Dr. Siddhartha Bhattacharyya is a Professor in the Department of Computer Science and Engineering at Christ University, Bangalore, with expertise spanning hybrid intelligence, quantum computing, and multimedia data processing. He has authored/edited 65 books and published over 300 research articles, focusing on interdisciplinary applications of machine learning and computational methods. Editorial Board Member, PeerJ Computer Science Holder of two PCT patents Active in academic leadership (organizing conference committees) His research integrates Artificial Intelligence , Computer Vision , and Quantum Computing to solve complex problems in education, healthcare, and environmental monitoring. Recent work includes multimodal student learning assessment, gas plume detection, and Metaverse applications. He leads an active academic lab focused on hybrid intelligence systems and their practical implementations. Key trends in his publications include: deep learning architectures for computer vision (YOLOv7, CNN-Transformer), quantum-inspired algorithms for graph coloring and bioinformatics, and educational technology innovations for remote learning environments. His work bridges theoretical advancements with real-world applications across diverse domains.
Pradeep Murukannaiah is an Associate Professor in the Interactive Intelligence group at the Faculty of Electrical Engineering, Mathematics, and Computer Science (EEMCS) at Delft University of Technology (TU Delft). He co-directs the Hippo Lab, a Delft AI lab focused on AI for fair, efficient, and interpretable analysis of climate policies. He also holds leadership roles as Use Cases Coordinator and Diversity Co-Chair in the Hybrid Intelligence center, and serves as Master Coordinator for the MSc in Data Science and Artificial Intelligence Technology (DSAIT). Dr. Murukannaiah received his PhD in Computer Science from North Carolina State University in 2016. Prior to joining TU Delft, he served as an Assistant Professor at Rochester Institute of Technology (2017-2019), completed an internship at Google, and worked as a Software Engineer at Alcatel-Lucent. His research centers on engineering socially intelligent agents through three interconnected thrusts: Natural Language Processing (focusing on argument mining, value alignment, and claim analysis), Multi-Agent Systems (exploring negotiation, social choice, and multi-objective reinforcement learning), and Hybrid Intelligence (developing frameworks for human-AI synergy in decision making). Cross-cutting these areas are his investigations into values (representing what matters to stakeholders) and norms (representing expectations between stakeholders), which together form sociotechnical systems where humans interact, make decisions, and stay accountable to each other while AI agents augment human intelligence. His recent publications demonstrate a strong focus on value-sensitive AI systems, with work spanning moral frame preservation in news summarization, multi-objective reinforcement learning for climate policy analysis, and mechanisms for responsible autonomy. His research consistently bridges theoretical advances in AI with practical applications in societal decision making, particularly around climate change and democratic processes. Dr. Murukannaiah has successfully mentored numerous graduate students, including current PhD candidates Zuzanna Osika (working on explainable multi-objective decision support), Shubhalaxmi Mukherjee (focusing on fact checking using LLMs), and several recent PhD graduates whose work has contributed to the fields of opinion diversity through hybrid intelligence and context-specific value inference. As co-director of the Hippo Lab and through his leadership roles in the Hybrid Intelligence center, he actively shapes research directions that emphasize fairness, interpretability, and human-centered AI approaches for addressing complex societal challenges.
