جستجوی استاد راهنمای دانشگاهی

Gaurav Rattanمشاهده پروفایل

استادیار

• Graph Theory
• Machine Learning on Graphs
• Graph Neural Networks
• +۵ مورد دیگر

Gaurav Rattan is an Assistant Professor in the Department of Applied Mathematics at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), where he joined in May 2024. His research focuses on the mathematical foundations of machine learning on graphs and discrete structures, with particular emphasis on theoretical aspects of graph neural networks. University of Twente, Department of Applied Mathematics (May 2024-present) TU Darmstadt, Postdoctoral Researcher in Pascal Schweitzer's group RWTH Aachen, DFG Eigene Stelle Researcher in Martin Grohe's group Dr. Rattan completed his PhD at IMSc Chennai under V. Arvind and earned his B. Tech. from IIT Bombay, establishing a strong foundation in theoretical computer science and mathematics. His research spans graph theory, algorithms, and machine learning on graphs, with specific expertise in graph isomorphism, graph homomorphisms, and the theoretical underpinnings of graph neural networks. He applies mathematical techniques from logic and algebra to develop theory-driven approaches for graph learning systems, with practical applications in optimization, bioinformatics, and databases. Dr. Rattan's publication record reveals a consistent focus on the intersection of theoretical computer science and machine learning. His recent work explores Weisfeiler-Leman algorithms, symmetry breaking techniques, and parameterized complexity of graph problems, demonstrating how classical graph algorithms connect with modern graph learning methodologies. His research provides crucial theoretical foundations for understanding the capabilities and limitations of graph neural networks. Active in the academic community, Dr. Rattan regularly presents at conferences including the Netherlands Mathematical Congress, SIGAlgo, LOGAMS, and specialized workshops on graph learning. Recent presentations include "From Graph Homomorphisms Densities to Graph Learning" at the Graph Learning Workshop at NITMB Chicago and "Color Refinement: One Algorithm, Many Facets" at SIGAlgo 2024.

Gagan Agrawalمشاهده پروفایل

استاد

• Parallel Computing
• High-Performance Computing (HPC)
• Data Analytics
• +۵ مورد دیگر

Gagan Agrawal is the UGA Foundation Professorship in Computing and a Professor at the School of Computing, University of Georgia. He serves as Director of the School and is affiliated with the Franklin College of Arts & Sciences. Agrawal holds a PhD and MS in Computer Science from the University of Maryland (1994-1996). His research focuses on high-performance computing, parallel algorithms, compiler optimization for deep learning, GPU acceleration, and interdisciplinary applications in health informatics. Notable contributions include frameworks like ForensiBlock (blockchain for data forensics) and DELITE (tensorized instruction compilation). Agrawal has secured over $2.5M in NSF grants for projects addressing extreme-scale computing challenges. Recent grants include: SHF: Small: Memory Hierarchy Optimizations Meet Transformers (MITTEN) ($600K, 2024-2027) DELITE compilation system for deep learning models ($600K, 2023-2026) Publications span parallel computing methodologies, cybersecurity frameworks, and health outcomes analysis. His work on social determinants of health in cancer survival has been systematically reviewed in top-tier medical journals. Agrawal leads UGA's computing initiatives, emphasizing interdisciplinary research and student mentorship in HPC and AI domains.

Stefano Ferilliمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Data Mining
• +۶ مورد دیگر

Stefano Ferilli is a Professor of Computer Science at the University of Bari, Italy, where he leads the ARA (Apprendimento e Ragionamento Automatico) research lab within the Department of Computer Science. His academic roles include former Director of the Interdepartmental Center for Logic and Applications (CILA) and current head of the Artificial Intelligence & Intelligent Systems node in the CINI national laboratory. He holds a PhD in Computer Science and has been a key figure in advancing machine learning, logic programming, and digital library technologies. Education: Laurea (MSc equivalent) in Information Sciences (1996), Specialist Laurea in Computer Science (2003), and PhD in Computer Science (2001). His research focuses on foundational aspects of machine learning, multi-strategy reasoning, process mining, and applications in cultural heritage, bioinformatics, and smart environments. Research Contributions: Developed frameworks like INTHELEX (incremental theory learner), WoMan (process mining), and DoMInUS (document management). Over 370 publications, including a Springer monograph and multiple award-winning papers. Active in organizing conferences like ECML-PKDD, ICDM, and IRCDL, and serves on editorial boards of journals like Information Sciences . Projects: Led or participated in over 30 national and European projects, including EU-funded initiatives on digital libraries (COLLATE, DELOS) and AI applications. Collaborates with industries like Samsung and institutions like the Italian Police for traffic analysis and cultural heritage preservation. Awards: Recognized for outstanding peer review (MDPI, ECMLPKDD), best paper awards, and contributions to AI education and cultural heritage. Member of prestigious associations like AI*IA (Italian AI Society) and AICA (Italian Computing Society).

