Find Academic Supervisors

Dorsa SadighView profile

Associate Professor

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +5 more

Dorsa Sadigh is an Associate Professor of Computer Science and Electrical Engineering at Stanford University, and a Senior Fellow at the Stanford Institute for Human-Centered AI. Her work focuses on advancing robotics , particularly in areas such as human-robot collaboration , reinforcement learning , and vision-language models . She explores how robots can learn from human demonstrations, adapt to dynamic environments, and safely interact with humans in caregiving and assistive tasks. Her research interests span autonomous systems , improving robot generalization , and foundation models for robotics . Key projects include developing policies for dexterous manipulation, proactive human-robot teamwork, and scalable data collection methods. She emphasizes ethical considerations in robotics, including perceived safety and human trust. Recent work highlights include the ProVox framework for personalized collaboration, HoMeR for mobile manipulation, and Octo —an open-source generalist robot policy. Her contributions bridge theoretical advances in AI with real-world robotic applications, leveraging large language models and vision-language integration. Dr. Sadigh’s research is funded by grants from NSF, DARPA, and industry partnerships. She collaborates with interdisciplinary teams to address challenges in assistive robotics, autonomous driving, and socially intelligent AI systems.

Malcolm HeywoodView profile

Professor

• Genetic Programming
• Coevolution
• Reinforcement Learning
• +12 more

Dr. Malcolm Heywood is a Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He leads the Network Information Management and Security (NIMS) Lab and is actively involved in research on genetic programming, coevolution, reinforcement learning, and big data analytics. His research interests span: Genetic Programming and Evolutionary Computation Coevolution and Competitive Learning Problem Decomposition and Hierarchical Models Streaming Data Analysis and Anomaly Detection Network Security and Insider Threat Detection Reinforcement Learning in Games (Atari, ViZDoom, Dota 2) Dr. Heywood's recent publications focus on emergent behaviors in reinforcement learning using Tangled Program Graphs (TPG), benchmarking genetic programming for streaming data, and applications in cybersecurity and computational finance. His work demonstrates a strong trend toward scalable, efficient evolutionary models for complex, real-world problems. His scientific awards include: Silver placed at Human-Competitive (Humies) Competition (2018) Best Paper at EuroGP (2017) Best Paper at DETA track, ACM GECCO (2017) Best Paper at RWA track, ACM GECCO (2018) Nomination for Best Paper at DETA track, ACM GECCO (2019) He has supervised numerous graduate students, including PhD and Master's candidates, many of whom have continued research in evolutionary computation. His lab has developed open-source code distributions for Tangled Program Graphs and Symbiotic Bid-Based GP. Dr. Heywood teaches courses in Computer Organization, Introduction to AI with Gaming Applications, and Genetic Algorithms and Programming.

• Non-Destructive Evaluation
• Ultrasonic Testing and Imaging
• Eddy Current Testing
• +9 more

Dr. Ehsan Mohseni is a Senior Lecturer in the Department of Electronics and Electrical Engineering at the University of Strathclyde, Faculty of Engineering. He is a key member of the Centre of Ultrasound Engineering (CUE) research group and supports the Royal Academy of Engineering and Spirit AeroSystems research chair led by Professor Gareth Pierce. His work focuses on advancing robotic and intelligent Non-Destructive Evaluation (NDE) systems for industrial applications. B.Sc. in Materials Science and Metallurgical Engineering, University of Tehran, 2006 M.Sc. in Metal Forming Processes, University of Tehran Ph.D. in Automated Defect Detection using Electromagnetic NDE, École de Technologie Supérieure (ETS), Montreal, Canada Dr. Mohseni’s research is centered on NDE 4.0, integrating advanced sensing, multi-physics modeling, and machine learning. His expertise spans ultrasonic and eddy current testing, multi-sensor data fusion, and probability of detection studies. He focuses on applications in additive manufacturing, welding, composites, and metal processing, aiming to overcome current technological barriers in industrial inspection. The recent publications highlight a strong trend toward intelligent, automated, and robotic NDE systems. Key themes include self-supervised learning for ultrasonic segmentation, human-machine collaboration in data analysis, and advanced signal processing for weld and composite inspection. The integration of AI, flexible sensor arrays, and embedded navigation systems reflects a shift toward smart, adaptive inspection platforms aligned with Industry 4.0. Scientific Awards: The BINDT Annual Conference Award (2019) Dr. Mohseni is actively involved in research funding and knowledge transfer. He serves as Principal Investigator on KTP projects with ETHER NDE LIMITED and NATIONAL OILWELL VARCO UK LIMITED, focusing on in-process inspection for additive manufacturing and field calibration for ultrasonic testing. He contributes to large-scale collaborative projects funded by Innovate UK and industry partners, emphasizing practical deployment of NDE solutions. He also supervises research staff and collaborates with global aerospace firms including Pratt & Whitney Canada, Safran, and Bell Helicopter. He is a core member of the Centre of Ultrasound Engineering (CUE), a dynamic research group developing next-generation ultrasound technologies. The team works on advanced robotic sensing hubs, flexible transducer arrays, and AI-driven data interpretation tools, often in collaboration with the Royal Academy of Engineering research chair and industrial partners.

