Find Academic Supervisors

• Database Systems
• Data Warehousing
• OLAP Analysis
• +3 more

Professor Zbyszko Królikowski is a faculty member at Poznań University of Technology, where he works in the Faculty of Computer Science and Telecommunications, specifically in the Institute of Computer Science. He holds the academic title of Professor (indicated by "prof. dr hab. inż.") and has been actively contributing to the field of computer science, particularly in database systems and data warehousing. His scientific work spans multiple disciplines, with 75% focus on Computer and Information Sciences and 25% on Information and Communication Technology. Professor Królikowski has established himself as an expert in data warehousing, having authored a comprehensive monograph titled "Data warehouses: logical and physical data structures" published in 2007. His research interests primarily revolve around database systems, with specific focus on: Data warehousing and OLAP analysis Logical and physical data structures for databases Sequential data analysis Materialized views and query optimization Database performance evaluation Evolution of data warehouse systems Professor Królikowski has maintained an active research profile with publications spanning nearly two decades, from the early 2000s to 2021. His work shows a consistent focus on database technologies, evolving from traditional relational database systems to more contemporary approaches including in-memory architectures and graph databases. Analysis of his publication trends reveals a progression from theoretical database modeling to practical applications in production planning and modern database technologies. He has contributed to the academic community not only through his publications but also by supervising doctoral research. Notably, he supervised Mikołaj Morzy's dissertation on "Advanced database structures supporting effective association discovery" in 2004 and has served as a reviewer for numerous other doctoral dissertations in related fields. Professor Królikowski has collaborated extensively with other researchers in the database community, particularly with scholars from Poznań University of Technology including Tadeusz Morzy, Bartosz Bębel, and Robert Wrembel. These collaborations have resulted in numerous joint publications that have contributed significantly to the field of data warehousing and database systems.

Uday VenkatadriView profile

Professor

• Supply Chain Management
• Facilities Planning and Design
• Production Planning and Control
• +5 more

Dr. Uday Venkatadri is a Full Professor and Department Head in the Department of Industrial Engineering at Dalhousie University's Faculty of Engineering, where he has taught since 2001. His industrial experience includes roles as Lead Architect at Baan and Research Associate at Université Laval. His research bridges facilities planning, supply chain optimization, and Industry 5.0 technologies, utilizing Mixed Integer Linear Programming as a core methodology. Research interests span: Hyperconnected logistics systems Production planning under uncertainty Healthcare supply chain resilience Sustainable operations AI/ML applications in industrial systems His publication trends show strong focus on: Optimization under uncertainty (35% of recent works) Sustainable supply chains (25%) Healthcare operations (20%) Maintenance systems (15%) Industry 5.0 applications (5%) Notable scientific awards: 2021 IISE Teaching Award (Logistics Division) Dalhousie Teaching Excellence Awards (2016, 2004) Purdue University Fellowships (1991-1993, 1987-1990) IIE Master's Thesis Award (1988) Advising and grants: Supervises graduate students in supply chain optimization and production systems Research funded through industry partnerships and institutional grants Developed 15+ graduate courses including Advanced Facilities Planning Laboratory involvement includes leading computational research groups focused on logistics optimization and sustainable operations, though no dedicated physical lab is specified.

