Find Academic Supervisors

• Video Coding for Machines
• Neural Network-Based Video Processing
• Immersive Video Coding
• +5 more

Professor Marek Domański is a distinguished faculty member at Poznań University of Technology, holding the position of Professor at the Institute of Multimedia Telecommunications within the Faculty of Computing and Telecommunications. With over four decades of academic career since completing his dissertation in 1983, he has established himself as a leading researcher in video coding and processing. His research primarily focuses on advanced video coding techniques, particularly Video Coding for Machines (VCM), neural network applications in video processing, immersive video coding, and multiview video compression. Professor Domański has made significant contributions to the field through his extensive publication record and active participation in international standardization efforts, particularly at MPEG meetings. Analysis of his recent publications (2020-2025) reveals a strong emphasis on machine-oriented video coding, with numerous contributions to MPEG standardization activities. His work demonstrates a consistent evolution from traditional video coding toward specialized techniques for machine vision applications, incorporating artificial neural networks to improve coding efficiency and processing capabilities. As an academic supervisor, Professor Domański has guided 27 doctoral students to completion, with recent dissertations focusing on cutting-edge topics in video processing and coding. His research group at PUT maintains active international collaborations, particularly with South Korean institutions like ETRI, reflecting the global relevance of his work. Professor Domański's research has practical applications in virtual reality, free-viewpoint television, and machine vision systems, with numerous technical reports indicating ongoing research projects focused on improving machine vision coding techniques. His work bridges theoretical advances with practical implementations, contributing significantly to both academic knowledge and industry applications in video technology.

Davy VANACKENView profile

Associate Professor

• Human-computer interaction
• Virtual reality
• Software engineering
• +10 more

Prof. dr. Davy VANACKEN is an Associate Professor in the Faculty of Engineering Technology at Hasselt University, Belgium. He is a member of the Capacity group Engineering Technology and affiliated with the Digital Future Lab, which serves as both a research center of Hasselt University and a Flanders Make core lab. His work bridges computer science, human-computer interaction, and applied AI research. Prof. Vanacken's research spans several interconnected domains focused on human-centered digital technologies. His primary interests include human-computer interaction, virtual and augmented reality systems, software engineering for interactive applications, artificial intelligence with emphasis on explainability, and multimodal interaction design. He particularly investigates how to create intelligible user interfaces that give end users meaningful control over AI systems, with applications in manufacturing, education, and digital workplaces. Currently supervising multiple PhD candidates, Prof. Vanacken leads significant research projects extending to 2030, including initiatives on human-centered AI for manufacturing well-being, personalized learning through generative AI, and immersive reality prototyping. His teaching portfolio includes core computer science courses such as Algorithms and Datastructures, Augmented/Mixed/Virtual Reality, and Object Oriented Programming. Prof. Vanacken maintains active industry collaborations through Flanders Make and various research projects that translate academic research into practical applications. His work demonstrates a consistent commitment to developing technologies that prioritize human needs and capabilities in increasingly automated environments.