Doeun ChoeView profile

Assistant Professor

• Artificial Intelligence
• Deep Learning
• Probabilistic Modeling
• +5 more

Doeun Choe is an Assistant Professor in the Department of Civil Engineering at New Mexico State University, where he has been serving since January 2021. His expertise spans the intersection of civil engineering, artificial intelligence, and structural reliability with applications to critical infrastructure systems. Education: Ph.D. in Civil Engineering/Structures (2007, Texas A&M University) M.S. in Architectural Engineering/Structural Engineering (2002, Inha University, South Korea) B.S. in Architectural Engineering (2000, Inha University, South Korea) Research Focus: Dr. Choe's work integrates Artificial Intelligence methodologies with traditional structural engineering to solve complex infrastructure challenges. His research in Probabilistic Modeling & Structural Reliability addresses corrosion effects on bridges and coastal structures, while his recent work focuses on Deep Learning applications for structural health monitoring of offshore wind turbines. The AISSRR research group he leads develops innovative approaches to enhance infrastructure resilience against extreme environmental conditions including seismic events and climate change impacts. Publication Trends: Dr. Choe's scholarly output demonstrates an evolving research trajectory from foundational reliability analysis (2008-2009 publications on corrosion effects) to advanced AI applications (2019-2021 work on deep learning for structural monitoring). His publications consistently address structural safety challenges through computational methods, with increasing emphasis on renewable energy infrastructure systems in recent years. The research spans civil engineering, computer science, and materials science disciplines. Academic Leadership: Dr. Choe leads the Artificial Intelligence for Structural Safety, Risk, & Reliability (AISSRR) research group at NMSU and has developed specialized coursework including the 'Artificial Intelligence for Civil Engineers' series (Machine Learning in Fall 2023 and Deep Learning in Spring 2024), which produces student projects applying AI techniques to real-world civil engineering problems.

• Artificial Intelligence
• Computer Science
• Swarm Robotics
• +5 more

Ramón Luis Rizo Aldeguer is a University Professor in the Department of Computer Science and Artificial Intelligence at the Higher Polytechnic School of the University of Alicante. He has held this position since 1996 and continues to be actively involved in teaching and research as recently as 2025. He previously served in various leadership roles including Director of the Department of Computer Science and Artificial Intelligence (1997-2004) and Deputy Director of the Institutional Projects Area at the University of Alicante (2012-2020). His educational background includes a PhD in Computer Science from the Polytechnic University of Valencia (1992) and a degree in Mathematics from the University of Valencia (1977). He has been a member of the Spanish Association for Artificial Intelligence since 1990 and has held leadership positions within the organization. Rizo Aldeguer's research focuses on artificial intelligence with particular emphasis on swarm robotics, UAV deployment, and deep reinforcement learning. His work bridges theoretical foundations with practical applications in robotics and autonomous systems. He has made significant contributions to educational methodologies, particularly in integrating computational tools into engineering education. His publication record shows a consistent trajectory in swarm intelligence and robotics, with recent publications (2018-2023) demonstrating increasing sophistication in applying deep reinforcement learning to complex multi-agent systems. His research spans both theoretical advancements and practical implementations in robotics and autonomous systems. Fifteen five-year research periods (trienios) Six teaching merit periods Five six-year research periods (sexenios) President of Organizing Committee of VI Conference of Spanish Association for Artificial Intelligence (1995) President of Scientific Committee of CAEPIA (1999) Rizo Aldeguer has supervised 14 doctoral theses, with many receiving the highest honors (SOBRESALIENTE CUM-LAUDE). He has participated as a researcher in over 30 competitive public research projects, serving as principal investigator in 12 of them. His educational projects include innovative teaching methods and the development of computational tools for engineering education. He has been instrumental in the design and implementation of computer science programs at both the University of Alicante and the Polytechnic University of Valencia. He is a founding member of the University Institute for Computer Research and directed the Industrial Computing and Artificial Intelligence research group from 1992 to 2004. His current research continues to focus on swarm robotics and intelligent systems, with active participation in the Valencian Graduate School and Research Network of Artificial Intelligence since 2021.

