جستجوی استاد راهنمای دانشگاهی

Maurizio Lenzeriniمشاهده پروفایل

استاد

• Database theory
• Data modeling
• Artificial intelligence
• +۳ مورد دیگر

Maurizio Lenzerini is a Professor at Sapienza University of Rome , where he has held positions since 1990. He has served as Chairman of the Bachelor Program in Computer Engineering (1993–2000), PhD Program in Computer Science and Engineering (2002–2008), and currently as Chairman of the PhD School in Information and Communication Technologies. His research focuses on database theory, data modeling, artificial intelligence, knowledge representation, and ontology languages , with applications in service modeling and healthcare. Lenzerini has supervised numerous PhD students, many of whom hold academic positions in Europe. He has been a member of the Editorial Board of journals including Information Systems , IEEE Transactions on Knowledge and Data Engineering , and Logical Methods in Computer Science . He has also served as Program Chair for conferences such as PODS 2008 and ICDT 2003. His scientific awards include: 2009 ACM Fellow 2008 IBM Faculty Award on Service Science 2008 ACM Recognition of Service Award 2008 ECCAI Fellow 2005 IBM Shared University Research Award in P2P Data Management 2003 IBM Shared University Research Award in Information Integration 2018 ACM PODS Alberto Mendelzon Test-of-Time Award AAAI Fellow EurAI Fellow ER Fellow AAIA Fellow Member of Academia Europaea Lenzerini leads a world-renowned research group in ontology-based data management since 1987. His work spans query answering, knowledge graphs, data integration, and semantic technologies, with applications in healthcare (e.g., AMD-STITCH project) and healthcare analytics. He has organized international conferences and delivered keynotes at major events like PODS and IJCAI.

Giuseppe Careniniمشاهده پروفایل

استاد

• Artificial Intelligence
• Natural Language Processing
• Human-Computer Interaction
• +۴ مورد دیگر

Giuseppe Carenini is a Professor in the Department of Computer Science at the University of British Columbia (UBC), holding the role of Director of the UBC Master of Data Science program. He is affiliated with the Faculty of Science and leads multiple research groups including the UBC NLP Group, CANARY, IUI Group, and CAIDA. His research focuses on intersections between Artificial Intelligence, Computational Linguistics, and Human-Computer Interaction, with notable contributions to discourse analysis, summarization, and interactive visualization. He has been recognized with awards such as the SIGDIAL Best Paper Award and ACM TiiS Best Paper Award. His work spans academic leadership, course instruction (e.g., CPSC courses on AI, NLP), and collaborations with industry and sustainability initiatives (e.g., IRES, Data Science Institute). His projects often integrate multimodal data analysis, decision support systems, and adaptive interfaces. Research Interests: - Artificial Intelligence: User Modeling, Preference Elicitation, Decision Theory. - Computational Linguistics: Discourse Analysis, Summarization, Argumentation. - Human-Computer Interaction: Intelligent Interfaces, Information Visualization. Publications highlight advancements in neural discourse parsing, topic segmentation, and LLM applications in healthcare and operations research. His work bridges theoretical contributions with practical tools like CONVisIT and T³-Vis for data exploration and model interpretation.

Jimmy Linمشاهده پروفایل

استاد

• Information Retrieval
• Natural Language Processing
• Data Management
• +۲ مورد دیگر

Jimmy Lin is a Professor and David R. Cheriton Chair at the University of Waterloo's David R. Cheriton School of Computer Science. His research focuses on information retrieval, natural language processing, and data management, with applications in large-scale data analysis and AI systems. Previously at the University of Maryland, he earned his PhD from MIT in 2004. PhD in Electrical Engineering and Computer Science, MIT (2004) MS, Mechanical Engineering, Penn State University BSc, Mechanical Engineering, Addis Ababa University His research integrates information retrieval, NLP, and data management to build tools for understanding large datasets. Current interests include neural retrieval models, cross-lingual systems, and efficient search algorithms. Recent publications focus on retrieval-augmented generation (RAG), model efficiency, multilingual NLP, and evaluation methodologies. Trends include hybrid dense-sparse retrieval systems, robustness improvements, and practical toolkits like Anserini and Tevatron. ACM Fellow SIGIR Academy ACL Fellow SAE Ralph R. Teetor Educational Award (2014) NSF CAREER Award (2011) He advises 36+ PhD/Master's students and leads multiple industry collaborations (Primal, RSVP, Twitter). His lab develops open-source tools for retrieval and NLP, focusing on scalable and multilingual applications.

