جست‌وجوی استاد و پژوهشگر

Kian-Lee Tanمشاهده پروفایل

استاد

• Query Processing
• Big Data Analytics
• Database Systems
• +۵ مورد دیگر

Kian-Lee Tan is a Tan Sri Runme Shaw Senior Professor and Professor of Computer Science at the School of Computing, National University of Singapore (NUS). He holds a Ph.D. (1994), M.S. (1992), and B.Sc. (1st Class Honours) from NUS. His academic career spans decades of contributions to database systems and data analytics. Ph.D. in Computer Science, National University of Singapore (1994) M.S. in Computer Science, National University of Singapore (1992) B.Sc. in Computer Science (1st Class Honours), National University of Singapore As a leading researcher in database systems, Tan focuses on query processing and optimization in multiprocessor/distributed systems, database performance, security, and multimedia information retrieval. His work extends to computational biology applications like genome databases and real-time influence analysis on social streams. His recent publications highlight trends in GPU-accelerated graph analytics, trajectory pattern mining, and computational journalism. These works emphasize parallel processing, performance optimization, and social/media data analysis. IEEE Technical Achievement Award (2013) President Science Awards, Singapore (2011) NUS Graduate School Excellent Mentor Award (2010/2011) Outstanding University Researchers Award (1997/1998) Tan has supervised numerous research projects and mentored students contributing to database systems. He secured significant grants including a US$1 million Ripple Foundation grant (2024) for financial technology education. His editorial roles include ACM Transactions on Database Systems and IEEE Transactions on Knowledge and Data Engineering. He leads the FinTech Lab at NUS Computing and has served on the VLDB Endowment Board (2012-2017). His work bridges database foundations with emerging applications in AI, fintech, and computational journalism.

Olle Järvمشاهده پروفایل

پژوهشگر

• Human mobility
• Big data analysis
• Spatial segregation
• +۷ مورد دیگر

Dr. Olle Järv is an Academy Research Fellow at the University of Helsinki's Department of Geosciences and Geography. As Docent in Human Geography and holder of a joint PhD from University of Tartu and Ghent University, his research focuses on leveraging big data sources like mobile phone records and social media for analyzing human mobility patterns across European cross-border regions. Key research areas: Spatial segregation, urban diversity, transnationalism, and pandemic-induced mobility changes Current projects: BORDERSPACE (2020-2025) – Mapping transnational spaces through mobility data MOBI-TWIN (2023-2026) – Regional mobility patterns in twin transitions WinWin4WorkLife (2024-2027) – Sustainable cross-border remote work His methodological innovations include developing tools for analyzing social media data and mobile phone records to study urban dynamics. Recent recognitions include the 2024 Research Council of Finland Award and 2021 Best Paper Award from Journal of Location Based Services.

Gustavo Alonsoمشاهده پروفایل

استاد

• Databases
• Distributed Systems
• Cloud Computing Architecture
• +۴ مورد دیگر

Gustavo Alonso is Full Professor at the Department of Computer Science (D-INFK) of ETH Zurich and Head of the Institute for Computing Platforms . He has been at ETH since 1995, first as a post-doc, then as Assistant Professor from April 1998, and promoted to Full Professor in October 2001. Within the Systems Group he leads the Information and Communication Systems Research Group . Education: 1989 – Telecommunications Engineering (undergraduate), Madrid Technical University (UPM-ETSIT), Spain 1992 – M.S. Computer Science, University of California, Santa Barbara (UCSB) 1994 – Ph.D. Computer Science, University of California, Santa Barbara (UCSB) Research Interests: His work spans databases, distributed systems, cloud-computing architecture, FPGAs, hardware acceleration for data science, parallel and reconfigurable computing . The group investigates how modern heterogeneous hardware—from GPUs to SmartNICs—can be integrated into data-processing systems to achieve orders-of-magnitude performance gains, energy savings, and new functionality such as in-network computation and serverless acceleration. Scientific Awards & Honors: Fellow of the ACM (Association for Computing Machinery) Fellow of the IEEE (Institute of Electrical and Electronics Engineers) Distinguished Alumnus, Department of Computer Science, UC Santa Barbara Four Test-of-Time / Most Influential Paper Awards across databases, programming languages, cloud computing, and software engineering Labs & Projects: He directs the Information and Communication Systems Research Group within the Systems Group ( systems.ethz.ch ). The lab develops open-source platforms such as Coyote v2 for FPGA abstractions, Shuhai for HBM benchmarking, and MicroRec for micro-second recommendation serving, while collaborating with industry on SmartNICs, serverless analytics, and cloud-scale data analytics.

