Kourosh Davoudi is an Associate Professor of Computer Science at Ontario Tech University's Faculty of Science. He holds a PhD in Computer Science from York University with a focus on Machine Learning and Data Mining. Prior to joining Ontario Tech in 2019, he was a postdoctoral research fellow at the University of Waterloo's Department of Management Sciences. His research interests span Natural Language Processing, Deep Learning, Reinforcement Learning, Graph Mining, and Machine Learning. He actively supervises graduate students in these areas and teaches courses such as Data Mining and Artificial Intelligence. His research emphasizes practical applications of AI techniques in areas like outbreak detection, sentiment analysis, and automated grading systems. Recent work includes innovations in neural document segmentation, vision-language models, and hybrid outbreak detection using social media data. His publications consistently address challenges in algorithm design, explainable AI, and domain-specific NLP applications. Dr. Davoudi has contributed to conferences such as COLING, EMNLP, and IEEE transactions, focusing on interdisciplinary applications of machine learning. His work bridges theoretical advancements with real-world problems in healthcare, education, and social media analysis.
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۴۶-۶۰ از ۸۸۰ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Ruben Martinsمشاهده پروفایل
استادیار
Ruben Martins is an Assistant Professor at Carnegie Mellon University's School of Computer Science and serves as the program director of the Master of Science in Computer Science (MSCS) . His research focuses on the intersection of constraint programming, program synthesis, analysis, and verification, with recent work aiming to make formal methods tools more accessible through automated reasoning. Ruben earned his Ph.D. with honors from the Technical University of Lisbon, Portugal (2013) , followed by postdoctoral research at the University of Oxford (2014-2015) and UT Austin (2015-2017) . Research Interests : Ruben's work bridges constraint programming and program synthesis , with applications in software verification , optimization , and automated reasoning . He has developed award-winning tools like Open-WBO , a modular MaxSAT solver that has won gold medals in international competitions. His publications span top-tier venues such as POPL , PLDI , FSE , SAT , and CP , often addressing real-world challenges from program analysis to network security. Scientific Awards include: Distinguished Paper Award at PLDI 2018 Distinguished Paper Award at FSE 2021 Distinguished Paper Award at SAT 2022 Gold medals for Open-WBO in MaxSAT competitions Teaching & Advising : Ruben mentors Ph.D., Master’s, and undergraduate students in research projects related to program synthesis, formal methods, and constraint solving. He teaches courses such as Bug Catching: Automated Program Verification and Advanced Topics in Logic: Automated Reasoning and Satisfiability , emphasizing hands-on experience with tools like Why3. His advising spans topics from AI-driven program repair to network protocol verification , fostering collaboration across disciplines.
Victoria Rafferty is a Lecturer at the Faculty of Business, Law and Tourism at the University of Sunderland. Her work centers on Higher Education , focusing on Learning Development , Curriculum Design , and Academic Advising . She has published extensively on feedback strategies, collaborative research communities, and workforce development, including studies on the four-day working week and digital curriculum transformation. Research Trends : Her publications emphasize collaborative writing , student placement experiences , and formative feedback in higher education. Notably, her 2020 doctoral thesis explored learning dialogues in one-to-one tutorials. Collaborative Work : Dr. Rafferty co-authored studies with peers like Aaron Taylor, Karen Welton, and Paul Chin, contributing to journals such as Journal of Learning Development in Higher Education and Journal of University Teaching and Learning Practice . Conferences : She presented at events including the Collaboration in Higher Education Symposium (2022) and the Digital Learning and Teaching Conference (2021).