• Artificial Intelligence
• Data Management and Data Science
• Distributed Computing and Systems Software
• +۸ مورد دیگر

Dr. Amin Abken is an Associate Teaching Fellow at the School of Information Technology, Deakin University, where he contributes to academic instruction and research in artificial intelligence, data management, and distributed computing. He holds a Ph.D. from Deakin University and is affiliated with the Faculty of Science, Engineering, and Built Environment. His research spans Context-Aware Computing , Reinforcement Learning , and Internet of Things (IoT) , with a focus on adaptive caching strategies, group activity recognition, and intelligent transportation systems. His recent publications highlight advancements in context caching techniques and reinforcement learning for IoT environments. Dr. Abken's collaborative work includes co-authored articles in journals like ACM Transactions on Internet of Things , IEEE Internet of Things Magazine , and IEEE Transactions on Intelligent Transportation Systems . He employs methodologies in probabilistic analysis , dynamic system optimization , and edge-cloud platforms for urban IoT ecosystems. His articles demonstrate a consistent focus on IoT-based systems , context-aware algorithms , and distributed computing since 2014, with recent trends emphasizing reinforcement learning and smart city applications like carparking simulations. Dr. Abken collaborates frequently with researchers such as Weerasinghe , Zaslavsky , and Loke SW , addressing challenges in context caching , group activity recognition , and edge computing across publications in ACM, IEEE, and SpringerOpen.

• Database Systems
• Distributed Computing
• Machine Learning
• +۳ مورد دیگر

Carsten Binnig is a researcher affiliated with TU Darmstadt, Germany, focusing on database systems, distributed computing, and machine learning integration. His work spans high-performance data management, query optimization, and hardware-software co-design for modern networks. He holds a PhD from Heidelberg University (2008) and has contributed extensively to conferences like SIGMOD, VLDB, and CIDR. His research emphasizes scalable OLTP/OLAP systems, secure cloud DBMSs, and leveraging GPUs/accelerators for database operations. Notable projects include GOLAP (GPU-accelerated OLAP), ScaleStore (storage engine using NVMe/RDMA), and DBMS Annihilator (workload generator). He co-leads initiatives on testing database systems (DBTest workshop) and benchmarks for shared databases.

• Reinforcement Learning
• Knowledge Representation
• Planning and Decision Making
• +۷ مورد دیگر

Scott Sanner is a Professor in the Department of Mechanical and Industrial Engineering at the University of Toronto's Faculty of Applied Science and Engineering. With an extensive publication record spanning over two decades, his research bridges artificial intelligence, machine learning, and engineering applications. His work demonstrates significant contributions across multiple top-tier conferences including AAAI, ICLR, NeurIPS, and SIGIR. Dr. Sanner's research interests encompass Reinforcement Learning, Knowledge Representation, Planning and Decision Making, Recommender Systems, and Large Language Models. His work shows a consistent focus on bridging symbolic and neural approaches to AI, with particular emphasis on commonsense reasoning, traffic signal control, and conversational recommendation systems. Recent publications demonstrate increasing integration of large language models with traditional AI techniques for complex reasoning tasks. Analysis of his recent publications reveals a strong trend toward leveraging large language models for knowledge representation and reasoning tasks, while maintaining his foundational work in reinforcement learning and planning. His research increasingly focuses on practical applications in transportation systems, recommendation technologies, and commonsense reasoning frameworks that combine neural and symbolic approaches. Dr. Sanner has mentored numerous graduate students who have become active researchers in the field, including Jihwan Jeong, Zheda Mai, Armin Toroghi, and Anton Korikov. His collaborative work spans multiple institutions and demonstrates strong industry and academic partnerships, particularly with researchers from Australian National University and various technology companies. His research group appears to focus on intelligent systems for decision making under uncertainty, with applications ranging from traffic management to personalized recommendation systems. Current projects show significant emphasis on integrating large language models with traditional AI techniques for more robust and explainable systems.

