جست‌وجوی استاد و پژوهشگر

Alessio Gambiمشاهده پروفایل

پژوهشگر

• Software Engineering
• Software Testing
• Self-Driving Cars
• +۴ مورد دیگر

Alessio Gambi is a Researcher at the Austrian Institute of Technology (AIT) within the Security & Communication Technologies department, specializing in software engineering for autonomous systems. His current work focuses on testing methodologies for self-driving cars, self-adaptive systems, and cloud environments. His research interests center on Software Testing for Autonomous Vehicles , where he develops novel techniques for scenario generation, safety validation, and uncertainty management. Key areas include search-based procedural content generation, simulation-based testing, and the integration of large language models for test learning. His work bridges theoretical advances with practical tools like Flexcrash and TEASER for real-world validation. Analysis of his recent publications (2023-2025) reveals a strong trend toward autonomous vehicle testing with increasing incorporation of AI techniques. Approximately 60% of his work addresses self-driving car validation, 25% focuses on general software testing methodologies, and 15% explores AI/LLM applications in testing. His subfield specialization shows consistent emphasis on critical scenario generation, mixed-traffic simulation, and safety monitoring. Gambi actively contributes to the software engineering community through program committee roles at major conferences including ASE (2023-2025), ICSE (2024-2026), ISSTA (2021-2025), and ESEC/FSE. He has served as session chair, workshop organizer, and track committee member across these venues, demonstrating leadership in software testing research. His professional activities include developing open-source testing tools (visible on GitHub), teaching engagements like the Database Systems course at AIT (2024), and industry collaborations through AIT's research infrastructure. Current projects focus on predictive safety monitoring and uncertainty management for automated driving systems.

Reid Holmesمشاهده پروفایل

استاد

• Software Engineering
• Software Testing
• Code Search
• +۷ مورد دیگر

Reid Holmes is a Professor in the Software Practices Lab at the University of British Columbia's Department of Computer Science, Faculty of Science. With over 15 years of academic experience, he has progressed from Assistant Professor at the University of Waterloo (2010-2015) to Associate Professor (2015-2022) and now full Professor (2022-present) at UBC. His research spans multiple dimensions of software engineering with a strong emphasis on the human aspects of development. Dr. Holmes' research interests focus on understanding the problems software engineers encounter when creating and evolving software systems. His work examines software testing and validation, source code reuse, code search, context-sensitive example location, API understanding, speculative analysis, code review, and team awareness. He believes that by better understanding how people create, explore, evolve, and reason about software systems, we can enhance developers' effectiveness and improve software quality. His research is characterized by its practical application to real-world software development challenges. His recent publications demonstrate a consistent focus on improving developer productivity through better tools and understanding of developer behavior. The publications reveal a trajectory from traditional software engineering topics toward newer areas involving AI-assisted development, with particular attention to how generative AI affects workflow and collaboration. His work consistently bridges theoretical software engineering concepts with practical developer experience considerations. ACM SIGSOFT Distinguished Paper Award (multiple times) FSE 2024 Most Impactful Paper Award 2024 ICSE Most Influential Paper Award Winner of the 2008 MSR Mining Challenge As an educator and mentor, Dr. Holmes has supervised numerous graduate students who have gone on to successful careers in academia and industry. He has served on program committees for major software engineering conferences including ICSE, FSE, and MSR, and has been an Associate Editor for TSE since 2016. His work on developer tools like CodeShovel, Devy, and Baker demonstrates his commitment to creating practical solutions that address real developer pain points.

• Software Engineering
• Machine Learning
• DevOps
• +۳ مورد دیگر

Emelie Engström is a software engineering researcher at Lund University, specializing in empirical studies at the intersection of academia and industry. Her work focuses on regression testing, machine learning applications in software quality, DevOps practices, and aligning requirements engineering with autonomous systems validation. University: Lund University Research Areas: Software Engineering, Machine Learning, DevOps, Autonomous Systems Her recent research examines ML-driven bug report classification, sustainable DevOps for cyber-physical systems, and optimizing anomaly detection through industrial case studies. She has developed taxonomies like SERP-test to improve communication between academic researchers and software practitioners. Key article trends show strong emphasis on: 2025: ML techniques for bug classification 2024: Ericsson case studies on automated bug assignment 2023: Anomaly detection in DevOps 2021: Autonomous driving system testing As a prolific collaborator, she works with researchers across Sweden including Per Runeson, Markus Borg, and Kai Petersen on projects ranging from cognitive load analysis in large-scale development to regression test scoping in software product lines.

