Tsvetomila Mihaylova is a Postdoctoral Researcher in the Department of Computer Science at Aalto University , specializing in machine learning and human-robot interaction. Her work bridges theoretical advancements in neural networks with practical applications in autonomous systems. Fields of Interest : Latent structure learning, autonomous driving, visual-language models, and natural language processing Email : tsvetomila.mihaylova@aalto.fi Research Focus : • Latent structure modeling using discrete and undirected neural networks • Autonomous driving safety through conflict simulation and trajectory prediction • Cross-modal integration in robotic vision-language systems Publication Trends : Recent work explores 2025 in structured neural architectures for autonomous vehicles and 2024 in human-robot interaction quality evaluation, with foundational contributions to natural language processing fact-checking systems since 2019 .
Jon Yngve Hardeberg is a Professor at the Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU). He is a member of the Norwegian Colour and Visual Computing Laboratory, where he supervises students, manages international programs, and leads research projects. His career spans roles at Gjøvik University College and NTNU, with visiting positions at institutions like the University of Washington and French universities. MSc in Signal Processing from NTNU PhD in Signal and Image Processing from École Nationale Supérieure des Télécommunications, Paris Research Interests: Multispectral colour imaging, cultural heritage documentation, medical imaging, and material appearance modeling. His work integrates optics, signal processing, and computational methods for applications like gilded surface analysis, 3D printing, and translucency perception. Recent publications focus on advanced imaging techniques (e.g., spectral reconstruction, reflectance transformation imaging), material analysis (pigment mapping, skin gloss), and 3D digitization. Keywords span Computer Science , Optics , and Medical Imaging , with subfields like Hyperspectral Imaging and Image Quality Metrics . Scientific Awards: Fulbright Scholar SPIE Senior Member Advising and Grants: Supervised 46 master students and 12 PhD graduates; currently guides 4 postdocs and 5 PhD candidates. Led major projects such as MUVApp (NOK 24.9M), IQ-MED (NOK 16.2M), and HyPerCept (NOK 33.6M), funded by the Research Council of Norway and EU. Labs/Teams: Director of The Norwegian Color Research Laboratory (2005-2012), member of the International Commission on Illumination (CIE) Division 8, and founder of Forum Farge (Norway’s color association). Collaborates with institutions like the Swiss National Museum and Edvard Munch Museum.
Peter Troxler is a Professor in Future of Working at Hogeschool Rotterdam (Rotterdam University of Applied Sciences), where he approaches the topic from both digital and business perspectives. He has been active at the Creating 010 knowledge center since 2012 and is involved in the AI-Translator master's program. His work bridges technological innovation with human-centered approaches to organizational design, focusing on how digital technologies can transform work while improving quality of life and working conditions. Professor Troxler's research centers on three key dimensions: Digital Transformation & People-Centered Working, Technology Implementation and Management, and Business Architecture for Future Work. He investigates how organizations can implement emerging technologies while ensuring and improving the quality of work, how socio-technical systems can be effectively managed from a collaborative paradigm, and how organizations can optimize technological capabilities while maintaining balance between social, technological, and commercial aspects. His work emphasizes networked collaboration paradigms and business models built on lateral governance and open-source principles, with a strong commitment to ensuring technological advancements serve human needs rather than the reverse. His extensive publication record spanning over 15 years reveals a consistent evolution from early explorations of open hardware and digital fabrication toward increasingly sophisticated examinations of equitable access, sustainable production models, and the social implications of technological change. Recent publications (2023-2024) demonstrate growing attention to boundary objects in collaborative spaces, the self-production economy, and the integration of digital and physical spaces for community-based innovation. His work spans theoretical frameworks for understanding networked collaboration and practical guides for implementing maker spaces in libraries and educational settings. As a key contributor to the Creating 010 knowledge center, Professor Troxler has led numerous collaborative projects including DESTRESS, NADR 3, Reading between the Lines, Makerlab, and Stadslab. These initiatives address real-world challenges in healthcare, urban development, retail, and education through applied design research. His work consistently bridges theoretical frameworks with practical applications, emphasizing community-based approaches to innovation and the development of business models that prioritize social value alongside commercial viability. He has particular expertise in understanding how new patterns in industry impact personnel requirements and production locations, with significant implications for urban development and spatial planning.