Mark Crowleyمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Deep Learning
• Manifold Learning
• +۱۲ مورد دیگر

Mark Crowley is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, with a cross-appointment in the Cheriton School of Computer Science. He is a member of the Waterloo Artificial Intelligence Institute (WAII) and the Waterloo Institute for Complexity and Innovation (WICI), and serves as National Secretary of the Canadian Artificial Intelligence Association (CAIAC). His educational background includes a Ph.D. and M.Sc. in Computer Science from the University of British Columbia, where he worked in the Laboratory for Computational Intelligence, and a B.A. in Computer Science from York University. He completed a postdoctoral fellowship at Oregon State University working with Tom Dietterich's machine learning group. Crowley's research focuses on developing dependable and transparent algorithms to augment human decision-making in complex domains with multiple agents, spatial structure, or uncertainty. His work spans Reinforcement Learning , Deep Learning , Ensemble Methods , and Manifold Learning . He frequently collaborates with researchers in applied fields including Computational Sustainability, Sustainable Forest Management, Autonomous Driving, Medical Imaging, and Material Design. His research is motivated by both theoretical opportunities and real-world challenges such as forest fire management, automotive applications, and medical imaging. His recent publications demonstrate a strong focus on addressing challenges in reinforcement learning, particularly around observation costs, multi-agent systems, and causal representation learning. His work on ChemGymRL provides a significant contribution to digital chemistry and material design through reinforcement learning frameworks. The textbook Elements of Dimensionality Reduction and Manifold Learning represents a major contribution to the theoretical foundations of machine learning. Crowley actively supervises graduate students, with recent thesis completions including Shayan Shirahmadi Gale Bagi (PhD, Feb 2025) and Oleksandra Nahorna (MASc, Dec 2024). His lab, UWECEML (Waterloo ECE Machine Learning Lab), focuses on developing new algorithms at the intersection of Machine Learning, Optimization, and Probabilistic Modeling. He teaches courses including ECE 457C (Reinforcement Learning), ECE 657A (Data & Knowledge Modelling & Analysis), and ECE 457B (Fundamentals of Computational Intelligence). His blog Computationally Thinking explores AI, machine learning, and the societal impact of these technologies.

Scott Sissonمشاهده پروفایل

استاد

• Computational Statistics
• Bayesian Statistics
• Big Data
• +۴ مورد دیگر

Professor Scott Sisson is Director of the UNSW Data Science Hub (uDASH) and Professor of Statistics and Data Science at the University of New South Wales, School of Mathematics and Statistics. His research focuses on computational statistics, particularly solving 'intractable' statistical problems through Bayesian methods, big data techniques, simulation algorithms, and extreme value theory with environmental applications. Education includes a PhD in Statistics from Bristol University (2002), MSc in Environmental Statistics from Lancaster University (1997), and BSc in Mathematics and Statistics from Lancaster University (1996). Research interests span: Bayesian statistics and uncertainty quantification Big data analytics and scalable algorithms Machine learning integration with statistical methods Extreme value modeling for climate/environment Computational techniques for intractable problems Recent publications (2022-2025) demonstrate strong emphasis on Bayesian computation, spatiotemporal modeling, and interdisciplinary applications in materials science, oncology, quantum computing, transportation policy, and ecology. Methodological innovations include likelihood-free inference, modular Bayesian analyses, and symbolic data modeling. Awards and honors: 2020 Service Award (Statistical Society of Australia) 2017 ARC Future Fellowship 2015 G. N. Alexander Medal (Engineers Australia) 2011 Moran Medal (Australian Academy of Science) 2010 J.G. Russell Award (Australian Academy of Science) 2010 Queen Elizabeth II Research Fellowship As Director of uDASH, he leads data science initiatives across UNSW. He maintains sustained ARC funding and supervises students in computational statistics, Bayesian methods, extreme value theory, and machine learning. Professional service includes editorial roles for Statistics and Computing and past presidency of Statistical Society of Australia.