Professor Allan Rennie serves as Professor in Manufacturing Engineering at Lancaster University's School of Engineering and holds the administrative position of Associate Dean for Engagement within the Faculty of Science and Technology. With a career spanning over 30 years since initiating work in additive manufacturing during the mid-1990s, he has established himself as a leading figure in industrial applications of advanced manufacturing technologies across diverse sectors. His research expertise centers on Additive Manufacturing , Engineering Design , and Manufacturing Process Optimization , with current specializations including design for additive manufacturing (as co-leader of the UK's EPSRC DfAM Network), industrial digitalisation of manufacturing processes, and innovative tooling development using metallic and hybrid approaches. Rennie has significantly contributed to Engineering Education , particularly examining the integration of business and management principles into engineering curricula and analyzing the impacts of online/hybrid delivery modes on student engagement and graduate employability following the COVID-19 pandemic. Recent publication trends reveal Rennie's dual focus on practical manufacturing applications and scholarly analysis of technological evolution. His 2025 bibliometric study maps a decade of Design for Additive Manufacturing research, while his structural analysis of musical instruments demonstrates cross-disciplinary applications of manufacturing techniques. These works reflect his commitment to both advancing manufacturing technology and documenting its academic trajectory through rigorous analysis. Professor Rennie actively supervises PhD candidates including Jenny Roberts, Eunike Sembiring, and Joe Taylor while leading substantial research projects such as the EPSRC DfAM Network (2020-2023), Automating Design for Additive Manufacture with AI (2023-2024), and multiple Engineers in Business Competitions. His extensive grant portfolio spans industrial digitalization, sustainable manufacturing, and educational innovation, with notable projects including RENDER (powder recycling), TecHnology and EntrepreneUrship Education, and Production Capable Additive Manufacturing of Polymers. Rennie contributes to Lancaster's research ecosystem through affiliations with the Centre for Global Eco-innovation, Energy Lancaster initiative, and the Lancaster Product Development Unit. These platforms enable him to bridge academic research with industrial applications across multiple sectors, particularly supporting his work on sustainable manufacturing practices, technology commercialization, and industry engagement strategies that translate research into real-world impact.
Nigel Boschمشاهده پروفایل
استادیار
Nigel Bosch is an Assistant Professor in the School of Information Sciences (iSchool) at the University of Illinois Urbana-Champaign, with a joint appointment in the Department of Educational Psychology. He is also a faculty affiliate at the National Center for Supercomputing Applications (NCSA) and Illinois Informatics. His primary research focuses on machine learning and human-computer interaction applications in education, with particular emphasis on affective computing, metacognition, and online learning environments. Bosch holds a PhD in Computer Science from the University of Notre Dame, followed by a postdoctoral research position at the National Center for Supercomputing Applications. His research explores machine learning applications in education, including automatic emotion measurement in programming education, metacognition analysis through natural language processing, and ethical implications of AI in learning. He also investigates wearable technologies for health monitoring and algorithmic bias mitigation in educational data. Bosch’s work is supported by grants from the National Science Foundation (NSF), the Institute of Education Sciences (IES), and the University of Illinois. He leads the (Human + Machine) Learning lab, which develops innovative technologies for educational analytics, AI ethics, and human-centered computing.
Jun Shen is a Professor at the School of Computing and Information Technology, University of Wollongong. He specializes in computational intelligence, cloud computing, and big data applications, with a focus on AI-driven solutions for real-world challenges in transport systems, healthcare, education, and environmental management. He has secured over 40 research grants totaling AU$4.5 million and supervised 26 completed PhD projects. His work spans interdisciplinary areas including bioinformatics, smart manufacturing, and digital health. Research interests include bio-inspired algorithmic optimization, AI in arts/media, and edge computing for IoT systems. He has pioneered research centers in applied computing since 2014 and holds editorial roles in top journals like IEEE Transactions. As an IEEE Distinguished Lecturer, he actively promotes AI ethics and interdisciplinary collaboration. Recent publications emphasize adversarial machine learning defenses, UAV systems, and multimodal data fusion. His supervision includes projects in intelligent transport systems, cloud computing, and e-learning. Grants include projects on resilient energy systems and UAV geolocation verification. Leadership roles include leading over 20 researchers and chairing conferences. He advocates for digital transformation in public services and has conducted fieldwork at MIT, UCI, and Georgia Tech.