Marco Maggesi is an Associate Professor at the University of Florence, affiliated with the Department of Mathematics and Computer Science 'Ulisse Dini'. His research focuses on the formal representation of mathematical and computational systems using proof assistants like HOL Light and UniMath. Research interests span mathematical logic, formal methods, computer science, theorem proving, and category theory, with applications in complex systems, quaternionic analysis, and type theory. His work emphasizes rigorous verification of emergent behaviors in adaptive systems and foundational computational structures. Publication trends show consistent contributions to formal verification (e.g., provability logic, universal algebra), interdisciplinary applications (e.g., IoT, blockchain), and educational outreach (e.g., visual machine learning guides). Research is frequently supported by national projects such as MIUR PRIN grants.
Mykola Shykula serves as a Lecturer at KTH Royal Institute of Technology within the unit of Probability, Mathematical Physics and Statistics. His academic base is at Lindstedtsvägen 25 in Stockholm, with direct contact via email (shykula@kth.se) and telephone (+46 8 790 66 44). His research spans core theoretical and applied domains: Probability theory foundations Mathematical statistics methodologies Machine learning algorithm integration Mathematical physics applications Shykula actively teaches and administers key courses including Applied Statistics (SF1910), multiple Probability Theory and Statistics iterations (SF1914-SF1923), and specialized instruction in Probability Theory with Machine Learning applications (SF1935). He frequently holds dual roles as examiner and course responsible, demonstrating significant curricular leadership. Additionally, he supervises second-cycle Degree Projects in Mathematical Statistics (SF290X), guiding Master's thesis research in statistical methodology.
Dr. Michael Behrisch is an Associate Professor for Visual Analytics in the Visualization and Graphics Group at Utrecht University's Department of Information and Computing Sciences. With a PhD from University of Konstanz, his career includes postdoctoral work at Harvard and Tufts Universities, and over six years as a research associate at Konstanz. Specializes in matrix-based representations for relational data Focuses on cognitive load reduction in visual analytics Develops interactive systems for pattern discovery Research Highlights: Combines algorithmic approaches with user-centric visualization techniques to address challenges in large-scale, multivariate, and dynamic datasets. Research themes include: Automated pattern quantification Matrix reordering algorithms Explainable AI integration Game research applications Scientific Contributions: Recognized through 61 publications and multiple awards, including the EuroVA 2022 Best Paper and IEEE VAST 2018 Honorable Mention. His work bridges theoretical research with practical applications across life sciences, network analysis, and big data domains.
Minghan Chen is an Associate Professor in the Computer Science Department at Wake Forest University and a Z. Smith Reynolds Foundation Faculty Fellow. Her research bridges computational methods with biological applications, focusing on developing innovative algorithms for understanding complex biological systems and diseases. Education: PhD from Virginia Tech (2019) Dr. Chen's research interests center on multiscale modeling, knowledge-guided machine learning, and parameter optimization algorithms specifically designed for bio-related applications. Her work particularly focuses on Alzheimer's disease progression, where she develops computational frameworks to model the spatiotemporal dynamics of neuropathological events. She integrates techniques from computational biology, bioinformatics, and machine learning to create models that can simulate and predict disease progression at multiple scales. Her recent publications demonstrate a strong trend toward increasingly sophisticated computational approaches to Alzheimer's disease research, with particular emphasis on brain network analysis, single-cell genomics, and multimodal data integration. Her work combines graph theory, deep learning, and multiscale modeling to create comprehensive frameworks that capture the complexity of neurodegenerative processes. Awards and Recognition: Z. Smith Reynolds Foundation Faculty Fellow Dr. Chen actively mentors students in her research group, currently recruiting both graduate and undergraduate students interested in computational biology and machine learning. She has successfully guided students to recognition, including Jingwen who received the best poster award at ACM-BCB in 2022. Her research is supported by stipend funding for students engaged in rigorous, publication-quality research. Dr. Chen has organized significant academic events including the Biological Modeling and Mining workshop (BMM, 2021) and the Virtual Deep Learning Bootcamp (May 2022), demonstrating her commitment to advancing computational methods in biological sciences.