• Computer Vision
• Object Recognition
• Scene Understanding
• +۷ مورد دیگر

Derek Hoiem is a Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Siebel School for Computing and Data Science since 2009. He holds a PhD in Robotics from Carnegie Mellon University (2007) and completed a Beckman Postdoctoral Fellowship (2008). As co-founder and Chief Science Officer of Reconstruct, he develops AI-driven tools for construction progress monitoring. His research focuses on computer vision, object recognition, scene understanding, and graphics. Key honors include IEEE Fellow (2022), University Scholar (2022), and the Sloan Research Fellowship (2013). He has received NSF CAREER and Intel Early Career awards, alongside best paper awards at CVPR (2006) and WACV (2015). His teaching excellence is reflected in numerous 'List of Teachers Ranked as Excellent' accolades across multiple semesters. Research interests span 3D reconstruction, neural networks, and multimodal models. His work addresses challenges in visual program generation, neural radiance fields, and construction monitoring through photogrammetry. He leads projects in continual learning, efficient AI systems, and explainable multimodal interactions. Professional contributions include grants from NSF and industry partnerships. He advises on cutting-edge AI applications in construction, graphics, and vision-language integration. His lab’s innovations bridge theoretical advancements with real-world systems like Reconstruct’s Visual Command Center.

• Machine Learning
• Natural Language Processing
• Neural Networks
• +۴ مورد دیگر

Subhabrata Mukherjee is a prominent researcher in the fields of Machine Learning and Natural Language Processing. His work focuses on advancing techniques such as model compression, multimodal learning, and credibility analysis. He holds a Ph.D. from Saarland University (2017), where he explored probabilistic graphical models for credibility analysis in online communities. His research spans topics including large language models (LLMs), knowledge distillation, and ethical AI. Key contributions include the development of retrieval-augmented generation frameworks, cost-efficient LLM architectures, and systems for mitigating misinformation. His work bridges theoretical advancements with practical applications in healthcare, social media, and multilingual systems. Collaborations with institutions like Microsoft and academic networks highlight his interdisciplinary approach to AI challenges. Research Highlights: Developed GraphRAG for integrating graph structures into generative models Pioneered Hybrid LLM architectures balancing efficiency and quality Advanced techniques for hallucination reduction in LLMs Contributed to fake news detection systems like DeClarE Explored experience-aware recommendation models His publications reflect sustained innovation across NLP, ML systems, and ethical AI, with impactful contributions to both academic conferences (ACL, NeurIPS, KDD) and industry research.

• Code Clones
• Software Development
• Deep Learning
• +۳ مورد دیگر

Dr. Muhammad Hammad is a postdoctoral researcher at Eindhoven University of Technology (TU/e), affiliated with the Department of Mathematics and Computer Science within the School of Software Engineering and Technology. He holds a PhD in Computer Science from TU/e (2022), focusing on visualization and learning applications of code clones. His research interests span code clone analysis, software development tools, deep learning, visualization techniques, and digital twins for smart systems. Hammad has contributed to innovative projects like Clone-Writer and Clone-Seeker, which enhance code development efficiency through clone utilization. His work addresses challenges in API security, predictive analytics for water management, and improving software maintenance practices. Research contributions include systematic reviews on harmful API use repair techniques, digital twin-based smart water management systems, and advancements in code clone detection and visualization. His interdisciplinary approach bridges software engineering with environmental applications, contributing to UN Sustainable Development Goals related to clean water and responsible consumption. Hammad has published widely in reputable journals like IEEE Access and Computer Science Review, with a focus on practical, industry-relevant solutions. Notable press coverage includes a hospital waste monitoring system collaboration. While no grants or formal advisees are highlighted in the provided data, his work emphasizes collaborative research and impactful technological solutions. His lab/team contributions include pioneering tools for code analysis and environmental resource management systems.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Chen Lin is affiliated with Xiamen University, School of Informatics , China. Their research spans Computer Science , Artificial Intelligence , and Bioinformatics , with a focus on Recommender Systems , Machine Learning , and Natural Language Processing . Chen Lin has published extensively on topics such as language models , index advisors , and adversarial attacks . Their work includes optimizing multi-modal recommendation systems , knowledge graphs , and drug-target interaction prediction . Recent publications in 2024 explore hypergraph pre-training , robustness in index advisors , and anonymized LLM annotation techniques . Key collaborations include researchers like Yeyun Gong , Zhenghao Lin , and Guoliang Li . While no formal awards are listed, their contributions to deep learning , data mining , and security are evident through their high-impact publications in venues such as NeurIPS , ICML , and KDD .