Kirill DjebkoView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Optimization Algorithms
• +4 more

Kirill Djebko is a researcher at the Chair of Computer Science VI - Artificial Intelligence and Knowledge Systems within the Institute of Computer Science, Faculty of Mathematics and Computer Science at the University of Würzburg . His work spans AI-based optimization systems , autonomous spacecraft operations , and deep reinforcement learning applications. Doctorate completed in 2020 Active in research since 2015 Key projects: KINERGY (heating system optimization), VeriKI (AI verification for space), LeLaR (AI attitude control for nanosatellites) Research focuses on metaheuristics , genetic algorithms , regression algorithms , and deep reinforcement learning for practical applications. His work integrates digital twin modeling , decision support systems , and AI robustness in industrial and space contexts. Recent publications (2024-2016) analyze heating system optimization , spacecraft fault detection , and autonomous nanosatellite operations . These studies emphasize sim-to-real-world transfer , data-driven calibration , and AI quality assurance in complex environments. Current affiliations include the Center for Artificial Intelligence and Data Science (CAIDAS) . Contact details: Email - kirill.djebko@uni-wuerzburg.de | Phone +49 931 31-86405 | Room 03.010, Emil-Fischer-Straße 50, Würzburg.

• Network Security and Privacy
• Open-RAN Security and Privacy
• Green Networking
• +2 more

Mohammad Shojafar (M'17-SM'19) is an Associate Professor at the Institute for Communication Systems within the Faculty of Engineering and Physical Sciences at the University of Surrey , UK. He has secured over £1.9M in research funding as Principal Investigator for projects like ORAN-TWIN (EPSRC), PRISENODE (MSCA-IF), TRACE-V2X (MSCA-SE), and D-XPERT (Innovate UK), among others. Previously held positions include Senior Researcher at University of Toronto and Toronto Metropolitan University, Senior Researcher at Italian universities (Telecom Italia Mobile), and Postdoc at University of Padua Key affiliations: Associate Editor for IEEE Transactions on Network and Service Management, Intelligent Transportation Systems, Green Communications and Networking, and Consumer Electronics Magazine Research Specialism: 5G/6G Security and Privacy Open-RAN Security Green Networking Adversarial Machine Learning Applied Cryptography Publication Trends: Focus on Open RAN security challenges (bearer context migration poisoning, KPI poisoning attacks), IoT/Fog security (GAN-based attacks, distributed intrusion detection), Lightweight Cryptography (multi-signature protocols, authentication schemes), and AI-driven Network Optimization (federated learning, reinforcement learning applications). Recent work addresses security in vehicular networks, smart grids, and video streaming frameworks. Scientific Recognition: Marie Curie Individual Fellowship (MSCA-GF-IF) Intel Innovator ACM Professional Member Sustainability Fellow at Institute for Sustainability IEEE Senior Member Supervision: Currently supervising 6 PhD students and has graduated 5 PhD/MSc students since 2021. Active in 5G/Open RAN security research with over 20 related publications since 2022.

Xia NingView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +7 more

Xia Ning is a Professor jointly appointed in the Department of Computer Science and Engineering , the Division of Medicinal Chemistry and Pharmacognosy (College of Pharmacy), and the Department of Biomedical Informatics at The Ohio State University . She also holds affiliation with the Translational Data Analytics Institute at OSU. Education: Ph.D. in Computer Science & Engineering, University of Minnesota, Twin Cities (2012) M.S. in Computer Science, University of Minnesota, Twin Cities M.S. in Statistics, University of Minnesota, Twin Cities B.S. in Computer Science, Chu Kechen Honors College, Zhejiang University, China Research Focus : The Ning Lab pioneers data-driven Artificial Intelligence, Machine Learning, and Big-Data analytics with targeted applications in drug discovery, medical informatics, health informatics, and e-commerce . Recent thrusts include generative AI for molecule design, graph neural networks for retrosynthesis, large-language models specialized for chemistry (LlaSMol) and e-commerce (eCeLLM), and reinforcement-learning frameworks for precision-medicine drug selection. The lab’s methodologies are intentionally generalizable, enabling spill-over benefits to domains such as social networks and system monitoring. Publication Trends : Over the past four years Professor Ning has released a steady stream of high-impact articles spanning retrosynthesis planning, LLM instruction tuning for scientific domains, reinforcement learning for drug discovery, and COVID-19 health-analytics . These works repeatedly integrate cutting-edge AI techniques (deep RL, graph Transformers, large-scale instruction datasets) with rigorous experimental validation in chemistry and biomedicine. Scientific Awards & Honors : Sanofi iDEA-TECH Award (2024) 10-Year Highest-Impact Award, International Conference on Data Mining (ICDM, 2020) Grants & Collaborations : Funding includes the Sanofi iDEA-TECH Award and collaborative grants with Amazon Web Services for COVID-19 knowledge graphs. Her open-source datasets (ECInstruct, SMolInstruct, CTKG) and models (G2Retro, LlaSMol, eCeLLM) are publicly released on HuggingFace and GitHub, fostering broad academic and industrial adoption. Labs & Teams : Professor Ning heads the Ning Lab at OSU, a multidisciplinary team focusing on AI/ML methodology and translational applications in health and medicine. The lab actively releases code and interactive web portals to accompany each major publication.

