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Masud RahmanView profile

Associate Professor

• Software Engineering
• Artificial Intelligence for Software Engineering (AI4SE)
• Software Maintenance and Evolution
• +11 more

Masud Rahman is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Canada, where he leads the RAISE Lab. Previously a tenure-track Assistant Professor, he completed his Ph.D. in Computer Science/Software Engineering from the University of Saskatchewan and a postdoctoral fellowship at Polytechnique Montreal. His academic career demonstrates strong progression with significant research impact in software engineering. Faculty of Computer Science, Dalhousie University (Current) University of Saskatchewan (Ph.D. studies) Polytechnique Montreal (Postdoctoral research) Dr. Rahman's research focuses on the intelligent automation of software maintenance and evolution, particularly targeting software debugging, code search, and code reviews. His work strategically combines Software Engineering with Artificial Intelligence techniques including Machine/Deep Learning, Information Retrieval, Mining Software Repositories, and Natural Language Processing. His industry experience as a professional developer for three years significantly shaped his research direction toward solving practical software maintenance challenges that cost the global economy billions annually. His research program addresses critical problems in software bug detection, diagnosis, explanation, and reproduction, with increasing focus on AI-powered and simulation modeling software. His publications demonstrate consistent output in top-tier venues including 7 papers at ICSE (A*), 3 at FSE (A*), 3 at ASE (A*), 8 at EMSE (A), 6 at ICSME (A), and 9 at MSR (A). The research trends show increasing focus on deep learning applications for software engineering problems, with particular attention to code search, bug localization, and debugging automation. His work has evolved from traditional information retrieval approaches to incorporate advanced neural network techniques and generative AI. Governor General's Gold Medal 2019 U of S Doctoral Thesis Award 2019 CS Best PhD Thesis Award 2019 TCSE Distinguished Paper Award Most Influential Paper Award Dr Keith Geddes Award Dalhousie Belong Research Fellowship President's Gold Medal (Bangladesh) Dr. Rahman has secured $500K+ in competitive research funding as Principal Investigator, including an NSERC Discovery Grant, Mitacs Accelerate International, and Climate Action and Awareness Fund. He actively collaborates with industry partners including Metabob Inc., Mozilla Firefox, and Vendasta Technologies. His service to the community includes extensive program committee work for major conferences and journal reviewing. He leads the RAISE Lab, which focuses on developing AI-powered solutions for software maintenance challenges, with current projects emphasizing sustainable software innovation and sustainable AI as part of Dalhousie's strategic goals.

Djamel Eddine KhelladiView profile

Researcher

• Software engineering
• Model-Driven Engineering
• Software Evolution
• +3 more

Djamel Eddine Khelladi is a CNRS Researcher at the IRISA laboratory within the DIVERSE team at University of Rennes, specializing in software engineering with emphasis on model-driven techniques and empirical validation. His work bridges theoretical frameworks and industrial-scale applications, particularly in evolving software ecosystems. His academic foundation includes a Ph.D. from Sorbonne University (formerly University Pierre et Marie Curie) at the Laboratory of Computer Science of Paris 6 (LIP6), followed by postdoctoral research at Johannes Kepler University Linz's Institute for Software Systems Engineering. This trajectory established his expertise in software evolution and model-driven approaches. Khelladi's research centers on software evolution challenges, particularly model-code co-evolution in highly-configurable systems like the Linux kernel. He develops scalable analysis tools (e.g., HyperAST, HyperDiff) and investigates empirical phenomena in build systems, configuration management, and polyglot programming environments. Recent work increasingly integrates large language models for automated co-evolution tasks while maintaining rigorous empirical validation. His publication trends reveal a consistent focus on practical tooling for software evolution, with growing exploration of AI-assisted engineering. Key themes include scalability in software history analysis, reproducibility in configurable systems, and debugging multi-language environments, often using Linux kernel ecosystems as testbeds. As an active community contributor, Khelladi serves on program committees for ASE, ICSE, and ESEC/FSE while advancing research through the DIVERSE team at IRISA. This group specializes in variability-intensive software systems, providing the collaborative environment for his empirical and tool-building research.

