جست‌وجوی استاد و پژوهشگر

Anthony Clarkمشاهده پروفایل

استادیار

• Autonomous Robotics
• Evolutionary Robotics
• Computer Vision
• +۷ مورد دیگر

Anthony Clark is an Assistant Professor of Computer Science at Pomona College, where he has been teaching since 2020. Previously, he served as an Assistant Professor at Missouri State University from 2016 to 2020. He directs the ARCS (Autonomous Robotics and Complex Systems) Lab, which focuses on improving the robustness and adaptability of autonomous robots, particularly small-scale systems that can navigate unpredictable terrain and adapt to potential damage. Clark earned his Ph.D. in Computer Science from Michigan State University in 2016, where he worked under Dr. Philip K. McKinley, and his B.S. in Computer Engineering from Kansas State University, graduating magna cum laude. His research centers on making autonomous robots more robust and adaptive through optimization algorithms and multimodal systems. He specializes in evolutionary robotics, computer vision, neural networks, and simulation methods for developing control systems that leverage multiple locomotion mechanisms. His recent work demonstrates strong trends across several domains: developing hybrid locomotion systems (wheel/leg transformations), applying deep learning to terrain classification and pathfinding, using simulation environments for training, and exploring pretraining techniques for evolutionary robotics. His research shows a consistent focus on bridging simulation and real-world applications while addressing challenges in robot adaptability and robustness. Faculty Excellence in Teaching, Missouri State University (2018) Best Paper Award, Workshop on Evolutionary and Reinforcement Learning (2013) Best Paper Award, ALIFE Conference, Behavior and Intelligence Track (2012) Outstanding Reviewer, Elsevier (2018) Master Advisor Certification, Missouri State University (2017) Clark has advised numerous undergraduate and graduate students through the ARCS Lab, with current research involving projects like the Adabot (a robot with multiple locomotion mechanisms) and thermal semantic segmentation for aerial field robots. His teaching portfolio includes courses on data structures, algorithms, neural networks, computer systems, and mobile robotics. He has also served as a Visiting Associate at Caltech's ARC Lab from 2023-2024, working with Dr. Soon-Jo Chung. The ARCS Lab develops simulation environments, optimizes control systems, and fabricates physical robots. Current projects include the Adabot with its geared coaxial shaft mechanism for hybrid locomotion, thermal semantic segmentation using satellite data, and creating dynamic simulation environments with Unreal Engine 5. The lab emphasizes practical applications of theoretical research while training students in both hardware and software aspects of robotics.

Mauro Barniمشاهده پروفایل

استاد

• Multimedia Security
• Digital Forensics
• Machine Learning
• +۳ مورد دیگر

Mauro Barni serves as a Full Professor in the Department of Information Engineering and Mathematical Sciences at the University of Siena, where he teaches Cybersecurity, Information Theory, and Mathematical Statistics. His office hours are held Fridays from 3:00 PM to 5:00 PM via online appointment, reflecting his active engagement with students. Professor Barni's research spans multimedia security and digital forensics, with emphasis on deep learning applications for digital watermarking, deepfake detection, and synthetic image attribution. His work addresses critical challenges in adversarial machine learning, steganography, and image manipulation detection, contributing significantly to cybersecurity and intellectual property protection frameworks. Analysis of his 2021-2025 publications reveals dominant trends in neural network watermarking robustness, synthetic media detection, and defenses against backdoor attacks. His research consistently bridges theoretical foundations with practical implementations, focusing on real-world applications like printer source attribution and physical-domain adversarial scenarios. He leads the VIPP (Vision, Image Processing, and Pattern Recognition) research group, which maintains dedicated virtual classrooms for collaborative projects in computer vision and multimedia security. The group actively develops methodologies for image forensics, synthetic media analysis, and security countermeasures against emerging threats.