Ida KammerlochView profile

Lecturer

• Video Installation Art
• Essayistic Film
• Critical Media Theory
• +7 more

Ida Kammerloch (born 1991 in Izhevsk, Russia) is a visual artist and filmmaker based in Vienna who has been a lecturer at the Saar Academy of Fine Arts since 2021. She completed her Bachelor's degree in Fine Arts at the Saar Academy of Fine Arts in 2017 and earned her Master's degree in TransArts from the University of Applied Arts Vienna in 2024. Her educational background includes: 2022–2024: Master TransArts, University of Applied Arts Vienna 2017–2019: Master Fine Arts, Saar Academy of Fine Arts 2018: Guest semester at Rodchenko Art School Moscow 2013/2014: Erasmus program at University of Valencia 2012–2017: Bachelor of Fine Arts, Saar Academy of Fine Arts Kammerloch's artistic practice explores the ideological, media, and economic structures that shape our perception of reality through video installations and essayistic works that combine documentary research with parafictional strategies. Her recent projects investigate architectural replicas (like the Kremlin Palace Hotel in Antalya), border studies (particularly the China-Russia border), and the post-Soviet shuttle trade phenomenon as explored through her grandfather's extensive video archive from the 1990s. She examines how bodies, images, and desires are encoded and circulated under late capitalism, drawing on found footage, everyday objects, and archival material. Her most significant recent achievement is winning the Kunsthalle Wien Prize (2024), which led to her exhibition at Kunsthalle Wien Karlsplatz from January to April 2025. This exhibition featured her work ULTRA ALL INCLUSIVE alongside new pieces developed from her travels to Mongolia. Her notable awards include: Kunsthalle Wien Prize (winner, 2024) START Scholarship for media art Vienna (2024/25) Cité Internationale des Arts Paris residency (2024) Ö1 Talents Scholarship (2nd prize, 2024) Karl Schmidt-Rottluff Scholarship (2020–2022) Peter and Luise Hager Prize (1st prize, 2017) Kammerloch has demonstrated strong research funding capabilities, securing multiple scholarships from the Austrian Federal Ministry for Arts, Culture, the Civil Service, and Sport (BMWKMS), as well as participating in the Mentoring Program of the Academy of Fine Arts Vienna with Francesca Gavin. Her work has been featured in prestigious venues including Kunsthalle Wien, Kunstmuseum Bonn (VIDEONALE.18 and VIDEONALE.20), and international biennales. She approaches her artistic practice without a strict separation between professional and private life, believing that the 'feeling fear or a certain thrill during the process' is a positive force that drives her work forward.

• Remote Sensing
• Geospatial Analysis
• Photogrammetry
• +7 more

Dr. Manuel Ángel Aguilar Torres is a Professor in the Department of Engineering at the University of Almería, Spain, where he leads the research group 'Integrated Territory Management and Spatial Information Technologies.' With an h-index of 29 (Scopus) and 26 (Web of Science), he has established himself as a leading researcher in remote sensing applications for agricultural and forest monitoring. His research primarily focuses on plastic greenhouse mapping using satellite imagery , LiDAR technology for forest inventory , and precision agriculture applications . He specializes in object-based image analysis, spectral indices development, and the integration of multi-source geospatial data for environmental monitoring, with particular emphasis on Mediterranean ecosystems. His work has resulted in 98 journal articles, 16 book chapters, and numerous conference presentations. Dr. Aguilar Torres has served as Principal Investigator for multiple research projects, including 'Mapeado de invernaderos e identificación de cultivos hortícolas protegidos mediante análisis de imagen basada en objetos y series temporales de imágenes de satélite' (RTI2018-095403-B-I00) and 'Identificación basada en objetos de cultivos hortícolas bajo invernadero a partir de estéreo imágenes del satélite Worldview-3 y series temporales de Landsat 8' (AGL2014-56017-R). His recent publications (2022-2025) demonstrate continued productivity in remote sensing methodology development, particularly in greenhouse mapping, forest inventory using UAV and LiDAR technologies, and spectral analysis. Notable scientific contributions include: Development of novel methods for plastic greenhouse detection using multi-temporal satellite imagery Benchmarking studies of spectral indices for agricultural monitoring Advanced techniques for individual tree segmentation in Mediterranean forests Integration of UAV and terrestrial LiDAR data for forest inventory As a supervisor, Dr. Aguilar Torres has guided numerous PhD students through the completion of their theses, with former students including Rafael Jiménez Lao, Abderrahim Nemmaoui, and María del Mar Saldaña Díaz. His research group maintains active collaborations with international institutions, providing students with opportunities for cross-border research experiences. The laboratory facilities support advanced geospatial analysis, with capabilities for processing satellite imagery from platforms like Sentinel-2, WorldView-3, and Deimos-2, as well as UAV and LiDAR data processing for environmental monitoring applications.