Massimo Mecellaمشاهده پروفایل

استاد

• Service-Oriented Computing
• Business Process Management
• Process Mining
• +۱۲ مورد دیگر

Massimo Mecella serves as Full Professor at Sapienza University of Rome's Faculty of Information Engineering, Computer Science and Statistics, where he leads the Processes, Services and Software Management research group and participates in the CINI National Cyber Security Lab. His academic foundation stems from a PhD in Computer Engineering earned at Sapienza in 2002. His research expertise spans service-oriented computing, business process management, and cyber-physical systems with emphasis on service composition, process mining, and adaptive distributed architectures. Key technical domains include IoT integration, digital twin development, and security frameworks for complex software ecosystems. Recent publications (2024-2025) demonstrate a pronounced shift toward Large Language Model (LLM) integration in business processes and smart manufacturing. Dominant themes include LLM-driven process modeling, multimodal quality control systems, and digital twin implementations for energy management and production optimization. Methodological innovations focus on retrieval-augmented generation (RAG) for service discovery and fuzzy cognitive maturity assessments. His distinguished recognition includes: ICSOC 2013 Most Influential Paper Award (2003-2012 decade) 2017 Best Paper Award As research group leader, Mecella directs projects in smart manufacturing, process mining, and cyber-physical security, with funding typically sourced from EU initiatives and industry partnerships in aerospace, healthcare, and energy sectors. His team maintains active collaborations with aerospace manufacturers through the MICS SPOKE8 project and develops frameworks like SAMBA for human-in-the-loop manufacturing systems. The Processes, Services and Software Management group operates within Sapienza's engineering faculty infrastructure, utilizing specialized labs for CPS/IoT testing and digital twin simulation, with strong ties to the CINI Cyber Security Lab for security validation.

Susanne Gerberمشاهده پروفایل

استاد

• Bioinformatics
• Computational Genomics
• Systems Biology
• +۲ مورد دیگر

Susanne Gerber is a Professor at iDNA and Adjunct Director at the Institute of Molecular Biology (IMB), Johannes Gutenberg University Mainz (JGU), affiliated with the Faculty of Biology's Bioinformatics department. Her academic journey includes an Assistant Professorship in Bioinformatics at JGU (2015-2020) and postdoctoral research at Università della Svizzera italiana. Her educational background comprises a PhD in Biophysics from Humboldt University of Berlin (2011), an M.Sc. in Bioinformatics from Free University of Berlin and Konrad Zuse Institute (2007), and a B.Sc. in Bioinformatics from Free University of Berlin and Max Planck Institute (2004). Dr. Gerber's research spans Bioinformatics, Computational Genomics, Systems Biology, Molecular Evolution, and Neuroinformatics , focusing on developing computational frameworks for genomic analysis, neurodegenerative disease modeling, and microbiome interactions. Her work integrates machine learning with multi-omics data to address complex biological questions in molecular evolution and neural systems. Analysis of her 15 most recent publications (2024-2025) reveals a strong emphasis on nanopore sequencing applications for RNA modification detection, deep learning frameworks for genomic data enhancement, and neurobehavioral modeling using AI-driven approaches. Key thematic clusters include epitranscriptomics, chromatin dynamics, and computational psychiatry with ethical AI considerations. Her methodological innovations include tools like COMET for network analysis, CCUT for chromatin data enhancement, and ModiDeC for RNA modification classification, demonstrating translational impact across genomics and neuroscience. Dr. Gerber leads research groups at IMB and iDNA focusing on computational genomics, advising students in bioinformatics and securing grants for AI-driven genomic analysis. Her labs develop open-source tools for nanopore data processing and neuroimaging analysis, fostering collaboration between computational and experimental biologists.

• Service-oriented Computing
• Web Services
• Workflow Technology
• +۷ مورد دیگر

Jun Yan is a Professor at the University of Wollongong's School of Computing and Information Technology within the Faculty of Engineering and Information Sciences. His roles include academic leadership and research supervision, with active involvement in committees like the Student Academic Experience Sub-Committee and Quality Assurance Review Group. Current research focuses on service-oriented computing, workflow technology, adaptive process management, and AI-driven systems. His work intersects with IoT, UAV systems, federated learning, and multi-agent reinforcement learning. Research interests span service-oriented software engineering, decentralized workflow management, and cybersecurity challenges in autonomous systems. Notable projects include an ARC-funded initiative on robust defenses against adversarial ML for UAV systems (2025–2027). He supervises Masters/PhD projects on topics like diffusion model-based MRI, graph prompt learning, and industrial defect detection. His publications from 2023–2025 emphasize scalable multi-agent systems, federated learning with non-IID data, and UAV applications in intelligent transportation. Key areas of contribution include trust models for e-commerce, privacy-preserving cloud computing, and fault-tolerant service architectures.

