جست‌وجوی استاد و پژوهشگر

Chanh Le Tan Nguyenمشاهده پروفایل

پژوهشگر ارشد

• Cloud Computing
• Edge Computing
• Machine Learning
• +۴ مورد دیگر

Chanh Le Tan Nguyen is a Postdoctoral Fellow at Umeå University's Department of Computing Science, working from MIT House (MIT.D.247) in Umeå, Sweden. Their research focuses on advancing cloud-edge computing systems through innovative middleware development, resource management strategies, and machine learning applications. Autonomous Distributed Systems Lab member Active in wearable cognitive assistance research Expert in mobile edge cloud technologies Research interests span cloud computing, edge computing, machine learning, and distributed systems. Their work addresses critical challenges in wearable systems' integration with edge computing, dynamic resource allocation, and latency-intolerant application management. Recent publications (2021-2024) reveal a strong focus on location-aware resource allocation, elasticity control for edge applications, and cloud-edge orchestration. Key themes include wearable cognitive assistance, mobile edge cloud optimization, and machine learning robustness in dynamic environments. Dr. Nguyen's work builds on their 2016-2020 research project Mobility-aware Resource Management for Mobile Edge Clouds , which laid foundational insights for current advancements in edge computing. Their research continues to shape policies for efficient small-scale cloud data centers and large-scale cloud robotics systems.

Christof Schöchمشاهده پروفایل

استاد

• Digital Humanities
• Computational Literary Studies
• Quantitative Text Analysis
• +۷ مورد دیگر

Professor Christof Schöch is a leading academic at the University of Trier and co-director of the Trier Center for Digital Humanities (TCDH) since 2017. He co-edits the Journal of Computational Literary Studies (JCLS) and has held significant leadership roles including chair of the German-speaking Digital Humanities Association (DHd 2018–2022) and president of the Alliance of Digital Humanities Organizations (ADHO 2023–2024). Completed doctorate in French Literature (binational Kassel/Paris, 2008) Thesis: La Description double dans le roman français des Lumières 1760-1800 Degree: Romance Languages, English, Psychology Schöch's research focuses on Computational Literary Studies , particularly statistical text analysis , data modeling , and quantitative methods applied to French Enlightenment literature and modern/contemporary French novels . He champions Open Science practices including open access, open data, and open source software. His work also intersects with digital publishing , copyright issues , and reproducibility in Digital Humanities . Recent publications highlight trends in multilingual stylometry , LLM applications , and historical data modeling . Notable contributions include the European Literary Text Collection (ELTeC) and MiMoTextBase RDF dataset. His academic awards include the 2010 Prix Germaine de Stael for his dissertation. Schöch actively develops digital infrastructure through projects like Wikibase instance for academic activity tracking and coleto text collation software. He participates in international collaborations such as the Text+ AG Legal and Ethical Issues and CLS INFRA infrastructure project. His teaching includes Advanced Digital Humanities courses on law and digital humanities and data analysis methods.

• Cybersecurity
• Artificial Intelligence
• Computer Networks
• +۷ مورد دیگر

Pere Barlet-Ros is a Full Professor in the Department of Computer Architecture at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics (FIB) and the Barcelona Neural Networking Center (BNN-UPC). He leads research in AI-driven network technologies and is a key member of the UPC CBA and IDEAI-UPC research groups. Ph.D. in Computer Science, UPC (2008) M.Sc. in Computer Science, UPC (2003) His research focuses on applying artificial intelligence, particularly graph neural networks, to solve critical challenges in network monitoring, traffic classification, congestion control, and cybersecurity. His work bridges theoretical innovation with practical deployment, as evidenced by integration into commercial tools like Auvik TrafficInsights and Talaia Polygraph. He explores digital twins for network optimization, privacy-preserving technologies, and next-generation web tracking detection. The recent publications highlight a strong trend toward leveraging graph-based machine learning for network modeling, anomaly detection, and traffic compression. His work spans top-tier journals like IEEE/ACM Transactions on Networking and conferences such as IEEE EuroS&P and ACM SIGCOMM workshops, emphasizing robust, real-world AI/ML applications in networking. 2nd VALORTEC prize for best business plan (2014) Fiber Entrepreneurs award, best entrepreneur at FIB (2015) Pere Barlet-Ros has served as Principal Investigator on seven competitive research projects and has advised at least 14 PhD students, including Paul Almasan, Miquel Ferriol, and Ismael Castell. His lab, BNN-UPC, fosters interdisciplinary collaboration in neural networking and AI for infrastructure. His entrepreneurial experience with Talaia Networks underscores a commitment to translating research into impactful technology.