Sezer Karaogluمشاهده پروفایل

مدرس

• Artificial Intelligence
• 3D Computer Vision
• SLAM
• +۱۰ مورد دیگر

Sezer Karaoglu is a Lecturer and part-time postdoctoral researcher at the Computer Vision Group, Informatics Institute, University of Amsterdam. He is also the CTO and Co-Founder of 3DUniversum, a technology spin-off of the University of Amsterdam that provides state-of-the-art 2D/3D computer vision solutions. Additionally, he has co-founded other startups including Scanm and 3DHealthScan. Dr. Karaoglu received his PhD from the Computer Vision Group, Informatics Institute, University of Amsterdam, with research funded by the COMMIT project. His educational background includes a double master's degree: an optics, image and vision master's degree from University Jean Monnet in France and a media technology master's degree from Gjovik University College in Norway. He completed his undergraduate studies with honors at Istanbul Technical University in Telecommunication Engineering. His research focuses on Artificial Intelligence and 3D Computer Vision, with specific interests in SLAM, re-localization, 3D reconstruction, 3D object detection and segmentation, synthetic media, generative AI, deep fake creation and detection, and VR/AR technologies. His work has significant applications in healthcare, particularly in using deepfake technology for therapy for victims of sexual violence-related PTSD and moral injury, as documented in a Frontiers in Psychiatry article. Analyzing his recent publications reveals a strong trend toward neural scene reconstruction, intrinsic image decomposition, and the application of diffusion models to computer vision problems. His research increasingly integrates 3D scene understanding with language models, as evidenced by his work on language-to-3D scene generation. The applications span from healthcare (deeptherapy.ai) to media authenticity (deepfake detection) and industrial applications. ICT.OPEN Poster Award (3rd Position), Oct'13 Pascal VOC'12 Classification challenge, 2nd Position, Sep'12 Pascal VOC'12 Detection challenge, 3rd Position, Sep'12 Best project award at Nokia and CIMET project competition Outstanding reviewer at CVPR'21 PROVADA Future Startup Battle winner Best Dutch AI startup by Valuer Dr. Karaoglu has supervised numerous PhD, Master's, and Bachelor's students, demonstrating his commitment to academic mentorship. His research has attracted significant media attention, with features on Dutch national TV programs including NPO, VPRO, RTL, and international outlets like BBC News. He has received research funding through the COMMIT project during his PhD studies and has successfully translated his research into commercial applications through his startups. His work on deepfake technology has been applied in innovative therapeutic contexts through DeepTherapy.ai, showing the real-world impact of his research. Dr. Karaoglu leads research efforts at the Computer Vision Group Amsterdam and through his company 3DUniversum, which has developed applications like weScan, DeepTherapy, and FairFake.ai. His team collaborates with various institutions including the Netherlands Film Academy for grief therapy applications using deepfake technology. The DeepTherapy project represents a particularly impactful application of his work, using deepfake technology to help victims of sexual violence confront perpetrators in therapeutic settings.

Julia Eckelمشاهده پروفایل

عضو هیئت علمی

• Film Studies
• Animation Studies
• Documentary Theory
• +۴ مورد دیگر

Jun.-Prof. Dr. Julia Eckel is Junior Professor for Film Studies at the Institute of Media Studies, Faculty of Cultural Studies, University of Paderborn, and has served as Deputy Managing Director of the institute since October 2023. Her research integrates film theory, animation studies, documentary theory, and media epistemology, with special attention to temporality, anthropocentrism, and digital visualities. Education & Career: PhD (Dr. phil.), Ruhr-Universität Bochum, 2018 MA Media Studies, Ruhr-Universität Bochum & Universität Zürich, 2008 BA Media Studies & Social Psychology/Anthropology, Ruhr-Universität Bochum, 2006 Junior Professor, University of Paderborn, since 2022 Research & teaching positions at Ruhr-Universität Bochum (2018–2022, 2016–2022, 2008–2009) and Philipps-Universität Marburg (2013–2018) Research Interests: Julia Eckel's scholarship focuses on the intersections of animation, documentary, and demonstration within contemporary audiovisual culture. She investigates how temporal complexity and anthropomorphic motifs shape filmic narratives, while also interrogating screencasting practices and the epistemic role of selfies in digital media ecologies. Her theoretical framework questions anthropocentric assumptions in media theory, exploring how human–media relations are reconfigured through animated and documentary forms. Recent work examines the documentation of digital processes and the ontological status of animated images in the era of artificial intelligence and deep fakes. Publications & Editorial Work: Eckel co-edits Rabbit Eye – Journal of Film Research and has co-edited special journal issues on Tech|Demo and Visual Clarity . Her monograph Das Audioviduum is available open access, advancing a media-theoretical account of anthropomorphic figuration in audiovisual media. Professional Service & Outreach: Co-speaker, AG Animation (Society for Media Studies) Member, Society for Animation Studies (SAS), Society for Cinema and Media Studies (SCMS), NECS Organizer/co-organizer of numerous international workshops and conferences on animation, documentary, and digital media (2024–2010) Reviewer for leading journals and funding bodies Labs & Research Networks: Eckel co-leads the interdisciplinary DFG research network Animation and Contemporary Media Culture , coordinates an inter-university habilitation colloquium in media cultural studies, and mentors early-career researchers through the Research Academy Ruhr.

