جستجوی استاد راهنمای دانشگاهی

• Edge Computing
• Fog Computing
• Distributed Systems
• +۵ مورد دیگر

Umakishore Ramachandran is a Professor in the School of Computer Science within the College of Computing at Georgia Institute of Technology. His research spans edge computing, distributed systems, and real-time video analytics, with significant contributions to fog computing infrastructure, mobile systems, and sensor networks. Over a prolific 38-year career, he has authored 142 publications with major contributions in 2022-2025. His research interests focus on bridging the gap between cloud and edge computing, with pioneering work in video analytics systems like EVA and MicroEdge. He investigates resource optimization for latency-sensitive applications, developing novel approaches for load shedding, data management, and container runtime efficiency at the network edge. His work addresses fundamental challenges in distributed camera networks, autonomous vehicle systems, and real-time stream processing. Ramachandran's recent publications reveal a strong emphasis on practical edge computing solutions, with 75% of his 2021-2025 work focusing on video analytics and infrastructure optimization. His research shows increasing collaboration with industry partners while maintaining academic rigor, with publications appearing in top venues like SIGMOD, Middleware, and DEBS. The work consistently addresses real-world constraints of resource-constrained edge environments. Ramachandran has mentored numerous researchers who have become principal investigators on edge computing projects, with notable collaborators including Harshit Gupta, Enrique Saurez, and Zhuangdi Xu appearing as first authors on multiple papers. His work has received significant grant support for projects related to mobile fog computing and distributed video analytics. He leads research in the Edge Computing Laboratory at Georgia Tech, focusing on the development of practical frameworks for real-world deployment of edge infrastructure. Current projects include eCAV for connected autonomous vehicles and MicroEdge for multi-tenant camera processing systems.

• Network Security
• Privacy-Preserving Technologies
• Machine Learning for Networking
• +۵ مورد دیگر

Ke Xu is a Professor in the Department of Computer Science at Tsinghua University's School of Information Science and Technology. With extensive research contributions in network security, privacy-preserving technologies, and machine learning applications for networking, Professor Xu has established himself as a leading researcher in computer science. Professor Xu's research interests span network security, privacy-preserving technologies, machine learning for networking, federated learning, internet protocols, encrypted traffic analysis, blockchain applications, and AI in networking. His work bridges theoretical foundations with practical implementations, focusing on real-world security challenges and network optimization problems. He has developed novel frameworks for secure network operations, privacy-preserving data sharing, and efficient AI deployment in distributed environments. Professor Xu's publication record shows a clear trend toward integrating artificial intelligence with traditional networking challenges. His recent work explores federated learning security, encrypted traffic analysis using deep learning, and novel approaches to network security that leverage machine learning techniques. The interdisciplinary nature of his research spans computer networking, security, privacy, and artificial intelligence. Professor Xu has received recognition for his contributions to network security and privacy-preserving technologies through publications in top-tier venues including IEEE journals, ACM conferences, and security symposia. His work has appeared in IEEE Transactions on Dependable and Secure Computing, IEEE/ACM Transactions on Networking, and security conferences like CCS and NDSS. Professor Xu actively collaborates with researchers across institutions, supervising students and junior researchers in exploring cutting-edge problems in network security and AI. His research has been supported by significant grants focusing on network security, privacy, and intelligent networking infrastructure. He leads projects that address fundamental challenges in secure communication, privacy-preserving data analysis, and intelligent network management. Professor Xu is involved with research laboratories focusing on network security and intelligent systems at Tsinghua University. His team works on developing practical security solutions, privacy frameworks, and AI-enhanced networking protocols that address real-world challenges in today's increasingly connected world.

Sebastian Schmidtمشاهده پروفایل

پژوهشگر

• Active Learning
• Out-of-distribution Detection
• Uncertainty Estimation
• +۳ مورد دیگر

Sebastian Schmidt is a researcher at the Technical University of Munich (TUM) within the Department of Informatics - I26 (Data Analytics and Machine Learning) under Prof. Dr. Stephan Günnemann. His work is situated in the TUM School of Computation, Information and Technology, where he contributes to cutting-edge research in machine learning applications. His research focuses on active learning methodologies with particular emphasis on out-of-distribution detection and uncertainty estimation in open-world perception systems. His work bridges theoretical machine learning with practical applications in computer vision and robotics, as evidenced by publications in top-tier conferences like CVPR, ICRA, and IROS. His recent publications demonstrate a consistent research trajectory in developing active learning frameworks that address real-world challenges in perception systems. The publications show increasing sophistication from temporal stream processing to multi-agent robotic applications and safety-constrained sensor fusion. As a member of the Data Analytics and Machine Learning group, he collaborates extensively with colleagues including Leo Schwinn, Stephan Günnemann, and others across multiple publications. His research has practical implications for autonomous systems requiring reliable perception under uncertainty.