• Information Processing Systems
• Internet of Things (IoT)
• Edge Computing
• +۴ مورد دیگر

Marcello Pietri is a Researcher (td art. 24 c. 3 lett. A) and Contract Professor at the Department of Engineering Sciences and Methods (DISMI) at the University of Modena and Reggio Emilia. His work focuses on Information Processing Systems, with expertise in IoT, Edge Computing, and Digital Twins. He teaches courses like 'Sistemi Informativi' (Information Systems) for the Engineering Management program, emphasizing database design, SQL, and web application development with Python. His research explores advanced topics including Digital Twin integration in Industry 5.0, Fluid Computing in IoT ecosystems, and Smart City data fusion using 5G MEC architectures. He collaborates with institutions like the DIPI Lab (Distributed and Pervasive Intelligence Group), contributing to projects on telecom security, energy forecasting, and human-centric manufacturing systems. Recent publications highlight innovations in operator digital twins for workplace well-being, distributed data mesh models for IoT-edge-cloud systems, and adaptive monitoring algorithms for large-scale cloud environments. His work bridges theoretical advancements with practical implementations, addressing challenges in scalability, real-time data processing, and interdisciplinary collaboration.

Divya Mahajanمشاهده پروفایل

استادیار

• Computer Architecture
• Distributed Systems
• Machine Learning Systems
• +۲ مورد دیگر

Divya Mahajan is an Assistant Professor holding dual appointments in the College of Computing and College of Engineering at Georgia Institute of Technology. She directs the Systems Infrastructure and Architecture Research Lab, focusing on sustainable computing platforms for large-scale AI, machine learning, and data storage systems. Her research integrates computer architecture, distributed systems, and database technologies to optimize end-to-end data pipelines. Mahajan earned her PhD from Georgia Tech and Bachelor's from IIT Ropar (President's Gold Medal). Prior to academia, she was a Senior Researcher at Microsoft Azure, leading communication collective designs for distributed DNN training. Awards include NCWIT Collegiate Award (2017) and HPCA Distinguished Paper Award (2016). Research spans hardware-software co-design for ML systems, efficient recommendation architectures, federated learning optimization, and PIM/NPU acceleration. Recent projects address challenges in large-scale model serving, edge-cloud integration, and sustainable computing. Student advisees include PhD and MS researchers in systems and architecture. Publications demonstrate consistent innovation in accelerating ML workloads through novel hardware/software techniques, with work appearing in ISCA, MICRO, ASPLOS, NeurIPS, and VLDB. Current projects explore energy-efficient AI infrastructure and domain-specialized systems for emerging economies.

Longxiang Liمشاهده پروفایل

استادیار

• Environmental Health
• Exposure Assessment
• Spatial Analysis/GIS
• +۴ مورد دیگر

Longxiang Li is an Assistant Professor in the Gangarosa Department of Environmental Health at Emory University's Rollins School of Public Health. His research focuses on environmental pollutants' human health impacts, particularly unconventional oil and gas development (fracking) and radon exposure. Funded by NIH, he develops satellite-based machine learning models to assess exposure risks. Before joining Emory in 2024, he studied/worked at Harvard T.H. Chan School of Public Health. Education: Sc.D. from Harvard T.H. Chan School of Public Health; M.S. from Chinese Academy of Sciences; B.S. from China University of Geosciences (Beijing). Research interests include exposure assessment, spatial analysis/GIS, and machine learning applications in environmental health. Notable work includes high-resolution radon models and collaborations with the White House on radon-related lung cancer prevention strategies. Key articles focus on radon's effects on asthma, cancer, and pregnancy outcomes, using advanced modeling techniques. His work bridges environmental data science with public health policy. Affiliations: International Society of Environmental Epidemiology, International Society of Exposure Science, American Chemistry Society.