Mahmoud Alfadelمشاهده پروفایل

استادیار

• Software Ecosystems
• Software Releases
• Build Systems
• +۵ مورد دیگر

Mahmoud Alfadel is an Assistant Professor in the Department of Computer Science at the University of Calgary, Faculty of Science. His research focuses on software ecosystems, build systems, security vulnerabilities, and mining software repositories. He holds a PhD in Software Engineering from Concordia University (2021), an M.S. from KFUPM (2017), and a B.S. from Damascus University (2013). Education: Bachelor of Science in Information Technology, Damascus University (2013) Master of Science in Software Engineering, King Fahd University of Petroleum and Minerals (KFUPM, 2017) Doctor of Philosophy (Ph.D.) in Software Engineering, Concordia University (2021) His research emphasizes empirical studies in DevOps practices, continuous integration, and software security. Recent work explores vulnerability lifecycle analysis in Golang, fuzz testing adoption in open-source projects, and dependency-induced waste in CI pipelines. His publications reflect a focus on improving software quality and security through automated tools and empirical analysis. Awards and Grants: No specific awards or grants are listed in the profile. His work is supported through empirical studies and academic collaborations. Advising and Labs: No student advisees or specific lab affiliations are noted in the provided materials. His research is likely conducted through collaborative projects with students and industry partners.

Onet-Marian Zsuzsannaمشاهده پروفایل

مدرس

• Software Engineering
• Machine Learning
• Data Mining
• +۳ مورد دیگر

Dr. Onet-Marian Zsuzsanna is a Lecturer in the Department of Computer Science at Babes-Bolyai University in Cluj-Napoca, Romania. Her research focuses on applying machine learning techniques to software engineering challenges, particularly software defect prediction and restructuring. Academic Rank: Lecturer Affiliation: Babes-Bolyai University Department: Computer Science Email: zsuzsanna.onet@ubbcluj.ro Research Interests : Dr. Marian specializes in developing machine learning models (clustering, association rules, reinforcement learning) for software defect detection, package-level restructuring, and test order optimization. She also explores applications of Formal Concept Analysis in text summarization and music pattern discovery. Recent Publications highlight her work in source-code embeddings, unsupervised learning for software analysis, and comparative studies of online/traditional learning environments. Her methods often integrate domain-specific metrics and AI-driven optimization. Contact : zsuzsanna.onet@ubbcluj.ro | Office: Teodor Mihaly street, Room 440

Steven J Jonesمشاهده پروفایل

استاد

• Bioinformatics
• Gene Expression
• Gene Regulation
• +۷ مورد دیگر

Dr. Steven J. Jones is a Professor in the Department of Medical Genetics at the University of British Columbia (UBC) and Associate Director/Head of Bioinformatics at the Genome Sciences Centre, BC Cancer Research Centre. He serves as Director of the CIHR/MSFHR Bioinformatics Training Program and the UBC Bioinformatics Graduate Program, with associate memberships at the Peter Wall Institute for Advanced Studies and Michael Smith Laboratories. Dr. Jones' research focuses on computational genomics and cancer bioinformatics , analyzing mutational landscapes and epigenetic targets in oncology. His work includes next-generation sequencing , structural variant detection , and long-read sequencing applications for resolving complex genomic architectures. He has extensive experience with transcriptome analysis , gene regulation , and comparative genomics across diverse organisms including C. elegans , conifers, and human pathogens. His 15 most recent publications highlight expertise in long-read sequencing for cancer genomics , imprinting disorders , and structural variation analysis. Key contributions include developing the Circos visualization tool for comparative genomics and pioneering bioinformatics approaches for epigenetic modifier discovery in oncology. Scientific Awards : Spencer Award for IT innovation (2005) 2007 Medical Genetics teaching award (UBC) 2011 Fellow of the Royal Society of Canada 2012 UBC Killam teaching prize 2014 Distinguished Achievement Award (UBC Faculty of Medicine) 2014 Fellow of the Canadian Academy of Health Sciences Clarivate Analytics Highly Cited Researcher (2016, 2018) Dr. Jones actively advises graduate students and serves as a data analysis center for the Canadian Epigenetics, Environment and Health Research Consortium (CEEHRC). He co-founded Ifowonco Bioinformatics Inc. and Alamya Health, PBC, and consults for Apotheca Systems Inc.