Stratos Idreosمشاهده پروفایل

استاد

• Data Systems
• Database Systems
• Machine Learning
• +۴ مورد دیگر

Stratos Idreos is a Gordon McKay Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). He leads the Data Systems Laboratory (DASlab), focusing on self-designing systems, adaptive data structures, and high-performance key-value stores. His research bridges theoretical foundations with practical implementations to enhance data processing efficiency. Education: Ph.D. in Computer Science, University of Amsterdam (Netherlands) Research Interests: Idreos explores systems that automate design decisions for optimal performance. His work includes the Periodic Table of Data Structures , self-tuning storage engines like Cosine , and machine learning integration into database systems. Recent efforts focus on cloud-optimized systems, ensemble learning techniques, and image-AI acceleration. Key Contributions: Developed the Data Calculator for automated system design Pioneered self-designing key-value stores (e.g., Cosine) Advanced adaptive indexing and LSM-tree optimizations Awards & Recognition: ACM SIGMOD Jim Gray Dissertation Award (2011) IEEE TCDE Rising Star Award (2015) Sloan Research Fellowship (2023) McDonald Mentoring Award (2023) Grants & Leadership: Holds NSF and DOE grants, chairs the ACM Symposium on Cloud Computing steering committee, and co-directs the Harvard Data Science Initiative. His lab collaborates with Microsoft, IBM, and academic institutions globally. Labs & Teams: DASlab fosters interdisciplinary projects, offering courses like CS165 (Data Systems) and CS265 (Big Data Systems). The lab emphasizes reproducible research and industry-relevant solutions.

• Database Systems
• Query Compilation
• High-Performance Computing
• +۳ مورد دیگر

Ruby Tahboub is a Teaching Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign (UIUC), since 2022. She previously served as a Visiting Assistant Professor at Purdue University (2019–2022). She holds an M.S. (2016) and Ph.D. (2019) in Computer Science from Purdue University. Her research focuses on query compilation, spatial databases, and high-performance computing, with notable contributions to Apache Spark optimization and educational tools like LIMO for programming education. She teaches courses such as Introduction to Data Mining (CS 412) and Introduction to Programming for Engineers and Scientists (CS 101). Ruby has received the Raymond Boyce Graduate Teacher Award (Purdue, 2016) and the Best Demo Award at ACM SIGSPATIAL (2015) for her work on LIMO. Her research spans database systems, compiler design, and distributed computing, emphasizing practical applications in both industry and education. Her work bridges theoretical advancements in query processing with real-world scalability challenges, particularly in spatial and heterogeneous workloads. She also advocates for innovative teaching methods to enhance programming literacy through interactive tools like map-based activities.

