جست‌وجوی استاد و پژوهشگر

Arif Ali Khanمشاهده پروفایل

دانشیار

• Quantum Software Engineering
• Software Process Improvement
• Multi-criteria Decision Analysis
• +۸ مورد دیگر

Arif Ali Khan is an Associate Professor (Tenure Track) and Docent (Adjunct Professor) in the M3S Unit of Empirical Software Engineering at the University of Oulu, Finland. He holds a PhD in Software Engineering from City University of Hong Kong and has previously served at the University of Jyväskylä, Finland, and Nanjing University of Aeronautics and Astronautics, China. His expertise spans Quantum Software Engineering , DevOps , Microservices Architecture , and AI Ethics . Education: Bachelor of Science in Software Engineering, University of Science and Technology, Bannu, Pakistan MSc in Information Technology, Universiti Teknologi PETRONAS, Malaysia PhD in Software Engineering, City University of Hong Kong Research Interests: Khan focuses on advancing empirical software engineering practices in quantum computing, global software development, and ethical AI integration. Key areas include quantum software architecture, agile methodologies for quantum projects, and decision-making frameworks for DevOps and microservices systems. Projects: CLASSİ|Q⟩: Bridging Classical and Quantum Programming – Developing translation frameworks for hybrid systems SeQuSoS M3S – Securing quantum software stacks Labs/Teams: Member of the M3S Empirical Software Engineering Research Unit, focusing on evidence-based methodologies and industry collaboration.

• Database Systems
• Data Management
• Data Visualization
• +۳ مورد دیگر

Eugene Wu is a Professor affiliated with Columbia University, specializing in database systems, data management, and visualization. His work bridges theoretical foundations with practical applications, focusing on systems that empower users through interactive analysis and human-in-the-loop processes. Key contributions include Smoke (lineage tracking), AlphaClean (automated data cleaning), and PI2 (interactive visualization generation). He has also explored privacy-preserving data systems (Saibot) and leveraging large language models (LLMs) for tasks like table profiling (Cocoon) and prompt development. Education: PhD from MIT in Computer Science Research spans database theory, visualization frameworks, and machine learning integration. Recent work emphasizes data quality (SPADE), privacy in distributed markets, and scalable training data debugging. He actively contributes to conferences like SIGMOD, CIDR, and VLDB, often chairing workshops on human-in-the-loop analytics. Articles focus on system design, theoretical advancements, and LLM applications in data management. Awards and recognition include sustained innovation in data systems and visualization. Advises on projects blending databases with emerging technologies like LLMs and decentralized data markets.

• Software Engineering
• Cybersecurity
• Artificial Intelligence
• +۲ مورد دیگر

Petar Kresoja is a Researcher at Singidunum University's Faculty of Informatics and Computing. He completed his Doctoral studies in Intelligent Software Engineering (2024–present), Master's in Contemporary Information Technologies (2023–2024), and Bachelor's in Informatics and Computing (2019–2023). His research focuses on software engineering, AI applications, cybersecurity, and web development. Education: Doctoral Studies: Singidunum University, Intelligent Software Engineering (2024–present) Master's: Singidunum University, Contemporary Information Technologies (2023–2024) Bachelor's: Singidunum University, Informatics and Computing (2019–2023) High School: Četvrta Gimnazija u Beogradu, Mathematics-Oriented Track (2015–2019) Research Interests: Explores innovative software migration strategies, AI-driven automation, network management systems, and the performance implications of database encryption. His work bridges theoretical computer science with practical software development challenges. Recent Research Trends: Active in developing efficient web application frameworks, leveraging LLMs for automation, and optimizing cybersecurity measures for modern applications. Awards: None explicitly mentioned. Advising & Grants: No information provided. Labs/Teams: No specific lab or team affiliation mentioned.