Katrin Korfmann is a German artist, photographer, and Senior Tutor at the Royal Academy of Art, The Hague (KABK), where she teaches Image in the Bachelor Graphic Design program and Post-Photography in the Master Non Linear Narrative program. She has been a faculty member since 2010 and is currently pursuing a PhD in artistic research at PhDArts ACPA, Leiden University, focusing on temporality in photography. Korfmann's artistic practice centers on urban spaces and public interaction, challenging conventional photographic perspectives. She transforms public spaces into evolving studios, where the urban environment becomes integral to her creative process. Her work examines how people claim and reinterpret urban spaces according to their interests and needs, as seen in her documentation of surfers in Munich's Eisbach river and freerunners navigating Amsterdam's architecture. Korfmann's research explores photographic temporality across three aspects: the 'temporality of making,' the 'temporality of representing,' and the 'temporality of experiencing,' questioning what photographic time means in the age of AI and machine learning. Her major works, including 'Augenblick,' 'Back Stages,' and 'Ensembles Assembled,' examine the relationship between individuals and their urban environments, challenging Henri Cartier-Bresson's concept of the 'Decisive Moment' through layered visual narratives that collapse multiple temporal dimensions. Korfmann's work has been exhibited internationally in galleries and museums worldwide, including the Photography Museum Rotterdam, GEM Museum of Contemporary Art, Kemper Museum of Contemporary Art, and Aperture New York. Prix de Rome (2nd) Mama Cash Award Esther Kroon Award (NL) Bieler Fototage Prize (CH) Mondriaan Fund Stipend for established artists Korfmann has realized numerous public art commissions for institutions including the Ministry of Finance, Rijksgebouwendienst, Schiphol Airport, and University medical centers. She is currently creating a new art commission for the renovated Radiology Department of Leiden University Medical Centre, bridging art and science. Her work is held in prestigious collections including the Würth Foundation, European Patent Office, Robert Bosch Foundation, and various Dutch art collections.
Dr. G Smid is a Researcher in the Department of Psychology within the Faculty of Social and Behavioural Sciences at Utrecht University, specializing in Clinical Psychology. His expertise centers on advanced statistical methodologies applied to psychological research, with particular emphasis on structural equation modeling under constrained data conditions. His primary research domains include Structural Equation Modeling, Bayesian Statistics, Adolescent Mental Health, Clinical Psychology, Data Synthesis, and Systematic Reviews. He has pioneered critical methodological frameworks for Bayesian SEM applications with small samples, developed interpretable synthetic data techniques, and investigated adolescent mental health determinants including harmful sexual behavior and immigration-related impacts. His work bridges complex statistical theory with clinical psychology applications. Analysis of Dr. Smid's 13 publications (2014-2023) reveals a dominant methodological trajectory focused on overcoming small-sample limitations in psychological research. His most influential contributions address Bayesian prior specification in SEM, measurement equivalence validation, and adolescent mental health risk factors. The publications demonstrate consistent innovation in statistical methodology while maintaining strong clinical relevance, particularly in adolescent psychology and mental health assessment.
Dr. Pablo Mosteiro Romero is an Assistant Professor at Utrecht University's Faculty of Social and Behavioural Sciences within the Department of Methodology and Statistics . His work bridges Natural Language Processing (NLP) , Applied Data Science , and Human-centered Artificial Intelligence , focusing on language change, morphology-syntax trade-offs, and applications in healthcare and open societies. Education: B.Sc. in Physics, Rutgers University (Summa Cum Laude, 2007) Ph.D. in Computer Science, Princeton University (2014, Centennial Fellowship) Research Interests revolve around computational linguistics, information-theoretic approaches to linguistics, and ethical AI. He investigates synonym evolution, morphosyntactic interactions, and fairness in mental health-focused AI systems. Recent work includes de-identification in medical texts and taxonomy induction via reinforcement learning. Publications span top venues like ACL, IEEE, and Springer, covering topics such as topic modeling interpretability, bias discovery in ML, and multimodal negation processing. Collaborations with colleagues like Scheepers, Spruit, and Kaymak highlight interdisciplinary applications in electronic health records. Awards include the Centennial Fellowship at Princeton and Summa Cum Laude at Rutgers. As a University Teaching Qualification holder, he teaches courses in data science, Python programming, and research methods, supervising theses in AI and Business Informatics. His work aligns with Utrecht's Institutions for Open Societies (IOS) theme, addressing gender diversity and global justice in data-driven systems.