Manil Maskeyمشاهده پروفایل

استاد مدعو

• Artificial Intelligence
• Machine Learning
• Data Science
• +۱۲ مورد دیگر

Manil Maskey is a Senior Research Scientist and Project Manager at NASA and serves as an Adjunct Faculty member in the Atmospheric Science Department at the University of Alabama in Huntsville (UAH). He plays a key leadership role in NASA's Science Mission Directorate, focusing on artificial intelligence, data systems, and Earth science innovation. He has contributed significantly to the development of NASA's AI strategy and leads the Visualization, Exploration, and Data Analysis (VEDA) project. B.S. in Computer Science and Mathematics M.S. in Computer Science Ph.D. in Computer Science His research interests lie at the intersection of artificial intelligence and Earth science, with a focus on machine learning, data-centric AI, geospatial intelligence, and high-dimensional data visualization. He is particularly interested in applying deep learning to environmental challenges such as tropical cyclone intensity estimation, hail detection, and smoke monitoring using satellite and radar data. His work emphasizes robust data systems, ethical AI, and scalable computing frameworks for scientific discovery. The recent publications reflect a strong trend toward foundation models, data-centric machine learning, and AI-driven Earth system science. Topics include Clifford neural operators, metadata for ML-ready datasets, and traceability to prevent data swamps, indicating a shift from purely model-centric to holistic data-systems thinking in AI for science. Senior Member, IEEE Chair, IEEE GRSS Earth Science Informatics Technical Committee Co-chair, NITRD Big Data Interagency Working Group Steering Committee Member, National AI Research Resource (NAIRR) Manil Maskey has mentored numerous students and served on thesis committees across computer science and atmospheric science. He has led competitive NASA programs including ACCESS and CSDA, and played a central role in establishing data management frameworks for commercial smallsat data. His work bridges research, policy, and infrastructure development in support of AI for Earth science. He is actively involved in several scientific teams and initiatives, including the IMPACT project at NASA, where he helped establish a data science team, and leads the GRSS Working Group on High-Performance and Disruptive Computing in Remote Sensing (HDCRS). He also contributes to international collaborations through the NASA-ESA-JAXA Earth Observation Dashboard and promotes open science through workshops and tutorials on cloud computing and machine learning.

Dirk Jordanمشاهده پروفایل

پژوهشگر

• Photovoltaics
• Renewable Energy Engineering
• Degradation Mechanisms
• +۴ مورد دیگر

Dirk Jordan is a Researcher VI at the National Renewable Energy Laboratory (NREL) within the Materials Science Photovoltaics Research Topic . His work focuses on photovoltaic system reliability , degradation mechanisms , and advanced statistical analysis for energy applications. Education: PhD in Physics, Arizona State University Master in Physics, Arizona State University Bachelor in Physics, Heidelberg University of Education Six Sigma Black Belt, Motorola Research Interests include AI-driven machine learning for energy systems, extreme weather resilience , performance loss in solar infrastructure, and big data analytics applied to photovoltaics. His recent work emphasizes embedded sensors and multivariate analysis to optimize PV longevity. Research Trends: Recent publications highlight degradation rate analysis (2023), reliability in terawatt-scale systems (2022), and large-scale field data interpretation (2020-2016). Key themes include climate adaptation , quality control , and material science for photovoltaics. Scientific Recognition: 2023: NREL Distinguished Member of Research Staff 2018, 2014, 2005: NREL President's Award 2005: Scientific American Top 50 & Nanotech Briefs Top 50 Collaborates with cross-functional teams on photovoltaic module testing , weather resilience , and energy system validation . Mentions over 150 total publications with peak activity in 2023-2021.