Enes AlgülView profile

Lecturer

• Graph Machine Learning
• Geometric Deep Learning
• Bioinformatics
• +5 more

Enes Algül serves as a Lecturer in the Faculty of Engineering and Architecture at Bingol University, bringing deep expertise in Graph Machine Learning and Geometric Deep Learning developed through his PhD at the University of York and postdoctoral work at the University of Copenhagen. His research bridges theoretical computer science with practical applications in bioinformatics and computer vision. His educational foundation includes: PhD in Computer Science, University of York, UK (2017-2022) MSc in Software Engineering, University of Hertfordshire, UK (2016-2017) Bachelor's in Computer Engineering, Ankara University, Turkey (2009-2014) Research Interests: Algül pioneers methods for transforming complex data into graph and 3D point cloud representations, applying Graph Kernels and Graph Neural Networks to solve challenging problems in RNA structure classification and face recognition. His work uniquely combines graph theory, deep learning, and domain-specific knowledge in bioinformatics, with recent focus on novel graph representations for RNA molecules based on sequence free energy and 3D structural properties. This interdisciplinary approach enables breakthroughs in analyzing biological data and visual recognition systems. Scientific Recognition: Turkish Ministry of National Education Scholarship (2014) - Awarded to top 900 undergraduate students nationally Academic Contributions: Algül teaches advanced courses including Software Engineering, Natural Language Processing, and Object-Oriented Programming at Bingol University. His graduate teaching experience at York demonstrates strong pedagogical skills across Python, Java, and data science curricula. The scholarship supporting his entire graduate education reflects exceptional academic promise, and his recent publication surge in 2023 indicates active research momentum with potential for significant future contributions in geometric deep learning applications. Research Environment: While no dedicated lab is specified, Algül's work spans multiple high-impact domains including computational biology and computer vision. Students would engage with cutting-edge techniques in graph representation learning and neural network architectures applied to real-world data challenges.

Izabella Adamczewska-BaranowskaView profile

Assistant Professor

• Literary Studies
• Reportage
• Polish Journalism
• +9 more

Izabella Adamczewska-Baranowska is an Assistant Professor at the Department of Theory of Literature, Faculty of Philology, University of Lodz. She has been working at the Institute of Contemporary Culture since 2009, after defending her doctoral dissertation in 2007 titled "Evolution of the environmental novel in contemporary Polish literature." She teaches courses including Literature Promotion and Publishing Market, Fact and Fiction, Text Editing, Narrative Techniques, and Reportage Laboratory. Her primary research interests focus on literary theory, particularly the theory of the novel and reportage, literary journalism, and biopoetics. She has published three monographs: "Jacek Hugo-Bader. Włóczęga" (2022), "False Reportages and True Fictions. Journalistic Novel and Reportage in the Post-Truth Era and Performative Turn" (2020), and "Landscape after Masłowska. Evolution of the Environmental Novel in Contemporary Polish Literature" (2011). Her work explores the boundaries between fact and fiction, especially in journalistic and reportage literature. Her recent scholarly output demonstrates a consistent focus on contemporary reportage, autotheory, and the intersection of literature with urban studies, ecocriticism, and gender studies. She frequently examines how reportage functions in the post-truth era and how performative approaches transform traditional journalistic forms. Her work shows particular interest in Polish authors like Jacek Hugo-Bader and the international reception of Polish literature. Nagroda Rektora UŁ II stopnia za monografię "Krajobraz po Masłowskiej" Nagroda Rektora UŁ II stopnia za cykl publikacji o reportażu i powieści dziennikarskiej Nagroda Rektora UŁ II stopnia za książkę "Jacek Hugo-Bader. Włóczęga" Złote Pióro im. Jerzego Katarasińskiego – "Redaktor sezonu 2017/2018" She has supervised two theses and has been actively involved in the "Leksykon terminów medialnych" project funded by the Ministry of Education and Science. Beyond academia, she works as a journalist for the Lodz branch of "Gazeta Wyborcza" since 2002, reviewing theater performances and books, and maintains a literature blog "Wchodzę w słowo" on Facebook.

Gang MeiView profile

Associate Professor

• Numerical Simulation and Computational Modeling
• GPU Computing
• Machine Learning
• +5 more