Zhuoyue Zhaoمشاهده پروفایل

استادیار

• Database systems
• Security
• Query processing
• +۶ مورد دیگر

Zhuoyue Zhao is an Assistant Professor in the Department of Computer Science and Engineering at the University at Buffalo, School of Engineering and Applied Sciences. His office is located at 338I Davis Hall, Buffalo, NY 14260, and he can be reached at zzhao35@buffalo.edu or by phone at (716) 645-4735. Dr. Zhao received his PhD in Computer Science from the University of Utah in 2021, where he was advised by Prof. Feifei Li and Prof. Jeff Phillips. Prior to that, he earned his BS in Computer Science from Shanghai Jiao Tong University in 2016, where he was part of the prestigious ACM Class. During his undergraduate studies, he conducted research under Prof. Kenny Zhu and spent Fall 2015 as a research assistant at Hong Kong Polytechnic University supervised by Prof. Eric Lo. Dr. Zhao's research focuses on database management systems, with specific emphasis on traditional and approximate query processing, query optimization, database systems on modern hardware, transaction processing, indexing, and storage. His work bridges theoretical foundations with practical implementations, often resulting in systems that address real-world database challenges. He has made significant contributions to probabilistic query processing, transaction scheduling, and learned indexing techniques. His recent publications demonstrate a clear trajectory toward optimizing database performance in hybrid transactional/analytical processing environments. His research increasingly integrates systems techniques with machine learning approaches, particularly in the area of learned indexes. There's also a strong focus on making database operations more efficient through innovative scheduling mechanisms and query processing techniques that can handle concurrent updates. Google PhD Fellowship (2019-2021) Best Paper Award at SIGMOD 2016 for "Wander Join: Online Aggregation via Random Walks" Best Paper Award at SIGMOD 2025 for "Low-Latency Transaction Scheduling via Userspace Interrupts" Dr. Zhao currently advises several PhD students including Yunnan Yu, Congying Wang, Gaoxiang Liu (co-advised with Prof. Ziming Zhao), and Zhuoran Li. He has successfully guided MS student Nithin Sastry Tellapuri to graduation (Fall 2023), who is now employed at AirPay. His research is supported by significant funding including an NSF CAREER award (#2339596) totaling $599,977 for research on "Speedy and Reliable Approximate Queries in Hybrid Transactional/Analytical Systems" (2024-2029) and an unrestricted Google gift of $30,000 (2021). Dr. Zhao leads the ADBLab research group at UB, where students work on cutting-edge database systems research. His lab focuses on building practical database systems that address real-world challenges in query processing, transaction management, and indexing. The lab maintains strong connections with industry partners and regularly contributes to open-source database projects.

• Computational Social Science
• Political Participation
• Big Data
• +۳ مورد دیگر

Dr. Jonathan Bright is a Senior Research Fellow and Associate Professor at the Oxford Internet Institute, University of Oxford , specializing in computational approaches to the social and political sciences. His research explores how digital technologies reshape political participation and how new data sources enhance governmental decision-making. Research Interests: Focused on social media, political behavior, computational social science, and big data , Bright’s work addresses digital governance, algorithmic bureaucracy, and the dynamics of political fragmentation online. His empirical studies span open government data usage, crowdsourced geographical information, and the impact of neighborhood effects on data completeness. Scientific Contributions: Leading studies on algorithmic governance in UK local authorities (2020) Investigating geodemographic biases in crowdsourced platforms like OpenStreetMap (2018) Mapping political fragmentation in social media ecosystems (2018) Grants & Collaborations: Funded by UK taxpayers, ESRC, and Google, his work intersects with projects on vaccine misinformation, hate speech monitoring, and digital economies. Formerly held positions as Research Fellow and Research Associate at OII from 2013 to present.