Thilo StadelmannView profile

Professor

• Artificial Intelligence
• Machine Learning
• Representation Learning
• +3 more

Thilo Stadelmann is the Founding Director of the Centre for Artificial Intelligence at the Zurich University of Applied Sciences (ZHAW) . A computer scientist by training, he earned his Doctor of Science degree from Marburg University, Germany, and has held engineering and leadership roles in the automotive industry before transitioning to academia. His research interests lie at the intersection of representation learning and the societal implications of artificial intelligence . He is particularly focused on understanding how AI systems can be designed to enhance human capabilities while addressing ethical concerns and societal challenges. Stadelmann is a prolific speaker and educator, delivering TEDx talks and lectures on topics such as "How Not to Fear AI" , "AI vs Human: Understanding the Fundamental Differences" , and "Decoding AI Fear: The Philosophy Behind It" . His work emphasizes the importance of demystifying AI and fostering a balanced perspective on its potential and limitations. His recent publications span a wide range of AI applications, from safety-critical network infrastructures and medical imaging to industrial process control and AI governance . Notable works include studies on AI risk assessment for public policy, document recognition, and the societal impact of AI technologies. Beyond his academic role, Stadelmann is actively involved in the digital ecosystem as a (co-)founder and senior leader in several organizations, bridging the gap between research and practical implementation in the AI space.

• Natural Language Processing
• Information Extraction
• Deep Learning
• +4 more

Thien Huu Nguyen is an Associate Professor in the Department of Computer Science at the University of Oregon, part of the College of Arts and Sciences. His research focuses on advancing natural language processing through deep learning approaches, with particular emphasis on multilingual capabilities and information extraction systems. Dr. Nguyen earned his B.S. in Computer Science from Hanoi University of Science and Technology, followed by M.S. and Ph.D. degrees in Computer Science from New York University, where he worked with Professors Ralph Grishman and Kyunghyun Cho. He completed postdoctoral research at the University of Montréal with Professor Yoshua Bengio at the Montreal Institute for Learning Algorithms. His research explores mechanisms to understand human languages for computers so that they can perform cognitive language-related tasks. He is especially interested in distilling structured information and mining useful knowledge from massive and multilingual human-written text across various domains. His lab employs and designs effective learning algorithms for information extraction and text mining, with current focus on deep learning approaches. They are among the first groups to develop deep learning models demonstrating effectiveness for information extraction, while also targeting other language-related problems including reading comprehension, machine translation, natural language generation, chatbots, and language grounding. Dr. Nguyen's research has resulted in significant contributions to multilingual NLP, including the development of Vistral (a state-of-the-art conversational LLM for Vietnamese), CulturaX (a massive multilingual dataset with 6.3 trillion tokens in 167 languages), and the Okapi framework for evaluating multilingual LLMs in 26 languages. His work has been adopted by major organizations including Stability AI for training their state-of-the-art multilingual language models. NSF CAREER Award (2023) AI 2000 Most Influential Scholar Honorable Mention in NLP (2022) EACL 2021 Best Demo Paper Award EACL 2021 Outstanding Demo Paper Award IBM Ph.D. Fellowship (2016) Dr. Nguyen actively mentors students, currently supervising four PhD students (Minh Nguyen, Nghia Ngo, Hieu Man, and Chien Nguyen) and has successfully guided numerous alumni to positions at leading tech companies and academic institutions. His research has been supported by multiple grants including funding from NSF, IARPA, and Adobe Research. He leads the UO-NLP research group which has developed influential software including FourIE (a neural information extraction system), Trankit (a multilingual NLP toolkit), and FAMIE (a multilingual active learning framework).

