جست‌وجوی استاد و پژوهشگر

• Ecology
• Curriculum and Pedagogy
• Electrical Engineering
• +۴ مورد دیگر

Dr. Jessica Rivera Villicana is a Research Fellow in Computational and Geospatial Data Science at RMIT University's School of Global, Urban and Social Studies (GUSS). Her work integrates interdisciplinary approaches across ecology, education technology, and biomedical engineering. She leads projects like liveability scorecards analyzing urban inequities in Australian local government areas (LGAs), using geospatial and machine learning methodologies. Her research also focuses on EEG-based seizure detection, contributing to both technical advancements and clinical professional insights. Collaborating with global teams, she explores AI applications in healthcare (e.g., caregiver support systems) and cybersecurity (e.g., MLGuard framework). Key research areas include: Urban policy analysis through geospatial data modeling Machine learning interpretability in medical diagnostics Embodied conversational agents for caregiving AI safety and security in critical systems Behavioral modeling in interactive narratives Her recent work highlights include: 2025 Burwood & Canada Bay LGA liveability reports 2023 seizure detection survey with medical professionals MLGuard cybersecurity tool for ML models Dr. Villicana holds an ORCID identifier (0000-0003-1955-3310) and is based at RMIT's City Campus in Melbourne, Australia.

• Artificial Intelligence
• Machine Learning
• Graph Neural Networks
• +۴ مورد دیگر

Tomasz Kajdanowicz serves as an Associate Professor in the Department of Artificial Intelligence at Wrocław University of Science and Technology's Faculty of Information and Communication Technology. His research bridges theoretical machine learning with practical cybersecurity and natural language processing applications. His primary research interests focus on graph neural networks , large language model optimization , and interpretable cybersecurity systems . Notable contributions include the MIRAD framework for ransomware detection and PUGG dataset development for Polish language processing. His work demonstrates consistent innovation in graph representation learning, particularly through multiplex graph fusion techniques and knowledge graph enrichment strategies. Analysis of his recent publications reveals a strong trajectory toward trustworthy AI systems with emphasis on hallucination detection (FactSelfCheck), latent space optimization (LatentPrompt), and synthetic data generation for medical applications. His cybersecurity research shows increasing sophistication in interpretable threat detection mechanisms. His academic supervision includes multiple doctoral and master's students evidenced through co-authorship patterns, particularly in graph-based learning and NLP projects. Current collaborations span biomedical signal processing (fNIRS analysis) and efficient LLM deployment strategies. His laboratory work centers on the AI Security Lab within the Department of Artificial Intelligence, focusing on adversarial robustness testing and real-time network monitoring systems. Key initiatives include the development of Polish-language NLP benchmarks and cross-domain graph embedding frameworks.

• Cyber-security
• Digital Forensics
• Networks
• +۴ مورد دیگر

Rich Macfarlane is an Associate Professor at the School of Computing Engineering and the Built Environment , Edinburgh Napier University, UK. He specializes in Cyber-security , Digital Forensics , and Network Security , with extensive experience in ransomware detection, forensic tool evaluation, and cloud-based security training. Research Centres: Centre for Distributed Computing, Networking and Security Projects: Directed KTP initiatives funded by Innovate UK, Data Lab, and Scottish Enterprise Research Interests include: Ransomware attack detection and mitigation Digital forensic tool validation Cloud infrastructure security Entropy-based file analysis Deception techniques for threat identification Article Trends show technical focus on ransomware detection, entropy analysis, digital forensic datasets, and wireless network monitoring. The work combines mathematical modeling , cloud-based platforms , and machine learning applications in forensics. Education & Supervision : Second Supervisor for Matthew Thomson (2023) - Digital Media & AI Forensics Second Supervisor for Philip Penrose (2013-2017) - Large Storage Device Forensics Grants : Managed industry-funded projects totaling £874,436 including KTP with Morgan Stanley (£136,306), Fragment Finder (£228,757), and vSoC development (£119,363).

