جست‌وجوی استاد و پژوهشگر

Qin Zhangمشاهده پروفایل

استاد

• Algorithms for Big Data
• Theoretical Foundations of Machine Learning
• Quantum Information and Computing
• +۴ مورد دیگر

Roles and Affiliations: Professor of Computer Science at Indiana University Bloomington, Adjunct Professor in Mathematics. Previously at IBM Almaden Research Center and Aarhus University's Center for Massive Data Algorithmics. PhD from HKUST in Computer Science and Engineering. Education: PhD in Computer Science, HKUST, advised by Mordecai Golin and Ke Yi Research Interests: Focuses on algorithms for big data, including communication-efficient distributed computation, streaming/sketching algorithms, and quantum data management. Explores theoretical foundations of machine learning, particularly distributed learning. Leads projects funded by NSF grants on parallel reinforcement learning, distributed graph algorithms, and noisy data processing. Publications and Awards: Over 100 papers in top venues like FOCS, SIGMOD, and NeurIPS. Won the Best Paper Award at SPAA 2017 for distributed clustering work. Invited to special journal issues for top conferences. Teaching and Grants: Teaches advanced courses on sublinear algorithms and algorithm design. PI on NSF grants totaling over $2M, including collaborative projects on distributed computing and epidemiological modeling. Advises PhD students in areas like quantum algorithms and reinforcement learning. Service: Served on program committees for SIGMOD, NeurIPS, ICML, and PODS. Organizes theory seminars and Midwest Theory Day workshops.

Daniele Zambonمشاهده پروفایل

پژوهشگر

• Graph Representation Learning
• Machine Learning
• Learning in Non-Stationary Environments
• +۷ مورد دیگر

Daniele Zambon is a postdoctoral researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA), affiliated with Università della Svizzera italiana (USI) in Lugano, Switzerland. He is a member of the Faculty of Computer Science and the Graph Machine Learning Group, as well as the IEEE Task Force on Learning for Graphs. PhD : Informatics, Università della Svizzera italiana (USI), 2022 Master’s & Bachelor’s : Mathematics, University of Milan, Italy Visiting Researcher : University of Florida, University of Exeter Internship : STMicroelectronics, Italy His research lies at the intersection of machine learning and graph-structured data, with a strong emphasis on graph representation learning , learning in non-stationary environments , and time series analysis . He explores how to model dynamic graphs, detect anomalies and changes over time, and develop deep learning methods for spatiotemporal forecasting. His work integrates statistical testing, geometric deep learning, and neural architectures like Graph Neural Networks (GNNs) and Neural ODEs. The recent publications highlight a clear trend toward temporal and dynamic graph modeling , especially for time series forecasting and irregularly sampled data . There is a growing focus on generative and foundation models for graphs , uncertainty-aware learning , and the creation of benchmark datasets like PeakWeather. His work bridges theoretical contributions (e.g., statistical tests, Kalman filters on graphs) with practical applications in sensing, environmental modeling, and system monitoring. Co-author of patent: Method for the Detecting Electrocardiogram Anomalies and Corresponding System (US10610162B2) PhD thesis featured in D22 Excellent Computer Science Dissertations (2022) Associate Editor, IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS) Organizer of tutorials and special sessions at ICML, LoG, KDD, and ESANN Daniele actively contributes to the academic community through advising and teaching at USI’s Bachelor’s and Master’s programs. He has co-supervised research projects and co-organized educational initiatives such as tutorials on graph deep learning. His collaborative work involves grants and partnerships with institutions like MeteoSwiss, leading to impactful datasets and applied research. He is deeply involved in building research capacity through workshops and community engagement in the graph learning field. He is a core member of the Graph Machine Learning Group at IDSIA and contributes to the IEEE Task Force on Learning for Graphs , fostering international collaboration and setting research agendas in the domain of graph-based AI.