• Database Systems
• Graph Databases
• Data Management
• +۷ مورد دیگر

Amol Deshpande is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, College of Engineering. With over 160 publications spanning from 2000 to 2025, his research has significantly impacted the database systems community. His work bridges theoretical foundations with practical systems, evidenced by numerous publications in top-tier venues including SIGMOD, VLDB, and ICDE. Professor Deshpande's research focuses on database systems, with particular expertise in graph databases, data management, probabilistic databases, query optimization, and data provenance. His work addresses fundamental challenges in managing complex data, including efficient graph analytics, dataset versioning, streaming data processing, and privacy-preserving data management. Recent research directions include entity-relationship abstractions beyond traditional relations, standalone catalog engines for large data systems, and graph theoretical approaches to dataset versioning. His publication trends show a consistent focus on evolving database technologies, with early work on probabilistic databases and query optimization, transitioning to graph analytics and data provenance, and more recently addressing modern challenges in data cataloging, privacy-first data management, and serverless stream processing. His research spans both theoretical contributions (e.g., approximation algorithms for stochastic optimization) and practical systems building (e.g., RStore, TreeCat). Professor Deshpande has mentored numerous PhD students who have become active researchers in the database community, including Hui Miao, Souvik Bhattacherjee, and Konstantinos Xirogiannopoulos. His collaborative work spans across institutions, with frequent collaborations with researchers from MIT, University of Maryland, and other leading institutions. His research has been supported by major funding agencies and has influenced both academic research and industry practices in data management. The evolution of his work reflects the changing landscape of data management, from traditional relational systems to modern graph and streaming data challenges.

Farzan Banihashemiمشاهده پروفایل

پژوهشگر ارشد

• Urban Building Energy Modeling
• Building Energy Simulation
• Machine Learning and Deep Learning
• +۱ مورد دیگر

Farzan Banihashemi serves as a Research Fellow at the Chair of Energy Efficient and Sustainable Design and Building at the Technical University of Munich (TUM), maintaining this affiliation since 2019 while concurrently working as a Data Scientist at Climateflux GmbH since 2023. His work bridges sustainable building design and data science, focusing on computational approaches for urban energy systems. His academic credentials include: Master in Management from TUM School of Management (2019) Master in Energy Efficient and Sustainable Building from TUM (2017) His research centers on data-driven urban building energy modeling (UBEM) , building energy simulation , and machine learning applications for occupant behavior analysis . He develops non-intrusive sensing methodologies to model window operations and occupancy patterns using environmental data streams, with significant contributions to CO2-based occupancy detection systems and predictive modeling for office environments. His work integrates climate change considerations into early-stage building design processes. Analysis of his 2022-2024 publications reveals a concentrated research trajectory applying artificial intelligence to building energy challenges. Over 60% of his recent work addresses occupant behavior modeling—particularly window operations and space occupancy—using explainable AI techniques. His publications also demonstrate growing engagement with urban-scale applications, including urban heat island mitigation and vertical densification strategies, often incorporating life cycle assessment frameworks. No scientific awards were documented in the source materials. While specific advising activities aren't detailed, his collaborative publication pattern (average 4.3 co-authors per paper) indicates active participation in research teams. Grant involvement is implied through project affiliations though specific funding mechanisms aren't specified. He operates within TUM's Chair of Energy Efficient and Sustainable Design and Building, contributing to major initiatives including Building Climate–Municipal (BauKlima-Kommunal), CircularFTmehrRAUM, CircularGreenSimCity, and the NAWAREUM project. These efforts focus on sustainable urban development, climate adaptation strategies, and circular economy implementation in the built environment, particularly examining urban densification under climate change scenarios.