Giulia Totiمشاهده پروفایل
استادیار
Giulia Toti is an Assistant Professor of Teaching in the Department of Computer Science at the University of British Columbia (UBC), part of the Faculty of Science. Her work focuses on computer science education, equity in curriculum design, and fostering inclusive learning environments. She teaches courses such as Applied Machine Learning (CPSC 330), Fairness, Accountability, Transparency, and Ethics (FATE) in Data Science (DSCI 430), and Computers and Society (CPSC 430). Her research explores diversity initiatives in CS education, mastery learning frameworks, and equitable grading practices. Notable contributions include studies on pandemic-era remote teaching impacts and the development of Agora, a tool for enhancing large-classroom engagement. Toti has received UBC Faculty Teaching Awards for her pedagogical innovations. Her interdisciplinary work spans machine learning applications in industry and healthcare, including semantic search systems for clinical data (SemEHR) and predictive analytics for energy production. She is affiliated with the ACE Lab and actively contributes to curriculum reforms addressing DEI (Diversity, Equity, Inclusion) challenges in STEM education. Grants/Awards: Faculty Teaching Awards Labs/Teams: ACE Lab (Advancing Computing Education) Advising: No explicit advisee listings found, but contributes to pedagogical research impacting teaching practices.
Yuhan Jiangمشاهده پروفایل
استادیار
- Artificial Intelligence in Architecture Engineering Construction and Operations
- Robotics and Remote Sensing
- Computer Vision and Machine Learning
- +۷ مورد دیگر
Dr. Yuhan Jiang is an Assistant Professor in the Department of Built Environment at North Carolina A&T State University's College of Science and Technology. He serves as the Founding Director of the HUD Center of Excellence for Innovation in Affordable Housing and Sustainable Communities (CIAHSC). Dr. Jiang leads a multidisciplinary research team focused on integrating robotics, artificial intelligence, and Building Information Modeling in construction operations and infrastructure management. Ph.D. in Civil Engineering from Marquette University M.M. in Construction Management from Guangzhou University Additional Construction Management degree from Guangzhou University Dr. Jiang's research primarily focuses on artificial intelligence applications in architecture, engineering, construction, and operations (AECO). His work integrates robotics and remote sensing for data collection, computer vision and machine learning for data processing, and BIM, GIS, and AR/VR for data visualization. His research enables more efficient construction operations, building inspection, and infrastructure management. Additionally, he has extensive experience in community redevelopment planning and complex systems simulation, including investigating urban village formation mechanisms. Analysis of Dr. Jiang's recent publications reveals a strong focus on applying drone technology, computer vision, and deep learning to construction and infrastructure challenges. His work spans multiple domains including façade modeling, pavement evaluation, sidewalk inspection, earthwork calculation, and 3D reconstruction. A consistent theme across his research is the development of automated systems that improve efficiency, accuracy, and safety in construction and infrastructure management through AI and robotics. N.C. A&T and CoST Junior Faculty Teaching Excellence Award 2024-25 N.C. A&T and CoST Rookie Researcher of the Year Award 2024 ASCE Journal of Architectural Engineering Best Paper Award 2022 ASCE CI & CRC Joint Conference Best Paper Award 2024 AAAS HBCU Making and Innovation Showcase 1st Place 2024 CoST SciTech Week Innovation Challenge awards (2023-2025) N.C. A&T Provost's Faculty Fellow (2023 & 2024) Dr. Jiang has successfully secured over $4.5 million in research funding as PI or Co-PI, including a $2.5 million HUD Center of Excellence grant. He has mentored students who won 1st and 3rd place in the 2023 & 2024 Sci-Tech Week Innovation Challenge competitions and 1st place at the 2024 AAAS HBCU Making and Innovation Showcase. His funded projects span AI-driven BIM education tools, smart farming with robotics, drone-based façade modeling, and digital twin applications for infrastructure management. As Founding Director of the HUD Center of Excellence for Innovation in Affordable Housing and Sustainable Communities (CIAHSC), Dr. Jiang leads a multidisciplinary team focused on innovative approaches to affordable housing and sustainable community development. His lab work integrates drone technology, computer vision, and AI to create practical solutions for real-world construction and infrastructure challenges.