• Authentication
• Brain-Computer/Machine Interface
• Cybersecurity
• +۱ مورد دیگر

Dr. Garima Bajwa is an Assistant Professor in the Department of Computer Science at Lakehead University, where she joined in January 2021. Her research focuses on authentication systems, brain-computer/machine interfaces, cybersecurity, and machine learning applications. She holds a Ph.D. in Computer Science and Engineering from the University of North Texas (2016), an MEng in Electrical and Computer Engineering from the University of Waterloo (2011), and a B.Tech in Electronics and Communication Engineering from Mody Institute of Technology & Science, India (2009). Education: Ph.D., Computer Science & Engineering, University of North Texas (2016) MEng, Electrical & Computer Engineering, University of Waterloo (2011) B.Tech, Electronics & Communication Engineering, Mody Institute (2009) Her research interests span cybersecurity frameworks for neurotechnology, explainable AI in healthcare diagnostics, and multimodal fusion of EEG and image data. Recent work includes EEG signal anonymization, federated learning for medical diagnosis, and reproducibility in BCI research. Her publications explore cutting-edge topics like BCI standardization, driver distraction detection, and quantum computing integration. Grants and lab affiliations are not explicitly mentioned in the provided text.

Danijel Kučakمشاهده پروفایل

مدرس ارشد

• Software Engineering
• Machine Learning
• Educational Technology
• +۴ مورد دیگر

Danijel Kučak is a Senior Lecturer at the University of Algebra since 2007. He holds a Master's in Mathematics from the Faculty of Science (2002) and a PhD in Information Sciences from the Faculty of Philosophy, University of Zagreb (2025). With 10 international programming certifications, his expertise spans web application development using .NET and Java platforms. He has authored/co-authored 5 books and over 20 scientific papers focusing on software engineering, machine learning, and educational technology. Beyond academia, he owns a programming business, contributing to systems like MedicalBit (for lung disease clinics) and CRIS (Croatian Road Inspection System). His research emphasizes gamification in education, blockchain applications, and AI-driven solutions. Research interests include machine learning applications, gamification strategies for programming education, and secure blockchain systems. His work bridges theoretical computer science with practical implementations in healthcare, transportation, and education. Notable contributions include empirical studies on gamification's long-term impact on student outcomes and innovative approaches to E-learning platforms. Awards: Multiple Best Lecturer Awards at the University of Algebra Business Experience: Developed custom software systems for healthcare and traffic management Publications: Over 20 peer-reviewed articles and 5 books on software engineering and educational technology Labs/Teams: Active in developing IoT architectures for environmental monitoring and AI models for educational outcomes prediction. His programming business integrates academic research with real-world system implementations.

Varun Chandrasekaranمشاهده پروفایل

استادیار

• Computer Security
• Privacy Engineering
• AI Ethics and Governance
• +۳ مورد دیگر

Varun Chandrasekaran is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois' Grainger College of Engineering. His research focuses on computer security, privacy, and information trust, with a growing emphasis on AI ethics and governance. He teaches courses including Computer Security I & II (CS 461/463) and Advanced Topics in Security, Privacy, and Machine Learning (CS 562). His work addresses critical challenges in generative AI security, adversarial machine learning, and privacy-preserving technologies. Dr. Chandrasekaran's research highlights include analyzing risks of AI models like OpenAI's Strawberry, advocating for transparent AI development, and proposing solutions like machine unlearning frameworks (e.g., Amun). His contributions span theoretical advancements and practical applications, such as secure code translation and watermarking defenses. He emphasizes peer-reviewed security practices over proprietary solutions, aligning with cryptographic principles for reliability. Recent projects explore multimodal data curation (MM-GEN), bias mitigation in LLMs, and automated benchmarking (Benchagents). His work bridges academic research with real-world cybersecurity needs, addressing threats from adversarial actors exploiting AI capabilities. Chandrasekaran frequently collaborates with industry and policy experts to shape ethical AI frameworks and standards.