• Database Systems
• Distributed Databases
• Transaction Processing
• +۵ مورد دیگر

Daniel J. Abadi is a prominent researcher in database systems at Yale University. With over two decades of impactful research, he has made significant contributions to the fields of distributed databases, transaction processing, and column-oriented database systems. His work bridges theoretical foundations with practical implementations that have influenced both academia and industry. Dr. Abadi's research primarily focuses on database system architecture, with particular emphasis on: Distributed and geo-replicated database systems High-performance transaction processing Column-oriented and analytical database systems Stream processing and real-time analytics Cloud and serverless database technologies Integration of machine learning with database systems His recent work shows a continued focus on addressing scalability challenges in modern database systems, with particular attention to multi-region transaction processing, automated data management, and the integration of machine learning techniques. The trend in his publications indicates a strong emphasis on practical, deployable systems that solve real-world problems faced by industry. Dr. Abadi has been instrumental in several major research initiatives and reports that have shaped the direction of database research, including the Seattle Report and the Cambridge Report on Database Research. Throughout his career, Dr. Abadi has mentored numerous students and collaborated extensively with leading researchers in the field. His work has received significant recognition through widespread citations and adoption of his ideas in both academic and industrial database systems.

• Business Process Management
• Process Mining
• Complex Event Processing
• +۵ مورد دیگر

Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Min Peng is a Professor at Wuhan University's School of Computer Science. His research focuses on artificial intelligence, machine learning, natural language processing, and knowledge graphs. He has collaborated extensively with institutions like Hefei University of Technology and the University of Chinese Academy of Sciences. His work bridges theoretical advancements in AI with practical applications in finance, social media analysis, and network optimization. Recent contributions include neural-symbolic reasoning frameworks, contrastive learning for knowledge graphs, and financial benchmarking with large language models. Research interests emphasize scalable machine learning models for complex reasoning tasks, explainable AI, and domain-specific applications in finance and social networks. Over 100 publications span venues like WWW, ACL, and NeurIPS, highlighting interdisciplinary impact. Notable projects include SymAgent (neural-symbolic agent frameworks), PIXIU (financial LLM benchmark), and DTC (commonsense machine comprehension). Key technical trends include integrating large language models with structured data, temporal knowledge graph reasoning, and transfer learning across domains. His work often addresses real-world challenges in data efficiency, interpretability, and cross-domain scalability. Current efforts explore financial LLMs, agent-based reasoning systems, and multimodal applications. While no specific grants or awards are listed in the provided data, his prolific publication record indicates sustained research excellence. Collaboration networks include teams in computer science, electrical engineering, and finance disciplines.

Riccardo Tommasiniمشاهده پروفایل

دانشیار

• Stream Processing
• Knowledge Graphs
• Query Languages
• +۳ مورد دیگر

Riccardo Tommasini is an Associate Professor at INSA Lyon , a leading engineering institution in France. He leads the Stream Processing and Knowledge Graphs research within the DB Team at LIRIS laboratory under Professor Angela Bonifati. His academic journey began with a PhD in Computer Science from Politecnico di Milano under Emanuele Della Valle, with a dissertation titled Velocity on the Web to be published as a Springer book. Research Interests : Advancing stream processing for real-time data systems Extending knowledge graphs with dynamic data Designing graph databases for big data applications Creating query languages for heterogeneous data environments Building data engineering pipelines with Apache Airflow Enabling big graph processing in distributed settings Key Contributions : Developed Zodiac framework for Datalog reasoning under rule amendments (ICDE 2025) Co-authored foundational Streaming Linked Data book with Springer (2023) Created RSP4J API for RDF stream processing (ESWC 2021) Designed challenge-based learning curriculum for Data Engineering courses Scientific Recognition : Received ANR JCJC grant for POLYFLOW project (2024) Awarded Best Resource at ESWC 2021 Managed industrial collaborations with Neo4j, InfluxData, and Confluent Advising & Teaching : Supervises Mohamed Ragab (PhD candidate at University of Tartu) Course Leadership : Foundational Data Engineering course at INSA Lyon and University of Tartu Structured around Apache Airflow , Docker, and graph databases

• Planetary Science
• Solar System Formation
• Cometary Studies
• +۳ مورد دیگر

Harald Krüger is a Research Scientist at the Max Planck Institute for Solar System Research (Göttingen), specializing in planetary science and space dust analysis. He leads and contributes to multiple space missions, including the DESTINY+ Dust Analyzer (DDA), the Rosetta COSIMA instrument, and the PHILAE Dust Impact Monitor (SESAME-DIM). His research focuses on cometary dynamics, interstellar dust interactions, and solar system formation processes. Krüger holds a PhD from the University of Göttingen and has held research roles at prestigious institutions like the Max Planck Institutes for Nuclear Physics and Astronomy (Heidelberg). He chairs ESA’s working group on comet 67P/Churyumov-Gerasimenko and participates in international astronomical societies. His work integrates advanced instrumentation design with observational data from spacecraft missions, contributing to our understanding of cosmic dust properties and their role in planetary systems. Key projects include analyzing interstellar dust via the DESTINY+ mission and studying Martian moons through the MMX mission. His publications emphasize instrument constraints, cometary dust trail detection, and spacecraft outgassing effects on measurements.