Massimo Piccardiمشاهده پروفایل

استاد

• Natural Language Processing
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Massimo Piccardi is a Professor of Natural Language Processing (NLP), Computer Vision, and Machine Learning at the University of Technology Sydney (UTS) , where he has been since 2002. He currently serves as the Head of the School of Electrical and Data Engineering and leads the Big Data Analytics program at the Global Big Data Technologies Centre. His research focuses on advancing NLP, machine learning applications in healthcare, and cybersecurity in IoT systems. He has authored over 200 journal papers and conference proceedings, secured significant ARC and CRC grants, and holds the IEEE Computer Society Distinguished Contributor Award (2022). Education & Professional Roles: Joined UTS in 2002, progressing from Associate Professor (2002–2007) to Professor (2008–present). Serves as Associate Editor for IEEE Transactions on Big Data and Editor for Artificial Intelligence in Medicine. Active in professional societies including IEEE, ACL, and ALTA (President, 2023–2024). Research Interests: Core areas include NLP (translation, summarization, adversarial attacks), healthcare informatics (clinical NLP, health service analysis), and cybersecurity (IoT security, privacy-preserving systems). Cross-cutting themes include generative models, cross-lingual systems, and ethical AI. Grants & Projects: Principal Investigator on ARC Discovery/Linkage projects and CRC grants. Recent projects include controllable machine translation (Amazon), privacy-preserving digital agriculture, and STEM innovation (ASTRID project with NBN Co). Labs & Collaborations: Leads the UTS Global Big Data Technologies Centre, collaborating on projects like adversarial NLP attacks, medical machine translation, and secure IoT frameworks.

Yang Zhouمشاهده پروفایل

دانشیار

• Big Data Algorithms
• Data Mining
• Machine Learning
• +۵ مورد دیگر

Yang Zhou is an Associate Professor in the Department of Computer Science and Software Engineering at Auburn University, part of the Samuel Ginn College of Engineering. His research focuses on big data algorithms, machine learning, data mining, and distributed computing. He has contributed to advancements in federated learning frameworks, graph mining tools, and spatial machine learning for environmental applications like flood mapping. Education includes a Ph.D. in Computer Science from Georgia Tech (2021), M.E. in Computer Application Technology from Chongqing University (2016), and B.E. in Engineering from Jiangnan University (2014). His work emphasizes scalable algorithms for large-scale systems, with tools like DirDense for dense subgraph mining and FedASMU for federated learning optimization. Recent publications explore adversarial robustness, blockchain strategies in IoT, and curriculum-based learning for large language models. He advises on interdisciplinary projects at the intersection of AI and environmental science.

Katja Hoseمشاهده پروفایل

استاد

• Knowledge Graphs
• Semantic Web
• Data Management
• +۷ مورد دیگر

Katja Hose is a Full Professor of Data Management at TU Wien's DBAI research unit, heading the Data Management and Knowledge-Driven AI Lab. She previously held a Poul Due Jensen Foundation Professorship at Aalborg University. Her research focuses on data and knowledge engineering, including graph databases, knowledge graphs, querying, analytics, and machine learning, with interdisciplinary applications in bioscience, healthcare, and environmental assessment. Education: PhD in Computer Science (Ilmenau University of Technology, 2009), Postdoc at Max Planck Institute for Informatics (2009–2012). Academic roles include Program Co-Chair for ISWC 2024 and EDBT 2023, and editorial board membership at VLDBJ and TGDK. She leads projects like TARGET (health virtual twins) and ARMADA (data management). Research Interests: Knowledge Graphs, Semantic Web, Big Data, Machine Learning, Data Integration, and Provenance Systems. Key contributions include SHACL shape extraction, conversational data analytics, and environmental knowledge graphs. Awards include the 2025 Distinguished Meta-Reviewer Award and 2024 Manfred Paul Award. Advising and Grants: Supervised students including E. Pürmayr (Diploma Thesis 2025). Active in EU projects (TARGET, ARMADA) and grant coordination. Labs/Teams: DMKI Lab at TU Wien, collaborating with interdisciplinary teams in healthcare and environmental science.