Christian FiebergView profile

Professor

• Financial Economics
• Risk Management
• Portfolio Management
• +5 more

Christian Fieberg serves as a Professor of Data Science at Hochschule Bremen (City University of Applied Sciences) in Bremen, Germany, and holds an Affiliate Professor position at Concordia University in Montreal, Canada. He is actively affiliated with the DTX research cluster within the Faculty of Business and Economics at Hochschule Bremen, where his work bridges theoretical modeling with practical financial applications. Professor Fieberg's research spans financial economics with particular expertise in risk and portfolio management, sustainable investments, and data-intensive financial analysis. His methodological approach integrates advanced statistical techniques, machine learning algorithms, and econometric modeling to address complex financial problems. He specializes in working with large datasets and developing practical software tools that translate research findings into actionable insights for financial practitioners. His publication record reveals a strong focus on market predictability, factor modeling, and cross-sectional analysis across various asset classes. Recent work demonstrates increasing attention to cryptocurrency markets and the application of machine learning techniques to international financial markets. His research consistently appears in top-tier finance journals including the Journal of Finance, Journal of Financial and Quantitative Analysis, and Review of Finance, reflecting both methodological rigor and practical relevance. Top 50 most research-intensive economists under 40 years of age in German-speaking countries (Wirtschaftswoche) Professor Fieberg maintains active collaborations with researchers across international institutions, as evidenced by his co-authored publications with scholars from Concordia University, Montpellier Business School, and the University of Bremen. His work with the DTX research cluster emphasizes innovative research approaches and strong academic-industry networking. His current projects include Bond Factor Prediction (2025-2026) and participation in international workshops such as the STARS EU workshop in Sweden. His laboratory work focuses on developing computational tools for financial analysis using multiple programming environments including Matlab/Octave, R, Stata, Python, and Excel/VBA. The research group maintains active data repositories on Harvard Dataverse for replication purposes, demonstrating commitment to research transparency and reproducibility.

• Process Systems Engineering
• Chemical Engineering
• Optimization
• +6 more

Alexander Mitsos is a Professor at Forschungszentrum Jülich in Germany, where he leads research at the intersection of process systems engineering, chemical engineering, and computational methods. His work spans optimization theory, machine learning applications, and energy systems, with a focus on developing novel methodologies for complex engineering problems across multiple domains. Dr. Mitsos's research interests center on the application of advanced optimization techniques to chemical engineering problems. His primary areas of focus include: Process systems engineering and optimization Machine learning applications in chemical engineering Energy systems and hydrogen technologies Bioprocess engineering and control systems Ammonia energy storage and carbon capture His recent publications reveal a strong trend toward integrating machine learning with traditional chemical engineering approaches. He has pioneered work on graph neural networks for molecular property prediction, reinforcement learning for control systems, and bilevel optimization for energy systems. His research demonstrates a consistent focus on developing computationally efficient methods that bridge theoretical advances with practical engineering applications, particularly in sustainability-focused domains like hydrogen technologies and carbon emission reduction. The analysis of his 15 most recent publications shows a balanced portfolio between theoretical method development (e.g., optimization algorithms) and practical applications (e.g., cement production, hydrogen compression). Dr. Mitsos has mentored numerous graduate students and postdoctoral researchers, as evidenced by his extensive publication record with junior authors. His research has been supported by various grants focused on energy transition, process optimization, and sustainable chemical engineering solutions, with significant collaborations across European institutions. The funding landscape for his work appears to emphasize sustainability transitions and industrial decarbonization, particularly in energy-intensive sectors. His work appears to be conducted within a research group focused on process systems engineering, with strong connections to both computational mathematics and practical chemical engineering applications. The group maintains laboratories for experimental validation of computational models, particularly in bioprocess engineering and hydrogen technologies, while maintaining strong theoretical foundations in optimization and control theory.

• Cryptography
• Cryptology
• Information Security
• +2 more

Ludger Krohs serves as a Lecturer within the Faculty of Electrical Engineering, Media and Computer Science at Amberg-Weiden University of Applied Sciences. His primary academic responsibility centers on teaching Cryptology II, a specialized course in cryptographic systems and information security protocols. His research profile focuses intensely on Cryptography and Cryptology, with strong foundations in Computer Science and Mathematics. This expertise supports the faculty's technical curriculum in secure communications and data protection frameworks, though specific research outputs or methodologies remain undocumented in available materials.