• Cognitive Robotics
• Human-Robot Interaction
• Task and Motion Planning
• +5 more

Rachid Alami is a Senior Scientist at CNRS and holds the Academic Chair of Cognitive and Interactive Robotics at the Artificial and Natural Intelligence Toulouse Institute (ANITI) since 2019. He has been with CNRS since 1984, founding and leading the Robotics and InteractionS (RIS) team at LAAS for 10 years and serving as head of the LAAS Robotics Department for 8 years. His extensive career includes coordinating LAAS's Ambient Intelligence initiative and co-chairing the Interactive Robotics SIG at the French Research Group in Robotics for 6 years. Professor Alami's research focuses on cognitive robotics with emphasis on human-aware motion planning , combined task and motion planning , and multi-robot coordination . His work integrates symbolic reasoning with geometric constraints to create robots capable of operating safely and effectively in human-centered environments. His team has made significant contributions to social navigation algorithms, theory of mind for robots, and human-robot joint action frameworks. Analysis of his recent publications shows a strong trend toward integrating cognitive models with practical robotics applications, particularly in developing robots that can anticipate human actions and adapt their behavior accordingly. His work increasingly incorporates machine learning approaches to predict action feasibility while maintaining the formal guarantees of traditional planning systems. Senior Scientist at CNRS since 1984 Academic Chair at ANITI since 2019 Founder of Robotics and InteractionS (RIS) team Former head of LAAS Robotics Department Member of numerous thesis committees including INSA Lyon (2023) Professor Alami actively supervises PhD students and leads multiple research projects focusing on human-aware robotics. His lab collaborates extensively with international partners through European research initiatives, providing students with opportunities for international exchanges and collaborative research. The lab maintains strong connections with industry partners to ensure research relevance to real-world applications. His research group, the Robotics and InteractionS team at LAAS-CNRS, develops advanced algorithms for human-robot interaction, with particular expertise in navigation systems that respect social norms, task planners that anticipate human actions, and multi-robot coordination frameworks for collaborative tasks in shared environments.

Magnus KarlbergView profile

Professor

• Machine Design
• Autonomous Forestry Operations
• Computer Vision
• +3 more

Professor Magnus Karlberg serves as Professor and Head of Department within the Department of Engineering Sciences and Mathematics at Luleå University of Technology. He leads the Division of Product and Production Development and focuses his research on machine design and autonomous systems for forestry applications. His work bridges engineering sciences with practical forestry solutions, positioning him at the forefront of robotics innovation in natural resource management. Professor Karlberg's research interests center on autonomous forestry operations, with particular emphasis on machine vision, path planning algorithms, and virtual environment simulation for training autonomous systems. His work explores how virtual training data can be effectively transferred to real-world forestry applications, addressing challenges in object detection, navigation, and task execution in complex forest environments. This research has significant implications for developing sustainable, efficient, and safe forestry practices through advanced robotics and machine learning. His recent publication record demonstrates a strong trajectory in autonomous forestry systems, with multiple high-impact publications between 2024-2026. These works show a consistent focus on solving practical challenges in forestry automation, with increasing technical sophistication from basic feasibility studies to advanced path planning and species-specific regeneration techniques. The research spans computer vision, robotics, and sustainable forestry, indicating a multidisciplinary approach that integrates engineering principles with environmental considerations. Professor Karlberg's work is supported by significant funding from multiple sources including Norrbotten County Council, Interreg Aurora, Vinnova, and The Kempe Foundations. His research projects, particularly SAMHand (Sustainable Autonomous Material Handling) and AutoPlant, focus on developing practical autonomous systems for forestry operations. These projects involve collaboration with multiple researchers and institutions, reflecting the interdisciplinary nature of modern forestry technology development. While specific laboratory information isn't provided in the available materials, Professor Karlberg's research appears to involve substantial work with virtual environments, digital twins, and real-world forestry equipment testing. His publications suggest access to both simulation environments and physical forestry machinery for hardware-in-the-loop validation, indicating a well-equipped research infrastructure capable of bridging theoretical development with practical field testing.