Sourav Chakrabortyمشاهده پروفایل

استاد

• Theoretical Computer Science
• Boolean Functions
• Quantum Complexity
• +۴ مورد دیگر

Sourav Chakraborty is a Professor in the Advanced Computing and Microelectronics Unit (ACMU) of the Computer and Communication Sciences Division at the Indian Statistical Institute (ISI), Kolkata, India. He joined ISI in July 2018 after serving as faculty at Chennai Mathematical Institute from 2010-2018. Previously, he held postdoctoral positions at Centrum Wiskunde & Informatica (CWI) in Amsterdam and Technion in Israel. Education: Ph.D. in Computer Science, University of Chicago (2008) M.S. in Computer Science, University of Chicago (2005) B.Sc. in Mathematics, Chennai Mathematical Institute (2003) Research Focus: Professor Chakraborty specializes in Theoretical Computer Science with emphasis on classical and quantum complexity of Boolean functions, including sensitivity analysis, property testing, and quantum database search. His work extends to graph algorithms, electronic commerce mechanisms, and coding theory. His research explores fundamental questions in computational complexity through innovative mathematical frameworks. Publication Trends: Recent work demonstrates a strong focus on property testing, sampling algorithms, and complexity theory, with significant contributions to streaming algorithms, Boolean function analysis, and quantum query complexity. His publications frequently appear in top theoretical computer science venues and exhibit consistent innovation in algorithm design and complexity boundaries. Awards & Honors: Praise from Donald E. Knuth for streaming algorithms research Inclusion in Oded Goldreich's 'my choices' list for Conditional Sampling and Huge-Object Model work Chakraborty's function named in his honor for Sensitivity Conjecture contributions Academic Service: Teaches courses in discrete mathematics and theoretical computer science, with detailed course materials available through institutional pages. Organized workshops including the 2020 Workshop on Sensitivity and Query Complexity at ISI.

Omri Ben-Eliezerمشاهده پروفایل

مدرس ارشد

• Sublinear-Time Algorithms
• Algorithms and Society
• Privacy in Machine Learning
• +۳ مورد دیگر

Omri Ben-Eliezer is a Senior Lecturer (Assistant Professor) and Taub Fellow at the Faculty of Computer Science in the Technion — Israel Institute of Technology. He holds a PhD from Tel Aviv University (2020, supervised by Noga Alon), followed by postdoctoral positions at the Weizmann Institute (hosted by Moni Naor), Harvard University (mentored by Madhu Sudan), and a research fellowship at the Simons Institute. He also served as an instructor in applied mathematics at MIT. His research focuses on the theoretical and algorithmic foundations of big data, including sublinear-time algorithms, privacy in machine learning, beyond worst-case analysis, and complex networks. He has contributed to topics such as adversarial robustness, approximation algorithms, and knowledge representation. Ben-Eliezer's work spans algorithm design and analysis, with notable awards including the PODS 2020 Best Paper Award and the 2021 ACM SIGMOD Research Highlight Award. His publications appear in top venues like FOCS, SODA, and ITCS, and he frequently serves on program committees for conferences such as FOCS and PODS. He actively mentors students interested in algorithmic research bridging theory and practice and is affiliated with multiple academic institutions and collaborative projects.