• Database Systems
• Distributed Systems
• Cloud Computing
• +۴ مورد دیگر

Dr. Kenneth Salem is a Professor at the University of Waterloo, Canada , with a distinguished career spanning over three decades in database systems, distributed computing, and data management. His work has significantly influenced transaction processing, replication, and scalability in modern databases. Institution: University of Waterloo Collaborations: Frequent co-author with Hector Garcia-Molina, Ashraf Aboulnaga, Umar Farooq Minhas, Khuzaima Daudjee Research Interests: Dr. Salem’s research focuses on database systems, particularly in transaction processing , distributed databases , cloud computing , and energy-efficient data storage . He has pioneered techniques for schema design in NoSQL , adaptive caching , and RDMA-based data transfer . His work often addresses challenges in high availability , workload-aware optimization , and cardinality estimation . Recent Article Trends: His latest publications (2023-2025) explore ad hoc transactions , eventual durability , and summary-based cardinality estimation , emphasizing practical solutions for real-world database scalability and consistency. Earlier works (2018-2022) covered memory power optimization , RDMA abstractions , and workload-aware CPU scaling , reflecting a sustained focus on performance and efficiency. Advising and Collaborations: Dr. Salem has mentored numerous co-authors in database research, including Ashraf Aboulnaga, Umar Farooq Minhas, and Khuzaima Daudjee. His collaborative projects span Apache Spark caching , RemusDB high availability , and DimmStore memory optimization . Labs and Teams: He has been a key contributor to the DaMoN and Versatile Database Systems workshops, focusing on hardware-aware database design. His work with teams at Waterloo and collaborations with institutions like ETH Zurich and MIT has advanced self-managing databases and edge replication technologies.

Vasco Ribeiro Santosمشاهده پروفایل

پژوهشگر

• Tourism Management
• Marketing
• Sustainability
• +۵ مورد دیگر

Vasco Ribeiro Santos serves as a Full Researcher at ISLA - Higher Institute of Management and Administration of Santarém, Portugal, affiliated with the Competitiveness, Innovation and Sustainability (CIS) research group. His scholarly profile is indexed in major academic databases including SCOPUS and Web of Science, reflecting active contributions to tourism and business management literature. Dr. Santos' research centers on tourism management , with specialized expertise in wine tourism , luxury branding , and sustainable practices . He investigates the transformative impact of digital technologies —including IoT, geospatial analysis, and AI—on consumer behavior and destination marketing. His work extends to employer branding , talent management , and innovation within hospitality sectors, consistently applying Portuguese case studies to global theoretical frameworks. Analysis of his 2024-2025 publications reveals dominant trends in sustainable tourism innovation and digital transformation . Key themes include metaverse applications in luxury branding, influencer marketing dynamics on social media, and geospatial approaches to urban resilience. These studies demonstrate methodological diversity—from systematic literature reviews to empirical case studies—while maintaining focus on Southern European tourism contexts. He actively contributes to the DSS-Lab (Decision Support Systems Laboratory) , developing analytical dashboards and policy tools for tourism management. His work within the CIS research group drives interdisciplinary investigations into competitiveness and innovation, particularly examining how sustainability initiatives intersect with economic development in regional tourism ecosystems.

Russell Schwartzمشاهده پروفایل

استاد

• Cancer Genomics
• Tumor Evolution
• Computational Phylogenetics
• +۳ مورد دیگر

Russell Schwartz is Professor of Biological Sciences and Computational Biology at Carnegie Mellon University, with courtesy appointments in Computer Science and Machine Learning. He serves as Head of the Ray and Stephanie Lane Computational Biology Department and Co-Director of the Joint CMU-University of Pittsburgh PhD Program in Computational Biology. His research develops computational models for complex biological systems, focusing on cancer genomics and macromolecular assembly. Key areas include reconstructing tumor clonal evolution through integration of bulk and single-cell sequencing data, developing algorithms for stochastic simulation of self-assembly systems, and creating optimization methods for fitting models to experimental data. Recent publications demonstrate advancements in single-cell lineage tracing, circulating tumor DNA analysis, and generative AI for biological databases. His team's BrM-Phylo software enables phylogeny reconstruction from matched transcriptome data in breast cancer brain metastases. NSF CAREER Award Alfred P. Sloan Research Fellowship Schwartz leads multiple NIH-funded projects on tumor evolution modeling and mentors students in computational cancer research. His laboratory develops open-source tools including FISHtrees for tumor phylogenetics and TUSV for integrating structural variants in clonal reconstruction.

