Albert Gatt is a researcher at the University of Malta , with extensive contributions to Natural Language Generation (NLG) , Vision-and-Language (V&L) models , and evaluation practices in NLP . His work spans multimodal reasoning, data pruning efficiency, and reproducibility challenges in human evaluations. Key collaborations include studies on temporal grounding in image sequences (TempVS benchmark) and automated legal violation detection in cookie banners. Research highlights include bridging linguistic theory with computational models (e.g., VALSE benchmark for multimodal grounding) and improving generation quality through contrastive learning frameworks. Scientific awards are not explicitly mentioned in the provided texts. His work emphasizes rigor in automatic metric validation and cross-modal interpretability , particularly in multimodal model attention mechanisms and logical formula minimization for text generation.
Alessandro Palma is a researcher affiliated with Sapienza University of Rome , Italy, focusing on Cybersecurity and Information Security Governance . His work involves attack graph analysis, incident management compliance, and network vulnerability evaluation. Palma collaborates extensively with researchers like Marco Angelini, Silvia Bonomi, and Fabian J. Theis. Recent publications highlight his contributions to: Attack Graph Scalability (2025): Developing frameworks for evaluating network security. Incident Management Compliance (2024): Creating systems for quantitative process assessment. Multi-Modal Single-Cell Generation (2025): Applying machine learning to computational biology. His research integrates graph theory , visual analytics , and machine learning to address cybersecurity challenges in IoT and federated data systems. No scientific awards or student advisement information is publicly available in the provided data.
Amoon Jamzad is an Adjunct Assistant Professor at the School of Computing, Queen's University, and a postdoctoral fellow at Med-I Lab. He holds a BSc in Electrical Engineering (Electronics) from University of Tehran (2007), an MSc in Biomedical Engineering (2010), and a PhD in Biomedical Engineering (2015). His doctoral research focused on noninvasive ultrasound analysis of kidney stones. He later served as a guest lecturer and lab instructor at University of Tehran for 3 years before joining Queen's University in 2019. BSc: Electrical Engineering (Electronics), University of Tehran (2007) MSc: Biomedical Engineering, University of Tehran (2010) PhD: Biomedical Engineering, University of Tehran (2015) Dr. Jamzad's research centers on cancer cell detection, particularly in developing ultrasonic and optical spectroscopic devices for surgical margin detection. His work integrates advanced machine learning techniques with clinical applications, focusing on breast and prostate cancer surgery. He has pioneered the use of mass spectrometry imaging for cancer margin assessment and developed open-source platforms like ViPRE and MassVision for AI-driven medical data analysis. His recent publications demonstrate expertise in self-supervised learning, domain adaptation, and uncertainty quantification within medical imaging contexts. While no specific awards are mentioned, his contributions to surgical oncology tools and multi-center prostate cancer studies indicate significant clinical impact. He has also explored temporal enhanced ultrasound for composite defect detection and electrosurgical cautery state recognition through deep learning.
Christopher Pal is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. With a Ph.D. from the University of Waterloo, he has held academic positions at the University of Rochester and the University of Toronto, and industry roles at Interval Research and Microsoft Research's Interactive Visual Media Group. Fields of Expertise: Artificial Intelligence, Computer Vision, Pattern Recognition, Machine Learning, and Natural Language Processing Affiliations: CIFAR Chair in Artificial Intelligence, Institute for Data Valorization (IVADO) Member His research focuses on deep learning applications in visual question answering , medical image segmentation , and generative models . Recent work involves multimodal data analysis for climate modeling and vision-language systems for code generation. Key projects include CarbonSense for climate flux modeling and GeoCoder for geometry problem-solving AI. His 15 most recent publications (2023-2025) span topics from diffusion models to multi-agent systems , with emphasis on video generation , 3D animation , and environmental applications . Scientific recognition includes: CIFAR Chair in Artificial Intelligence IVADO Institute Membership Top-2% cited researcher (2021) He has supervised 22 Ph.D. and Master's students, with recent graduates working on generative AI , reinforcement learning , and medical imaging . Current research grants include MITACS-funded projects in software engineering agents and drone imagery analysis for tropical forest conservation.