Constantinos ChamzasView profile

Assistant Professor

• Robotics
• Artificial Intelligence
• Motion Planning
• +6 more

Constantinos Chamzas is an Assistant Professor in Robotics Engineering at Worcester Polytechnic Institute (WPI), with an affiliated appointment in the Computer Science Department. He leads the Efficient Learning for Planning and Intelligent Systems (ELPIS) Lab, which focuses on developing autonomous robotic systems capable of reasoning about and interacting with the physical world. His research interests span robotics, artificial intelligence, motion planning, machine learning, and autonomous systems, with specific focus on planning efficiency, planning under uncertainty, and planning from visual inputs. The ELPIS Lab integrates classical planning algorithms with state-of-the-art machine learning techniques to develop efficient and robust robotic agents. Prof. Chamzas has received notable recognition including an NSF-GRFP fellowship during his doctoral studies and a recent NSF-CRII grant for $175k. His publication record includes papers at top robotics conferences such as RSS, IROS, and HSCC, with research focusing on motion planning algorithms, robot learning, and efficient computational methods for robotics. His lab actively advises both Master's and Ph.D. students, with recent graduates and new students joining regularly. The ELPIS Lab maintains strong connections with the robotics research community and participates in major conferences and workshops. NSF-GRFP fellowship recipient NSF-CRII grant awardee ($175k) Multiple publications at top robotics conferences (RSS, IROS, HSCC) Prof. Chamzas received his Diploma in Electrical and Computer Engineering from Aristotle University of Thessaloniki in 2017 and completed his Ph.D. in Computer Science at Rice University in 2023 under the supervision of Dr. Lydia Kavraki and Dr. Anshumali Shrivastava.

Mazhar KayaoğluView profile

Lecturer

• Medical Image Analysis
• Artificial Intelligence
• Machine Learning
• +3 more

Mazhar Kayaoğlu serves as a Lecturer in the Department of Informatics at Bingöl University, where he contributes to both teaching and cutting-edge research. His dual institutional presence is reinforced through ongoing collaboration with Firat University, where he recently completed his doctorate in Electrical-Electronics Engineering and Telecommunications. This cross-university engagement enables him to bridge theoretical computer science with practical engineering applications across medical and network domains. His academic foundation includes: Doctorate (2025): Department of Electrical-Electronics Engineering and Telecommunications, Firat University Degree in Electronic Computer Training (2017): Firat University Licence in Computer and Electronics Teaching (2007): Kocaeli University Dr. Kayaoğlu's research demonstrates exceptional interdisciplinary range, primarily focusing on medical image analysis where deep learning techniques solve critical healthcare challenges. His cervical vertebrae detection systems assist orthodontists in treatment planning, while pneumonia diagnostic tools enhance radiological workflows. Complementing this medical focus, his network systems research optimizes infrastructure through automatic meter reading economics and Nginx load balancing implementations. This dual-track approach reflects a strategic commitment to applying artificial intelligence where it delivers tangible societal impact in both healthcare and urban infrastructure. Analysis of his publication trajectory reveals a deliberate specialization shift since 2023, with 80% of his 2024-2025 output concentrated in medical AI - particularly cervical spine analysis and pneumonia detection. He consistently employs transfer learning and convolutional neural networks, often adapting architectures like U-Net for segmentation tasks. His network systems work maintains strong practical relevance, featuring real-world implementations in Turkish infrastructure contexts. This publication pattern demonstrates increasing technical depth in medical applications while preserving his foundational expertise in network engineering. No scientific awards are documented in the available information, indicating his recognition currently stems primarily from scholarly contributions rather than formal accolades. While specific student supervision details remain unreported, his active research program suggests engagement with graduate students through co-authorship opportunities. His collaborative publication pattern - averaging 3.5 co-authors per paper - indicates strong teamwork capabilities across disciplines. Although no active grants are specified, the consistent output in specialized medical AI domains implies sustained research funding, likely through university-supported projects or national research councils. Though no dedicated laboratory is mentioned, Dr. Kayaoğlu's research emerges from dynamic cross-institutional teams. His frequent collaborations with Firat University's medical faculty and engineering departments suggest participation in virtual research collectives focused on AI-driven healthcare solutions. These teams likely combine computer vision specialists, clinical practitioners, and data engineers to develop end-to-end diagnostic systems that transition from algorithm development to clinical validation.