Dr. Gang Mei is an Associate Professor in Scientific Computing within the School of Engineering and Technology at China University of Geosciences (Beijing), where he has held academic positions since 2014. His career progression includes Postdoctoral Researcher (2014-2016), Lecturer (Oct-Dec 2016), and current Associate Professor (since Jan 2017). His research bridges computational science and engineering applications with significant editorial contributions to computer science literature. Education: Ph.D. in Computer Science, University of Freiburg, Germany (2014) Research Interests: Dr. Mei specializes in Numerical Simulation and Computational Modeling, GPU Computing, Machine Learning, and Data Mining, with strong applications in Network Science and Spatial Information Systems. His work integrates Distributed and Parallel Computing techniques for large-scale scientific simulations, particularly in geospatial modeling and network analysis. The research demonstrates consistent focus on computational efficiency through hardware acceleration and algorithmic optimization across diverse domains including satellite imagery processing, financial event detection, and medical image classification. Publication Trends: His editorial portfolio reveals strong interdisciplinary patterns connecting computer science fundamentals with domain-specific applications. Recent works emphasize GPU-accelerated methods for data-intensive problems (2020-2022), spatial-temporal modeling (2019-2020), and network science applications (2021). The publications consistently address computational scalability challenges while maintaining practical relevance across geospatial, financial, medical, and engineering contexts. Professional Recognition: As an IEEE Member, Dr. Mei serves on editorial boards for IEEE Access and PeerJ Computer Science, reflecting peer recognition in computational fields. His editorial contributions span 15+ publications demonstrating expertise in evaluating cutting-edge computer science research. Academic Service: Beyond editorial work, Dr. Mei's service includes advising on computational methodology across multiple disciplines. His role as Academic Editor demonstrates commitment to scholarly communication, particularly in bridging theoretical computer science with practical engineering applications. No grant funding details were specified in available materials.

Viorel GrigorceaView profile

Senior Lecturer

• Artificial Intelligence
• Case-Based Reasoning
• Programming Technologies
• +3 more

Viorel Grigorcea is a Senior Lecturer at the Department of Computer Science within the Faculty of Mathematics and Informatics at the University of State of Moldova (USM). He has been with USM since 1997, becoming a senior lecturer by competition in 2002, and has served as Vice-Dean of the Faculty of Mathematics and Informatics since that same year. His academic journey began with a Bachelor's degree in Applied Mathematics from USM (1991-1996), followed by a Doctorate in Computer Programming from the Institute of Mathematics and Informatics of the Romanian Academy of Sciences (1996-1999). His teaching portfolio includes a comprehensive range of computer science courses: Artificial Intelligence Logic Programming Functional Programming Object-Oriented Programming Programming Fundamentals Programming Techniques Computer Architecture Operating Systems Expert Systems Grigorcea's research has evolved over time, with early work focusing on analogical reasoning, case-based reasoning, and attribute modeling in artificial intelligence systems. In recent years, his research has expanded to include educational assessment, particularly evaluating school results and basic skills of primary and secondary school graduates in Mathematics, Romanian, and Russian. His scientific activity includes continuous collaboration since 2001 on the institutional project 'Research and development of theoretical and practical aspects of modern programming technologies,' building on his earlier work in artificial intelligence systems. Grigorcea is associated with the Web Programming Section at USM, reflecting his engagement with practical programming technologies and applications.

• Machine Learning
• Data Analytics
• Artificial Intelligence
• +3 more

Maria-Cristina Marinescu serves as a Lecturer in the Department of Mathematics and Data Analytics at IQS School of Management. With a strong foundation in computational methods and data analysis, she contributes significantly to both teaching and research within the institution. Her academic profile demonstrates consistent scholarly activity with 44 documented scientific production items spanning over two decades. Dr. Marinescu's research interests center on Machine Learning, Data Analytics, and Artificial Intelligence, with notable applications in epidemic modeling, wireless sensor networks, and programming models. Her work bridges theoretical computer science with practical applications addressing real-world challenges in public health, digital well-being, and medical diagnostics. The fingerprint analysis of her work shows strong emphasis on Machine Learning (100%), Learning Systems (100%), and Transportation Models (100%), with substantial contributions to Programming Models (84%) and Wireless Sensor Networks (77%). Her recent publications reveal a trend toward interdisciplinary research that combines computational methods with societal challenges. She has made significant contributions to epidemic modeling during the COVID-19 pandemic, developing methods for accurate incidence rate estimation in Spain and analyzing information gains from multiple epidemic model outputs. Additionally, her work extends to medical applications including research on ocular ischemia and glaucoma, as well as innovative approaches to automated metadata annotation using machine learning techniques. Dr. Marinescu actively leads and participates in multiple research projects including Uncovering patterns of unconscious reactions to fake content (as Principal Investigator), MobilePressure (focused on reducing children's smartphone exposure), and ADAMIQS: Applied Data Analytics and Modelling IQS . These projects demonstrate her commitment to addressing contemporary issues through data-driven approaches while mentoring students and collaborating with interdisciplinary research teams.