Anil Aswani serves as an Associate Professor and Head Undergraduate Advisor in the Department of Industrial Engineering and Operations Research (IEOR) at the University of California, Berkeley's College of Engineering. His work centers on developing statistical and optimization techniques for big data to model human behavior in complex systems, enabling better system design and management across healthcare, energy, and social domains. Education: Ph.D. in Electrical Engineering and Computer Sciences, UC Berkeley (2010) Research interests include operations research, machine learning, optimization, and statistical modeling, with applications in healthcare systems (e.g., personalized disease management, mechanical ventilation), energy systems (e.g., EV charging, HVAC), and human behavior analytics. His methods integrate causal inference, reinforcement learning, and tensor completion to address real-world challenges in resource allocation and system optimization. Analysis of recent publications reveals dominant trends in applying reinforcement learning to healthcare (mechanical ventilation, disease management), contract design for end-of-life care and cybersecurity, and fair decision-making frameworks. Key methodological themes include off-policy evaluation, tensor completion for high-dimensional data, and optimization under uncertainty across dynamic systems. Scientific Awards: NSF CAREER Award (2019) for "Data-Driven Personalized Chronic Disease Management" As Head Undergraduate Advisor, Aswani guides academic planning and curriculum development for IEOR students. His research is primarily funded by the NSF CAREER award, focusing on data-driven healthcare optimization, with additional support for interdisciplinary projects like food assistance program analysis and medical data privacy. Collaborations span public health, medicine, and engineering domains. His interdisciplinary team bridges operations research, computer science, and domain-specific applications, evidenced by joint studies on nutritional assistance programs, NICU admissions prediction, and step-tracker re-identification. Current work emphasizes scalable methods for personalized interventions in complex socio-technical systems.
Elie Adam, Ph.D., is an Investigator and Instructor at the Massachusetts General Hospital (MGH) and a member of the Faculty of Anaesthesia at Harvard Medical School (HMS) . His research spans neuroscience, bioengineering, pharmacology, mathematics , and hibernation , focusing on neurometabolic mechanisms and artificial hibernation for neuroprotection. Education: Ph.D. in Electrical Engineering and Computer Science (MIT, 2017). His work combines biophysical modeling, in-vivo experiments , and systems theory to study brain dynamics under anesthesia, develop miniature NMR technology for brain energetics, and derive adaptive systems principles from hibernation. Recent publications emphasize neuropharmacology, neural oscillations , and critical care applications . Contact: eadam@mgh.harvard.edu
Julian McAuley is a Professor in the Department of Computer Science at the University of California, San Diego (UCSD). His research bridges machine learning, natural language processing, and computer music, with a focus on generative models, recommender systems, and multimodal learning. He leads a lab that has produced influential datasets and frameworks for recommendation tasks. Primary Affiliation: UCSD, Department of Computer Science Research Themes: Generative AI, Recommender Systems, Music-Cognition Interfaces, Multimodal Learning McAuley's work explores the intersection of large language models (LLMs) with sequential recommendation, causal inference, and creative applications in music generation. His lab develops novel architectures like CoMMIT (multimodal instruction tuning) and SAND (LLM agent deliberation), while also advancing ethical AI through normative alignment techniques. Recent publications highlight trends in code-augmented reasoning , symbolic music processing , and contextual preference optimization . Notable applications include video-guided music synthesis, Explainable Chain-of-Thought systems, and tools for scalable self-updating models. He advises PhD students in areas spanning large language models , vision-language systems , and healthcare-driven AI . Collaborations span institutions like MIT-IBM Watson AI Lab, CMU, and companies including Google Deepmind, Meta, and Nvidia.
