جست‌وجوی استاد و پژوهشگر

Brook Wuمشاهده پروفایل

دانشیار

• Text Mining
• Information Extraction
• Natural Language Processing
• +۵ مورد دیگر

Brook Wu is an Associate Professor in the Informatics Department at New Jersey Institute of Technology (NJIT) and serves as Associate Dean for Academic Affairs. She holds a Ph.D. in Information Science from SUNY Albany (2001), an M.S. in Information Resources Management from Syracuse University (1996), and a B.B.A. in Management Information Systems from Tamkang University (1993). Her research focuses on text mining, natural language processing, and information retrieval, with applications in fake news detection on social media and automated feedback systems for education. She developed systems like Fned for fake news detection and tools for writing-to-learn activities. Her work integrates machine learning, user behavior analysis, and educational technology to enhance learning outcomes and information access. Her research portfolio includes over 30 publications, spanning topics from conceptual term extraction in medical documents to personalized query refinement in digital libraries. She is also a proponent of leveraging computational methods for educational assessment, such as analyzing class discussion messages to evaluate student performance. Her contributions have been presented in venues like the International Conference on Data Mining and the Journal of the American Society for Information Science and Technology. She maintains an active research website at http://web.njit.edu/~wu . In academic leadership, she has championed initiatives like the NJIT ADVANCE program, applying social network analysis to support faculty mentoring. Her interdisciplinary approach bridges computer science, education, and information science, addressing challenges in both technological innovation and pedagogical practice.

Abby Stylianouمشاهده پروفایل

دانشیار

• Computer Vision
• Deep Learning
• Image Processing
• +۴ مورد دیگر

Dr. Abby Stylianou is an Associate Professor in the Department of Computer Science at Saint Louis University, affiliated with the Taylor Geospatial Institute and SLU Research Institute. Her research focuses on computer vision, deep learning, and explainable AI, with applications in social justice (e.g., combating human trafficking through hotel image retrieval) and environmental science (e.g., climate change studies using time-lapse imagery). She emphasizes citizen science and non-expert engagement, designing accessible tools for data collection and analysis. Education: Ph.D., M.S., and B.A. in Environmental Studies/Geoscience from Washington University in St. Louis. Prior roles include Research Scientist at WashU's Media and Machines Lab and Postdoc at George Washington University. Research Interests: Image Processing, Deep Learning, Citizen Science, Genotype-Phenotype Interactions, and Geospatial Analysis. Awards: Google Research Scholar (2022), NSF Biology Integration Institute Member, St. Louis Business Journal’s 40 Under 40 (2023). Grants: SLU School of Medicine grant for implant classification in spinal surgery, DOE/ARPA-E funding for plant phenotyping. Teaching includes courses on Artificial Intelligence, Computer Vision, Databases, and Deep Learning. She advises students in her lab, focusing on strong programming skills and applications in vision and AI.

Valerie Kingمشاهده پروفایل

استاد

• Graph Algorithms
• Randomized Algorithms
• Probabilistic Analysis
• +۳ مورد دیگر

Valerie King is a Professor in the Department of Computer Science at the University of Victoria, Canada. Her research focuses on graph algorithms, randomized algorithms, probabilistic analysis, and fault-tolerant distributed computing. She holds a PhD from UC Berkeley. Her work emphasizes scalable and secure distributed systems, with contributions to consensus protocols, network algorithms, and adversarial resilience. Dr. King's research bridges theoretical foundations and practical applications in computing systems. Education: PhD in Computer Science from UC Berkeley. Research Interests: Graph algorithms, randomized algorithms, fault-tolerant distributed computing, and algorithmic defense mechanisms against cyberattacks. Her work often addresses challenges in network security, consensus protocols, and efficient distributed algorithms. Key research trends in her articles include Byzantine agreement protocols, communication-efficient distributed algorithms, and sublinear-time graph algorithms. She explores geometric network models, secure multi-party computation, and energy-efficient communication strategies. Her contributions span algorithm design, complexity analysis, and practical implementations in adversarial environments. Advising and Grants: While specific student names or grant details are not listed here, her extensive publication record reflects sustained research activity in distributed systems and algorithms. She contributes to foundational research in theoretical computer science with applications to real-world distributed networks. Labs/Teams: Affiliated with the Computer Science department at UVic, collaborating on projects related to distributed computing and algorithmic security. Her work often involves interdisciplinary approaches to network resilience and computational efficiency.