Paulo Alencarمشاهده پروفایل

استاد مدعو

• Machine Learning
• Software Engineering
• Artificial Intelligence
• +۵ مورد دیگر

Paulo Alencar is an Adjunct Professor at the University of Waterloo, located in DC 1315. His research spans Machine Learning, Software Engineering, and Health Informatics, with a focus on applying AI to healthcare, software development tools, and IoT systems. He leads work on conversational agents, model selection frameworks, and spatial-temporal data analysis. Key research interests include: Machine Learning applications in clinical assessment (e.g., depression detection via NLP) Context-aware software development tools using AI IoT-based health monitoring systems leveraging wearable devices Multi-agent systems and adaptive decision-making architectures Recent work emphasizes: LLM effectiveness in testing and clinical contexts Graph-based cluster evolution analysis for transportation and urban planning Agentic frameworks for recommender systems mHealth platforms for stress and public health surveillance He has developed the AKIP Process Automation Platform for process-aware web applications and contributed to metadata-driven testing methodologies for research software. His work bridges AI, software engineering, and healthcare innovation without listed formal advisees.

Souvika Sarkarمشاهده پروفایل

استادیار

• Natural Language Processing (NLP)
• Information Retrieval (IR)
• Artificial Intelligence (AI)
• +۳ مورد دیگر

Souvika Sarkar is an Assistant Professor at the School of Computing within the College of Engineering at Wichita State University. Her research focuses on enhancing AI and data science accessibility through interdisciplinary work at the intersection of Natural Language Processing (NLP), Information Retrieval (IR), and AI. She aims to develop context-aware and scalable AI systems capable of semantic understanding of natural language, particularly for broader societal benefit. Dr. Sarkar holds a Ph.D. from Auburn University, where she was honored with prestigious awards including the 100+ Women Strong Outstanding Departmental Annual Graduate Award and Auburn University’s Outstanding Doctoral Student Award. She also earned a master’s in software engineering from Jadavpur University. Prior to academia, she worked as an IT Analyst at Tata Consultancy Services, managing Microsoft SharePoint migrations and enterprise process workflows. Her research interests span NLP applications in education, multilingual AI systems, and ethical AI practices. Notable projects include developing conversational frameworks for K-12 physics education and analyzing annotator bias in hate speech detection systems. She has also explored deploying NLP models on embedded devices to address computational constraints. Publications highlight her work on digital twin security, LLM-driven meta-review systems, and Bangla language processing. Her industry experience bridges academic research with real-world enterprise solutions, reflecting a commitment to practical AI applications.

• Pattern Recognition
• Applied Machine Learning
• Document Analysis
• +۷ مورد دیگر

Fischer Andreas is a Professor at HES-SO (University of Applied Sciences and Arts Western Switzerland) , specifically affiliated with the School of Engineering and Architecture of Fribourg (HEIA-FR) . His research spans Pattern Recognition , Applied Machine Learning , Handwriting Recognition , and Graph-based Methods . He has secured significant grants from TAINA Technology , Swisscom , and the Hasler Foundation for projects including automatic handwriting recognition for tax forms , Swiss German translation , and graph-based keyword spotting in Vietnamese steles . Education : BSc in Computer Science from HEIA-FR. Research Highlights : Graph Neural Networks for Büchi automata classification Annotation-free alignment in historical documents Graph edit distance optimization for keyword spotting Hybrid deep learning for Vietnamese stele analysis Graph-based tumor budding analysis in digital pathology Collaborative Impact : Fischer's work bridges historical document analysis and medical imaging , demonstrated through frameworks like DIVA-DAF and tools like GammaFocus for histopathology. His 2024 conferences suggest ongoing exploration of LLM integration in document processing and OCR-free models for information extraction.

• Human-Computer Interaction
• Recommender Systems
• Deep Reinforcement Learning
• +۴ مورد دیگر

Ingram Sandy is a Professor at the Haute école d'ingénierie et d'architecture de Fribourg (HES-SO), part of the iSIS Institute for Intelligent and Secure Systems. Her research focuses on Human-Computer Interaction, Recommender Systems, and Digital Wellbeing, with a strong emphasis on applying technology to education and workplace environments. She leads and collaborates on interdisciplinary projects addressing challenges in smart environments, user experience design, and AI integration in learning platforms. Key projects include developing serious games for mental health support (Sesame Project), workplace comfort systems (SpotOn), and chatbot-enhanced educational tools. She has also contributed to frameworks like eLogBook and Graasp, aiming to bridge formal and informal learning through social software innovations. Her work spans over 15 years, with publications in top venues like IEEE, ACM, and international conferences on human-computer interaction and learning technologies. Ingram Sandy’s research interests span from foundational AI methods (reinforcement learning, deep learning) to applied domains like digital wellbeing assessment, smart office comfort modeling, and chatbot-mediated education. She actively collaborates with industry partners (e.g., OrchardAI) and academic networks across Switzerland and Europe. Her contributions highlight the intersection of technology, human behavior, and educational design.