Cristian Spitoni is an Assistant Professor in the Mathematical Modeling group at the Mathematical Institute within the Faculty of Science at Utrecht University. His office is located in the Hans Freudenthal Building at Budapestlaan 6, Room 510, 3584 CD Utrecht. He maintains an active research profile spanning mathematical physics, statistics, and interdisciplinary applications in medical informatics and neuromorphic computing. His primary research interests include Stochastic Modeling, Statistical Physics, Non-Equilibrium Statistical Physics, and Survival Analysis. Dr. Spitoni's work demonstrates a remarkable interdisciplinary range, bridging theoretical mathematics with practical applications in healthcare and computing. His research trajectory shows a fascinating evolution from fundamental statistical physics problems to medical applications and more recently to neuromorphic computing. Analysis of his recent publications (2023-2025) reveals three major research thrusts: 1) Theoretical work on probabilistic cellular automata and metastability in statistical physics; 2) Medical statistics applications focusing on ICU infections, sepsis, and survival analysis; and 3) Cutting-edge research in neuromorphic computing, particularly on memristors, fluidic circuits, and brain-inspired computing architectures. His work increasingly shows interdisciplinary convergence, with mathematical techniques from statistical physics being applied to both medical informatics and neuromorphic engineering problems. Dr. Spitoni has established productive collaborations with researchers across multiple disciplines, including medical researchers at Utrecht University Medical Center and physicists working on novel computing architectures. His research has been published in high-impact journals spanning physics, mathematics, medical informatics, and computer science, demonstrating the breadth and significance of his contributions. His teaching responsibilities include courses such as Interacting Particle Systems in the Lattice and Continuum, Introduction to Complex Systems, and Mathematical Statistics, reflecting his expertise in both theoretical and applied mathematical modeling.
Dr. ir. Cynthia C.S. Liem is an Associate Professor in the Multimedia Computing department at Delft University of Technology, specializing in ethical and reliable AI frameworks. Her work bridges machine learning, human-computer interaction, and societal impact. Academic Rank: Associate Professor Department: Multimedia Computing School: Electrical Engineering, Mathematics and Computer Science Liem’s research focuses on Explainable AI , Algorithmic Transparency , and AI Ethics , emphasizing fairness in machine learning and societal implications of autonomous systems. Recent publications explore adversarial testing, energy-constrained counterfactuals, and critiques of unscientific AGI claims. Her most recent articles highlight trends in autonomous vehicle testing , AI explainability , and ethical algorithmic design , leveraging techniques like conformal prediction and differential evolution . Scientific Awards : SBFT 2023 Best Paper Award TU Delft Education Fellow WWW 2018 Challenge Winner 2024 Women in AI Netherlands Diversity Leader Award Liem serves as an advisor for DUO’s research ethics audits and frequently contributes to public discourse on AI’s societal role, appearing in national media outlets like Delta and Trouw . She also leads datasets on music representation learning and algorithmic recourse.
L. Beinborn and N. Hollenstein are academic researchers recognized for authoring the book Cognitive Plausibility in Natural Language Processing , which was critically reviewed in Computational Linguistics (Volume 50, Issue 3, September 2024). Their work bridges computational methods with cognitive modeling in language processing. Research interests span: Natural Language Processing (core focus) Cognitive Science foundations Computational Linguistics methodologies Machine Learning applications Artificial Intelligence interpretability No institutional affiliations, awards, or academic appointments are specified in the source material. The absence of student advisement records, grant details, or laboratory affiliations suggests the provided context is limited to their scholarly publication output.