Lucia Seminara serves as an Associate Professor in the Department of Naval, Electrical, Electronic, and Telecommunications Engineering (DITEN) at the University of Genoa's Polytechnic School. She teaches Electronic Devices, Sensors, and Sensing Systems for Master's programs in Electronic Engineering and Engineering for Natural Risk Management, while also contributing to Philosophy of Medicine for Philosophical Methodologies. As a member of the Joint Teacher-Student Commission, she bridges academic governance with pedagogical innovation in engineering education. Her research pioneers tactile sensing systems using piezoelectric polymers (PVDF) and electronic skin for robotics and prosthetics. She investigates indentation mechanics on soft electronic skin, grasping speed sensitivity, and hierarchical sensorimotor control frameworks for human-in-the-loop robotic hands. Key innovations include machine learning-based contact force estimation and electrotactile feedback systems that restore natural touch perception in prosthetic devices, addressing critical gaps in sensory substitution technology. Analysis of her 2021-2025 publications reveals escalating integration of machine learning with tactile sensing, particularly in symmetry detection for efficient haptic exploration and transdisciplinary human-in-the-loop applications. Recent work emphasizes real-world implementations like post-stroke rehabilitation systems and high-bandwidth human-machine interfaces, demonstrating a strategic shift from foundational sensor development toward clinically viable solutions with measurable user impact. Dr. Seminara's research lineage includes significant contributions to the Roboskin project (2013), which established large-area tactile sensor arrays for robotics. Her current work extends this foundation through investigations into viscoelastic properties, stress transmission modeling, and AI-driven tactile perception, positioning her at the forefront of intelligent electronic skin development with active collaborations across engineering, neuroscience, and clinical rehabilitation domains.
D. (Dimka) Karastoyanova is a Professor of Information Systems at the University of Groningen , Faculty of Science and Engineering, Computer Science Department. She leads the Information Systems Group and serves as vice-chair of the Bernoulli Institute Board since 2024. Previously, she was Head of the Computer Science Department (2020-2024) and a member of the Informatics Europe Board since 2023. Dimka's research focuses on data-driven, service-based process automation for cross-organizational collaboration, emphasizing runtime adaptability, security, and sustainability . Her work spans applications in manufacturing, logistics, healthcare, and eScience , integrating Scientific Workflows, Cloud Computing, and Middleware Systems to enhance process flexibility. Her scientific contributions include 15+ publications on topics like Choreography Lifecycle Management, BPEL Adaptation, and Cloud Migration Frameworks , reflecting her expertise in Service-Oriented Computing, Workflow Technologies, and Distributed Systems . She received the Rosalind Franklin Fellowship and has served on program committees for IEEE, ICWS, and BPM conferences . Dimka holds a PhD in Computer Science (TU Darmstadt, 2006), an MSc in Computational Engineering (University of Erlangen-Nuremberg), and MSc/BSc in Industrial Engineering (Technical University of Sofia). She previously held roles as Associate Professor of Data Science at KLU Hamburg and Senior Researcher at Hasso Plattner Institute, University of Potsdam.