Marco Magginiمشاهده پروفایل

استاد

• machine learning
• neural networks
• kernel machines
• +۶ مورد دیگر

Marco Maggini is a Full Professor in the Department of Information Engineering and Mathematics at the University of Siena, a position he has held since joining the university in 1996. His academic career spans over 25 years with foundational expertise in computer engineering and artificial intelligence, focusing on theoretical and applied machine learning research. His educational background includes: Laurea degree (cum laude) in Electronics Engineering from the University of Florence (1991) Ph.D. in Computer Engineering and Control Systems from the University of Florence (1995) Prof. Maggini's research encompasses machine learning, neural networks, kernel machines, and the integration of symbolic and sub-symbolic knowledge systems. He extends these foundations into practical applications including web mining, search engine technology, pattern recognition, natural language processing, and computer vision. This interdisciplinary approach bridges theoretical computer science with real-world implementation challenges across multiple domains. Analysis of his 15 most recent publications (2023-2025) reveals a dominant focus on multilingual NLP applications, particularly educational puzzle generation for low-resource languages (Italian, Arabic, Persian, Turkish) using LLMs. His work demonstrates consistent innovation in named entity recognition, commonsense reasoning evaluation, and cross-lingual adaptation techniques. Secondary research threads include medical imaging segmentation, molecular property prediction, and AI security vulnerabilities, reflecting his broad technical mastery across computer vision, bioinformatics, and adversarial machine learning. No specific scientific awards were mentioned in the provided documentation, though his editorial roles indicate peer recognition within the academic community. While student mentoring details are absent from the source material, his position as Full Professor and leadership of SAILab imply active graduate supervision. His extensive publication record (120+ papers) and editorial service suggest significant research grant involvement, though specific funding sources remain undocumented. He directs the Siena Artificial Intelligence Laboratory (SAILab), which serves as an interdisciplinary hub for advancing machine learning theory and applications. The lab's current projects emphasize educational technology, multilingual NLP systems, and the integration of symbolic reasoning with neural architectures, maintaining strong industry and international academic collaborations.

Carson Leungمشاهده پروفایل

استاد

• Big Data Science
• Data Mining
• Machine Learning
• +۵ مورد دیگر

Dr. Carson Kai-Sang Leung is a Full Professor in Computer Science at the University of Manitoba's Faculty of Science. He founded and directs the Database & Data Mining Lab. His research focuses on big data science, data mining, machine learning, health informatics, and visual analytics. He holds SMIEEE and SMACM fellowships, reflecting his contributions to the field. Education: B.Sc., M.Sc., and Ph.D. from the University of British Columbia (UBC). Research interests include human-centered exploratory data mining, image databases, and scalable algorithms. He emphasizes user-driven constraints in mining processes and has developed techniques like the segment support map and OSSM for optimized frequency counting. His work on subimage queries in large image databases addresses real-world challenges in visual data retrieval. Publications span data mining, healthcare analytics, and transportation systems. His lab collaborates on projects like visual analytics for motor vehicle accidents and environmental data science for smart cities. He is affiliated with institutions such as the Institute of Industrial Mathematical Sciences (IIMS) and TRLabs. Key awards: Senior Member of IEEE (SMIEEE) and Senior Member of ACM (SMACM).

• Astronomy
• Astrophysics
• Machine Learning
• +۵ مورد دیگر

Robert Brunner serves as Professor of Astronomy at the University of Illinois at Urbana-Champaign, where he bridges astrophysical research with computational innovation. His work focuses on extracting knowledge from massive astronomical datasets through advanced statistical and machine learning techniques, while also extending methodologies to finance and agricultural applications. Research interests center on developing machine learning algorithms (random forests, deep neural networks, Bayesian estimation) for astronomical data analysis, cosmological parameter constraints via n-point clustering measurements, and hardware acceleration using GPUs/cloud systems. His interdisciplinary approach spans source classification, transient phenomena detection in surveys like SDSS and DES, and applications in financial time-series analysis and agricultural remote sensing. Recent publications (2019-2025) reveal strong cross-domain expertise: astronomical catalogs for Rubin Observatory and Spitzer surveys coexist with financial market analysis using community detection methods and agricultural computer vision systems. Key methodological threads include spatio-temporal forecasting, multimodal learning for earnings calls, and anomaly detection via extended isolation forests, demonstrating consistent innovation in handling petascale datasets across scientific boundaries.