Dr. Merel Scholman is an Assistant Professor at Utrecht University within the Department of Languages, Literature and Communication and affiliated with the Institute for Language Sciences . Her research focuses on how people use language to create meaning through discourse relations , investigating cognitive processing of coherence markers in written, spoken, and visual communication. PhD in Language Science and Technology from Saarland University (summa cum laude, 2019) Research Master in Linguistics from Utrecht University (cum laude, 2015) Visiting researcher at Lancaster University and University of Edinburgh Her work combines theoretical psycholinguistics with computational modeling , using quantitative empirical methods like corpus analysis and crowdsourcing experiments. Recent research examines cross-modal discourse signals across Nigerian Pidgin , English , and European languages , while methodological contributions explore annotation reliability and experimental design. Publications demonstrate expertise in discourse markers , coherence relations , and low-resource NLP , with applications to gesture synthesis , spoken language analysis, and educational tools . She received the Veni grant for her project on natural discourse structure signals across modalities. As an educator, she contributes to the Communication and Information Sciences (BA) and Linguistics (RMA) programs, teaching courses like Writing Style as a Choice . Her methodological innovations in crowdsourcing and annotation frameworks have shaped discourse analysis practices in computational linguistics.
Yingqian Zhang is an Associate Professor in the Information Systems group at the Industrial Engineering and Innovation Sciences department of Eindhoven University of Technology (TU/e). She is affiliated with the Eindhoven Artificial Intelligence Systems Institute (EAISI), specifically with the EAISI High Tech Systems and EAISI Foundational groups. Her research focuses on applying Artificial Intelligence to solve complex decision-making problems across various domains including logistics, transportation, manufacturing, and e-commerce. Dr. Zhang received her PhD in Computer Science from the University of Manchester, UK. Prior to joining TU/e, she served as an Assistant Professor in the Econometrics Institute at Erasmus University Rotterdam and as a postdoc researcher in the Algorithmics group at TU Delft. She was also a visiting professor at the Institute for Advanced Computer Studies at University of Maryland, College Park, USA. Her research expertise lies at the intersection of Artificial Intelligence and optimization, with particular focus on machine learning, deep reinforcement learning, and trustworthy data-driven optimization. Dr. Zhang develops socially aware algorithms that can optimize decisions in data-rich environments. Her work bridges the gap between theoretical AI advancements and practical applications in industrial settings, addressing real-world challenges through innovative algorithmic solutions. She is particularly interested in how AI can support human decision-making while maintaining transparency and trustworthiness. Dr. Zhang's recent publications reveal a strong trend toward applying graph neural networks and reinforcement learning to complex scheduling and optimization problems. Her work demonstrates increasing sophistication in handling stochastic elements in decision-making processes, with applications spanning healthcare diagnostics, logistics, transportation, and manufacturing. She has made significant contributions to the field of neural combinatorial optimization, particularly for job shop scheduling problems and vehicle routing. Dr. Zhang has received several prestigious awards recognizing her contributions to the field: Winner of the MLVRP2023 GECCO competition (2023) Best Paper Award from Omega-International Journal of Management Science (2017) Best Industrial Paper Award (2020) Best Student Paper Award (2019) Best Student Paper Award of ICAART 2022 (2022) As a dedicated mentor, Dr. Zhang supervises numerous PhD students including Mohsen Abbaspour Onari, Abdo Abouelrous, Luca Begnardi, Xia Jiang, Chengpeng Hu, Minshuo Li, Robbert Reijnen, Jesse van Remmerden, Bart von Meijenfeldt, Ya Song, and Igor Smit. Her research is supported by various grants, including the LEO (Learning and Explaining Optimization) project co-funded by Holland High Tech | TKI HSTM via the PPP allowance scheme for public-private partnerships. Dr. Zhang actively contributes to the academic community as the Chair of the Benelux Association for Artificial Intelligence (BNVKI) and as a member of the Technical Board for the European Big Data Value Association (BDVA). She serves as an associate editor for the "Annals of Mathematics and Artificial Intelligence" journal and participates in the technical Program Committee for major AI conferences such as IJCAI, AAAI, AAMAS, and ECAI. She is also on the executive committee of the Data Science meets Optimisation (DSO) working group of EURO to promote collaboration between AI and Operations Research communities.