Fernando Diaz is an Associate Professor at Carnegie Mellon University's Language Technologies Institute within the School of Computer Science . His research spans Information Retrieval , Recommender Systems , and the Societal Impacts of Artificial Intelligence , with a focus on fairness, ethics, and evaluation metrics. Education : PhD in Computer Science from University of Massachusetts Amherst Research Interests include: Information Retrieval (web search, crisis informatics, search latency) Recommender Systems (multi-interest personalization, cultural content recommendation) AI Fairness (exposure fairness, data minimization, bias mitigation) Evaluation Methodology (metric robustness, contextual meta-evaluation) Human-AI Collaboration (mouse behavior analysis, preference-based evaluation) Retrieval-Augmented Generation (fair ranking, model synthesis) His recent work analyzes scaling laws , tip-of-the-tongue retrieval , and multisided fairness in AI systems. Diaz also explores the cultural implications of AI in music recommendation and content curation. Teaching : Leads courses on Search Engines and LTI Colloquium Advisees : Shaily Jagat Bhatt, Athiya Deviyani, Alfredo Gomez, Jessica Huynh, To Eun Kim
Dr. W.J. (Wilson) dos Santos Silva is an Assistant Professor at the Faculty of Science , University of Utrecht, specializing in AI & Data Science and Biology . His research focuses on creating explainable and robust AI models for multimodal multi-centre medical data , with emphasis on privacy-preserving machine learning and out-of-distribution generalization . PhD in Electrical and Computer Engineering (2022), University of Porto Master's and Bachelor's in Electrical and Computer Engineering (2016), University of Porto Research Interests include: Explainable AI for medical decision-making transparency Privacy-Preserving Machine Learning in healthcare Multi-Centre Data Analysis across institutions Medical Imaging applications in oncology and neurology Recent Publications demonstrate expertise in: Medical image segmentation techniques Cross-modal learning approaches Federated learning for privacy Biomedical data interpretation Generalization in heterogeneous datasets Scientific Contributions include organizing the iMIMIC workshop at MICCAI 2024 and mentoring students receiving competitive awards. Students & Collaborators : PhD Candidates: Valentina Corbetta, Daan Boeke, Miriam Cobo, Aniek Eijpe, Jan van Eck Postdoctoral Researchers: Soufyan Lakbir Former Students: Tingyang Jiao, Laura Latorre, Filipe Campos, etc. Laboratory develops AI solutions for medical imaging , multi-centre collaboration , and ethical AI in healthcare contexts.
Annisa Puspa Kirana is a Ph.D. candidate and researcher at the Department of Geo-information Processing (ITC-GIP), Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente. She is a Lecturer in the Department of Information Technology at State Polytechnic of Malang, currently on study leave to focus on her Ph.D. research. Her work integrates Artificial Intelligence , Computer Vision , and Geospatial Analytics to analyze satellite/aerial imagery for Climate Change Mitigation , Disaster Monitoring , and Resource Management . PhD in Geo-Information Science @ University of Twente (Netherlands) Master of Computer Science @ IPB University (Indonesia) Her research emphasizes Deep Learning applications in Earth Observation, including Vision-Language Models and Agentic AI for multimodal data analysis. She collaborates with interdisciplinary teams , government agencies , and industry partners . Selected Publications Trends: Focus on AI agents , LLMs , VLMs , and Vision Transformers for geospatial and environmental applications Technical tutorials on Streamlit , TalkToEBM , and LangChain integration Conceptual breakdowns of agentic vs. agent-based systems , interpretability in AI , and prompt engineering Scientific Awards: LPDP Awardee (Indonesian Endowment Fund for Education) Microsoft Certified Educator She actively mentors students in AI/geospatial fields and advocates for open-source science and ethical AI practices in environmental decision-making. Her work bridges academic research and practical policy tools .
Seonwook Park is a Researcher at Lunit Inc. , focusing on advancing eye-tracking technology through deep learning. He earned his PhD in June 2020 from ETH Zurich 's Department of Computer Science under the supervision of Prof. Dr. Otmar Hilliges. His research bridges eye tracking and deep learning , aiming to make eye-tracking accessible in uncontrolled, everyday environments. This includes gaze estimation from monocular RGB images, real-time 3D gaze tracking, and self-learning transformations for gaze redirection. His work often intersects with computer vision and human-computer interaction , as seen in his contributions to UI adaptation via eye movement analysis. His publication record spans top conferences like NeurIPS , ECCV , and ICCV , reflecting expertise in gaze estimation , hand pose estimation , and visual SLAM . Notably, he won the Best Presentation Award at ETRA 2018 . He has supervised multiple theses, including MA and BA projects, and served as a teaching assistant for courses such as Machine Perception and Human-Computer Interaction at ETH Zurich.