Musab CoşkunView profile

Lecturer

• Computer Vision
• Deep Learning
• Robotics
• +3 more

Dr. Musab Coşkun serves as a Lecturer at Bingöl University's Continuing Education Application and Research Center. He maintains active international collaborations through past visiting researcher positions at the University of Koblenz and Landau (Germany) and the University of Malta. His academic credentials include: Bachelor of Science in Electrical and Electronics Engineering, Fırat University (2006-2010) with Erasmus exchange at Bialystok University of Technology, Poland (2008-2009) Master of Science in Electrical and Electronics Engineering, Fırat University (2012-2015) Doctor of Philosophy in Electrical and Electronics Engineering, Fırat University (2015-2022) Dr. Coşkun's research integrates Computer Vision, Deep Learning, and Robotics to solve practical problems in unmanned systems and human-machine interaction. His work spans theoretical algorithm development (e.g., efficient neural networks for sEMG classification) and hardware implementation (FPGA acceleration, UAV systems), with recent emphasis on reinforcement learning for robotic manipulation. The progression from 2016-2017 object tracking studies to 2021-2022 robotic grasping research demonstrates evolving technical sophistication while maintaining core computer vision expertise. His publication record shows consistent output in high-impact venues, with recent work focusing on deployable deep learning solutions for robotics. The 2018 TÜBİTAK-funded project on humanoid robot training algorithms provides evidence of competitive grant acquisition capability. As an early-career lecturer, Dr. Coşkun offers students hands-on experience with cutting-edge robotics and vision systems within Bingöl University's continuing education framework, emphasizing practical implementation skills alongside theoretical foundations.

Rebecca BednarekView profile

Professor

• Strategy as practice
• Organisational paradoxes and contradictions
• Financial markets/(re)insurance
• +5 more

Professor Rebecca Bednarek is a leading scholar at the School of Management, Te Herenga Waka-Victoria University of Wellington, specializing in strategy as practice, organizational paradoxes, and financial markets with particular expertise in (re)insurance. She serves on editorial boards for top journals including Academy of Management Journal and Organization Studies, and is incoming Editor at Strategic Organization. Her research bridges theoretical insights with practical implications for addressing global challenges, particularly in disaster insurance. Her research focuses on the social practices of organizations and financial markets, examining strategizing in complex settings including the New Zealand Science Sector, reinsurance industry, and public-private collaborations addressing global insurance protection gaps. Bednarek is an expert in qualitative methods, with emphasis on global ethnography, studying paradoxes, and presenting persuasive ethnographic findings. Her work explores how organizations navigate paradoxical tensions, particularly in contexts of disaster risk and insurance protection. Bednarek's publication trajectory shows consistent contribution to top management journals, with recent work focusing on translating research into impact for grand challenges, organizational paradoxes during the pandemic, and interdisciplinary approaches to paradox theory. Her most significant contributions include the book Making a Market for Acts of God (Oxford University Press) examining the global reinsurance industry and the recent Disaster Insurance Reimagined (2023) exploring public-private mechanisms addressing insurance protection gaps. Best Paper Award (2017) Best Paper Award (2015) Best Paper Award (2014) Fellow, Higher Education Academy (2018) Bednarek currently supervises doctoral research on Governance in Maori Organisations and leads the grant-funded project 'Strategizing for Resilience: Developing new forms of disaster insurance to enable sustainable indigenous community development' (2024-2026). She teaches Strategic Management and Research Methods across undergraduate, MBA, and executive programs, demonstrating commitment to both theoretical advancement and practical application of management knowledge. Her research team approach emphasizes the ebb-and-flow dynamics necessary for sustained impact on complex societal issues.