Miria Grisotمشاهده پروفایل

استاد

• Information Infrastructures
• Digital Infrastructures
• Digital Ecosystems
• +۸ مورد دیگر

Miria Grisot is a Professor at the Department of Informatics, University of Oslo, specializing in Digital Innovation. Her work focuses on information infrastructures, digital ecosystems, and healthcare transformation, with particular emphasis on remote care systems and AI implementation in clinical settings. She is actively involved in multiple research projects including AI4Users, C3 - Center for Connected Care, and the DIGI Remote project. Her research interests span Information Infrastructures, Digital Infrastructures, Digital Ecosystems, Digitalisation of work, Design of Information Infrastructures, Healthcare, remote care, AI in context, data work, and CSCW (Computer-Supported Cooperative Work). Her work demonstrates a consistent focus on how digital technologies transform healthcare practices and organizational structures. Her recent publications reveal a strong trend toward examining digital remote care services, AI implementation in healthcare, and infrastructure design across different cultural contexts (particularly Norway and China). Her research adopts sociotechnical perspectives to understand how digital transformations reshape work practices, particularly in healthcare settings. Miria Grisot teaches courses including IN2150 - IT in Organizations, HINF1102 - Health Informatics and Logistics, and INFO901 - Introduction to AI Ethics (a PhD course at UiB). She is an active member of the Digital Innovation (DIN) research group and has been involved in numerous projects examining digital transformation in public services. Her research demonstrates significant contributions to understanding how digital infrastructures evolve through collaborative processes, with particular attention to the interplay between standardization and flexibility in healthcare contexts. Her work bridges theoretical insights from information systems research with practical challenges in healthcare digitalization.

Ben Collierمشاهده پروفایل

مدرس ارشد

• Criminology
• Science and Technology Studies
• Cybercrime
• +۷ مورد دیگر

Ben Collier is a Senior Lecturer in Digital Methods at the University of Edinburgh, specializing in intersections between Criminology and Science and Technology Studies. His work examines digital infrastructure's role in power, crime, privacy, and control. Senior Lecturer, Science, Technology and Innovation Studies Chair, Foundation for Information Policy Research Research spans three main strands: Large-scale ethnographic studies of digital infrastructure (Tor network, generative AI adoption by governments, elite content moderation) Digital crime and resistance (cybercrime industrialization, hacker communities, statistical analysis of harm signals) Digital infrastructure and state power (FBI takedowns, law enforcement innovation, National Technical Authorities) Recent publications include book chapters and journal articles analyzing Tor's cultural significance, cybercrime market dynamics, and digital influence campaigns by government agencies. Collaborations with Cambridge Computer Lab Security Group highlight technical-sociological intersections. Methodologically combines qualitative research with computational and statistical approaches, focusing on critical social data science and ethical implications of digital technologies. Supervises PhD students across relevant topics in digital methods and cybercrime research.

Wenjie Zhangمشاهده پروفایل

استاد

• Databases
• Big Data
• Data-centric AI
• +۹ مورد دیگر

Professor Wenjie Zhang is a faculty member at the School of Computer Science and Engineering, University of New South Wales (UNSW), Australia . She holds the ARC Future Fellowship and serves as Head of the Data and Knowledge Research Group and Deputy Head of School (Research). Her research spans Databases, Big Data, Data-centric AI, Machine Learning, and Graph Data Processing , with a focus on spatial-temporal data, uncertain data analysis, and scalable graph algorithms. Affiliation: University of New South Wales, Australia Roles: ARC Future Fellow, Head of Data and Knowledge Research Group, Deputy Head of School (Research) Research Interests include: Dynamic graph analytics (heterogeneous, multi-dimensional, bipartite graphs) Spatial-social data processing & keyword search AI/ML integration for database optimization Foundational work on large language models for data science Scientific Leadership : 2025 PC Chair for ICDE and ADC conferences Associate Editor for IEEE TKDE and VLDB Journal Steering committee member for DASFAA and ADC conferences Research Funding : $495,000 ARC Discovery Project (DP230101445) for cloud-based temporal graph processing $35M ARC Centre of Excellence for cellular systems modeling $5M FRIASA Hub for fire-resilient infrastructure Industry grants totaling $1.01M–$2M for AI trust, mining IoT, and financial risk detection Scientific Recognition : Recipient of the CORE Chris Wallace Award , multiple best paper awards at ICDE/DASFAA, and ACM SIGMOD Research Highlight for influential database work.

Fabricio Jailson Barthمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +۵ مورد دیگر

Fabricio Jailson Barth is a Brazilian academic with a PhD in Electrical Engineering (University of São Paulo, USP) and dual expertise in Computer Science and Computer Engineering. Since 2003, he has built a career at Insper Institute of Education and Research focusing on machine learning and artificial intelligence applications across finance, internet technologies, public safety, education, and human resources. Education: PhD in Electrical Engineering (USP), Computer Science graduate Research Focus: Autonomous systems development, large-scale AI implementation, and educational technology Technical Expertise: Python and R programming for machine learning, reinforcement learning, sensor data analysis Key Projects: ai_gym library, human activity recognition systems, Watson Studio training materials