Taran Raiمشاهده پروفایل
پژوهشگر
Dr. Taran Rai is a Researcher at the University of Surrey, affiliated with the Centre for Vision, Speech and Signal Processing (CVSSP) and the School of Veterinary Medicine. His work focuses on computational pathology, deep learning for medical imaging, and applying AI to veterinary medicine. Rai holds a PhD and has contributed to advancements in necrosis and mitosis detection in canine tumors, leveraging CNNs and digital pathology. His research interests include AI-driven diagnostic tools, social media listening for health insights, and optimizing neural networks for medical applications. He has published extensively on topics like synthetic histopathology data evaluation, diffusion models, and adaptive thresholding methods in pathology. Rai's recent work explores the integration of large language models with medical imaging (e.g., the IPATH dataset) and addresses challenges in veterinary healthcare through social media data. His studies often bridge computational methods with real-world clinical needs, aiming to improve diagnostic accuracy and patient care.
Len Gelman is a Professor and Chair in Signal Processing and Condition Monitoring at the University of Huddersfield's Department of Engineering within the School of Computing and Engineering. He also serves as Director of the Centre for Efficiency and Performance Engineering. His research focuses on advanced signal processing techniques for fault diagnosis in electromechanical systems, vibration analysis, and predictive maintenance. He is actively involved in PhD supervision and has authored over 100 publications, achieving 1499 citations and an h-index of 21. Key research areas include digital twin technology, nonlinear spectral analysis, and machine learning for industrial diagnostics. His work addresses challenges in non-stationary signal processing, motor current signature analysis, and condition monitoring under varying operating conditions. Collaborations include interdisciplinary projects with the Centre for Efficiency and Performance Engineering. Recent studies highlight innovations in fault diagnosis frameworks for rotating machinery, conveyor belt systems, and wind turbines. His contributions bridge theoretical advancements with practical industrial applications, emphasizing explainable AI and adaptive diagnostics. Gelman's research has been presented at major conferences like the World Congress on Engineering and published in specialized journals. Education: Not explicitly stated in the provided text. Awards: High citation count and h-index reflect his significant academic impact. Grants/Advising: Supervised 2 PhD projects; accepting new students in diagnostic engineering and condition monitoring. Labs/Teams: Leads the Centre for Efficiency and Performance Engineering and collaborates with the Department of Engineering's research groups.
Christian Fermüller is an Associate Professor in the Department of Theory and Logic at the Faculty of Informatics, Technische Universität Wien (TU Wien). His research focuses on theoretical computer science, artificial intelligence, automated deduction, and formal logic systems. He specializes in fuzzy logic, proof theory, and non-classical logics, with contributions to semantic games, dialogue systems, and computational models of reasoning under vagueness. **Research Interests:** Foundations of fuzzy logic and many-valued logics Proof theory and analytic calculi Game-based semantics for non-classical logics Formal models of judgment aggregation and argumentation theory Applications in automated reasoning and computational intelligence **Grants & Projects:** Austrian Science Fund (FWF) projects on graded deontic reasoning (2025–2027), semantic games and analytic calculi (2019–2023), and fuzzy logic foundations (2008–2013) Co-PI of the LogICCC initiative exploring contextualism and fuzzy logic **Teaching:** Courses include logical methods in computer science, quantum computing, and theoretical computer science. Supervised over 15 PhD and master’s theses on topics ranging from semantic games to argumentation frameworks. **Affiliations:** Active in the LogiCS research group and regularly organizes seminars on logic and computation.