Torsten Hoeflerمشاهده پروفایل

استاد

• Deep learning Systems
• Parallel programming models
• High performance and datacenter networking
• +۸ مورد دیگر

Torsten Hoefler is a Full Professor of Computer Science at ETH Zurich, Switzerland, with an adjunct appointment in Electrical Engineering. He previously held roles at the National Center for Supercomputing Applications (University of Illinois at Urbana-Champaign) and Indiana University. Full Professor of Computer Science, ETH Zurich (2020–present) Adjunct Professor of Electrical Engineering, ETH Zurich (2020–present) Member at Large, ACM SIGHPC Executive Committee (2013–present) Leadership roles in the MPI Forum and Blue Waters project His research focuses on performance-centric system design , with emphasis on scalable networking, parallel programming models, and performance modeling. Key contributions include the Slim Fly network topology, Data-Centric Python framework, and innovations in parallel graph computations and RDMA-based systems. Recent publications span topics like LLM training networks , quantization geometry , chiplet interconnects , and AI-driven climate modeling , reflecting his interdisciplinary approach combining HPC, AI, and hardware-software co-design. ACM Gordon Bell Prize (2019) ERC Consolidator Grant (2020) IEEE TCSC Award for Excellence (2019) SIAM SIAG/SC Junior Scientist Prize (2012) Latsis Prize of ETH Zurich (2015) He has received multiple best paper awards at top conferences (SC10, SC13, SC14, SC19, IPDPS'15, HPDC'15, OOPSLA'16) and contributed to MPI-3 standardization.

• Database Systems
• Healthcare Information Systems
• Artificial Intelligence
• +۵ مورد دیگر

Carlo Combi is a Full Professor at the Department of Computer Science, University of Verona. He serves as Department Director and holds roles in various academic committees, including the Faculty Board of PhD in Computer Science and the Computer Science Teaching Committee. His research focuses on databases, healthcare information systems, temporal data mining, and business process management. He leads the STARS laboratory and has contributed to projects like PREPARE (prostate cancer evaluation) and EDIPO (neuroimaging genetics). His teaching spans courses on biomedical databases, software engineering, and Python programming. Combi's work integrates ethical considerations into process models and addresses challenges in healthcare workflows, temporal analytics, and AI applications in medicine. Education: Not explicitly listed in provided texts. Research Projects: Includes 30+ projects since 2008, emphasizing healthcare informatics, temporal systems, and data warehousing. Labs/Teams: STARS (Semistructured Temporal clinical Geographical Systems) and collaborations with CBMC (Computational Biomedicine Center). Grants: Multiple national/international grants, including funding for temporal data mining and healthcare process modeling. Awards: None explicitly mentioned, though his leadership roles and extensive publications highlight academic recognition. His research bridges clinical processes, temporal databases, and AI, with applications in acute kidney injury prediction, BPMN extensions for healthcare, and explainable AI in medical systems. Combi actively contributes to open-source solutions for clinical data analysis and integrates ethical frameworks into process management.

Ninghui Liمشاهده پروفایل

استاد

• Information Security
• Data Privacy
• Cybersecurity
• +۴ مورد دیگر

Ninghui Li is the Samuel D. Conte Professor and Associate Department Head in the Department of Computer Science at Purdue University. He holds a B.S. from the University of Science and Technology of China and a Ph.D. from New York University. His research focuses on information security, privacy, and database systems, with notable contributions to differential privacy and secure data publishing. He has authored over 200 papers, including influential works like the 2007 t-Closeness paper, and has received multiple awards, including being named an ACM and IEEE Fellow. Li’s academic roles include Editor-in-Chief of ACM Transactions on Privacy and Security (TOPS) and leadership in organizations like ACM SIGSAC. He advises over 30 graduate students and has been instrumental in coaching Purdue’s ICPC teams to top global rankings. His current projects include NSF-funded initiatives like the Center for Distributed Confidential Computing (CDCC) and privacy-focused AI research. Key contributions span privacy-preserving data synthesis, federated learning security, and cybersecurity for IoT systems. He actively contributes to conferences as a program chair and through editorial roles, ensuring advancements in both theoretical and applied security domains.