• Biometrics
• Machine Learning
• Deep Learning
• +5 more

Raghavendra Ramachandra is a Professor at the Department of Information Security and Communication Technology (IIK) , within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU) in Gjøvik. His research focuses on biometric systems, particularly in face, fingerprint, and finger vein recognition, with emphasis on presentation attack detection, morphing attack detection, and deep learning applications. Current research projects include: SALT (2022-2026) : Developing privacy-preserving facial biometric authentication systems. OffPAD (2022-2025) : Creating cryptographic tools and presentation attack detection for fingerprint biometrics. SWAN (2015-2020) : Developing biometric countermeasures against presentation attacks. His recent publications demonstrate technical expertise in: Face morphing attack detection using vision transformers and point cloud networks Image fusion techniques for multispectral biometrics GAN-based synthetic data generation for security evaluation Explainable AI approaches for biometric verification Professor Ramachandra also supervises PhD and Master’s students, and has extensive experience in leading national and EU research initiatives.

Rui NingView profile

Assistant Professor

• Cybersecurity
• Privacy-preserved AI
• Secure AI systems
• +3 more

Rui Ning is an active Assistant Professor in the Department of Computer Science at Old Dominion University (ODU), within the Batten College of Engineering & Technology. His academic journey includes a B.S. in Computer Science & Engineering from Lanzhou University (China), an M.S. in Computer Science from the University of Louisiana at Lafayette, and a Ph.D. in Electrical & Computer Engineering from ODU. Dr. Ning's research focuses on cybersecurity, privacy-preserved AI, and secure AI systems, with particular emphasis on backdoor detection in neural networks, federated learning security, and privacy-preserving deep learning. His work bridges theoretical security mechanisms with practical implementations in real-world AI systems, addressing critical vulnerabilities in modern machine learning frameworks. Analysis of his publication trends reveals a strong focus on adversarial machine learning, with increasing attention to multimodal AI security since 2022. His research shows consistent growth in addressing sophisticated attack vectors while developing practical defense mechanisms applicable to industry settings. Notably, his work spans both theoretical contributions and practical implementations, often achieving high acceptance rates at top-tier conferences. Mark Weiser Best Paper Award, IEEE PERCOM, 2018 Best In-session Presentation Award, IEEE INFOCOM, 2019 NSF CRII Award, 2022 Ph.D. Researcher of the Year, ODU ECE, 2019 Dr. Ning actively mentors graduate students, currently supervising multiple Ph.D. candidates and an M.S. student at ODU. His grant portfolio demonstrates significant research impact, with over $1.5 million in funding as PI or Co-PI from sources including NSF, DoD, NSA, and industry partners like Interdigital. His research addresses critical challenges in AI security with practical applications for cybersecurity infrastructure. Dr. Ning also contributes substantially to academic service as a reviewer for top conferences and journals, and serves on program committees for major AI and security venues.

Richard NaudView profile

Assistant Professor

• Computational Neuroscience
• Neural Coding
• Statistical Methods in Neuroscience
• +5 more

Richard Naud is an Assistant Professor in the Department of Cellular and Molecular Medicine at the Faculty of Medicine, University of Ottawa, with a cross-appointment in Physics at the Faculty of Science. He holds dual research positions at the Center for Neural Dynamics (CND) and Brain and Mind Research Institute (BMRI), focusing on computational approaches to decode neural signaling mechanisms and develop brain-machine interfaces. His educational background includes: PhD in Neuroscience from École Polytechnique Fédérale de Lausanne (EPFL), 2011 MSc in Physics from McGill University, 2006 BSc in Physics from McGill University, 2004 Dr. Naud's research investigates how neurons encode information through spikes, bursts, and silences using mathematical models and statistical analysis of electrophysiology data. His lab develops computational protocols for synaptic dynamics analysis, studies dendritic computation in neurological diseases, and creates neuromorphic algorithms for spiking neural networks. Current work emphasizes serotonin system dynamics, burst coding mechanisms, and neural network simulations for demyelinating conditions. Analysis of his recent publications reveals three dominant research vectors: (1) Burst coding as an independent information channel beyond firing rates, (2) Serotonin-mediated value coding in decision systems, and (3) Neuromorphic implementation of biologically plausible learning rules. His work bridges theoretical neuroscience with clinical applications in stroke recovery and neurological disorders. Dr. Naud leads the Neural Coding Lab, which actively recruits postdoctoral fellows, graduate students, and undergraduates for projects in neural coding theory, computational psychiatry, and neuromorphic engineering. The lab maintains collaborations with experimental neuroscience groups for model validation and develops open-source tools like SRPlasticity for synaptic dynamics analysis.