• Machine Learning
• Artificial Intelligence
• Bayesian Statistics
• +۵ مورد دیگر

Dr Cuong Nguyen is a Lecturer in the Department of Mathematical Sciences at Durham University, specializing in Machine Learning, Artificial Intelligence, and Statistics. His research bridges theoretical foundations with practical applications, with particular expertise in Bayesian methods, transfer learning, and multimodal systems. His educational background includes a PhD in Computer Science or a related field (specific institution not mentioned in provided data), with research focusing on machine learning theory and applications. Nguyen has established himself as a researcher with publications spanning top conferences including NeurIPS, UAI, and ACM Web Conference. Research Interests: Nguyen's work centers on lifelong learning systems that overcome catastrophic forgetting, transferability estimation between tasks, and multimodal learning applications. His research integrates Bayesian principles with deep learning to create more robust and adaptable AI systems. Recent Trends: Analysis of his 15 most recent publications reveals a strong focus on practical applications of theoretical machine learning concepts, particularly in security (CAPTCHA systems), real-world problem solving (fake advertisement detection), and fundamental learning theory (transferability metrics). Dr Nguyen has made significant contributions to understanding the theoretical underpinnings of transfer learning and continual learning, with his work on LEEP providing a practical metric for transferability estimation. His research on CAPTCHA systems demonstrates both theoretical rigor and practical security implications. Advising: While specific students aren't listed in the provided data, his publications show collaborations with researchers across institutions, suggesting active supervision of PhD and Master's students. Research Groups: He is affiliated with the Statistics research center within Durham's Department of Mathematical Sciences, contributing to the university's strength in mathematical and computational research.

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۴ مورد دیگر

Rizwan Qureshi is an active researcher and academic specializing in artificial intelligence, machine learning, and their applications in medical imaging and bioinformatics. With a robust publication record spanning from 2017 to 2025, he has established himself as a significant contributor to the fields of computer vision and biomedical AI. His research interests focus on Artificial Intelligence , Machine Learning , Medical Imaging , Computer Vision , and Biomedical Engineering . Qureshi's work demonstrates particular expertise in object detection systems (especially YOLO variants), medical image segmentation, vision-language models, and applications of AI to healthcare problems including lung cancer research and diabetic retinopathy detection. Analysis of his recent publications (2023-2025) reveals a strong trend toward medical applications of AI, with approximately 60% of his work focusing on healthcare-related problems. His research shows increasing emphasis on model robustness, explainability, and handling distribution shifts in real-world applications. The publications span top venues including IEEE Access, IEEE Transactions on Medical Imaging, CVPR, and BIBM. Qureshi maintains extensive collaborations with researchers across multiple institutions, with frequent co-authorship with Hong Yan, Tanvir Alam, Jia Wu, and Sheheryar Khan. His work demonstrates both technical depth in machine learning methodologies and practical application to significant healthcare challenges. While specific details about his academic advising are not evident from the publication record alone, his numerous publications with multiple co-authors suggest active participation in research teams and likely supervision of graduate students. His work shows consistent funding support through publication in reputable journals and conferences.

• Artificial Intelligence
• Machine Vision
• Distributed Computing
• +۱۱ مورد دیگر

Dr. Dan Ionescu is a Professor at the University of Ottawa's School of Electrical Engineering and Computer Science, Department of Electrical and Computer Engineering. He has been affiliated with the university since 1985, contributing extensively to teaching and research. His career includes visiting professorships at École Nationale Supérieure de Télécommunications (Paris) and Universitat Politècnica de Catalunya (Barcelona). He founded the Machine Intelligence Research Laboratory (1988) and the Network Computing and Control Research Laboratory (NCCT, 1999), which he currently directs. His research spans Artificial Intelligence, Machine Vision, Distributed Computing, Network Control, and Formal Methods. Notable contributions include methodologies in Expert Systems, Image Processing, Temporal Logic, and Network Management. His work has received industrial and governmental grants from CITO, Nortel, NSERC, and others. Recent research focuses on Web-based collaborative platforms (e.g., UC-IC, Watch-Together), Autonomic Computing, and AI applications in gesture control and 3D IR camera systems. Dr. Ionescu’s technical leadership includes pioneering the first distributed network management platform with industry partners and designing the NCIT*net 2 network architecture. His current interests include AI-driven solutions for cybersecurity, medical imaging, and IoT-enabled disaster response systems. He has advised numerous projects in AI ethics, quantum computing, and real-time control systems. His research trends emphasize interdisciplinary innovation, combining AI with healthcare (e.g., MRI analysis, deepfake detection), cybersecurity (GAN-based intrusion detection), and IoT (Salv AIoT platform). Collaborations with IBM CAS and Diatem Networks highlight his industry-academia integration. His legacy includes over 40 years of impactful contributions to computing and engineering education.