Cherise Chen is an Assistant Professor in Computer Vision at the Department of Computer Science, University of Sheffield. She also holds positions as a Visiting Researcher in the Oxford BioMedIA Group at the University of Oxford and an Honorary Research Fellow at Imperial College London. As a core member of the Insigeno Institute and Shef.AI community, Dr. Chen leads research at the intersection of artificial intelligence and healthcare, focusing on translating cutting-edge AI techniques into practical medical applications. Dr. Chen's research program centers on developing robust, data-efficient machine learning algorithms for medical image analysis. Her work spans adversarial data augmentation, robust machine learning frameworks, and data-efficient learning techniques including self-supervised, few-shot, and semi-supervised approaches. She has made significant contributions to multi-task and multi-modal learning, adaptive machine learning systems, and algorithms with built-in considerations for fairness, privacy, robustness, and interpretability. Her research specifically targets clinical applications in cardiac image analysis (including segmentation, registration, and shape remodeling with quality control), prostate image analysis integrated with pathological image analysis, and brain image segmentation for clinical use cases. Analysis of Dr. Chen's recent publications reveals a strong focus on addressing the practical challenges of deploying AI in real-world medical settings. Her work on test-time adaptation methods (2023), adversarial style composition (2022), and cooperative training frameworks (2021) demonstrates her commitment to creating AI systems that maintain performance despite domain shifts and limited labeled data. She has consistently contributed to top-tier medical imaging conferences, with multiple papers accepted to MICCAI from 2018-2023, reflecting her standing in the medical imaging research community. IEEE TMI Gold-level Distinguished Reviewer Award (2022-2023) MICCAI 2023 Outstanding Reviewer Award Winner of the Fetal Tissue Annotation and Segmentation Challenge (FeTA) 2022 Winner of the Multi-sequence Cardiac MR Segmentation Challenge 2019 China National Scholarships (twice, top 0.2%) Dr. Chen actively mentors students and has delivered invited talks at prestigious institutions including Johns Hopkins University, Technical University of Munich, and the German Cancer Research Center. Her laboratory at Sheffield focuses on advancing deep medical image segmentation with particular attention to robustness, reliability, and real-world applicability in clinical workflows.
Karla Miller is a Professor of Biomedical Engineering at the University of Oxford , where she serves as Director of the Oxford Centre for Integrative Neuroimaging (OxCIN) and leads the MRI Physics Group . Her research focuses on advancing MRI technology to study tissue microstructure, brain connectivity, and neuronal health, with applications in UK Biobank harmonization and post-mortem imaging validation.
Trung Nghia Vu is a Principal Researcher at the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet in Stockholm, Sweden. His research group focuses on developing and applying statistical and bioinformatics methods for molecular biology and medicine, with particular emphasis on cancer genomics and pharmacogenomics. Dr. Vu received his PhD from the Department of Mathematics and Computer Science at the University of Antwerp, Belgium (2009-2014), following an MSc in Computer Engineering from Korea Aerospace University (2006-2008) and a BSc in Technology from Vietnam National University in Hanoi (2000-2004). His research focuses on developing statistical and bioinformatics methodologies for analyzing high-throughput omics data, with specific interests in single-cell sequencing analysis, characterization of cancer omics, and pharmacogenomics. His group has developed 20 statistical and bioinformatics tools available through Biostat Wiki Primary research areas include gene/isoform quantification, detection of abnormal RNAs, single-cell RNA-seq analysis, and drug response prediction Current projects focus on breast cancer and Acute Myeloid Leukemia (AML) Analysis of Dr. Vu's recent publications reveals a strong focus on cancer genomics, particularly in AML and breast cancer. His work bridges computational biology with clinical applications, developing methods for spatial transcriptomics, drug synergy prediction, and circular RNA analysis. The research demonstrates a clear trajectory toward precision medicine applications, with increasing emphasis on personalized drug response prediction and pathway activation modeling. Dr. Vu actively supervises multiple students and researchers, including both completed PhD students (Quang Thinh Trac, Lu Pan, Wenjiang Deng) and ongoing supervisees (Gulser Caliskan, Meghana Alugunoolla, Yuying Li, Aron Arzoomand). His research is supported by significant grants from the Swedish Research Council, CancerFonden, and Karolinska Institutet. He leads the Computational Medicine and Bioinformatics research group at Karolinska Institutet, which collaborates with international clinical and systems-biology researchers. The group maintains active development of bioinformatics tools and maintains the Biostat Wiki platform for academic use. Dr. Vu also contributes to teaching through master's and doctoral courses in biostatistics and R programming at Karolinska Institutet.