Alessandro Orsoمشاهده پروفایل

استاد

• Software Engineering
• Software Testing
• Program Analysis
• +۳ مورد دیگر

Alessandro (Alex) Orso is a Professor in the School of Computer Science and Interim Dean of the College of Computing at Georgia Institute of Technology. He holds an M.S. in Electrical Engineering (1995) and a Ph.D. in Computer Science (1999) from Politecnico di Milano, Italy. Since 2000, he has been a faculty member at Georgia Tech. Affiliations: School of Computer Science, Scientific Software Engineering Center, Center for Experimental Research in Computer Systems (CERCS), and Online Master of Science Computer Science (OMSCS). Research Focus: Software engineering with emphasis on testing, program analysis, and improving software reliability/security through formal methods and tools. His research has been funded by DARPA, NSF, IBM, and Microsoft, among others. He co-founded the Scientific Software Engineering Center to advance methodologies for high-performance scientific software. Orso is a Distinguished Member of the ACM and an IEEE Fellow. Key contributions include developing techniques for automated REST API testing, program debloating, and cross-browser web application testing. His work bridges theory and practice, emphasizing real-world system validation. Awards: Four impact awards: ISSTA (2017, 2021), ASE (2020), IBM Haifa (2013) Editorial roles: ACM TOSEM, IEEE TSE Program chairs: ISSTA 2010, FSE 2014, ICSE 2017 Advising & Grants: Supervised over 40 students (PhD, Master's, undergrad). Secured funding from government/industry partners. Tools developed include AutoRestTest, Barista, and X-PERT. Labs/Teams: Leads the Arktos Research Group, focusing on software testing, analysis, and tool development. Collaborates with industry and government on applied research projects.

Andrea Edgintonمشاهده پروفایل

استاد

• Pharmacokinetics
• Clinical Pharmacology
• Hemophilia Treatment
• +۳ مورد دیگر

Andrea Edginton is a Professor and Hallman Director at the University of Waterloo's Faculty of Science, serving as Associate Dean. Her research focuses on pharmacokinetics and physiologically based pharmacokinetic (PBPK) modeling, particularly in hemophilia treatment optimization, pediatric drug dosing, and obesity-related pharmacology. She leads initiatives like the WAPPS-Hemo platform for clotting factor concentrates and has contributed to studies on maternal medication exposure during breastfeeding. Edginton's work bridges clinical practice and computational modeling, addressing drug efficacy and safety in diverse populations, including critically ill children and patients with rare coagulation disorders. Educational background: While not explicitly detailed in the text, her roles and research imply advanced training in pharmacy, pharmacology, or biomedical sciences. Research interests emphasize translational pharmacology, drug delivery mechanisms, and pediatric pharmacotherapy. She collaborates on global health projects, integrating real-world data with PBPK models to tailor treatments for hemophilia, neonatal care, and chronic illnesses. Key contributions include developing predictive models for factor VIII prophylaxis, optimizing anakinra dosing in extracorporeal life support, and evaluating drug interactions in obese pediatric populations. Her work on maternal drug exposure via breast milk highlights translational research impacting child health policies. Edginton also contributes to open-source pharmacology tools like the Open Systems Pharmacology Suite. Grant and advising activities are inferred from her research scope, though specific grants or student names are not listed. She is affiliated with the ConcePTION project for breastfeeding medication safety and the Kompetenznetz Hämostaseologie Ost (KHDO) in Germany. Her interdisciplinary approach spans academia, clinical practice, and industry collaborations, advancing personalized medicine strategies in hematology and pediatric pharmacology.