• Wireless Communications
• Network Security
• Quality of Experience (QoE)
• +۷ مورد دیگر

Tasos Dagiuklas is a Professor in the Department of Computer Science and Technology within the School of Engineering and Technology at the University of Bedfordshire. With over 168 publications spanning from 1995 to 2025, he has established himself as a leading researcher in telecommunications and network systems. His extensive publication record demonstrates continuous scholarly contribution across multiple decades in the field. Professor Dagiuklas' research focuses on wireless communications, edge computing, 5G/6G networks, quality of experience (QoE), and federated learning . His work bridges theoretical networking concepts with practical applications, particularly in multimedia delivery and security. He has developed significant expertise in video streaming optimization, network security mechanisms, and resource management in emerging network architectures. His research consistently addresses the evolving challenges of modern communication systems, with recent work increasingly focusing on AI integration in networking. Analysis of his recent publications (2023-2025) reveals a strong trend toward edge computing, federated learning, and security applications in next-generation networks. His work demonstrates a strategic shift from traditional networking concerns to more complex systems involving AI integration, energy efficiency, and heterogeneous environments. The publications show consistent collaboration with researchers across multiple institutions, with particularly strong partnerships with Muddesar Iqbal, Ilias Politis, and Stavros Kotsopoulos. Professor Dagiuklas has made substantial contributions to the academic community through his extensive publication record in high-impact venues including IEEE journals and conferences. His work has evolved from foundational networking research to cutting-edge investigations of AI-enhanced communication systems, reflecting the broader trajectory of the field itself. His research demonstrates both technical depth in specific networking challenges and breadth across multiple application domains.

Saman Amarasingheمشاهده پروفایل

استاد

• Programming Languages
• Compilers
• Domain-Specific Languages
• +۷ مورد دیگر

Professor Saman Amarasinghe is a full Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), and Principal Investigator at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Commit compiler research group, which focuses on programming languages and compilers that maximize application performance on modern computing platforms. His work spans multiple academic departments and research centers, with strong affiliations to both MIT's School of Engineering and CSAIL. Professor Amarasinghe's research interests center around high-performance domain-specific languages and compiler technology . His work combines language design with sophisticated compilation techniques to deliver unprecedented performance for targeted application domains. His research spans multiple areas including image processing (Halide), sparse tensor algebra (TACO), graph analytics (GraphIt), stream computations (StreamIt), and bioinformatics (Seq). A significant thread throughout his work is the application of machine learning for compiler optimizations, from Meta optimization in 2003 to the OpenTuner autotuner framework. Analysis of Professor Amarasinghe's recent publications reveals a strong focus on sparse computing , compiler vectorization , and domain-specific language implementation . His work consistently bridges theoretical compiler concepts with practical performance gains across diverse application domains. The progression from earlier work on StreamIt and Halide to more recent projects like GraphIt and TACO shows an evolution toward more specialized, high-performance DSLs targeting specific computational patterns. His 2020-2025 publications particularly emphasize sparse tensor operations, GPU acceleration, and machine learning integration with compiler technology. ACM Fellow (2019) Professor Amarasinghe has made significant contributions to academic entrepreneurship and student development. He founded Determina, Inc. (acquired by VMware) based on security research from his MIT lab and co-founded Lanka Internet Services, Ltd., Sri Lanka's first ISP. As faculty director of MIT Global Startup Labs, his programs across 17 countries have helped create over 20 successful startups. His teaching includes the popular Performance Engineering of Software Systems (6.172) course with Professor Charles Leiserson, as well as innovative project-based courses like the Open Source Software Project Lab and Bring Your Own Software Project Lab. His educational approach emphasizes hands-on experience with compiler and language design concepts. Professor Amarasinghe leads the Commit compiler research group at MIT CSAIL, which has produced numerous influential domain-specific languages and compilers including Halide, TACO, Simit, StreamIt, and GraphIt. The lab maintains strong industry connections through projects like OpenTuner and Determina, and collaborates with researchers worldwide on compiler technology. The group's work spans both theoretical compiler research and practical implementation, with a consistent focus on bridging the performance gap between high-level programming abstractions and hardware capabilities.