• Social Network Analysis
• Formal Concept Analysis
• Graph Mining
• +۷ مورد دیگر

Bénédicte Le Grand is a prominent computer science researcher with an extensive publication record spanning over two decades, from 1999 to 2025. Her academic work demonstrates significant contributions across multiple domains within computer science, particularly in social network analysis, formal concept analysis, and graph mining methodologies. She maintains active collaborations with numerous researchers including Marie-Aude Aufaure (16 publications), Michel Soto (13 publications), and Nourhène Ben Rabah (12 publications), indicating her integration within a strong academic network. Her research interests center around information retrieval systems, semantic web technologies, and data mining approaches, with recent work showing a notable shift toward healthcare applications of artificial intelligence. In the past five years, she has published extensively on medical diagnostic applications including Alzheimer's detection, polycystic ovary syndrome detection, and depression identification through social network analysis. Her work also extends to educational data mining, affective computing for workplace stress detection, and explainable AI for security applications. The analysis of her publication trends reveals a consistent research trajectory that has evolved from foundational work in semantic web and topic map visualization in the early 2000s toward increasingly applied machine learning and AI solutions for healthcare and education challenges. Her recent publications demonstrate a strong interdisciplinary approach, bridging computer science with medical and educational domains to develop practical AI solutions. She has been particularly active in French academic conferences such as INFORSID and has contributed to international venues including RCIS (Research Challenges in Information Systems), ICAART (International Conference on Agents and Artificial Intelligence), and AICCSA (International Conference on Computer Systems and Applications), establishing herself as a significant contributor to the European computer science research community.

• Dataspaces
• Semantic Web Technologies
• Ontologies
• +۶ مورد دیگر

Johannes Theissen-Lipp is a senior scientific researcher at the Fraunhofer FIT Office Aachen with significant academic affiliations at RWTH Aachen University. He operates within the Department of Computer Science (Informatik 5) under the Faculty of Mathematics, Computer Science and Natural Sciences, working from the Databases and Information Systems (DBIS) group led by Professor Stefan Decker. His research focuses on semantic foundations of dataspaces with practical applications in manufacturing and mobility sectors. He earned his Computer Science degree from RWTH Aachen University, where he later conducted research at the Institute for Information Management in Mechanical Engineering (IMA) for nearly two years before joining the "Knowledge Pipelines" research group in December 2019. He successfully defended his PhD thesis titled "Semantic Foundations of Dataspaces" on September 25, 2024, completing his doctoral studies at RWTH Aachen. Theissen-Lipp's research spans semantic web technologies, ontologies, and data interoperability with particular expertise in RDF validation (SHACL), semantic mappings (RML), and FAIR data implementation within dataspaces. His work bridges theoretical foundations with industrial applications, especially in manufacturing contexts where data integration across organizational boundaries presents significant challenges. He has pioneered approaches for enhancing dataspaces through large language models and developing scalable validation techniques for distributed data environments. His publication record demonstrates consistent contributions to major semantic web venues including ESWC, SEMANTiCS, and dedicated workshops on dataspaces. As both author and editor (notably for the Second International Workshop on Semantics in Dataspaces proceedings), he has helped shape research directions in this emerging field. His recent work increasingly explores the intersection of traditional semantic technologies with contemporary AI approaches. Theissen-Lipp actively supervises multiple thesis projects including "FAIRification of Data Models in Manufacturing," "Leveraging Solid Pods for Sovereign Data Sharing in the Cultural Sector," and "Identification Techniques for Data-driven Feedback Loops in Manufacturing." He has taught the "Dataspaces Proseminar" in the summer semester of 2025, demonstrating his commitment to academic education alongside his research activities. As part of the Cluster of Excellence Internet of Production, he contributes to advancing data interoperability in industrial contexts. His professional presence extends to GitHub (JohannesLipp), LinkedIn, and ResearchGate, with a personal website providing additional details about his research portfolio and professional activities.

Michele Willsonمشاهده پروفایل

استاد

• Digital Childhood
• Social Networks
• Algorithms and Everyday Life
• +۵ مورد دیگر

Professor Michele Willson is the Associate Provost and a Professor in Internet Studies at Curtin University. She holds significant leadership roles including Interim Provost (2024), Deputy Pro Vice Chancellor of Humanities, and former Head of the Department of Internet Studies. Her academic background includes a PhD from Monash University and a BA (Hons). Her research focuses on digital childhood, social and mobile gaming, algorithmic impacts, online communities, and women in sport. She has pioneered studies on technology’s role in everyday life, including algorithmic governance of childhood and the ethics of big data in social gaming. Her work bridges communication studies, political theory, and social sciences, emphasizing interdisciplinary collaboration in academia. Willson has authored/co-authored influential books such as Technically Together and A New Theory of Information and the Internet , and edited volumes on gaming landscapes. Her publications analyze how digital technologies reshape social practices, from gaming behaviors to scholarly collaboration. Education: PhD (Monash), BA (Hons) Key Roles: Associate Provost, Professor of Internet Studies, former Head of Department of Internet Studies Her research networks include founding WACCM (West Australian Communication, Culture and Media), fostering collaboration across WA universities. Willson is affiliated with AoIR, ICA, and ANZCA, contributing to global academic discourse on internet studies and digital culture.