• Algorithms
• Data Structures
• Combinatorics
• +۹ مورد دیگر

Conrado Martinez Parra is a Professor in the Department of Computer Science at the Faculty of Computer Science, Universitat Politècnica de Catalunya (UPC). He is a core member of the ALBCOM research group, which focuses on Algorithmics, Bioinformatics, Complexity, and Formal Methods. Affiliation : Department of Computer Science, Faculty of Computer Science (FIB), UPC Research Group : ALBCOM - Algorísmia, Bioinformàtica, Complexitat i Mètodes Formals Email : conrado@cs.upc.edu ORCID : 0000-0003-1302-9067 Researcher ID : G-4629-2015 His research spans theoretical computer science with a strong emphasis on the design and analysis of algorithms and data structures. His work includes average-case analysis of algorithms, combinatorial generation, probabilistic methods in algorithmics, and applications in information retrieval and data stream processing. He has extensively studied multidimensional data structures such as quadtrees, K-d trees, and skip lists, analyzing their performance under various query models including partial match and orthogonal range searches. His recent publications reveal a sustained focus on algorithmic efficiency, sampling techniques, and probabilistic modeling in data structures. Trends indicate a deep engagement with randomized algorithms, unbiased estimation, and cache-efficient selection methods, reflecting both theoretical rigor and practical applicability in modern computing environments. Scientific Contributions Extensive publication record spanning over three decades, from 1989 to 2024. Active in major algorithmic conferences such as ANALCO, AofA, and AAAI. Contributions to foundational algorithm analysis including Hoare’s FIND, Quickselect variants, and deletion in binary search trees. Collaborative research with prominent figures in theoretical computer science across Europe. Professor Martinez Parra has advised or collaborated with several doctoral students, including Gustavo Lau, whose thesis on partial match queries he supervised. He has participated in numerous competitive R&D projects funded by national and regional programs, focusing on large-scale information processing and graph-based computing models. His work is supported by long-standing grants from Spanish and Catalan research councils. He is affiliated with the ALBCOM research group, a leading team in algorithmic research at UPC, contributing to both theoretical advances and practical implementations in combinatorics and data structure optimization.

• Computer Science
• Distributed Systems
• Blockchain Technology
• +۳ مورد دیگر

Hans-Arno Jacobsen is a Professor at the Faculty of Computer Science (Technische Universität München, TU Munich) and affiliated with the Department of Electrical and Computer Engineering at the University of Toronto. His work spans Computer Science , Distributed Systems , and Artificial Intelligence . Research interests include Blockchain Technology , Consensus Algorithms , Graph Neural Networks , and Quantum Computing . Recent projects focus on decentralized consensus , energy-efficient databases , and federated learning in edge environments. His 15 most recent articles (2024–2025) explore topics such as dynamic resource orchestration , CRDT-based blockchains , and multimodal depression recognition . Collaborates with researchers like Ruben Mayer , Gengrui Zhang , and Shiqiang Wang on systems for federated computing , blockchain benchmarking , and distributed GNN training .

Lauri Lovénمشاهده پروفایل

استادیار

• Distributed AI
• Edge computing
• Edge intelligence
• +۶ مورد دیگر

Lauri Lovén is a tenure-tracked Assistant Professor at the University of Oulu's Faculty of Information Technology and Electrical Engineering. As vice-director of the Center for Ubiquitous Computing (UBICOMP) and leader of the Future Computing Group (20+ researchers), he coordinates the Distributed Intelligence strategic research area within Finland's 6G Flagship program. Education: D.Sc.(Tech.) 2021, Docent (Edge Intelligence) 2025, University of Oulu Prior Affiliations: TU Wien (2022), ETH Zürich (2023) Research Focus: Specializing in edge intelligence and distributed AI, his work explores cognitive computing continuums across 6G networks, IoT systems, and industrial metaverse applications. Recent Trends: Recent publications reveal two key directions: 1) AI optimization for 6G wireless networks (handover management, semantic slicing), and 2) intelligent data management frameworks (data fabric, message brokers) for distributed systems. Industry Experience: Combines 20 years of software industry expertise with academic research, having served as founder, CTO, and advisor in AI startups.

• Computer Science
• Knowledge Graph
• Representation Learning
• +۷ مورد دیگر

Huijuan Xu is an Assistant Professor in the Department of Computer Science and Engineering. Her research spans artificial intelligence, computer vision, and knowledge representation, with a focus on temporal modeling, semantic reasoning, and multimodal learning. She has contributed to advancements in virtual reality streaming, knowledge graph completion, and weakly-supervised video analysis. Research output: 32 publications (2015-2025), including 15 peer-reviewed articles and conference contributions Core research areas: Representation Learning (100% match), Knowledge Graph (100% match), Temporal Action Detection (86% match), and Motion Feature Learning (73% match) Her recent work explores: 2025 : Bandwidth-optimized VR streaming for edge devices 2024 : Neural concept reasoning for image retrieval and avatar generation from sparse data 2023 : Zero-shot scene graph generation and bias mitigation in visual QA