Chunbo Chuمشاهده پروفایل

استاد

• Distributed Systems
• Applications of Artificial Intelligence
• Computer Science Education
• +۳ مورد دیگر

Dr. Chunbo Chu is a Professor in the Department of Computing Sciences and Mathematics at Franklin University, where he has served as Lead Faculty of Computer Science since 2008. He holds a Ph.D. in Computer Science from Wayne State University (2008) and B.S. and M.S. in Computer Science from Fudan University (1997, 2000). As academic leader, he chairs both the B.S. and M.S. in Computer Science programs. Ph.D. in Computer Science, Wayne State University, 2008 M.S. in Computer Science, Fudan University, 2000 B.S. in Computer Science, Fudan University, 1997 His research focuses on distributed systems, with significant contributions to predicate detection and network monitoring. He also investigates artificial intelligence applications and leads innovations in computer science education through learning analytics and retention studies. His work bridges theoretical foundations with practical implementations in software architecture and network traffic modeling. Dr. Chu has made 15 publications spanning 1999-2024, emphasizing educational improvement and distributed system reliability. His scholarship includes key contributions to overlay network design (2007), multifractal traffic modeling (2001-2002), and modern educational approaches using BRIDGES platform (2024). Academic Leadership Fellow (2024-25) Robert L. Bailey Teaching Award (2024, graduate faculty) Provost's List for teaching excellence (2020-2021) Dr. Chu actively contributes to computer science education through NSF-funded initiatives, program committee memberships (IEEE Frontiers in Education 2024, EAAI-24), and invited talks on AI's educational impact. He serves as technical reviewer for journals including IEEE Transactions and Journal of Parallel and Distributed Computing .

• Cosmology
• Transient Astrophysics
• Machine Learning
• +۴ مورد دیگر

Dr. Anais Moller is a Research Fellow at Swinburne University of Technology's School of Science, Computing and Emerging Technologies. She leads the Fink broker (https://fink-broker.org) with real-time access to Vera C. Rubin Observatory transients, focusing on cosmology and transient astrophysics through machine learning-driven analysis of large astronomical datasets. Doctorat de physique de l'Univers (PhD), Université Paris Diderot 7, France Master 2 NPAC, Université Paris Diderot 7, France Licenciatura en Física, Universidad Simón Bolívar, Venezuela Her research intersects three major domains: Cosmological constraints using Type Ia supernovae as standardizable candles for dark energy studies Multi-messenger astrophysics through searches for gravitational wave counterparts and fast transients Machine learning algorithm development for transient classification and data-intensive astronomy Recent publications demonstrate her leadership in: Photometric redshift estimation for supernovae Host galaxy dust modeling Weak lensing analysis Time dilation measurements through supernova light curves Kilonova detection algorithms Scientific recognition includes: 2025 School of Science, Computing and Engineering Technologies Research Excellence Award 2024 Asia-Pacific Women in AI Award (space category) She actively supervises HDR (PhD) students in topics spanning: Primordial black hole density determination Fast transient detection Gamma-ray burst analysis Multi-wavelength transient studies Machine learning applications Gravitational wave host galaxy identification As Associate Investigator at ARC Centre of Excellence for Gravitational Wave Discovery (OzGrav), she contributes to the detection of electromagnetic counterparts to gravitational waves, while leading two Australian Research Council grants focused on explosive astrophysics and dark universe illumination.

Jean-Daniel Feketeمشاهده پروفایل

پژوهشگر

• Visual Analytics
• Information Visualization
• Human-Computer Interaction
• +۳ مورد دیگر

Jean-Daniel Fekete is a Senior Research Scientist (DR1) at Inria Saclay and Scientific Leader of the AVIZ Project Team, affiliated with Université Paris-Saclay. His research focuses on Visual Analytics , Information Visualization , and Human-Computer Interaction , particularly for network visualization and progressive data analysis . He has held visiting positions at NYU-Poly and Harvard and serves in leadership roles for IEEE VIS and EuroVis. Senior Research Scientist, Inria Saclay (2006–present) Founder, AVIZ Research Team (2007) Member, Université Paris-Saclay Computer Science Department His work addresses scalable visualization of dense, dynamic networks , including matrix-based representations and tools for blockchain , medical pathways , and historical data . He received the IEEE VIS Test of Time Award (2024) and IEEE VGTC Visualization Technical Award (2020) , among others. Best Paper Awards: CHI 2025, ACM UIST 2016, CHI 2013 ACM SIGCHI Academy (2020) Google Research Award (2011) He has supervised numerous PhD students and developed open-source systems like ProgressiVis , Zooids , and ParcoursVis . Current projects include ParcoursVis (medical care pathways) and URGE (emergency services optimization).