Michael HiltonView profile

Teaching Professor

• Software Engineering
• Continuous Integration
• Structured Editors
• +3 more

Michael Hilton is an Associate Teaching Professor at Carnegie Mellon University's School of Computer Science, where he serves as the Associate Department Head for Education in the Software and Societal Systems Department. He directs the Software Engineering Minor and the Software Engineering Concentration programs, and teaches various software engineering courses including Foundations of Software Engineering, Introduction to Software Construction, and Crafting Software. His research interests focus on improving the lives of software engineers, with particular emphasis on flaky tests , continuous integration , and structured editors . His work bridges both the research and educational aspects of software engineering, aiming to make software development processes more reliable and efficient while educating the next generation of software engineers. Analysis of his recent publications reveals a strong focus on flaky test detection and mitigation, with significant contributions to understanding test reliability in continuous integration environments. His research spans empirical studies of testing practices, machine learning approaches to flakiness prediction, and educational aspects of software engineering. A notable trend is his increasing focus on the intersection of software testing and education, particularly examining how students interact with modern development tools and practices. Professor Hilton is highly active in the software engineering research community, serving on program committees for major conferences including ASE, ICSE, and SIGCSE. He has contributed to numerous workshops and symposia focused on software engineering education and testing research. As an educator, he has developed and taught multiple software engineering courses covering object-oriented design, testing, concurrency, and software construction principles. He emphasizes practical, hands-on learning experiences that prepare students for real-world software development challenges. His educational research explores best practices for teaching software engineering and the impact of new technologies like generative AI on software engineering education. He maintains an active research lab focused on software engineering tools and education, collaborating with both academic and industry partners to address practical challenges in software development workflows. His current projects include investigations into flaky test patterns, continuous integration practices, and the integration of modern development tools into software engineering curricula.

Simone ScalabrinoView profile

Assistant Professor

• Software Quality
• Software Testing
• Software Security
• +2 more

Simone Scalabrino is an Assistant Professor at the University of Molise, Italy, where he is part of the STAKE lab. He also serves as CSO at Datasound. His academic career includes teaching courses such as Automated Software Delivery and Object-Oriented Programming at the University of Molise. Dr. Scalabrino received his Ph.D. from the University of Molise in 2019 with a thesis entitled "Automatically Assessing and Improving Code Readability and Understandability," supervised by Prof. Rocco Oliveto. He earned his Master's Degree in Computer Science from the University of Salerno in 2015 and his Bachelor's Degree in Computer Science from the University of Molise in 2013. His research interests focus on Software Quality, Software Testing, Software Security, and Empirical Software Engineering . Dr. Scalabrino's work spans multiple areas including code readability assessment, software testing methodologies, Docker container analysis, game quality assessment, and voice user interface testing. His research often combines empirical methods with machine learning techniques to address practical software engineering challenges. Analysis of his recent publications reveals a strong focus on improving software quality through various approaches. His work spans code readability assessment, Docker container analysis, game quality testing, and voice user interface evaluation. There's a clear trend toward applying machine learning techniques to traditional software engineering problems, particularly in the areas of code understanding, defect prediction, and quality assessment. His research often involves large-scale empirical studies with real-world data from open source projects and commercial applications. Scientific Awards Distinguished Reviewer Award @ FSE 2025 Distinguished Reviewer for TSE (2023) Best Reviewer Award for JSS (2022) ACM Distinguished Paper Award @ MSR 2019 ACM Distinguished Paper Award @ ASE 2017 ACM Distinguished Paper Award @ ICPC 2016 Dr. Scalabrino has been actively involved in mentoring students through research projects, though specific student names are not listed in the provided information. He has served on program committees for numerous prestigious conferences including ASE, ICSE, ICPC, and FSE. His service extends to reviewing for top-tier journals such as Transactions on Software Engineering and Empirical Software Engineering. He leads or contributes to several research projects including DevProDev, which focuses on developer-centered recommendation systems, and ATTICUS, a tele-monitoring system for ambient-assisted living. Dr. Scalabrino has also developed multiple research tools such as Code Readability Predictor, TIRESIAS, OCELOT, CLAP, and ACRyL to address various software engineering challenges.