• Cultural Studies
• Globalization and Culture
• Entrepreneurship
• +۳ مورد دیگر

Tina Dacin is a Professor of Strategy and Organizational Behavior at the Smith School of Business, Queen's University, Canada. She holds the Stephen J.R. Smith Chair and serves as Director of the Community Impact Research Program. Her roles include former Director of the Smith School’s Centre for Social Impact, former University Senate member, and former Chair of the Principal’s Innovation Fund Committee. She co-founded the Community of Social Innovation Chair and holds affiliations with the Academy of Management and Informs. Education: Ph.D. (1993) in Business Administration from the University of Toronto and M.A. in Psychology from the University of New Brunswick. Research focuses on cultural heritage, social innovation/entrepreneurship, strategic alliances, and globalization. Key themes include custodianship, tradition preservation, place-making, and institutional dynamics. Recent work explores craft-based ventures, place-sensitive organizational practices, and institutional renewal in crises like the Detroit water wars. Awards: None explicitly listed, though her chair position reflects academic distinction. Teaching includes courses on global social impact and analytics/ethics policy. Labs/Teams: Leadership in the Community Impact Research Program and the Scotiabank Centre for Customer Analytics.

• Data Visualization
• Human-Computer Interaction
• Big Data Analytics
• +۱۳ مورد دیگر

Huamin Qu is a Chair Professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST). He serves as the Founding Dean of the Academy of Interdisciplinary Studies (AIS), Founding Head of the Division of Emerging Interdisciplinary Areas (EMIA), and was the Founding Acting Head of Computational Media and Arts (CMA) at HKUST(GZ). Qu directs the VisLab and coordinates the Human-Computer Interaction (HCI) group. He obtained his BS in Mathematics from Xi'an Jiaotong University and MS/PhD in Computer Science from Stony Brook University. Qu's research integrates Data Visualization , Human-Computer Interaction , and Human-Centered AI , with applications in urban informatics, social networks, and explainable AI. His work focuses on developing interactive systems for big data analytics, visual storytelling, and AI-driven decision support. Research extends to multimodal communication, fintech, and augmented reality applications. His publications emphasize visual analytics for complex datasets (mobility, social media, financial), interaction techniques for immersive environments, and AI-enhanced visualization tools. Recent works explore explainable AI interfaces and large-scale data communication frameworks. IEEE Visualization Academy (2020) IEEE VGTC Technical Achievement Award AI 2000 Most Influential Scholar (2019, 2023, 2024) 21 Best Paper/Honorable Mention awards IBM Faculty Award (2009) APICTA Merit Award (2015) Yelp Dataset Grand Prize (2018) Qu has advised 48 PhD graduates (21 now faculty at institutions like UC Davis, University of Minnesota, Texas A&M) and 30 MPhil students. He secured major grants including RGC theme-based projects (digital citizenship, air pollution), UGC AoE (slope safety), and China's 973 Program. As VisLab director, he leads 20+ researchers in visualization/HCI projects adopted by Microsoft, IBM, Huawei, and Tencent.

Zhenman Fangمشاهده پروفایل

دانشیار

• Customizable Computing
• Heterogeneous Computing
• FPGA Acceleration
• +۸ مورد دیگر

Dr. Zhenman Fang is an Associate Professor at the School of Engineering Science , Simon Fraser University (SFU) , where he founded and directs the HiAccel Lab . He also holds an associate membership in the School of Computing Science at SFU. His research focuses on customizable computing with software-defined hardware acceleration , addressing performance, energy-efficiency, and reliability in post-Moore’s law computing across domains like machine learning , big data analytics , quantum chemistry , and precision medicine . Education: Ph.D. in Computer Science from Fudan University (2014), with a visit to University of Minnesota during his studies. Postdoctoral Work: University of California, Los Angeles (UCLA) (2014-2017). Industry Experience: Staff Software Engineer at Xilinx (2017-2019). Dr. Fang’s research spans the entire computing stack , including application characterization , accelerator-rich architecture design , and programming/tool support . He has developed frameworks like HiSpMV , SyncNN , and SQL2FPGA , emphasizing FPGA acceleration for vision transformers , quantum chemistry , and spiking neural networks . His work has been recognized with 3 best paper awards (FPL 2024, TCAD 2019, MEMSYS 2017) and 3 best paper nominees (FCCM 2025, HPCA 2017, ISPASS 2018). Recent publications highlight trends in low-precision machine learning ( ShiftQuant , ESRU ), quantum chemistry acceleration ( SERI ), and vision transformer optimization ( Quasar-ViT ). His HiAccel Lab actively mentors PhD and MASc students , with notable graduates like Alec Lu (PhD 2024, now at Meta) and Philip Stachura (MASc, now with BC Graduate Scholarship). Scientific Awards: Inaugural SFU Research Excellence Award - Horizon Award (2025) FPL 2024 Stamatis Vassiliadis Best Paper NSERC Alliance Award (2020) CFI JELF Award (2019) Xilinx University Program Award (2019) IEEE Senior Member (2023) Grants: NSERC Discovery Grant (2019) CFI JELF Funding (2019) Huawei and Xilinx sponsorships Dr. Fang leads open-source initiatives like SyncNN , PASTA , and SQL2FPGA , and serves as General Chair for ASAP 2025 and Program Co-Chair for RAW 2025 . His lab collaborates globally with institutions such as UCLA , Northeastern University , and Xidian University .