Michael Grossniklausمشاهده پروفایل

استاد

• Data Management
• Information Systems
• Stream Processing
• +۳ مورد دیگر

Michael Grossniklaus is Professor for Databases and Information Systems at University of Konstanz's Department of Computer and Information Science. His research develops data management and processing technologies supporting data science applications, with emphasis on stream processing, context-aware systems, and web engineering solutions. Key research areas: Continuous query processing for streaming data Context-aware mobile data management Machine learning for database optimization Multi-channel web information systems Semantic web and RDF stream processing Publications demonstrate sustained contributions to database query processing, with recent work focusing on cardinality estimation using geometric deep learning and cloud database optimization. Developed C-SPARQL language extension for continuous RDF stream queries.

Wei Xiangمشاهده پروفایل

استاد مدعو

• Internet of Things
• Machine Learning for IoT
• Big Data Analytics
• +۳ مورد دیگر

Wei Xiang is an Adjunct Professor and Founding Chair of Electronic Systems and Internet of Things Engineering at James Cook University. He leads the development of Australia's first accredited IoT Engineering program and conducts research at the intersection of IoT, machine learning, and big data analytics. Research encompasses: IoT systems for environmental monitoring Machine learning applications for sensor data Edge computing architectures Wireless communication protocols Smart agriculture technologies Publication analysis reveals focus areas: Smart transportation systems (25%) Agricultural IoT (20%) Medical AI applications (15%) Industrial IoT (15%) Network optimization (25%) Honors include Pearcey Entrepreneurship Award, Engineers Australia Cairns Engineer of the Year, and TNQ Innovation Award for establishing Australia's first IoT Engineering degree. Current projects include closed-loop irrigation systems for sugarcane farms and marine monitoring using distributed deep learning.

David Anastasiuمشاهده پروفایل

دانشیار

• Machine Learning
• Data Mining
• Computational Genomics
• +۶ مورد دیگر

David Anastasiu is an Associate Professor in the Computer Science and Engineering department at Santa Clara University. He previously served as an Assistant Professor at San José State University. His research focuses on machine learning, data mining, computational genomics, and high-performance computing. He earned his Ph.D. from the University of Minnesota under George Karypis and holds a B.A. in Bible/Theology from Moody Bible Institute. Education: Ph.D., Computer Science, University of Minnesota (2016) M.S., Computer Science, Texas State University (2011) B.A., Bible/Theology, Moody Bible Institute (2001) Research Interests: Dr. Anastasiu's work spans machine learning, data mining, and high-performance computing. He develops algorithms for nearest neighbor search, time series forecasting, and hydrologic prediction. His lab focuses on applications in environmental science, healthcare, and smart cities. Recent projects include the LSPM and PFformer models for long-term time series analysis, and the AI City Challenge for urban analytics. Grants & Awards: NSF CAREER Award (2025) NVIDIA Academic Grant (2025) Outstanding Career Influencer Award (2021–2022) Best Research Paper, DSAA 2016 Advising & Labs: Leads the Anastasiu Lab, supervising over 50 students. Notable projects include kidney health monitoring apps, hydrologic flow prediction, and AI for traffic networks. Collaborates with NVIDIA, Santa Clara Valley Water District, and Infoblox.

Xi Niuمشاهده پروفایل

دانشیار

• Data and text analytics
• Knowledge discovery
• Search behavior
• +۳ مورد دیگر

Xi Niu is an Associate Professor at the College of Computing and Informatics, University of North Carolina at Charlotte. His research focuses on data and text analytics, knowledge discovery, search behavior, and interactive information retrieval with a strong emphasis on computational serendipity and cybersecurity applications. His work bridges theoretical advancements with practical applications, particularly in developing machine learning models for cross-domain recommendations, improving user experience through serendipity enhancement, and automating cybersecurity threat analysis. Notable contributions include frameworks for modeling user curiosity in recommender systems and methodologies for contradiction detection in text. Recent research trends show a focus on leveraging deep learning techniques for extreme multi-label classification, contrastive learning in recommendation systems, and integrating topological analysis for understanding complex text patterns. His work also explores human-centered design principles in crowdsourcing and active learning systems.