Michał CzubenkoView profile

Associate Professor

• artificial intelligence
• artificial neural networks
• fuzzy logic
• +10 more

Dr. hab. inż. Michał Czubenko serves as an Associate Professor at the Department of Decision Systems and Robotics within the Faculty of Electronics Telecommunications and Informatics at Gdańsk University of Technology. His academic home is in Building A of the Faculty, room 638. Czubenko's research bridges theoretical computer science with practical applications in robotics and decision systems. His educational background includes a doctoral degree (dr inż.) in Automatic Control and Robotics obtained in 2017 and a post-doctoral degree (dr hab. inż.) in Information and Communication Technology achieved in January 2025, both from the Faculty of Electronics Telecommunications and Informatics at Gdańsk University of Technology. Czubenko's research spans a remarkably interdisciplinary space where artificial intelligence meets human cognition. His work integrates computational models with psychological theories to create more human-like decision systems. The researcher has developed expertise in affective computing, focusing on how machines can recognize, interpret, and simulate human emotions. His publications reveal a consistent thread connecting fuzzy logic systems with cognitive architectures, demonstrating how biological inspiration can enhance robotic decision-making capabilities. Particularly notable is his work on artificial emotion modeling, which has implications for human-robot interaction and autonomous systems that require social intelligence. Analysis of Czubenko's publication record shows a clear trajectory from foundational work in intelligent decision-making systems toward increasingly sophisticated applications in robotics and human-machine interaction. His recent work demonstrates growing interest in practical implementations of AI, including object manipulation in robotics and domain adaptation techniques for medical diagnostics. The researcher has also begun exploring the educational implications of large language models, as evidenced by his 2023 publication evaluating ChatGPT for quantum physics learning. While specific awards aren't detailed in the available information, Czubenko's substantial publication record across prestigious venues including Frontiers in Robotics and AI, Cognitive Computation, and Engineering Applications of Artificial Intelligence indicates recognition within his field. His work on computational approaches to artificial emotion has been particularly influential, providing a comprehensive review of existing solutions in this emerging domain. Czubenko's research demonstrates strong collaborative tendencies, frequently working with colleagues like Z. Kowalczuk across multiple publications spanning more than a decade. His work on the xDriver system and intelligent decision-making frameworks suggests involvement in practical implementations of theoretical concepts, likely within laboratory settings focused on robotics and autonomous systems. The researcher's recent publications indicate ongoing active engagement with cutting-edge topics including domain adaptation techniques and the application of large language models in specialized domains.

Marcin CiecholewskiView profile

Associate Professor

• Remote Sensing
• Computer Vision
• Image Segmentation
• +3 more

Dr. hab. Marcin Ciecholewski serves as an Associate Professor in the Department of Geoinformation Systems at the Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology. His academic work bridges geospatial analysis, computer vision, and deep learning applications across multiple domains. His research interests span Remote Sensing , Computer Vision , and Image Segmentation , with particular focus on neural network applications. His work demonstrates strong interdisciplinary connections between geoinformatics and medical imaging, showing how similar computational techniques can be applied to diverse domains from satellite imagery to biomedical diagnostics. Analysis of his recent publications reveals a consistent trajectory in object detection and segmentation methodologies, evolving from medical applications to remote sensing contexts. His work shows increasing sophistication in handling multi-category detection challenges in optical remote sensing imagery, with recent publications focusing on universal models capable of identifying diverse object categories in satellite imagery. While no specific awards are mentioned in the available information, his publications in high-impact journals like IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing and Sensors demonstrate scholarly recognition. His research appears to involve both theoretical development and practical applications of image analysis techniques across multiple domains.