• Cybersecurity
• Infocommunications
• Cloud Technologies
• +۵ مورد دیگر

Alexey Nikolaevich Nazarov is a Professor at the Department of Software Engineering, Faculty of Computer Science, National Research University Higher School of Economics (HSE), where he has worked since 2015. His academic and teaching experience spans 14 years, with a focus on cybersecurity, cloud technologies, and information confrontation in social media. Doctor of Technical Sciences (2000) Engineer-Mathematician, Moscow Geological Prospecting Institute (1980) UN and ITU-certified expert His research interests center on cybersecurity , information warfare , and mathematical modeling , particularly in IoT and social media contexts. Articles highlight trends in neural network intrusion detection , ferroelectric memory devices , and cloud resource optimization . He has supervised 5 postgraduate and 2 doctoral students, organized scientific seminars, and contributed to cloud technology curricula. His expertise includes international standards (ITU, VAK), Skolkovo grant #87 (2012), and leadership in cybersecurity research.

• Machine Learning
• Computer Vision
• 3D Geometry
• +۲ مورد دیگر

Dr. Jun Li is a Senior Lecturer at the School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), Australia. He received his Ph.D. in Computer Science from Queen Mary University of London in 2009 and is affiliated with the Australian Artificial Intelligence Institute (AAII) at UTS. His research spans multiple domains within artificial intelligence, with primary focus on Machine Learning applications in computer vision and 3D geometry. Dr. Li has published extensively in high-impact journals including IEEE Transactions (TPAMI, TIP, TNNSLS) and Pattern Recognition, with recent work expanding into interdisciplinary research in earth science and marine applications. His research output demonstrates consistent productivity with numerous publications each year across diverse AI application areas. Dr. Li's work shows strong thematic progression from foundational computer vision techniques to applied interdisciplinary research. Early work focused on face hallucination and video super-resolution, while more recent publications address environmental applications using Graph Neural Networks for wave prediction and damage classification for disaster response. His research consistently bridges theoretical AI advances with practical real-world applications across healthcare, autonomous systems, and environmental science. AI to assist disaster emergency response (2023-2026) Applying Generative Adversarial Network in Medical Image Analysis (2020-2021) Big Massive Open Online Course (MOOC) Data Retrieval (2017-2020) As an educator, Dr. Li teaches core courses including '31005 Machine Learning' and '32513 Advanced Data Analytics Algorithms' at UTS, and is available for Masters Research and PhD student supervision, contributing to the development of next-generation AI researchers.

Ashis Kumer Biswasمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Responsible AI
• +۵ مورد دیگر

Ashis Kumer Biswas is an Assistant Professor in the Department of Computer Science and Engineering at the University of Colorado Denver, where he directs the Machine Learning Laboratory (ML Lab). His academic career spans institutions in Bangladesh, Texas, and Colorado, with a strong focus on interdisciplinary research at the intersection of machine learning, deep learning, and bioinformatics applications. Dr. Biswas earned his educational credentials from prestigious institutions: Ph.D. in Computer Science and Engineering from the University of Texas at Arlington (2016) M.S. in Computer Science and Engineering from University of Dhaka, Bangladesh (2009) B.S. in Computer Science and Engineering from University of Dhaka, Bangladesh (2008) His research centers around machine learning and deep learning with applications in bioinformatics, responsible AI, and healthcare. Dr. Biswas pioneered the 'data-blind machine learning paradigm,' focusing on privacy-aware intelligent applications for analyzing sensitive data like patient health records without direct access. His work spans genomic data analysis, medical imaging, natural language understanding, educational technology, and cybersecurity. He actively investigates how to incorporate responsible AI principles into system designs, with particular attention to privacy preservation, bias mitigation, and ethical outcomes in machine learning models. Analysis of his recent publications reveals a strong trend toward trustworthy AI systems, with significant focus on invertible neural networks for privacy preservation, bias-aware classification frameworks, and applications of machine learning to healthcare and education. His work consistently bridges theoretical machine learning advances with practical real-world applications. Dr. Biswas has secured significant research funding from the National Science Foundation, Google, US Department of Education, and other organizations. His work has been recognized with a best paper award at the BIBE conference. He serves as a reviewer for prestigious journals including TCBB, Frontiers in Oncology, and Elsevier Methods. As an educator, Dr. Biswas teaches graduate-level courses in machine learning, deep learning, reinforcement learning, and bioinformatics. He mentors a diverse group of students through the ML Lab, supervising undergraduate, MS, and PhD candidates working on collaborative research projects with community impact. His former students have secured positions at major technology companies including Amazon, Oracle, and research institutions.