Rada Mihalceaمشاهده پروفایل

استاد

• Natural Language Processing
• Text Mining
• Computational Linguistics
• +۵ مورد دیگر

Rada Mihalcea is a Professor at the University of Michigan in the Computer Science and Engineering department. She actively promotes Natural Language Processing (NLP) applications for social good and founded the ACL Year Round Mentorship program. University: University of Michigan School: College of Engineering Department: Computer Science and Engineering Academic Rank: Professor Research Interests include: Natural Language Processing for social good applications Text Mining and Sentiment Analysis Deception Detection and Multimodal Analysis Cross-Cultural Computational Linguistics Educational Technology and Gender Studies Recent research trends focus on: Fake news detection using multimodal features Lexicon-based cross-cultural value analysis Thermal imaging for affective computing Deception detection through linguistic and physiological cues Applications in healthcare and counseling analysis Scientific Awards : Sarah Goddard Power Award (2019) Best Paper Award at CICLING (2019) Best Paper Award at SocInfo (2018) Additional Contributions : Co-authored textbooks on Text Mining and Graph-based NLP Developed FunPython tutorial for children Collaborative work with the School of Music on the Ada Lovelace Opera Active in diversity initiatives in computing

Srishti Yadavمشاهده پروفایل

پژوهشگر ارشد

• AI and Society
• Machine Learning
• Computer Science
• +۳ مورد دیگر

Srishti Yadav is a Research Fellow at the University of Copenhagen and University of Amsterdam , affiliated with the Pioneer Centre for AI and ILLC respectively. She is advised by Dr. Serge Belongie and Dr. Ekaterina Shutova . Education: M.Sc. (Research-Track) in Computing Science, Simon Fraser University , Canada Research Interests: AI and Society Cross-Cultural Competency in Multimodal Models AI Safety and Evaluation Frameworks Model Interpretability and Dataset Creation Scientific Awards: ELLIS PhD Fellowship Advising & Community: Board Member, Women in Computer Vision (WiCV) Advisor for WiCV@ICCV2023 and WiCV@CVPR 2021 Chaired workshops at CVPR 2024, CVPR 2023, CVPR 2020 Labs & Teams: Belongie Lab (University of Copenhagen) Shutova Lab (University of Amsterdam) Collaborator at MILA Biodiversity Monitoring Project

• Natural Language Processing
• Machine Learning
• Word Sense Disambiguation
• +۵ مورد دیگر

Hiroyuki Niiro is a Professor in the Department of Information Engineering at Ibaraki University's College of Engineering, where he has served since joining as an Assistant in 1993, becoming Lecturer in 1997, Assistant Professor in 2001, and Professor in 2015. Born in Nagasaki in August 1961, he graduated from Tokyo Institute of Technology's Department of Information Science in 1985 and earned his Doctorate in Engineering from the same institution in 1997. His research focuses on Natural Language Processing using machine learning and statistics , with additional interests in image and voice pattern recognition and data mining. His work has evolved from traditional NLP techniques to contemporary transformer-based approaches, particularly specializing in Japanese language processing, word sense disambiguation, and domain adaptation methods. Niiro's recent publications (2021-2025) demonstrate his shift toward large language models, with research spanning BERT adaptations, data augmentation techniques, multimodal learning, and Japanese-specific NLP challenges. His work shows a consistent trajectory from foundational NLP research to current state-of-the-art language modeling approaches. He currently supervises graduate students including Yang Lei (D2) and Yu Onodera (M2) in his laboratory. Niiro has authored numerous books including Fine-tuning of LLM and RAG (2024), Introduction to Natural Language Processing Starting with Document Classification (2022), and multiple deep learning programming guides. His laboratory maintains active research in natural language processing, with members participating in major conferences including the Annual Conference of the Association for Natural Language Processing. Niiro has also contributed to educational resources including statistical examination preparation materials and RSS technology introductions.