Ji-Yong ShinView profile

Assistant Professor

• Formal Methods
• Distributed Systems
• Operating Systems
• +4 more

Ji-Yong Shin is an Assistant Professor in the Khoury College of Computer Sciences at Northeastern University, where he has been faculty since 2020. Previously, he served as an Associate Research Scientist at Yale University after completing his Ph.D. at Cornell University. His research bridges systems design and formal verification to ensure correctness in complex distributed systems. His educational background includes: Ph.D. in Computer Science from Cornell University Professor Shin's research focuses on co-designing systems and formal verification methods to simplify correctness proofs without sacrificing performance. Key areas include distributed systems (consensus protocols like Raft/Paxos), serverless computing for stateful applications, cloud storage leveraging emerging hardware (persistent memory, RDMA), and system support for deep neural networks. His Atomic Distributed Objects (ADO) model abstracts network behaviors to enable compositional reasoning of distributed systems. His publication trend (2020-2025) shows consistent contributions to top-tier venues (PLDI, OOPSLA, NSDI, VLDB), with recent work emphasizing blockchain verification (LiDO-DAG), reconfiguration protocols (ReCraft), and storage systems (FusionFlow). This reflects a cohesive research trajectory where formal methods solve practical systems challenges in distributed computing. His scientific recognition includes: NSF CAREER Award (2025) He currently advises PhD student Bryant Curto and actively recruits new students, focusing on systems and formal methods. His funded research includes an NSF FMitF grant (2020) with Zhong Shao and Robert Soule for formal methods in cloud infrastructure. He also serves in leadership roles including HotStorage 2025 Co-General Chair and PC member for OOPSLA 2026, NSDI 2026, and USENIX ATC 2025. Professor Shin jointly runs the systems research group at Northeastern, collaborating on projects spanning distributed systems verification, serverless computing enhancements, and storage system optimizations using next-generation hardware.

• Human-Computer Interaction
• Task Modeling
• User Interface Design
• +3 more

Sybille Caffiau serves as a Lecturer in Data Engineering at ISAE-ENSMA's School of Engineering, affiliated with the LIAS laboratory across both ENSIP (Poitiers) and ISAE-ENSMA (Chasseneuil) campuses. Her research focuses on Human-Computer Interaction with specialization in task modeling and user interface validation. Her research interests span Human-Computer Interaction , Task Modeling , and User-Centered Design . She develops and evaluates tools like K-MADe for modeling user activities, with emphasis on empirical validation of task modeling approaches. Her work bridges cognitive science principles with practical software engineering applications, particularly in educational technology and interactive system design. Analysis of her publication trends reveals consistent contributions to HCI conferences and journals since 2006, with increasing focus on tool development (notably K-MADe) and empirical validation methodologies. Her recent work demonstrates strong interdisciplinary connections between cognitive modeling, software engineering, and educational technology. No scientific awards or fellowships are documented in the available materials. While her collaborative network includes prominent researchers like Laurent Guittet and Patrick Girard, specific details about student advising or research grants are not provided in the source materials. Her thesis work (2009) established foundations for model-driven interactive application design. She operates within the LIAS laboratory's Data Engineering team, contributing to research on interactive systems and task modeling frameworks. The KMADe environment represents her primary software contribution for activity description and task modeling.