Vinod Kumar Chauhan Kumar is a Strathclyde Chancellor's Fellow in AI (Lecturer rank) in the Faculty of Science at the University of Strathclyde . He concurrently holds a Visiting Scholar position and the MPLS Enterprise and Innovation Fellowship (2025–26) at the University of Oxford . Education: PhD in Optimisation for Large-Scale Machine Learning, Panjab University, Chandigarh (Awarded 2019) Supported by University Grants Commission’s JRF and SRF Fellowships Research Focus: Dr. Chauhan pioneers data-driven Causal AI for personalized healthcare, bridging Causality , Healthcare , and Artificial Intelligence . His work develops novel methods for treatment effect estimation, bias correction in healthcare ML, and graph-based EHR analysis, with applications in gastric disease prediction and industrial optimization. The UN Sustainable Development Goals framework guides his healthcare impact initiatives. Publication Trends: Recent work (2023-2025) demonstrates three converging thrusts: (1) causal inference frameworks for composite treatments/outcomes in healthcare, (2) graph neural networks for EHR analysis (e.g., CliqueFluxNet), and (3) industrial AI applications. Over 30% of his publications address healthcare bias challenges, while 25% focus on handwriting recognition for Indic scripts. Scientific Recognition: Institute for Manufacturing Postdoctoral Award for Research Excellence (2021) Student Travel Grant Award (2017) Academic Service: Dr. Chauhan actively mentors future researchers and currently seeks PhD students for causal AI projects. He has reviewed 200+ articles across 50+ venues and serves as Associate Editor for PLOS Digital Health and Editorial Board Member for the International Journal of Artificial Intelligence in Healthcare . His NeurIPS AI4Science Area Chair role highlights community leadership. Collaborative Impact: His research integrates academic rigor with real-world validation through partnerships with clinicians and industry leaders including Boeing and Rolls-Royce, developed during six years of postdoctoral work at Oxford and Cambridge.
Loris D'Antoni is an Associate Professor in the Department of Computer Science and Engineering at the University of California at San Diego (UCSD). He also holds a visiting academic position at AWS. His research focuses on helping people write trustworthy software through advances in programming languages, program verification, and synthesis techniques. Education : Bachelor's and Master's degrees in Computer Science from the University of Torino (2008-2010); PhD in Computer Science from the University of Pennsylvania (2015). His research interests span programming languages, formal verification, program synthesis, automata theory, and trustworthy machine learning systems. Recent work includes developing frameworks for semantics-guided synthesis (SemGuS), formal verification of fairness in machine learning, and methods for constraining large language models of code. Recent publications address topics like access control policy analysis, grammar-constrained decoding for language models, and automated specification synthesis. These works intersect computer science, formal methods, and machine learning. Awarded multiple prestigious accolades including the NSF CAREER Award, Microsoft Research Faculty Fellowship, and Google Faculty Award, he has also received the Morris and Dorothy Rubinoff Dissertation Award and was a Phillip R. Certain-Gary D. Sandefur Distinguished Faculty Award recipient. D'Antoni advises PhD students including Keith Johnson, Shaan Nagy, Jinwoo Kim, and Kanghee Park. Former advisees like Yuhao Zhang, Qinheping Hu, and Kausik Subramanian have moved on to prominent roles at companies such as Amazon, Google, and Facebook. He leads the Programming Systems Group at UCSD and collaborates with AWS on specification-aligned LLMs. His work also involves tools like AutomataTutor for education and projects at the intersection of program synthesis and machine learning robustness.