• Data Science
• Big Data
• Natural Language Query Processing
• +۴ مورد دیگر

Prof. Dr. Kurt Stockinger is a Professor of Computer Science at ZHAW School of Engineering and holds a doctorate at the University of Zurich . He serves as Head of the MAS Data Science program and co-leads the ZHAW Datalab . His research focuses on Intelligent Information Systems , bridging information systems, natural language processing, and machine learning. Affiliated with the University of Zurich, he contributes to Quantum Machine Learning and Open Data Exploration initiatives. Stockinger's educational background includes a PhD in Computer Science (University of Vienna & CERN), a Master in Business Informatics (University of Vienna), and a CAS in Didactics & Methodology (ZHAW). He has taught courses in Quantum Computing , Big Data for Natural Sciences , and Data Science programs at ZHAW and University of Zurich. His research spans Data Science , Big Data , Natural Language Query Processing , Knowledge Graphs , and Quantum Machine Learning . Recent publications focus on quantum autoencoders , hybrid quantum neural networks , and prompt engineering for knowledge graph question answering. He has developed frameworks like ScienceBenchmark for real-world NL-to-SQL evaluation and NQuest for natural language query exploration. Scientific awards include the Best Paper Award at 7th Swiss Conference on Data Science (2020) He leads major projects such as DataGEMS (Data Discovery Platform, Horizon Europe) Digital Health Zurich (Clinical Innovation Lab) INODE4StatBot.swiss (NL-to-SQL Translation) GraphQueryML (Graph Database Optimization) ScienceBenchmark (NL-to-SQL Evaluation) Stockinger's work intersects with computer vision , biomedical data , and industrial applications , demonstrated through collaborations with institutions like Lawrence Berkeley National Laboratory, CERN, and University of Washington. He has contributed to establishing QuantumBasel and ZHAW Datalab as research hubs.

• Data Science
• Internet of Things (IoT)
• Visual Analytics
• +۱۰ مورد دیگر

Professor Dirk J. Lehmann is a Professor of Data Science in IoT at Ostfalia University of Applied Sciences, Faculty of Computer Science, where he has been employed since May 2022. He holds significant leadership roles including Deputy Head of the Institute for Information Engineering (since 2024), Research Officer of the Faculty of Computer Science (since 2023), and membership in multiple committees including the Admissions Committee for Digital Technologies and the Digital Technologies Examination Board. Professor Lehmann's extensive academic journey includes: Part-time professorship in Data Science in IoT at Ostfalia University (2020-2022) Senior Specialist for Digitalization, AI, and Visual Analysis at IAV GmbH (2018-2023) Assistant Professor of Visual Data Analysis at Nazarbayev University, Kazakhstan (2017) Visiting professorships at TU Graz, Austria and Universidad Rey Juan Carlos, Spain (2016-2017) Researcher at Otto-von-Guericke University Magdeburg (2009-2017) His research expertise centers on Visual Analytics and Data Science, with particular emphasis on high-dimensional data visualization, categorical data analysis, and IoT applications. Professor Lehmann leads the Data Science in IoT working group, conducting research across three main areas: visual data analysis, distributed data analysis using AI methods, and applied data analysis in geology, climate data, medicine, and industrial processes. His methodological contributions include innovative visualization techniques for complex datasets across multiple domains. Analysis of Professor Lehmann's 15 most recent publications (2017-2025) reveals a consistent focus on advancing visualization techniques for complex data analysis. His work spans categorical data visualization (CatNetVis), biological data analysis (D. Melanogaster research), optimization of star coordinate systems, and interactive exploration methods for large datasets. These publications appear in top venues including IEEE Transactions on Visualization and Computer Graphics and EuroVis, demonstrating both theoretical rigor and practical application across diverse domains from healthcare to environmental science. As an educator, Professor Lehmann teaches a comprehensive range of courses from foundational mathematics to advanced machine learning and visualization techniques. He actively supervises student projects and theses, emphasizing clear project definitions with measurable acceptance criteria. His international collaborations span institutions in Israel, Saudi Arabia, China, Austria, and Spain, reflecting a global research perspective that bridges academic theory with industry applications, particularly through his previous role at IAV GmbH, a Volkswagen subsidiary.