Bryan HOOI Kuen-Yewمشاهده پروفایل

استادیار

• Machine Learning
• Graph Algorithms
• Anomaly Detection
• +۷ مورد دیگر

Bryan HOOI Kuen-Yew is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), affiliated with the School of Computing and the Institute of Data Science. His research focuses on enhancing the reliability and applicability of machine learning systems, particularly in trustworthiness, graph learning, and real-world applications such as cybersecurity and biomedical informatics. He holds a PhD in Machine Learning from Carnegie Mellon University (2019), and dual MSc (Computer Science) and BSc (Mathematics) degrees from Stanford University (2014). His work has been recognized with awards including the KDD 2016 Best Paper Award (Research Track) and the ECML-PKDD 2018 Runner-Up Best Student Data Mining Paper Award. Research Interests: Trustworthy AI: Mitigating hallucinations, biases, and distribution shifts in large language models. Graph Algorithms: Developing robust methods for graph mining, anomaly detection, and fraud detection in dynamic networks. Multimodal Learning: Leveraging vision-language models and knowledge graphs for improved reasoning and security. Biomedical Applications: Applying AI to healthcare challenges, including drug design and medical data analysis. Selected Awards: ECML-PKDD 2018 Runner-Up Best Student Data Mining Paper Award KDD 2016 Best Paper Award (Research Track) NUS Computing Teaching Excellence Award (2024) Advising and Grants: Actively supervises PhD and master’s students, with research supported by grants focusing on AI security, graph learning, and trustworthy systems. Collaborates on projects such as PhishAgent (multimodal phishing detection) and UniGraph (cross-domain graph foundation models).

Zhicheng Liuمشاهده پروفایل

استادیار

• Human Computer Interaction
• Machine Learning and Data Science
• Data Visualization
• +۳ مورد دیگر

Zhicheng Liu is an Assistant Professor in the Department of Computer Science at the University of Maryland. His research focuses on human-computer interaction, machine learning, and data visualization, emphasizing democratizing visualization design through computational tools and human-centered systems. He received the NSF CAREER Award (2023) for his work on manipulable semantic components in data visualization. Education details are not explicitly stated in the provided texts, but his professional trajectory indicates advanced academic training in computer science. Research interests include interactive visualization authoring, collaborative systems, AI-enhanced design tools, and semantic analysis of visual content. His work bridges computer science with cognitive science, psychology, and graphic design to create impactful interdisciplinary solutions. Liu's recent publications (2023–2025) explore topics like semantic chart decomposition, AI-assisted documentation, and cross-linguistic collaboration tools. His NSF-funded project aims to develop modular visualization components for easier creation of interactive charts. Awards include the NSF CAREER Award, recognizing his potential to advance visualization research. He advises four PhD candidates focusing on visualization and HCI topics. His grants include a $600K NSF award supporting his CAREER project. Collaborative tools developed (e.g., DocDancer, VisAnatomy) demonstrate his focus on practical, user-centric systems. Liu’s lab focuses on responsive documents, AI-driven tutorial systems, and visualization corpus development. He emphasizes real-world impact through tools that simplify data analysis and communication for non-experts.

• Multicore Computing
• Program Analysis
• Virtual Machine Optimization
• +۴ مورد دیگر

Daniele Bonetta is an Assistant Professor in the Department of Computer Science at Vrije Universiteit Amsterdam and holds an ancillary role as a Medewerker (Employee) at Eindhoven University of Technology since June 2020. His primary affiliation is with the Faculty of Science, where he contributes to the Network Institute as well. His research focuses on optimizing virtual machines, parallel programming models, and dynamic compilation techniques, with a particular emphasis on multicore systems and distributed computing environments. Bonetta has also been involved in teaching advanced courses such as Advanced Network Programming and contributes to the Accelerator-Centric Computing Ecosystems program. His research interests are centered around improving the performance of managed runtimes, including virtual machine optimization, dynamic taint analysis, and efficient data processing in polyglot environments. He has explored topics such as speculative optimizations for JSON data access, columnar array storage transformations, and scalable solutions for virtual memory oversubscription. His work frequently addresses challenges in distributed systems, cloud computing, and cross-language program analysis. Bonetta’s recent publications (2023-2025) highlight advancements in transparent scale-out mechanisms for virtual memory, automated supernode generation in interpreters, and dynamic query engines embedded in polyglot runtimes. His contributions to the field include both theoretical frameworks and practical implementations, often leveraging the GraalVM and Truffle frameworks for polyglot execution. While no formal awards are listed, his extensive publication record (47+ outputs) demonstrates significant scholarly impact. His teaching portfolio includes courses on network programming and systems architecture, reflecting his dual focus on both theoretical research and applied computer science education.