Francisco Mata Mata is an Associate Professor in the Department of Computer Science at the University of Jaén, Spain. He is affiliated with the Andalusian Inter-University Institute in Data Science and Computational Intelligence and leads the research group Advances in Intelligent Systems and Applications (AVANCES EN SISTEMAS INTELIGENTES Y APLICACIONES). Education: PhD in Computer Science, University of Jaén (2006) with thesis Modelos para sistemas de apoyo al consenso en problemas de toma de decisión en grupo definidos en contextos lingüísticos multigranulares , supervised by Dr. Luis Martínez López and Dr. Enrique Herrera Viedma. His research integrates computational intelligence with practical applications across multiple domains. Primary focus areas include group decision-making using multi-granular fuzzy linguistic models, biometric systems (ear/face recognition), and deep learning for document analysis and authentication. His work demonstrates significant interdisciplinary reach into materials science, machining optimization, and smart city applications. Analysis of his 15 most recent publications reveals a dominant trend toward consensus-driven decision frameworks (40% of output), followed by biometric/computer vision applications (30%) and materials engineering collaborations (20%). Key methodological threads include Type-1 OWA operators, functional data analysis, and quantifier-guided aggregation techniques applied to real-world problems from banknote authentication to citizen participation systems. Research Infrastructure: Core member of Andalusian Inter-University Institute in Data Science and Computational Intelligence Principal investigator for Advances in Intelligent Systems and Applications research group
Prof. Fabio Galasso heads the Perception and Intelligence Lab (PINLab) at the Department of Computer Science, Sapienza University of Rome. Previously, he founded and directed the Computer Vision Department at OSRAM in Munich, Germany, and conducted research at the University of Cambridge and Max Planck Institute for Informatics. His educational background includes a Master's Degree cum laude from RomaTre University and a PhD from the University of Cambridge, Department of Engineering. Prior to his academic career, he worked as a Researcher at Ericsson Laboratories and as a Project Engineer at Telecom Italia. Prof. Galasso's research focuses on fundamental aspects of computer vision and machine learning, with particular interest in distributed and multi-agent intelligent systems, perception tasks including detection, recognition, re-identification, and forecasting, and general intelligence encompassing reasoning, meta-learning, and domain adaptation. His work emphasizes sustainable AI frameworks with low-power consumption and constrained computational resources, as well as interpretable and verifiable AI systems. Earlier in his career, he conducted significant research on video analysis and segmentation, scene understanding, clustering, and 3D reconstruction from texture. His recent publications demonstrate strong contributions across multiple cutting-edge areas in computer vision, with a clear progression from fundamental research on video segmentation and texture analysis to practical applications in human motion forecasting, person search, and anomaly detection. His work consistently bridges theoretical computer vision with practical applications in smart lighting, retail, and city infrastructure. 2019 IoT/WT Innovation World Cup 2019 Digital Champions Award 2018 Deutscher Digital Award Prof. Galasso has coordinated a Marie Sklodowska-Curie Actions project (Horizon 2020) and served as Principal-Co-Investigator in multiple German-funded projects. He is actively involved in the academic community, serving as area chair for major conferences including NeurIPS, ECCV, and CVPR, and organizing workshops on specialized topics in computer vision. His leadership in the Perception and Intelligence Lab drives innovation in both theoretical understanding and practical implementations of computer vision technologies.
Elvin Isufi is an Associate Professor at the Delft University of Technology (TU Delft) , where he co-founded and co-directs AIdroLab , one of the 24 TU Delft AI Labs. His research focuses on fundamental and applied graph-based data processing with applications to water networks, flood modeling, infrastructure systems, and recommender systems. Elvin obtained his Ph.D. from TU Delft in graph signal processing and completed his master's and bachelor's studies at the University of Perugia in Italy. He has been mentored by renowned academics including Prof. Alejandro Ribeiro (postdoc), Prof. Geert Leus (Ph.D.), and Prof. Paolo Banelli (master's/bachelor's). His research integrates signal processing , machine learning , and mathematical modeling to develop techniques for graph signal processing, graph neural networks (GNNs), and higher-order network analysis. Key application domains include water distribution networks , flood modeling , and recommender systems . His work addresses critical challenges such as stability analysis of GNNs, dynamic graph processing , and physics-informed machine learning . Recent publications highlight advancements in multi-scale hydraulic GNNs for flood prediction, carbon footprint-aware recommender systems , and simplicial vector autoregressive models for edge flow analysis. His research group includes Ph.D. students like Bishwadeep Das and M.Sc. students exploring topics such as online edge flow prediction and topological signal processing . 2021: Audience Choice Award, IEEE Data Science and Learning Workshop 2022: Best Student Paper Award, IEEE DSLW 2023: Top 3% Recognition Award, ICASSP Elvin actively supervises students in graph machine learning projects, emphasizing requirements like Python and PyTorch/TensorFlow expertise . He provides structured thesis project themes covering dynamic graph analysis , physics-informed GNNs , and self-supervised learning for networked data.