Zachary G. Ivesمشاهده پروفایل

استاد

• Data Integration
• Data Provenance
• Machine Learning Systems
• +۶ مورد دیگر

Zachary G. Ives is the Adani President's Distinguished Professor and Chair of the Computer & Information Science Department at the University of Pennsylvania. His research focuses on data integration, provenance, machine learning systems, and scalable computing. Notable contributions include the Juneau project for data management in computational notebooks and the Orchestra system for collaborative data sharing. He leads initiatives like the ASSET Center for Safe, Explainable, and Trustworthy AI and the Warren Center for Network and Data Science. Education: While explicit degrees aren’t detailed, his roles and academic contributions imply advanced degrees in Computer Science. Current courses include CIS 2450 (Big Data Analytics) and NETS 212 (Scalable and Cloud Computing). Research spans data lakes, sensor networks (ASPEN project), and biomedical applications like seizure prediction via the IEEG Portal. His work bridges databases, ML, and distributed systems, with emphasis on trustworthiness and explainability. Key awards include the NSF CAREER Award, Lindback Teaching Award, and ACM Fellow status. Grants from NSF, NIH, DARPA, and industry partners (Amazon, Google) support his work. He advises numerous PhD students and collaborates with faculty across disciplines, including Neurology and Biostatistics. Labs/Teams: Penn Database Group, ASSET Center, Warren Center, and collaborations with Meta Research and Mayo Clinic. Active in academic service as SIGMOD PC co-chair and journal editor.

• Algorithmic Game Theory
• Approximation Algorithms
• Facility Location Problems
• +۲ مورد دیگر

Dimitris Fotakis is Professor of Computer Science at the National Technical University of Athens (NTUA) and Collaborating Senior Researcher at the Archimedes Unit/ATHENA RC. His research in theoretical computer science focuses on algorithmic game theory, approximation algorithms, and facility location problems. He leads research projects including BALSAM (Beyond Worst-Case Analysis in Approximation Algorithms) funded by HFRI. With publications in ICALP, NeurIPS, and STOC, his work develops efficient algorithms for optimization under uncertainty. Fotakis serves on program committees of major conferences including ICALP and SAGT.

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

Tala Talaei Khoei serves as an Assistant Teaching Professor at Northeastern University, conducting interdisciplinary research at the intersection of artificial intelligence, cybersecurity, and healthcare innovation. Her academic profile reflects active engagement in developing practical AI solutions for critical real-world challenges. Her research program spans three primary domains: (1) Healthcare AI applications including pediatric pneumonia/asthma detection through respiratory sound analysis using Google's HeAR model; (2) Cybersecurity systems focused on ransomware detection, GPS spoofing countermeasures for UAVs, and IoT security frameworks; and (3) Generative AI advancements in large language models, emotional AI, and sustainable computing practices. This tripartite focus demonstrates her commitment to translating theoretical AI research into tangible societal benefits. Analysis of her 15 recent 2025 publications reveals accelerating research velocity with particular emphasis on data-efficient AI techniques for small datasets, multimodal security threat detection, and ethical implementation of generative models. Her work consistently bridges theoretical machine learning innovations with domain-specific applications, showing growing influence in both healthcare informatics and cybersecurity communities through high-impact survey papers and novel framework development.

Bo Zhaoمشاهده پروفایل

استادیار

• Distributed Systems
• Machine Learning Systems
• Large-Scale Data Processing
• +۲ مورد دیگر

Bo Zhao is an Assistant Professor in the Department of Computer Science at Aalto University, leading the Aalto Data-Intensive System group (ADIS). His research focuses on building efficient data-intensive systems across multiple layers, from scalable machine learning systems to distributed data management systems. Research Focus: Scalable machine learning systems Distributed data processing Hardware-software co-design Quantum computing systems High-performance computing Current Projects: AthenaRL (scalable RL systems), LARA (quantum ML algorithms), FlexMoE (efficient mixture-of-experts systems). His group develops systems enabling ML deployment across hardware from quantum computers to mobile devices. Education & Career: PhD from Humboldt-Universität zu Berlin. Previously at Queen Mary University of London, Imperial College London, and Amazon Web Services. Research published in SOSP, VLDB, USENIX ATC.