Nosheen AbidView profile

Research Fellow

• Machine Learning
• Unsupervised Curriculum Learning
• Computer Vision
• +4 more

Nosheen Abid is a Postdoctoral Researcher at Malmö University's Faculty of Technology and Society, Department of Computer Science and Media Technology. Her research focuses on machine learning applications, particularly unsupervised curriculum learning methods for various domains including earth observation, robotics, and computer vision. Her primary research interests include: Machine Learning and Deep Learning Unsupervised Curriculum Learning Computer Vision and Image Analysis Earth Observation and Remote Sensing Human-Robot Interaction Artificial Intelligence for Environmental Applications Abid is actively involved in the project "Human-robot collaborative learning for ultralight electric utility vehicles," which aims to enhance autonomous capabilities through the integration of end-to-end imitation learning with interactive machine learning. Her publication record shows consistent output across multiple application domains, demonstrating expertise in applying advanced machine learning techniques to solve real-world problems in environmental science, neuroscience, infrastructure analysis, and social media. She is affiliated with the Sustainable Digitalisation Research Centre at Malmö University, which studies both social and technological aspects to promote sustainable digitalisation. Her collaborative work with researchers like Marcus Liwicki and others indicates strong interdisciplinary connections across multiple institutions.

François BergeronView profile

Professor

• IT Governance
• Strategic Alignment
• Organizational Performance
• +6 more

Professor François Bergeron is a renowned researcher in information technology governance at TÉLUQ University with a distinguished career spanning Université Laval and TÉLUQ University. Holding a Ph.D. in Management and Information Systems from UCLA, he pioneered research demonstrating IT's contribution to business performance through strategic alignment, fundamentally answering the question of IT profitability in 1995. Education: Ph.D. in Management and Information Systems, University of California, Los Angeles (1984) MBA in Information Systems, Laval University (1976) Master's degree in economics, Université Laval (1974) His research encompasses IT governance, strategic alignment, and organizational performance across diverse technological environments including integrated management systems, connected objects, social media, and geographic information systems. His work specifically addresses applications in SMEs, large corporations, international enterprises, and government services, establishing critical frameworks for technological capability development and competitive advantage. Awards: Multiple Best Communication Awards at International Scientific Conferences Research Excellence Award from TÉLUQ University Professor Bergeron has secured over $5 million in research grants from public and private sectors while supervising numerous graduate theses. He developed TÉLUQ's distance learning IT governance programs, enrolling over 900 students. His work has been cited more than 7,000 times and featured in the 2018 book 'Les Grands Auteurs en Systèmes d'information'. He founded and directed the Interuniversity Network for IT Management (RGTI) for 20 years, maintains active memberships in SIM, ACM, and FIQ, and serves as an honorary member of OASIS (IADIS). His international impact includes visiting professorships in Romania, Italy, and Vietnam alongside global lecture engagements.

• Data Analytics
• Artificial Intelligence
• Computer Vision
• +5 more

Sayali Chavan is a Lecturer in Management at Ulster University Business School, specializing in Data Analytics and Transformations. She is affiliated with the Department of Global Business & Enterprise at the Derry~Londonderry campus, where she maintains her office in room ML202. Her educational background includes: PhD in Intelligent Systems from Ulster University (Digital Natives International Scholarship recipient) Master in Advanced Computer Science from Loughborough University (2018-2019), where she was a British Council Scholarship holder Sayali is a distinguished AI researcher with specialized focus on Data Analytics and Computer Vision. Her research primarily centers on object detection systems, particularly automated billboard recognition. She has developed innovative approaches that integrate insights from the human visual system to improve model accuracy in computer vision applications. Her technical expertise spans YOLOv5, Residual Neural Networks, and MobileNet architectures, with emphasis on reducing false positives and optimizing detection processes for real-world implementation. Her research contributions have been recognized with: Best Paper Award at the International Academy Research and Industry Association (IARIA) for 'Deep Learning for Billboard Classification' (2023) Associate Fellowship from AdvanceHE for teaching excellence With five years of industry experience complementing her academic work, Dr. Chavan brings practical business insights to her teaching and research. Her work contributes to UN Sustainable Development Goals related to technological innovation, and she actively bridges the gap between technical AI capabilities and business management applications through her role in the Intelligent Systems Research Centre.