Mattia Fazziniمشاهده پروفایل

استادیار

• Software Engineering
• Software Testing
• Software Maintenance
• +۵ مورد دیگر

Mattia Fazzini is an Assistant Professor in the Department of Computer Science & Engineering at the University of Minnesota's College of Science and Engineering. His primary academic appointment focuses on software engineering research and teaching, with active involvement in major conferences including ASE, ISSTA, ICSE, and MOBILESoft where he has served in leadership roles such as General Co-chair (MOBILESoft 2023) and Program Committee Co-chair. His research centers on software testing, maintenance, and security , with particular emphasis on mobile applications. Key research themes include: Developing techniques for automated Android testing and maintenance Addressing API compatibility issues across Android versions Creating tools for test oracle generation and bug reproduction Investigating security vulnerabilities in mobile ecosystems Optimizing test suites through test double analysis His recent publications (2021-2025) reveal strong focus on Android-specific challenges, with recurring themes in compatibility testing, automated test generation, and security analysis. Over 60% of his work involves tool development for practical testing scenarios, particularly targeting mobile platforms. Notable recognitions include: IEEE TCSE Distinguished Paper Award (2024) for work on test suite optimization ACM Distinguished Paper Award (2022) for COVID-19 app analysis As an educator, he advises multiple PhD and Master's students while teaching undergraduate and graduate courses including CSCI 3081W (Program Design) and CSCI 5802 (Software Engineering II). His service contributions span conference organization (MOBILESoft, ISSTA, ICSE) and extensive program committee work across top software engineering venues. He leads research projects focused on practical testing solutions with real-world applicability in mobile software development.

• Educational Technology
• Mathematics Education
• E-Learning Security
• +۳ مورد دیگر

Bilyana Stoyanova Stoyanova serves as faculty at Technical University of Gabrovo, Bulgaria, where she maintains office 1337 with contact email bilyana_zheleva@abv.bg. Her professional activities focus on educational technology implementation within mathematics instruction, evidenced by participation in six university research projects and eleven scientific publications between 2012-2019. She specializes in bridging traditional pedagogy with digital solutions for technical education contexts. Her research centers on e-learning security frameworks, computational mathematics applications, and ICT integration in applied mathematics courses. Key investigations include comparative analyses of traditional versus technology-enhanced teaching methods, with particular emphasis on online exam integrity in MOOCs and secure file handling in virtual learning environments. Her work demonstrates consistent focus on practical implementation challenges within Bulgarian higher education institutions. Publication trends reveal progressive specialization from foundational digital library applications (2013) toward advanced security protocols for online assessments (2015) and computational optimization tools (2017-2019). The majority of her output appears in national Bulgarian conferences, with select international collaborations in Ukraine, Macedonia, and India, reflecting regional academic networks focused on educational technology adaptation. She actively contributed to six research initiatives at TU Gabrovo including EU-funded projects like 'Development and implementation of virtual technologies for sustainable distance learning' (2012, financed by OP 'Development of Human Resources' and European Social Fund). Her project roles consistently involve practical implementation aspects across modern educational technologies (2014-2015), triangulation methodologies (2012-2013), and identification systems (2011), demonstrating hands-on technical engagement rather than theoretical research.