Gustavo Adolfo Junipero Rodriguez-Riveraمشاهده پروفایل

دانشگاهی

• Operating Systems
• Computer Networks
• Memory Management
• +۱۱ مورد دیگر

Gustavo Rodriguez-Rivera is an Associate Teaching Professor in the Department of Computer Science at Purdue University, part of the School of Science. He joined the department in 2000 and holds a Ph.D. in Computer Science from Purdue University (1998), an M.S. in Electrical Engineering from ITESM Campus Monterrey (1990), and a B.S. in Electrical Engineering from the same institution (1985). His research focuses on Operating Systems, Computer Networks, Memory Management, Embedded Systems, Real-Time Systems, and broader areas like Numerical Analysis and Artificial Intelligence. Dr. Rodriguez-Rivera has received multiple teaching awards, including the ACM Faculty Award in Computer Science (2020, 2023) and Best Teacher in the School of Science (2014, 2016). His work spans software engineering education, structural health monitoring for wind turbines, and memory management algorithms. Notable publications include studies on garbage collection techniques, real-time project tracking in programming courses, and vibro-acoustic modulation for turbine blade inspection. He has advised numerous projects and contributed to grants such as the NSF-funded work on wind turbine blade monitoring. His academic contributions also include developing secure programming course modules and tools for interactive debugging systems.

Norbert Ritterمشاهده پروفایل

استاد

• Databases and Information Systems
• New Database Technology (NoSQL)
• Scalable Cloud Data Management
• +۹ مورد دیگر

Prof. Norbert Ritter is the Dean of the Faculty of Mathematics, Computer Science and Natural Sciences (MIN) at the University of Hamburg since August 2022. He holds a full professorship in the Department of Informatics, leading the Databases and Information Systems group. Previously, he served as an associate professor (2002–2005) and assistant professor (1998–2002) at the Technical University of Kaiserslautern and the University of Hamburg. His research focuses on advanced database technologies, including NoSQL systems, scalable cloud data management, big data analytics, and information integration. Key areas include service-oriented computing, federated database systems, and transaction management. He has authored over 149 publications, with recent work emphasizing polyglot data stores, spatio-temporal data processing, and web performance optimization. Education: M.Sc. (1991), Ph.D. (1997) in Computer Science from the University of Kaiserslautern Professional Activities: Dean of MIN Faculty (since 2022), former head of DBIS group Labs/Teams: Leads the Databases and Information Systems research group His advising record includes over 274 student theses, spanning PhD and master's projects in database design, data integration, and web performance engineering. Collaborative projects include Beaconnect (continuous web A/B testing) and Compaz (shared dictionary compression).

Shiyong Luمشاهده پروفایل

استاد

• Big Data Workflows
• Cloud Computing
• Scientific Workflows
• +۳ مورد دیگر

Dr. Shiyong Lu is Professor of Computer Science at Wayne State University and Director of the Big Data Research Laboratory. He holds a PhD from State University of New York at Stony Brook and has published over 140 papers in venues including IEEE Transactions on Services Computing and IEEE Transactions on Knowledge and Data Engineering. Research focuses on: Architecture of big data workflow systems Secure execution in cloud environments Optimization algorithms for distributed computing Provenance management for scientific workflows Recent work demonstrates strong emphasis on secure workflow execution, with 80% of last 15 publications addressing trusted computing environments. Leads NSF-funded DATAVIEW project for cloud-based big data analytics. Awarded IEEE TCSVC Outstanding Service Award (2023). Teaches core courses in Database Management Systems and Data Modeling. Supervised 17 PhD graduates, 10 of whom hold faculty positions. Editorial board member for International Journal of Big Data and International Journal of Big Data Intelligence.