language Processing , with pioneering work in Turkish language parsing evolving into modern Recommender Systems . She integrates semantic relations and multi-domain data to build hybrid engines for movies, books, and music, emphasizing user modeling through knowledge representation and data mining techniques. Analysis of her 2010-2015 publications reveals two dominant threads: adaptive recommender systems (80% of output) using semantic similarity and dynamic clustering, and renewable energy analytics (20%) applying machine learning to wind/hydrological data. This pivot from NLP to energy forecasting demonstrates methodological versatility while maintaining core AI expertise. Her scientific recognition includes: TUBITAK scholarships throughout education (1977-1988) Multiple national contest awards in high school (1979-1980) Leadership in TUBITAK-funded energy and healthcare projects Dr. Birtürk has supervised 17 Master’s theses in NLP and recommender systems while securing competitive grants including METU-ISTEC #17435 (2006-2008; 504,000 YTL) and HASAT (2010-2013; 601,637 YTL). Her industry consultancy spans medical form design (FormAnalitik), question-answering systems, and retail intelligence platforms, translating academic research into real-world tools.

• Spatial Statistics
• Software Development
• Open Source Geospatial Software
• +۱ مورد دیگر

Barry Rowlingson is a Research Fellow at Lancaster University Medical School , affiliated with the Chicas Research Group and DSI-Health . He specializes in spatial statistics applied to disease epidemiology and geospatial software development . Teaches Geospatial Data module for MRes in Global Health Developed online course on Spatial Statistics in R with DataCamp Focuses on open-source geospatial tools for public health Research Interests: His work bridges spatial statistical methodology and infectious disease modeling , with applications in antimicrobial resistance mapping , wastewater-based pandemic surveillance , and health inequality analysis . Recent projects include modeling ESBL-producing bacteria in Malawi and developing spatio-temporal frameworks for COVID-19 wastewater monitoring . Scientific Contributions: Over 15 years, he has developed critical R packages like stpp for spatio-temporal analysis and rgdal for geospatial data abstraction. His 2023 publications address health workforce disparities and disease transmission dynamics using advanced statistical methods. Collaborations: Works with multidisciplinary teams across Lancaster Medical School , DataCamp , and SAVSNet Agile research projects. Supervises PhD student Charlotte Appleton in biostatistics.

Morteza Zihayatمشاهده پروفایل

دانشیار

• Data Analytics
• Human-centered AI
• Decentralized AI
• +۶ مورد دیگر

Morteza Zihayat is an Associate Professor and Canada Research Chair (Tier 2) in Human-Centered Artificial Intelligence at Toronto Metropolitan University. He holds dual appointments in the Faculty of Engineering and Architectural Science (Department of Electrical, Computer, and Biomedical Engineering) and the Ted Rogers School of Management. Additionally, he serves as an Adjunct Professor at the University of Waterloo in Management Sciences and is a Faculty Fellow at IBM's Centre for Advanced Studies. Dr. Zihayat's educational background includes: PhD in Computer Science from York University (2016) MSc in Computer Engineering from University of Tehran (2011) Postdoctoral Research Fellowship at University of Toronto's Faculty of Information (2017) His research lies at the intersection of AI, security, and society with a focus on building fair and transparent AI systems. Dr. Zihayat's expertise spans human-centered AI, fair information retrieval systems, and blockchain-enabled AI infrastructures. His work emphasizes creating AI systems that are accountable and designed to serve the public good, with applications in healthcare, digital media, and social networks. Dr. Zihayat has received numerous accolades including the Canada Research Chair (Tier 2) in Human-Centered AI (2024), Dean's Outstanding Scholarly, Research, and Creative Activity Award (2023), Best Short Paper Award at ECIR (2023), and IBM CAS Faculty Fellowship (2021). His research has attracted over $1.7 million in external funding from agencies such as NSERC, Mitacs, and multiple industry partners including Toronto Transit Commission, The Globe and Mail, AT&T, and IBM. Dr. Zihayat serves as Associate Editor of the Computational Intelligence Journal and is an active reviewer for top-tier venues. He is also Co-director and Co-founder of the Digital Enterprise Analytics and Leadership (DEAL) Research Center.