Dr. Alexander Schulz is a Researcher at the University of Bielefeld within the Faculty of Engineering and its Machine Learning Group . He focuses on machine learning applications across diverse domains including biomedical engineering , fairness evaluation , and data visualization . Research Interests Transfer learning for medical applications Dimensionality reduction techniques Bias detection in language models Classifier visualization tools Collaborations Center for Cognitive Interaction Technology (CITEC) Machine Learning Reports publications His recent publications highlight trends in dynamic graph analysis , semantic bias measurement , and physiological data generation . He has contributed to tools like the Box and Beans test for prosthetics evaluation and DeepView for classifier boundary visualization. Collaborative projects include cardiovascular data synthesis for implantable devices and fairness-aware AI frameworks. Dr. Schulz works at CITEC 2-228 and is reachable at aschulz@techfak.uni-bielefeld.de . His work integrates theoretical machine learning with practical implementations in healthcare and industrial systems.
Dr. Viorica Vasilache serves as a Scientific Researcher II at the Interdisciplinary Department of Sciences, ARHEOINVEST Center of Alexandru Ioan Cuza University (UAIC), specializing in cultural heritage conservation through advanced scientific methodologies. With over 25 years of dedicated research, she directs analytical efforts at the Laboratory for Scientific Investigation and Conservation of Cultural Heritage, employing techniques such as SEM-EDX, micro-FTIR, and derivatography to authenticate artifacts and develop preservation protocols. Her research spans cultural heritage conservation , archaeological materials analysis , and environmental science applications , with a recent focus on integrating artificial intelligence for forgery detection. Key projects include interdisciplinary studies of prehistoric pottery, authentication of artworks by Rembrandt and Magritte, and analysis of historical musical instruments, reflecting her commitment to bridging scientific rigor with cultural stewardship. Publication trends reveal growing emphasis on AI-driven authentication frameworks and sustainable conservation materials, particularly in textile dye extraction and salt-related environmental research. Vasilache has mentored 25 doctoral students (23 Romanian, 2 international) and contributed to over 20 national research initiatives, including Romania's participation in EUROINVENT innovation exhibitions. As secretary of the International Journal of Conservation Science (ISI-indexed), she advances scholarly discourse in heritage conservation while leading component projects in salt-related environmental and therapeutic research. Her laboratory maintains cutting-edge capabilities in non-invasive analysis, supporting ongoing collaborations with international institutions for artifact authentication and preservation.
Ruchi S Gupta , MD, MPH, is a Professor at Northwestern University Feinberg School of Medicine , with cross-appointments in Pediatrics, Medical Social Sciences, Preventive Medicine, and Medicine. She serves as Director of the Center for Food Allergy & Asthma Research (CFAAR) and the Institute for Public Health and Medicine (IPHAM), focusing on food allergy epidemiology, prevention strategies, and health disparities. Education: MD from University of Louisville (1998), MPH from Harvard University (2004) Postgraduate training: Pediatrics intern at University of Florida, residency at University of Washington, fellowship in General Academic Pediatrics at Harvard Her research spans food allergy prevalence studies , asthma management , and socioeconomic disparities in allergy care . She leads community-engaged interventions to improve health equity and inform global policy. Recent articles highlight mental health in allergy patients , immigrant health trends , and payer practices in allergy management . Scientific awards include: 2023 Heritage Lectureship Award (AAAAI) 2022 Pediatrics Excellence Award for Scholarship 2021 Equalize Pitch Competition Winner 2020 Bram Rose Memorial Lectureship 2018 Lurie Children's Hospital Senior Scientist Award She mentors students and professionals across training levels, integrates clinical care (20% effort at Lurie Children's Hospital Resident Clinic), and contributes to editorial boards and advisory roles for organizations like FARE and the NIH.