Lora Aroyoمشاهده پروفایل

استاد

• Crowdsourcing and human computation
• Hybrid human-AI systems
• Video understanding and multimedia semantics
• +۴ مورد دیگر

Lora Aroyo is Full Professor in the Department of Computer Science at VU University Amsterdam , leading the Web & Media group and actively contributing to the university’s Network Institute . Her research centers on hybrid human-AI systems for video understanding, crowdsourcing, data quality, and semantic search, with strong impact on digital humanities, cultural heritage, and interactive television. Research Interests: Human computation and large-scale crowdsourcing Hybrid human-AI architectures for multimedia analysis Data quality, annotation reliability, and disagreement modeling Semantic search and recommendation in cultural heritage Responsible AI and pluralistic alignment of generative models Across 2023–2025 her publications reveal a sharp focus on AI safety for generative and multimodal systems, including red-teaming of text-to-image models, evaluation of safety perceptions across demographic groups, and the creation of open benchmarks and datasets to support these goals. Earlier work (2018–2022) addresses video discovery, cultural heritage metadata, and human-centered data practices. Scientific Service & Funding: She has led major Dutch and EU projects in semantic search, event-driven multimedia access, and digital infrastructures for humanities research. She currently serves on the editorial board of PeerJ Computer Science . Labs & Teams: Prof. Aroyo heads the Web & Media group at VU, collaborates closely with the Network Institute , and coordinates interdisciplinary teams spanning computer science, digital humanities, and AI ethics.

Yuval Shavittمشاهده پروفایل

استاد

• Internet Measurement
• Network Mapping
• Network Modeling
• +۸ مورد دیگر

Prof. Yuval Shavitt is a Professor at Tel Aviv University's School of Electrical Engineering within the Iby and Aladar Fleischman Faculty of Engineering. His research focuses on Internet measurement, network mapping, and security, where he leads the globally recognized DIMES project for large-scale Internet topology analysis. Based in the Computer and Software Engineering department, he maintains an active research program with continuous publications and international collaborations. His primary research interests encompass Internet measurement and characterization, network mapping and modeling, Internet routing security (particularly IP hijack attacks), artificial intelligence applications in networking, network motifs, transportation networks, QoS routing, peer-to-peer network data mining, and active networks. This work has established him as a leading figure in Internet topology research, with methodologies adopted by major scientific initiatives. His research bridges theoretical algorithms and practical network infrastructure challenges, emphasizing real-world applicability through distributed measurement systems. Analysis of his 2009-2018 publications reveals consistent focus on Internet topology mapping, IP geolocation accuracy, and network security vulnerabilities. Key trends include the development of structural approaches for PoP-level geolocation, quantification of measurement biases in topology mapping, and analysis of geopolitical impacts on network infrastructure (notably the 2011 Arab events). His work demonstrates increasing integration of AI techniques for networking challenges and growing emphasis on infrastructure security. Scientific Recognition: Best Student Paper Award at ConTel 2009 He has secured significant research funding, including an 8-year Israel Science Foundation Center of Excellence grant for Internet modeling (with Danny Dolev, Shlomo Havlin, and Sarit Kraus), and led DIMES through EU projects EVERGROW, MOMENT, OneLab II, and GN3. While student advisement isn't explicitly detailed in the source material, his extensive publication record suggests substantial mentorship activity. Current grants focus on AI-driven network analysis and infrastructure security. He directs the DIMES project, a volunteer-driven distributed measurement system with global reach that has generated foundational Internet topology data. The project operates through an international consortium including EU partners and has been featured in Science Magazine for its innovative approach. Current work involves AI4Net (Artificial Intelligence for Networking) and network motif analysis within the Computer and Software Engineering infrastructure at Tel Aviv University.

Pavithra Harshaمشاهده پروفایل

پژوهشگر

• AI
• Machine Learning
• Optimization
• +۳ مورد دیگر

Pavithra Harsha is a Principal Research Staff Member at IBM Research , where she focuses on integrating AI , machine learning , and optimization to solve complex problems in ITOps , supply chains , pricing , and revenue management . Her work bridges theoretical advancements with commercial applications, including contributions to IBM's pricing and inventory management solutions for retail and airline clients.