Michael Möhringمشاهده پروفایل

استاد

• Data Science
• Big Data Analytics
• Industry 4.0
• +۴ مورد دیگر

Prof. Dr.-Ing. Michael Möhring is a Professor of Data Science at Reutlingen University's Faculty of Informatics. He serves as Prodekan for the Herman Hollerith Zentrum (HHZ) and leads research in data analytics, Industry 4.0, and process mining. Previously, he held roles as an IT consultant, project manager at Bosch Group/BSH, and academic researcher. Education: Dr.-Ing. (PhD) in Business Informatics M.Sc. in Business Informatics B.Sc. in Business Informatics Research Interests: Focuses on leveraging structured/unstructured data for industrial applications, enterprise architecture management, digital twins integration, and AI-driven decision support. Specializes in bridging technical systems with organizational processes in manufacturing and service industries. Lab Affiliations: AI-Real Lab AIDA Future Mobility Lab Internet of Things Lab Virtual Reality Lab Articles Trends: Recent work emphasizes practical implementations of AI in production failure analysis (language models), energy optimization systems (HollerithEnergyML), and technical debt management in SMEs. Consistently explores data integration challenges across manufacturing, service ecosystems, and digital twin frameworks. Grants & Collaborations: Active in EU-funded projects like 5G-PreCiSe and bwHealthApp. Collaborates with industry partners on digital transformation initiatives through HHZ's applied research programs.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۴ مورد دیگر

Dr. Milos Hauskrecht is a Professor of Computer Science at the University of Pittsburgh's School of Computing and Information. He holds a PhD from MIT (1997) and an M.Sc. from Slovak Technical University (1988). His research focuses on AI, machine learning, and data mining, with applications in medicine and finance. He leads projects in real-time clinical monitoring, anomaly detection, and time-series analysis of EHR data. He has advised numerous PhD and MS students, including notable alumni now at Amazon, DeepMind, and Microsoft. Research interests include reasoning under uncertainty, optimization, and AI-driven medical decision support. Current grants include NIH funding for AI in renal therapy and clinical monitoring. He has published widely in top venues like ICML, NeurIPS, and journals such as Artificial Intelligence in Medicine. His work on conditional outlier detection earned the Homer Warner Award (AMIA 2010). He teaches machine learning and advises on interdisciplinary AI projects.

Stefan Wildermannمشاهده پروفایل

استاد

• Reconfigurable Systems
• Edge Computing
• Organic Computing
• +۷ مورد دیگر

Stefan Wildermann is a Professor at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), where he leads the Reconfigurable Computing Group within the Chair of Computer Science 12 (Hardware-Software Co-Design) in the Department of Computer Science. He has maintained continuous research activity at FAU since 2006, progressing from researcher to his current leadership position. Dr. Wildermann earned his Diploma degree in Computer Science from FAU in 2006 and completed his doctorate (Dr.-Ing.) in Computer Science at the same institution in July 2012. His academic career has been entirely rooted at FAU, demonstrating a strong institutional commitment and progression through the ranks. His research spans multiple cutting-edge areas in computer science and engineering, with particular emphasis on reconfigurable systems and hardware-software co-design. Wildermann's work in edge computing explores efficient processing at the network periphery, while his research in organic computing investigates self-organizing systems that can adapt to changing environments. His expertise extends to optimization techniques for embedded systems, applying game theory principles and convex optimization methods to solve complex resource allocation problems. More recently, he has integrated reinforcement learning approaches to enhance system adaptability and performance. His teaching portfolio includes courses on event-driven systems, computer engineering fundamentals, embedded systems, and hardware-software co-design. Analysis of Wildermann's publication record from 2021-2025 reveals a strong focus on hardware acceleration, security, and embedded systems. His work demonstrates consistent evolution from foundational research in reconfigurable architectures toward practical applications in IoT, robotics, and secure computing. A significant portion of his recent work addresses near-data processing using FPGAs for database acceleration, while maintaining parallel research streams in side-channel security analysis and energy-efficient embedded systems design. His publications frequently appear in top-tier conferences including DATE, FPL, ASP-DAC, and HOST, reflecting strong recognition within the computer architecture and embedded systems communities. Wildermann has held significant leadership roles including Head of the Reconfigurable Computing Group since 2015 and previously served as Head of the Self-organizing Systems Group (2012-2015) and Lab Leader of the Automotive Lab within the Embedded Systems Initiative (2016-2020). His research has been consistently funded through multiple projects investigating invasive computing, reconfigurable architectures, and embedded systems design methodologies. Currently based in Room 02.116 at Cauerstr. 11, 91058 Erlangen, Wildermann continues to lead active research in the Hardware-Software Co-Design group, supervising projects that bridge theoretical computer science with practical hardware implementation challenges.