Nico Lang is an Assistant Professor at the University of Copenhagen's Department of Computer Science, associated with the Pioneer Centre for AI and Global Wetland Centre. He holds a PhD from ETH Zurich where he developed methods for global forest structure mapping. His research bridges computer vision, machine learning, and remote sensing to address environmental challenges like deforestation monitoring and climate change mitigation. Lang's research focuses on: Developing probabilistic deep learning models for global canopy height estimation Advancing open-set recognition under adversarial conditions Creating multi-modal representation learning frameworks for geospatial data Applying computer vision to biodiversity monitoring and conservation His work frequently appears in top venues like Nature, CVPR, and ECCV. His publications show strong emphasis on: environmental applications of AI, uncertainty-aware deep learning, and global-scale geospatial analysis. Recent work explores vision-language models and fine-grained open-set recognition. Awards & Honors: Culmann Prize for outstanding doctoral thesis (2023) Outstanding Reviewer for CVPR 2023 U.V. Helava Award for best paper in ISPRS Journal (2019) Associate PhD Fellow at Max Planck ETH Center (2018) Collaborations & Labs: Directs research at the intersection of computer vision and environmental science. Key affiliations include NASA GEDI mission, Swiss Federal Institute for Forest, Snow and Landscape Research, and the Pioneer Centre for AI. Organizes workshops like FGVC at CVPR and SSL4EO summer schools.
Jan G. Bjålie is a Professor and Vice Dean for Research and Innovation at the Faculty of Medicine, University of Oslo. He has led neuroinformatics development since the 1990s and held leadership roles including Head of the Department of Basic Medical Sciences (2009-2016) and Head of Infrastructure Development for EBRAINS. His research bridges neuroanatomy, computational neuroscience, and digital brain atlases. Doctor of Medicine, University of Oslo (1990) Associate Professor (1991), Professor (1997) in Basic Medical Sciences 2016-: First Leader, International Neuroinformatics Coordinating Facility 2017-: Coordinator, Human Brain Project Neuroinformatics 2019-2021: Head, International Brain Initiative Research Interests span neuroinformatics, brain architecture, and digital brain atlases. His work focuses on: Developing standardized frameworks for brain atlas utilization 3D spatial mapping of neural systems Neuroanatomical connectivity in rodent models Integration of multi-modal neuroscience data International collaboration in brain research Computational modeling of brain structure-activity relationships Recent Publications highlight advancements in automated brain imaging registration, developmental brain mapping, cross-cultural neuroscience perspectives, and genetic influences on brain composition. His neuroinformatics tools (e.g., DeepSlice, Waxholm Space, AtOM) enable global standardization of brain data analysis. Leadership Roles include: Vice Dean for Research and Innovation (2023-2026) Infrastructure Development Lead for EBRAINS (2019-) International Brain Initiative Head (2019-2021) Coordinating EU-funded Human Brain Project neuroinformatics Collaborations extend across major brain projects in the USA, Japan, and Europe through initiatives like the International Brain Initiative and ESFRI roadmap projects.