• Natural Language Interfaces to Databases
• Data Integration
• Large Language Models
• +۱ مورد دیگر

Davood Rafiei is a Professor in the Faculty of Science at the University of Alberta, specializing in the Department of Computing Science. His research bridges databases, natural language processing, and web technologies, with current projects on large language models and data integration. He holds a B.Sc. in Computer Engineering from Sharif University of Technology (1990), an M.Sc. in Computer Science from the University of Waterloo (1995), and a Ph.D. from the University of Toronto (1999). His work explores semantic annotation, table transformations, and adversarial analysis in social media. Recent publications emphasize applications of LLMs to structured data tasks like text-to-SQL conversion and knowledge graph integration. Research trends show consistent innovation in NLP-driven data management solutions, with collaborations spanning Google, Kyoto University, and the University of Paris. No awards, grants, or lab/team details are documented.

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۳ مورد دیگر

Dale Schuurmans is a Professor in the Faculty of Science at the University of Alberta, specializing in Computing Science. His research focuses on developing systems that learn predictive models from massive data sources, particularly in complex domains like perception, language interpretation, and bioinformatics. Education: B.Sc. Mathematics, University of Alberta (1985) B.Sc. Computing Science, University of Alberta (1986) M.Sc. Computing Science, University of Alberta (1988) Ph.D. Computer Science, University of Toronto (1996) His research interests span artificial intelligence, machine learning, reinforcement learning, probability modeling, optimization, and search algorithms. Schuurmans investigates fundamental challenges in knowledge representation for learning and navigating complex model spaces to prevent over/under-fitting. Schuurmans' recent publications demonstrate a strong focus on advancing fundamental machine learning techniques, particularly in reinforcement learning, optimization methods, and generative models. His work shows consistent emphasis on theoretical foundations of deep learning, model generalization, and efficient training algorithms. While no specific awards are mentioned, Schuurmans maintains an active research program investigating statistical natural language modeling, reinforcement learning, and learning search control. He has developed novel methods for probabilistic inference, optimization, and constraint satisfaction.

Mohammad Alizadehمشاهده پروفایل

دانشیار

• Computer Networks
• Systems
• Machine Learning
• +۶ مورد دیگر

Mohammad Alizadeh is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT, affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). He holds additional roles as Director of the 6-A MEng Thesis Program and Industry Officer for EECS, overseeing the EECS Alliance industry outreach program. His research focuses on computer networks, systems, and their applications in datacenters, cloud computing, and edge computing. Notable areas include machine learning for systems, congestion control, and network protocols. Education: PhD (Stanford University), MS in Electrical Engineering (Stanford), BS (Sharif University of Technology). Industry Experience: Former roles at Microsoft Research, Insieme Networks, and Cisco Systems. Research Interests: Alizadeh’s work emphasizes improving network performance, robustness, and management in large-scale systems. Key projects include DCTCP, CONGA, and Pensieve (adaptive video streaming with reinforcement learning). His contributions span network architecture, programmable switches, and learning-based systems. Awards: NSF CAREER Award Alfred P. Sloan Research Fellowship SIGCOMM Rising Star Award Microsoft Research Faculty Fellowship Teaching: Courses include 6.888 (Advanced Topics in Networking), 6.829 (Computer Networks), and 6.02 (Digital Communication Systems). His teaching emphasizes cutting-edge networking research and practical system design. Labs/Teams: Active member of CSAIL, leading projects in distributed systems, network protocols, and cloud computing.

Samuel Maddenمشاهده پروفایل

استاد

• Database Systems
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Samuel Madden is the Faculty Head of Computer Science and Distinguished College of Computing Professor at MIT's Department of Electrical Engineering and Computer Science (EECS). He joined MIT in 2004, earning a PhD from UC Berkeley (2003) and BS/MEng from MIT (1999). His research focuses on database systems, machine learning integration, and high-performance data processing. He co-leads the Data Systems for AI Lab and Data Systems Group. Key achievements include co-founding Cambridge Mobile Telematics, receiving ACM Fellow (2020), and awards like the NSF CAREER Award and SIGMOD Codd Innovations Award. His work bridges database systems with modern AI applications, emphasizing scalable and efficient data processing. Research interests span database analytics, query processing, cloud computing, and applying machine learning to system design. He has published extensively on topics like query optimization (e.g., Abacus, Kairos), AI-driven data systems (Palimpzest, Symphony), and scalable analytics (RITA, Cackle).