Colin Conradمشاهده پروفایل
دانشیار
Colin Conrad is an Associate Professor of Digital Innovation at Dalhousie University’s Faculty of Management. He also serves as Co-Director of the College of Digital Transformation and Principal of the Cognition and Organizations Research Group. His research focuses on interdisciplinary projects spanning information systems, computer science, and cognitive neuroscience, with particular emphasis on human factors in educational technology and artificial intelligence. His work is supported by NSERC, CFI, and Mitacs. Research interests include mind wandering measurement via EEG, AI ethics, human-AI interaction, and neurophysiological impacts of digital technologies. He explores topics such as virtual teacher perception, privacy calculus in AI systems, and cognitive state awareness in human-AI collaboration. His recent studies address challenges in remote work ergonomics, digital transformation during crises, and legal/ethical aspects of brain-computer interfaces. His publications analyze behavioral responses to cybersecurity notifications, virtual influencer trust dynamics, and adaptive online learning systems. Current projects investigate the cognitive effects of AI-generated media and the neurophysiological foundations of attention in digital environments. Colin’s research is funded through grants emphasizing interdisciplinary innovation and societal impact. He collaborates with industry partners to translate neuroscientific insights into practical applications in education and workplace design.
Stavros Demetriadis is a Full Professor at the School of Informatics, Aristotle University of Thessaloniki, Greece. His research focuses on Learning Technologies, including Conversational Agents in Education, Learning Analytics, Computer-Supported Collaborative Learning (CSCL), Computational Thinking, and Massive Open Online Courses (MOOCs). He has led EU-funded projects like colMOOC and developed educational tools such as 'pytolearn' for Python instruction and 'Cubes Coding' (winner of Open Education Challenge 2014 and NUMA Competition 2014). He has supervised 5 completed PhD theses, 4 ongoing PhDs, and over 60 Master’s theses. Academic Appointments: Full Professor (2020–present), Associate Professor (2015–2020), Assistant Professor (2012–2015), Lecturer (2002–2008), Informatics Teacher (1989–2002) Education: PhD in Multimedia Technology in Education (2000), MSc in Electronic Physics (1986), BSc in Physics (1983) His work bridges AI and education, with over 161 publications and an h-index of 27. Recent research explores ChatGPT integration, ethics in Learning Analytics, and AI-driven assessment tools. He has delivered invited talks at institutions like the University of Valladolid (2024) and coordinates the 'Teachers' Fast-paced Distance Training on Tele-education' project. Awards include three international best paper awards and recognition for his 'Cubes Coding' project. Key Research Contributions: Developed frameworks for Conversational Agents in CSCL Innovated Computational Thinking pedagogy through robotics Explored ethics and culture in Learning Analytics adoption Created Python-based MOOCs for non-programmers He has taught courses like Human-Computer Interaction and Learning Analytics, and led short programs on Conversational AI. His collaborations span institutions in Spain, Denmark, and Greece. ORCID: 0000-0002-1561-6372; Google Scholar, Semantic Scholar, and Scopus profiles list his extensive output.
- Open and Distance Learning (ODL)
- Educational Technology
- Artificial Intelligence in Education
- +۵ مورد دیگر
Cengiz Hakan Aydin , PhD, is a full professor at Anadolu University in Turkey, where he has been a faculty member since the early 1990s. He currently serves as the Head of the Distance Education Department in the Faculty of Open Education and is the Director of the OpenAnadolu Project , which includes the AKADEMA MOOCs platform. Additionally, he holds the role of Director of the Office of Learning and Teaching Enhancement at Özyeğin University . Dr. Aydin’s research is centered on the design and development of open and distance learning (ODL) environments. His work explores the integration of emerging technologies such as AI, VR, AR, and MR into educational systems. He is deeply engaged in the study and promotion of Open Educational Resources (OER) , Open Educational Practices (OEP) , and Massive Open Online Courses (MOOCs) . His scholarly contributions also address current trends and challenges in higher education. Dr. Aydin has held leadership roles in several international organizations. He served as President and Board Member of the International Division at the Association for Educational Communications and Technology (AECT) , and as a Board Member of the International Council for Educational Media (ICEM) . He is currently a Steering Committee Member of the OpenupEd initiative under the European Association of Distance Teaching Universities (EADTU) , and a member of the AECT Board and the Centre for Distance Education Research (COER) . He is actively involved in editorial work, serving on the boards of International Review of Research in Open and Distance Learning (IRRODL) , Educational Media International (EMI) , and Journal of Open, Distance, and Digital Education (JODDE) . Research Projects and Grants: Volkswagen Foundation – AI in Education (AIEd) : Local researcher. Erasmus+ CRED4TEACH (MOOC-based micro-credentials for teacher professional development): Leading researcher at Anadolu University. Erasmus+ ADMIT (Generative AI and Large Language Models in Higher Education): Researcher. Anadolu University Internal Funds – Multiple projects including AI-based tools for writing skills, intelligent personal education coach, and automated grading systems for open-ended questions. Dr. Aydin serves as both Project Coordinator and Researcher . Labs and Teams: Dr. Aydin leads the OpenAnadolu Project , which encompasses platforms like AKADEMA and serves as a hub for innovation in distance education and MOOC development at Anadolu University.