George Bebis is a Foundation Professor at the Department of Computer Science & Engineering, University of Nevada, Reno, affiliated with the College of Engineering. He has published extensively in computer vision, machine learning, and image processing. Research Interests: Computer vision, Image processing, Pattern recognition, Machine learning, Evolutionary computing Email: bebis@unr.edu Recent research trends include crater detection using convolutional neural networks, horizon/sky line detection via semantic segmentation, low-resolution face recognition, Gabor feature optimization, and dynamic programming integration with machine learning. His work spans applications in remote sensing, transportation safety, medical imaging, and biometrics.
Monica Nicolescu is a Professor in the Department of Computer Science at the University of Nevada, Reno, and Director of the Robotics Research Lab. Her research focuses on human-robot interaction, social robotics, and multi-robot systems, emphasizing communication, learning, and teamwork in dynamic environments. She develops methodologies for integrating robots into human society, with a focus on autonomous control systems and adaptive learning capabilities. Her work explores heterogeneous human-robot teams, emphasizing social norms and collaboration in service robotics applications. She also investigates maritime safety through AI-driven solutions and multi-modal interaction frameworks combining gestures, speech, and visual cues for reliable human-robot task configuration. Recent publications highlight advancements in medical imaging segmentation, cryptocurrency forecasting, and socially-aware navigation systems. Her research integrates deep learning, computer vision, and behavioral algorithms to enhance robotic functionality and safety in complex environments. Dr. Nicolescu leads the Robotics Research Lab, fostering interdisciplinary collaboration in robotics, AI, and human-robot interaction. Her research addresses both technical and societal challenges, aiming to create robots that adaptively and ethically integrate into diverse human contexts.
Aythami Morales is a Professor at Universidad Autónoma de Madrid (UAM), supported by the Madrid Government under the V PRICIT program for Excellence in University Teaching Staff. His work bridges machine learning with real-world applications in auditable AI tools, security, privacy, and discrimination-aware learning . Key Research Themes: Biometrics, multimodal AI, fairness in machine learning, dietary assessment, and attention mechanisms Recent Projects: M2RAI (Multimodal and Multitask Responsible AI, 250K euros + PhD funding) and eMIRIAM (Risk Assessment in AI for Mental Health) His 15 most recent articles (2025) focus on AI auditability, biometric verification, and privacy-preserving techniques , spanning conferences like IEEE/IJCB, ICCV, and CVPR. He received the 2024 Stanford Top 2% Scientists in AI recognition and a Best Paper Award at CVPR 2025 . Morales actively engages in societal outreach through media (El País Retina) and public events like Pint of Science, addressing ethical AI and bias.
Dr. Michela Botticelli is an Affiliate Researcher at the Kelvin Centre for Conservation and Cultural Heritage Research within the School of Culture & Creative Arts at the University of Glasgow. Her work focuses on applying advanced analytical techniques to cultural heritage conservation and archaeometry. She specializes in material characterization of historical artifacts using methods like Raman spectroscopy, FTIR, and hyperspectral imaging. Recent projects include investigations into pigment residues, Roman infrastructure materials, and the authentication of archaeological artifacts. Her research interests span archaeological materials analysis, material degradation studies, and the development of non-invasive analytical methods for heritage conservation. She has contributed to studies on rock art pigments in Georgia, Roman aqueduct mineralogy, and the synthesis of ancient ochre pigments. She is affiliated with the PISTACHIO project (Photonic Imaging Strategies For Technical Art History And Conservation), advancing imaging technologies for art historical applications. Publications highlight interdisciplinary approaches combining material science with cultural heritage preservation, emphasizing collaboration with international teams across archaeology, chemistry, and conservation science.
Terence O'Neill is a Lecturer in the Department of Accounting and Business Computing at the Faculty of Business and Hospitality. His research focuses on advanced topics in image processing, pattern recognition, and deep learning applications. Notably, he contributed to a study on lightweight deep learning models for detecting image forgery, emphasizing post-processed attacks. His work integrates embedded systems and VGGNet architectures, reflecting interdisciplinary expertise. Collaborations span international teams, as seen in his 2021 publication co-authored with researchers from diverse institutions. While no specific awards are listed, his research has garnered significant attention, evidenced by 37 citations. Details about academic advising or grants are not provided in the available text.