Khaled R. Ahmedمشاهده پروفایل

دانشیار

• Big Data
• Deep Learning
• Parallel Computing
• +۶ مورد دیگر

Khaled R. Ahmed is an Associate Professor and Graduate Program Director at the School of Computing, Southern Illinois University. He serves as Director of the Big Data & Software Engineering Lab (BASE Lab), focusing on software engineering support for big data systems and deep learning algorithms. His research bridges computer science with practical applications in agriculture, safety, and environmental monitoring. Dr. Ahmed's research interests center on Big Data Software Engineering, Deep Learning, Parallel and Distributed Computing, and Computer Vision. His laboratory collaborates extensively with the agriculture college on precision agriculture projects, securing federal funding from USDA-NIFA for research on livestock monitoring and crop analysis. His work demonstrates a clear trajectory from foundational distributed systems research to applied deep learning solutions for real-world problems. His recent publications reveal a strong focus on agricultural applications of computer vision, including weed detection systems, livestock methane emission monitoring, and soybean growth stage analysis. The BASE Lab has developed innovative approaches using Vision Transformers, DETR, and RetinaNet for precision agriculture applications, with several papers published in top venues like CVPR and IEEE Transactions. USDA-NIFA Research Award for Detecting Subacute Ruminal Acidosis USDA-NIFA Research Award for Artificial Intelligence for Greener Livestock Illinois Innovation Network Seed Grant Outstanding Achievement Award at World Congress in Computer Science (2020) Dr. Ahmed has successfully mentored numerous graduate students, with several receiving Outstanding Thesis Awards. His BASE Lab actively recruits Ph.D. and MSc students with backgrounds in Deep Learning, Big Data, and Software Engineering. He maintains international connections through Fulbright programs, particularly with researchers from India and Egypt interested in AI and Deep Learning applications. The BASE Lab maintains strong industry and research collaborations, particularly in precision agriculture where computer vision techniques are applied to solve practical farming challenges. Current projects include real-time video analysis systems for safety applications, drone-based monitoring for natural disaster recovery, and weapon detection systems for public safety.

V. Menkovskiمشاهده پروفایل

دانشیار

• Graph Neural Networks
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Dr. V. Menkovski serves as an Associate Professor in Data Mining at Eindhoven University of Technology's Department of Mathematics and Computer Science. He also holds associate professor positions with EAISI Health and EAISI High Tech Systems, and is an ICMS Affiliated member. His work spans multiple domains of artificial intelligence and computational physics, with significant contributions to fusion energy research. Mathematics and Computer Science, Data Mining (Primary Appointment) EAISI Health (Associate Professor) EAISI High Tech Systems (Associate Professor) ICMS (Affiliated Member) Menkovski's research focuses on Graph Neural Networks, Machine Learning, Deep Learning, and their applications in diverse fields from plasma physics to metamaterials. His work demonstrates strong interdisciplinary connections, particularly between computer science and fusion energy research. He has developed novel approaches for crowd simulation, tokamak plasma monitoring, and metamaterials homogenization using advanced neural architectures. His fingerprint reveals expertise in Quality-of-Experience, Autoencoders, Neural Networks, Annotation, Graph Neural Networks, Video Streaming, Adversarial Machine Learning, and Anomaly Detection. Analysis of his recent publications (2023-2025) shows a clear trend toward applying Graph Neural Networks to complex physical systems, particularly in fusion energy research and materials science. His work increasingly integrates symmetry principles with neural architectures, as seen in his research on equivariant networks for metamaterials and symmetry-informed networks for zeolite analysis. There's also significant focus on practical applications in fake news detection, anomaly detection, and plasma state monitoring. Best Paper Award ICPM 2021 (with Sommers and Fahland) Best Paper Award of LoG 2022 (with multiple co-authors including Huang, Chen, Fang, Zhao, Yin, Pei, Mocanu, Wang, Pechenizkiy, and Liu) Menkovski teaches several advanced courses including Deep Learning, Advanced Topics in Artificial Intelligence, and Sociophysics 2, which runs through August 2025. His supervised work portfolio includes 79 projects, indicating substantial mentorship activity. He has received significant media attention for his research, including coverage by 11 news outlets, blog posts, and mentions on social media platforms. His work on 'Supervised Learning of Process Discovery Techniques Using Graph Neural Networks' was particularly noted in media coverage. His research involves collaboration with multiple institutions and teams, particularly in fusion energy research (Eurofusion Tokamak Exploitation Team, ASDEX-Upgrade team, EUROfusion MST1 Team). He works closely with researchers across disciplines, including physicists working on tokamak plasma and materials scientists studying metamaterials and zeolites.