• Conceptual Modelling
• UML
• Model Engineering
• +۴ مورد دیگر

Dominik Bork is an Associate Professor for Business Systems Engineering at TU Wien, specializing in conceptual modeling, enterprise architecture, and model-driven engineering. He holds a PhD (Dr. rer. pol.) from the University of Bamberg and has been a postdoc at the University of Vienna. His roles include membership in the Curriculum Commission for Business Informatics at TU Wien. Education: Diploma in Information Science (University of Bamberg), PhD in Information Systems (University of Bamberg) Visiting Researcher Collaborations: University of Technology Sydney, Instituto Tecnologico Autonomo de Mexico, University of Pretoria, Stockholm University, and Ecolé de Mines d’Albi His research focuses on conceptual modeling methodologies, AI integration in modeling tools, sustainability in systems engineering, and enterprise architecture knowledge graphs. Key interests include multimodal process mining, model interoperability, and inclusive design for accessibility in modeling tools. Recent projects include developing the CM2KGcloud platform for transforming conceptual models into knowledge graphs, and exploring hybrid quantum-classical workflows. His work spans grants from the Austrian Research Promotion Agency (FFG) and international collaborations. Notable Awards: Best Reviewer Award (SoSyM Journal 2024), Eclipse Cloud DevTools Contributor Award (2023) Advising: Supervised over 20+ students in topics ranging from AI-enhanced modeling tools to digital twin frameworks He is a domain expert for the German Informatics Society’s Modelling Business Information Systems SIG, contributing to academic leadership and tool development in enterprise modeling.

• Artificial Intelligence
• Software Development
• Large Language Models
• +۴ مورد دیگر

Kai-Kristian Kemell is a Research Fellow in the Computing Sciences department, focusing on interdisciplinary research at the intersection of Artificial Intelligence, Software Engineering, and Ethical AI. His work explores practical applications of Large Language Models (LLMs), autonomous agents in software development, and regulatory frameworks like the EU AI Act. Research Interests : His primary areas include AI-driven software development tools, ethical implementation of AI systems, multi-agent systems for code refactoring, and systematic literature reviews on trustworthiness in LLMs. He investigates how LLMs can automate tasks like microservice generation from API definitions and assist early-stage software startups through prompt engineering. Collaborations : Active collaborations include projects with researchers like Prof. Petri Abrahamsson, focusing on topics such as RAG systems, AI ethics governance, and hybrid work practices in software engineering. His work bridges theoretical AI concepts with practical industrial applications. Publications : His 2025 publications emphasize autonomous software development agents, EU AI Act critiques, and ethical alignment strategies for LLM-based systems. Earlier work (2023–2024) addresses AI ethics implementation via user stories, hybrid work impacts, and MLOps challenges. Awards : No specific scientific awards mentioned in the provided texts. Grants & Labs : No explicit details on grants or affiliated labs, but his research frequently involves collaborative projects with industry and academic partners.

• Automatic Assessment Tools
• Programming Education
• Large Language Models in Education
• +۴ مورد دیگر

Bruno Miguel Pereira Cipriano is a researcher at Universidade Lusófona de Humanidades e Tecnologias, affiliated with the College of Engineering and the Department of Electrical Engineering, Electronics and Informatics. His work centers on advancing programming education through innovative technological solutions. His primary research interests include automatic assessment tools , Large Language Models in education , and object-oriented programming pedagogy . He investigates how AI tools like GPT-3.5, GPT-4, and Bard interact with programming assignments and how their integration can be optimized to support, rather than undermine, student learning. A key focus is countering over-reliance on LLMs through diagram-based and project-oriented exercises. The recent publications highlight a strong trend toward integrating AI with educational software, particularly in automating feedback and assessment. His work bridges project-based and exercise-based evaluation systems, aiming to create scalable, intelligent learning environments. The development of tools like Drop Project and plugins for IntelliJ IDEA demonstrates a practical, tool-oriented research approach. Bruno has contributed to several peer-reviewed journals and conferences, with active research output from 2022 to 2024. He has not listed any formal advisees or received scientific awards in the provided text. His work appears to be primarily independent or team-based within his institution, though specific lab affiliations or collaborative teams are not mentioned.