Eva Tuba is a researcher at Singidunum University , Serbia. Her work focuses on swarm intelligence , metaheuristic optimization , and machine learning applications across domains like medical imaging, wireless sensor networks, and digital forensics. Research Interests: Swarm optimization algorithms (bat, firefly, fireworks), machine learning, image/signal processing, cloud computing, and explainable AI. Publications: 15+ recent papers in journals such as Journal of King Saud University , IEEE Transactions , and Springer volumes, often in collaboration with researchers like Milena Tuba and Nebojsa Bacanin. Her academic contributions include hybridized algorithms for convolutional neural network tuning , medical diagnostics , and environmental monitoring . While she has no listed awards, her work spans both theoretical and applied computational intelligence.
Zahra Farzadpour holds a Doctor of Philosophy from the Computer and Information Sciences Department at Northumbria University. Her research focuses on biometric security, particularly in fingerprint forgery detection and pattern recognition. She has contributed to advancements in cybersecurity and digital forensics through innovative methodologies like adaptive thresholding techniques. Her academic work bridges computer vision and information security, addressing critical challenges in authentication systems. While no grants or awards are listed, her publication history reflects a commitment to robust security solutions.
Roman Maev is a Professor in the Department of Physics at the University of Windsor’s Faculty of Science. His research focuses on non-destructive testing, artificial intelligence applications in automotive manufacturing, art conservation through advanced imaging, and nanotechnology. He holds the Sokolov Award for contributions to non-destructive testing and has advised students such as Maryam Shafiei Alavijeh and Vlad Tusinean. He co-founded ONtech Rapid Coatings, a company commercializing his research on metal surface treatments, and chairs major conferences like the Nano Ontario Conference. As an IEEE lecturer, he delivers global talks on physics and materials science. His work has been showcased at the Institute for Diagnostic Imaging Research, where he develops diagnostic tools for industrial and medical applications. He frequently collaborates with institutions on art conservation projects and security technology innovations.
Kalin Stefanov is an ARC DECRA Fellow and Research Fellow in the Department of Human Centred Computing at Monash University. He holds a PhD in Computer Science from KTH Royal Institute of Technology and an MSc in Artificial Intelligence from the University of Amsterdam. His research focuses on Affective Computing, exploring systems that recognize and simulate human affects, with applications in social robotics, neurodiverse communication, and multimodal interaction. He has led projects on sign language translation and large-scale deepfake detection datasets. Key collaborations include work at the University of Southern California’s Institute for Creative Technologies and National Institute of Informatics. He has received accolades such as the Discovery Early Career Researcher Award (2023) and Best Paper Awards (2019, 2024). His research also contributes to UN SDG 4 (Quality Education) through accessible technologies for neurodiverse groups and visually impaired learners. Projects include the 'Active Generation of fingerspelling in Australian Sign Language' and 'Research Towards automated Australian Sign Language translation,' funded by the Australian Research Council. Stefanov’s work spans AI ethics, multimodal data platforms (e.g., OpenSense), and systems for social signal processing in human-robot interaction.