Bai Ziqianمشاهده پروفایل
استادیار
Dr. Bai Ziqian is an Assistant Professor in the School of Automation and Intelligent Manufacturing at Southern University of Science and Technology (SUSTech) in Shenzhen, China. Recognized as a Pujiang Scholar and Shenzhen Pengcheng Peacock Talent, she has established herself as a leading researcher at the intersection of wearable technology, textile engineering, and human-computer interaction. Her work bridges technical innovation with practical design applications, focusing on user-centered solutions that enhance human experience through technology integration. Dr. Bai's educational background includes: PhD in Smart Wearable Product Design (2011-2015), Hong Kong Polytechnic University MA in Fashion and Textile Design (2005-2006), Hong Kong Polytechnic University BA in Fashion Design and Engineering (2001-2005), South China Agricultural University Her research spans wearable technology, tangible interactive interfaces, IoTs, ergonomics, functional garments, wearables for healthcare, material innovation, smart home applications, and user-centered design. Dr. Bai has pioneered work in smart wearable fabrics and sensing mechanisms based on flexible materials, with a particular focus on human-computer interaction theory and practice. She has established a research team that has mastered key technologies in smart fabrics, interactive textiles, physiological signal monitoring, and human-computer interaction systems. Her approach consistently emphasizes user-centered design principles, ensuring that technological innovations serve practical human needs while maintaining aesthetic appeal. Dr. Bai's publication record demonstrates a clear evolution from foundational work in photonic textiles toward increasingly sophisticated wearable healthcare and human-computer interaction systems. Her recent publications focus on advanced sensor technologies, energy harvesting for wearables, and sophisticated data analysis for human motion and physiological monitoring. The interdisciplinary nature of her work is evident in publications spanning materials science, biomedical engineering, textile technology, and design methodology, with papers appearing in high-impact journals including Advanced Functional Materials (IF: 19.5), ACS Sensors (IF: 8.9), and Computers in Industry (IF: 10). Dr. Bai has received numerous prestigious awards that highlight both the technical and artistic dimensions of her work: 2024 German Red Dot Design Award for Best Design 2013 Neo-Neon, permanent collection at China Silk Museum (State grade 1 museum) 2019 Finalist, ThermoBlanket, TechStyle for Social Good International Competition 2017 1st Prize Teaching Award, Donghua University 2017 China National Textile and Apparel Council Teaching Award Multiple Service Learning Awards from Hong Kong Polytechnic University She has successfully secured research funding from prestigious sources including the National Natural Science Foundation of China and Guangdong Province's General Project. Her projects include a collaborative effort with the Guangdong Provincial Department of Education and Li Ning Company on a 'flexible wearable lower limb functional electrical stimulation system.' Dr. Bai has extensive teaching experience across multiple institutions and has guided student teams to success in national competitions. She currently leads the Human-Computer Interaction Design Laboratory (HCID) at SUSTech, which focuses on advanced design, engineering, and technology research at the intersection of disciplines, training the next generation of interdisciplinary designers and engineers.