Jinan Fiaidhiمشاهده پروفایل

استاد

• Thick Data Analytics
• Deep Learning
• Few Shots Learning
• +۱۲ مورد دیگر

Dr. Jinan Fiaidhi is a Professor of Computer Science at Lakehead University, Canada since 2001. She served as Graduate Coordinator for the MSc and PhD programs in Computer Science and Biotechnology. She holds adjunct positions at the University of Western Ontario. Her academic journey includes degrees from Essex University (PgD, 1983) and Brunel University (PhD, 1986). She has held academic roles at institutions like Sultan Qaboos University and Philadelphia University prior to Lakehead. Her research focuses on Thick Data Analytics , Deep Learning , and Collaborative Learning , with applications in healthcare, medical imaging, and AI-driven diagnostics. She is a Professional Engineer (PEng) in Ontario and a Senior Member of IEEE. She chairs the IEEE Special Interest Group on Big and Thick Data for eHealth and founded the International Journal of Extreme Automation and Connectivity in Healthcare (IJEACH) as its Emeritus Editor-in-Chief. Her research is funded by NSERC and MITACS grants. Key projects include developing frameworks like QL4POMR for problem-oriented medical records and applying thick data analytics to Crohn’s disease, ulcerative colitis, and cataract severity analysis. Education: PhD in Computer Science, Brunel University (1986) PgD in Computer Science, Essex University (1983) Awards: None explicitly listed, but holds professional designations: PEng, IEEE Senior Member, ISP (CIPS), and MBCS (Chartered Computing). Grants: NSERC, MITACS (specific projects unspecified). Labs/Teams: Leads IEEE eHealth initiatives and collaborates on projects involving thick data analytics in healthcare interoperability and extreme automation.

• Responsible AI Engineering
• Human Values in Software Development
• Model-Driven Engineering
• +۳ مورد دیگر

Jon Whittle is a Professor at Monash University's Faculty of Information Technology and leads research at CSIRO/Data61 in Sydney, Australia. His work focuses on Responsible AI Engineering , Human Values in Software Development , and AI Governance Frameworks . He has published extensively on architectural patterns for foundation models, ethical AI implementation, and value-sensitive design methodologies. Current affiliations: Monash University, CSIRO/Data61 Key research areas: Responsible AI, Model-Driven Engineering, Human Values in Tech His recent publications (2023-2025) analyze reference architectures for generative AI, AI risk assessment frameworks, and human value integration in mobile applications. Collaborators include Qinghua Lu, Liming Zhu, and Waqar Hussain. Scientific contributions span AI Ethics , Software Architecture , and Collaborative Human-AI Systems . Notable work includes developing the QB4AIRA question bank for AI risk assessment and creating pattern catalogues for responsible AI.

Asta Slotkienėمشاهده پروفایل

دانشیار

• Software quality assurance
• Software development processes
• E-learning systems
• +۳ مورد دیگر