Franz Wotawaمشاهده پروفایل

استاد

• Model-Based Reasoning
• Qualitative Reasoning
• Theorem Proving
• +۶ مورد دیگر

Franz Wotawa is a Professor of Software Engineering at Graz University of Technology. He holds a M.Sc. (1994) and PhD (1996) from Vienna University of Technology. He has served as head of the Institute for Software Technology from 2003–2009 and since 2020. His research focuses on model-based reasoning, software testing, autonomous systems, and diagnosis, with over 390 peer-reviewed publications. He founded Softnet Austria (2006) to bridge research and industry. He leads the Christian Doppler Laboratory for Quality Assurance Methodologies for Autonomous Cyber-Physical Systems since 2017 and has supervised 90+ master and 36+ PhD students. His awards include the 2016 Lifetime Achievement Award from the International Diagnosis Community. He is a member of Academia Europaea, IEEE, and AAAI. **Education**: M.Sc. in Computer Science, Vienna University of Technology, 1994 PhD, Vienna University of Technology, 1996 **Research Interests**: Model-based reasoning, qualitative reasoning, theorem proving, mobile robotics, verification/validation, software testing/debugging, AI, and autonomous systems. **Notable Projects**: A-IQ Ready (2022–2026): Quantum sensing for autonomous systems. ALFA (2024–2027): AI for smart diagnosis in building automation. Bilateral AI (2024–2029): Combining symbolic and sub-symbolic AI. VARCOS (2025–2028): Vehicle-road cooperative systems for autonomous driving. **Awards & Memberships**: Lifetime Achievement Award (2016, International Diagnosis Community) Senior Member, AAAI Member of Academia Europaea, IEEE, ACM, and Austrian Computer Society **Labs/Teams**: Christian Doppler Laboratory for Quality Assurance Methodologies (since 2017). Active in Cluster of Excellence “Bilateral AI” at TU Graz.

Alexey Ignatievمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Satisfiability Solving
• Automated Reasoning
• +۲ مورد دیگر

Alexey Ignatiev is a Senior Lecturer in Monash University's Department of Data Science & AI. His research develops SAT/SMT-based methods for AI applications including explainable AI, automated software debugging, and neuro-symbolic systems. Key projects: Formal Explainability for Neuro-Symbolic AI (ARC-funded) Hierarchical Abstractions for Neuro-Symbolic Systems Recent publications focus on formal explanation techniques for machine learning models, including defect prediction systems and interpretable rule extraction.

• Software Engineering
• Developer Productivity
• IDE Interaction Analysis
• +۳ مورد دیگر

Andrea Mocci is a Lecturer at the Faculty of Informatics of the Università della Svizzera italiana (USI). His work focuses on software engineering methodologies, developer productivity, and IDE interaction analysis. He is affiliated with the Software Institute and actively contributes to academic events such as the IEEE International Workshop on Mining and Analyzing Interaction Histories (MAINT). His research explores empirical software engineering techniques, including developer behavior analysis, code documentation improvement, and the application of natural language processing to software artifacts. Key areas of investigation include: IDE interaction and navigation efficiency Code redundancy and quality metrics Video tutorial analysis for educational content Runtime systems and annotation APIs Defect prediction and software maintenance Publications from 2016-2020 highlight trends in developer-centric tools, holistic recommender systems, and visualization techniques for software evolution. His work often bridges theoretical formal methods with practical developer workflows, aiming to improve both software quality and developer productivity through empirical studies and tool development.

• Service-Oriented Architectures
• Data Management
• Cloud Computing
• +۵ مورد دیگر

Dimosthenis Kyriazis is a Professor at the University of Piraeus, affiliated with the Research Center. He currently teaches courses such as C Programming, e-Business, and Information Systems. His research focuses on service-oriented architectures, cloud/edge computing, data management, and healthcare informatics. Kyriazis holds a PhD from the National Technical University of Athens (NTUA) and has contributed to EU-funded projects like BigDataStack and CrowdHEALTH, addressing quality of service, workflow management, and IoT applications. He leads research on data management in cloud/edge environments, socially-enhanced IoT management, and big data applications in sectors like finance and e-health. His expertise spans distributed systems, software engineering, and interdisciplinary collaborations with industries and academia. He has participated in EU working groups on Future Internet Architecture and Cloud QoS&SLAs. Kyriazis actively seeks interns and collaborates on initiatives like AI-driven healthcare platforms (iHELP) and sustainable computing practices. His work emphasizes human-centric AI, data interoperability, and ethical AI frameworks such as AI4Gov for transparent governance. Education: Diploma in Electrical and Computer Engineering (NTUA, 2001), MSc in Techno-economics (NTUA/University of Athens/University of Piraeus, 2004), PhD in Service-Oriented Architectures (NTUA, 2007). Key research areas include federated data marketplaces (FAME), healthcare data integration (holistic health records), and AI applications in finance (DeepVaR). His publications address topics like explainable AI (XAI), defect detection in manufacturing, and environmental risk correlation with health. Grants and collaborations involve coordinating EU projects targeting data governance frameworks, energy-efficient mobility data spaces (Mobispaces), and AI for policy-making (e.g., OECD AI policy analysis). His contributions bridge technical innovation with societal impact through sustainable computing and ethical AI practices.