Luis Miguel BergasaView profile

Professor

• Perception Systems
• Autonomous Vehicles
• Deep Learning
• +4 more

Luis Miguel Bergasa is a Full Professor at the Department of Electronics, University of Alcalá (UAH), with a career spanning over 25 years. He leads the RobeSafe Lab (since 2010) and serves as Director of Digital Transformation at UAH (since 2022). His academic roles include heading the Department of Electronics (2004–2010) and coordinating multiple educational programs. He teaches Perception Systems (Master in Industrial Engineering) Intelligent Control Systems (Computer Science) Computer Vision (Computer Science) His research focuses on Perception Systems for Intelligent Vehicles , emphasizing driver behavior analysis, scene understanding, and sensor fusion via deep learning. He has authored over 300 papers and holds 9 patents. Notable recognitions include being ranked #81 in Computer Science (Spain) by Research.com (2025) and receiving 30+ awards in Robotics/Automotive fields. Recent publications highlight advancements in Transformer-based driver action recognition (2025) Infrastructure-vehicle cooperative frameworks (2025) 3D semantic segmentation for autonomous perception (2024) Simulation-to-reality gap bridging (2024) Scientific Leadership Senior Editor, IEEE Transactions on ITS (2025) International Program Committee roles in 15+ conferences (2024–2025) Co-founder of Vision Safety Technologies Ltd (2009–2016) He supervises 9 active PhD students and has advised 14 former PhD candidates. His group collaborates with institutions in Germany, USA, China, and Spain, including KIT, UC Berkeley, and Northwestern Polytechnic University.

Silvia CascianelliView profile

Researcher

• Computer Vision
• Document Analysis
• Handwritten Text Recognition
• +4 more

Silvia Cascianelli is an AI and Computer Vision Researcher at the University of Modena and Reggio Emilia (UNIMORE). She actively contributes to the computer vision and document analysis communities through research, conference organization, and academic mentorship. She serves as Area Chair for major computer vision conferences including CVPR2025, BMVC2025, and ECCV2024, demonstrating her standing in the field. Her research focuses on several key areas within computer vision and document analysis: Image Generation : Developing efficient and lightweight methods for image generation with desired characteristics, particularly using diffusion models Handwriting Imitation : Creating algorithms for generating images of text with specific content and handwriting styles, along with evaluation methods Document Understanding : Extracting information from 2D and 3D document images, ranging from modern documents to historical artifacts like carbonized Roman papyri Dr. Cascianelli's work shows a clear progression toward more sophisticated generative models and evaluation frameworks, with recent publications focusing on diffusion models for handwritten text generation, efficient token reduction for multimodal tasks, and innovative approaches to historical document analysis. Her research bridges theoretical advancements with practical applications across diverse document types. Her scientific contributions have been recognized through invitations to serve as Area Chair for top-tier computer vision conferences (CVPR, ECCV, BMVC) and opportunities to organize specialized workshops including VisionDocs at ICCV, AI4DH at ECCV, and ADAPDA at ICDAR. Area Chair at CVPR2025 Area Chair at BMVC2025 Area Chair at ECCV2024 Organizer of VisionDocs Workshop at ICCV2025 Organizer of AI for Digital Humanities Workshop at ECCV2024 Organizer of ADAPDA Workshop at ICDAR2024 Dr. Cascianelli actively mentors the next generation of researchers: Vittorio Pippi - PhD Student at UniMoRe (National PhD program in AI) Fabio Quattrini - PhD Student at UniMoRe (ICT program) Carmine Zaccagnino - Research Intern at UniMoRe (formerly MSc student) Kostantina Nikolaidou - PhD Student at Luleå University of Technology Pau Torras Coloma - PhD Student at Computer Vision Center, Universitat Autònoma de Barcelona Bram Vanherle - CV Engineer at Colruyt Group Smart Innovation (formerly PhD student) She is actively involved in several research initiatives including the AI Governance Lab where she serves as a lecturer, and collaborates with institutions worldwide. Her current projects focus on advancing diffusion models for image generation, improving handwritten text recognition systems, and developing novel methods for document understanding across historical and contemporary contexts.