Charles Rahalمشاهده پروفایل

دانشیار

• Social Science Methodology
• Applied Social Data Science
• High-dimensional Econometrics
• +۸ مورد دیگر

Charles Rahal is an Associate Professor in Data Science and Informatics at the University of Oxford, with additional affiliations as an Associate Member of Nuffield College and Researcher at the Gradel Institute, New College. He serves as a Co-Investigator at the ESRC Centre for Care and sits on the Steering Group of Reproducible Research Oxford. His academic career includes previous roles as a Senior Departmental Research Lecturer at the Leverhulme Centre for Demographic Science and a British Academy Postdoctoral Fellow. Dr. Rahal completed his PhD in 2016 and has established himself as a prominent social science methodologist and applied social data scientist with expertise in high-dimensional econometrics. His research spans multiple domains, focusing particularly on unique Big Data origination processes and their relationship to social inequality, mobility, and stratification. He is deeply engaged in machine learning methods, civic technology, spatial and time series econometrics, model uncertainty, and scientometrics. His recent publications reveal a strong trend toward computational social science, with significant contributions to understanding prediction limits, pandemic impacts, healthcare systems, and environmental sustainability. The articles demonstrate his interdisciplinary approach, bridging traditional social science with cutting-edge computational methods, particularly in the analysis of large-scale datasets and development of novel metrics like the InterModel Vigorish for model comparison. Dr. Rahal is actively involved in teaching and mentoring, co-convening courses in Demographic Analysis, Life Course Research, and the Oxford Partner site of the Summer Institute in Computational Social Sciences. He has developed workshops on machine learning, command line interfaces, and LaTeX, reflecting his commitment to methodological training in social science. He leads the Metrics and Models lab and maintains several open-source projects including the GWAS Diversity Monitor and RobustiPy. His editorial roles include Associate Editor-in-Chief at the Journal of Social Computing and Associate Editor at ACM Transactions on Social Computing, highlighting his influence in shaping computational social science methodology.

Alexandra M. Schmidtمشاهده پروفایل

استاد

• Biostatistics
• Bayesian Inference
• Spatial Statistics
• +۳ مورد دیگر

Professor Alexandra M. Schmidt is a leading academic in Biostatistics at McGill University, holding an endowed University Chair. She specializes in spatial and spatio-temporal modeling, particularly in epidemiology and environmental health. Previously, she served as a Full Professor at the Federal University of Rio de Janeiro (2012–2016). Her research focuses on Bayesian methodologies for analyzing complex processes, including disease spread, environmental hazards, and socio-economic disparities. She has authored influential books such as Spatio-Temporal Methods in Environmental Epidemiology with R (2023) and contributed to over 150 peer-reviewed articles. Key awards include the ISBA Fellowship (2024), ASA Fellowship (2020), and the Abdel El-Shaarawi Award (2008). Education: PhD in Statistics (2001, University of Sheffield, UK), MSc and BSc in Statistics (Federal University of Rio de Janeiro, Brazil). Research interests span Bayesian inference, spatial statistics, and environmental epidemiology. She has advised numerous PhD/MSc students and collaborated on projects linking statistical methods to public health challenges, such as modeling dengue outbreaks and air pollution impacts. Active in academic service, she has chaired major conferences (e.g., 2022 ISBA World Meeting) and serves on editorial boards of top journals like Bayesian Analysis and Canadian Journal of Statistics . Teaching includes advanced courses on generalized linear models, spatial epidemiology, and Bayesian analysis. Her work bridges theoretical statistics with practical applications, addressing global health issues through innovative spatio-temporal modeling techniques.