Assoc. Prof. Dr. Asta Slotkienė is an Associate Professor and Researcher at the Cybersocial Systems Engineering Group , Institute of Mathematics and Informatics, Vilnius University , Lithuania. She has been a faculty member since at least 2009 and is based at Akademijos St. 4, Vilnius. Education PhD in Computer Science Engineering, Kaunas University of Technology (2009) MSc in Computer Science Engineering, Kaunas University of Technology (2004) BSc in Physics and Computer Science, Šiauliai University (2002) Research Interests Dr. Slotkienė’s research spans software quality assurance , development process improvement , e-learning systems , and cognitive decision-making . She investigates how rigorous evaluation methodologies and AI techniques can enhance the reliability and usability of software products and services. Her recent work explores the intersection of large language models and automated software testing , as well as the modeling of web service quality using fuzzy logic and ontology-based approaches. Research Trends Across her latest publications, a clear trend emerges toward leveraging AI and machine learning , particularly large language models , to automate and improve software testing practices. Simultaneously, she continues to deepen the theoretical foundations of software and service quality modeling , integrating fuzzy logic , ontology engineering , and bibliometric analyses to build robust frameworks for quality evaluation. Scientific Service & Awards Program Committee Member: ACM/SIGAPP SAC (2023–2025) Organizing Committee Member: Baltic DB&IS 2024 Program Committee Member: SS AIEDUMED’25, CompSysTech’25 Reviewer for Informatica, Baltic Journal of Modern Computing, New Trends in Computer Sciences Projects & Funding 2022–2026 Master of Science in Smart, Secure and Interconnected Systems (EU Grant 101083531) 2022–2024 Embracing RAD Skills for Employability and Innovation 2020–2022 AI-Enhanced Accounting and Business Management Systems (Grant 01.2.1-LVPA-K-856-01-0083) 2014–2018 Erasmus+ “Open University for Virtual Mobility” (2014-1-LT01-KA203-000550) 2012–2015 Tempus “High Objectives of National Organizational Reform” (530284-TEMPUS+1-2012-1-ES-TEMPUS-JPHES) International Collaboration & Training Through multiple Erasmus+ teaching and learning visits (Las Palmas de Gran Canaria, Madeira, Azores, Maia), as well as participation in DeepLearn schools and professional training (Devbridge/Cognizant Softvision, Robotic Process Automation, Creative Commons, Contextual Modeling), Dr. Slotkienė actively fosters cross-border academic cooperation and continuous professional development.

• Artificial Intelligence for Software Engineering
• Software Engineering Analytics
• Process Analytics
• +۳ مورد دیگر

Hoa Khanh Dam is a Professor in the School of Computing and Information Technology at the University of Wollongong, Australia. He serves as Director of the Decision System Lab and holds leadership roles including Deputy Head of School (Research) and Head of Postgraduate Studies. His research focuses on integrating Artificial Intelligence with Software Engineering to enhance software quality, cybersecurity, and productivity. Key areas include AI-driven solutions for project management, autonomous systems resilience, and privacy engineering. He actively supervises PhD and MPhil students exploring AI applications in software development and quantum computing. Recent funding includes a project on 'Monitoring and Safeguarding Deep Reinforcement Learning Systems.' Research interests span AI for software engineering, process analytics, service-oriented architectures, and cybersecurity resilience. His work addresses challenges such as adversarial machine learning, explainable vulnerability prediction, and GDPR-compliant privacy policies. The Decision System Lab under his direction develops AI tools for decision-making in complex environments, including adversarial search strategies and normative multi-agent systems. Teaching and supervision include advanced topics like AI-driven chatbots for university systems, quantum code generation, and CRM application development. His publications emphasize practical AI applications in software lifecycle management, including defect prediction, agile iteration planning, and robotic process automation optimization.

• Data Analytics
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Prof. Youakim Badr is a Professor of Data Analytics in the Engineering Division at Temple University's Great Valley campus. His research focuses on advancing interdisciplinary fields such as artificial intelligence, blockchain technology, cybersecurity, and Internet of Things (IoT) systems. He has contributed to over 134 publications, emphasizing trust frameworks in decentralized systems, privacy-preserving machine learning, and smart service ecosystems. Research Interests: Prof. Badr’s work spans AI-driven systems, blockchain applications for identity management, secure IoT architectures, and innovative frameworks for data analytics. He actively explores how AI can enhance service ecosystems and address societal challenges aligned with UN Sustainable Development Goals (SDGs), particularly in healthcare and smart infrastructure. Collaborations: His recent research involves international collaborations in blockchain-based trust systems, cybersecurity for AI, and IoT-enabled medical devices. These efforts have led to impactful solutions like the TrustLend platform for decentralized lending and frameworks for resilient dynamic data-driven applications. Advising & Grants: While specific student names are not listed, his research has been supported by grants focusing on AI ethics, secure smart cities, and crisis management systems. He has co-authored seminal papers on federated learning, adversarial testing for AI, and artifact-centric process modeling. Labs/Teams: His contributions include foundational work in the TrustLend platform and collaborations on resilient DDDAS (Dynamic Data-Driven Application Systems) for crisis environments. He also leads initiatives integrating business artifacts with modern IT infrastructure.