Michał Woźniak is a Professor at the Department of Systems and Computer Networks, Wrocław University of Science and Technology. He serves as Head of the Department and leads the Machine Learning Research Team. His research spans machine learning, pattern recognition, data stream mining, and imbalanced data classification. He actively supervises MSc theses and leads multiple research projects including those on continual learning, classifier ensembles, and fake news detection. Research Interests: Machine learning, particularly inductive and continual learning Pattern recognition and classifier ensembles Data stream mining under concept drift Imbalanced data classification Fake news and disinformation detection Cybersecurity and medical decision support His recent publications (2024–2025) focus on continual learning under concept drift, deep learning for image fusion and forgery detection, ensemble methods for imbalanced data, and AI applications in network optimization. These works reflect strong trends in adaptive machine learning, robust classification, and real-world AI deployment. Scientific Awards: BEST PAPER AWARD FOR CLVISION CVPR WORKSHOP 2024 He supervises numerous students and collaborates extensively on interdisciplinary projects. He has been involved in projects such as LM LDS (2021–2024), MOO (2020–2025), IDStream (2018–2022), and others. He is also a project manager and active in academic service, including membership in the Committee on Informatics of the Polish Academy of Sciences. His research group maintains a strong presence in AI and machine learning applications. Laboratory and Teams: He leads the Machine Learning Team and is involved in multiple research groups including the Advanced Data Analysis Methods Team and Metaheuristics Team . His lab focuses on developing robust, adaptive AI models for real-world challenges.
Glenn Van Wallendael is an Associate Professor at Ghent University's Faculty of Engineering and Architecture , affiliated with the Department of Electronics and Information Systems . He leads research in video coding, digital watermarking, and immersive media technologies. Academic Focus: Video compression standards (HEVC, H.266), AI for multimedia, virtual reality Key Collaborations: iMinds, imec, European research consortia Research Interests include: Video compression algorithms (HEVC, SVC, MV-HEVC) Digital watermarking for copyright protection Machine learning applications in image/video analysis Quality of Experience (QoE) in immersive environments Recent Publications (2024-2025) show expertise in: Deepfake detection using vision transformers Medical image landmarking tools Lightweight geometric approximation methods AI-driven video quality assessment Doctoral Mentorship includes supervising: 2021: Hannes Mareen (video forensics) 2020: Vasileios Avramelos (light field coding) 2017: Johan De Praeter (adaptive video encoding)
Serkan Özbay serves as an Associate Professor in the Department of Electrical and Electronics Engineering at Gaziantep University's Faculty of Engineering, Turkey. With academic appointments since 2003, he progressed from Lecturer to his current position (2024), maintaining continuous affiliation with the institution where he also completed all his degrees. His career demonstrates deep institutional integration and vertical advancement within the same department. His academic credentials were entirely earned at Gaziantep University: Doctorate (2007-2015): Institute of Science, Electrical and Electronic Engineering Master's (2003-2006): Institute of Science, Electrical and Electronics Engineering (Thesis) Licence (1997-2002): Faculty of Engineering, Electrical and Electronics Engineering Özbay's research centers on signal processing and machine learning applications, with significant contributions to computer vision for medical diagnostics (sinus segmentation, lung cancer detection) and security systems (image/video forgery analysis). His work bridges theoretical algorithms with practical implementations in edge computing and biomedical devices, evidenced by projects like real-time fall detection systems and metamaterial antennas. Recent publications show intensified focus on deep learning since 2020, particularly convolutional neural networks for medical imaging and object tracking. His publication trajectory reveals strategic interdisciplinary expansion: early work (2007-2015) concentrated on spectrum sensing and wireless communications, while post-2020 output pivoted toward AI-driven medical applications and digital forensics. Approximately 70% of his 22 publications (2019-2023) involve deep learning implementations, with strong institutional collaboration patterns—85% co-authored with Gaziantep University researchers. Journal publications predominantly appear in Q2-Q4 SCI-indexed engineering journals, while conference papers target international venues in computer vision and biomedical engineering. As an academic advisor, Özbay has supervised 16 graduate theses (1 PhD, 15 Master's) between 2017-2025, with 11 completed in 2020-2023 alone. Current advisees include students developing Raspberry Pi-based plate recognition systems and real-time fall detection solutions. He additionally mentors 15 undergraduate students in senior design projects annually, as evidenced by 2024 EEE498/499 project meetings. Administrative leadership includes Deputy Head of Department (2016), Erasmus Coordinator (2015-2016), and Institute Board membership (2020-2023).