Yaoxin Wu is an Assistant Professor at the Eindhoven University of Technology, affiliated with the Department of Industrial Engineering and Innovation Sciences. His research bridges deep learning and combinatorial optimization to solve complex problems in transportation, scheduling, and network design. Education : PhD in Computer Science from Nanyang Technological University (2023). Wu specializes in artificial intelligence and operations research , focusing on graph neural networks, stochastic programming, and multi-objective optimization. His work has significant applications in UAV routing and on-demand delivery systems. His 2025 publications highlight trends in neural combinatorial optimization for stochastic job shop scheduling, ride-hailing, and drone logistics. Key subfields include deep reinforcement learning, preference modeling, and topological graph learning. He has supervised 9 students, including PhD candidates Xia Jiang and Igor Smite, and Master’s students like Venkata Roshan Mannepu and Floor Halkes. Wu's research is funded by projects like LEO (Holland High Tech | TKI HSTM) and SURF Cooperative grants. His educational activities include teaching Fundamentals of Algorithmic Programming and AI-Driven Business Operations , emphasizing data-driven methods for manufacturing processes.
Alberto De Luca is an Assistant Professor at the Image Sciences Institute, Division Imaging & Oncology, University Medical Center Utrecht. He holds a BSc in Biomedical Engineering (2011), MSc in Bioengineering (2013), and PhD (2017) from the University of Padova, Italy, where his thesis focused on non-Gaussian diffusion in the brain and skeletal muscle. His research specializes in diffusion MRI methodologies for neurological diseases and cancer, with key interests including: Fiber-specific quantification in grey/white matter Multi-center MRI data harmonization Physiological parameter estimation via inverse methods Response prediction modeling He leads projects in cerebral small vessel disease and pediatric oncology, utilizing advanced 7T MRI and AI techniques. Recent publications (2025) demonstrate strong focus on: AI-driven lesion-symptom mapping in vascular cognitive disorders Diffusion MRI harmonization across research sites Advanced tractography validation frameworks Pediatric cancer imaging biomarkers He contributes to the Translational Neuroimaging Group and International Society for Tractography, with external collaborations including Erasmus MC (Frontotemporal dementia research) and Hogeschool Utrecht (guest lecturing on brain networks).
Claudio Persello is an Adjunct Professor at the University of Twente , affiliated with the Faculty of Geo-Information Science and Earth Observation (ITC) and the Department of Earth Observation Science (EOS). Prior to this, he held a Marie Curie research fellowship at the Max Planck Institute for Intelligent Systems and the Remote Sensing Laboratory at the University of Trento. His research focuses on Deep Learning for Earth Observation , developing AI methodologies tailored to remote sensing data (RGB, multispectral, SAR, LiDAR) and geospatial applications. Key themes include urban deprivation mapping, agricultural monitoring, glacier dynamics, and 3D urban modeling. He emphasizes user engagement, open science, and the creation of interpretable AI solutions for societal challenges. Recent publications highlight trends in deep learning for remote sensing , with applications spanning climate change impact assessment, urban planning, and sustainable development. His work often integrates multi-source data (e.g., satellite imagery, socio-economic layers) to address real-world problems. 2018 : Top five teacher at ITC 2012 : Best PhD thesis in Pattern Recognition (GIRPR) 2011 : Marie Curie fellowship for MaleRS project He actively contributes to editorial boards (e.g., Remote Sensing journal) and collaborative networks like the Digital Society Institute. His datasets (e.g., CadastreVision, Svalbard glacier mapping) are publicly available via Zenodo and DANS.
Michel J.A.M. van Putten is a Full Professor of Clinical Neurophysiology at the TechMed Centre, University of Twente , with an h-index of 52 and 9261 Scopus citations. His research focuses on: EEG-based outcome prediction in post-cardiac arrest coma Deep learning applications for seizure and interictal discharge detection Neurophysiological modeling of epilepsy and traumatic brain injury Cultured cortical neural networks for disease mechanism inference AI-driven analysis of brain connectivity and excitation-inhibition balance His work contributes to UN Sustainable Development Goals for Health and Well-being , Human-AI Interaction , and Economic Impact of AI . Recent research includes: 2025: Automated inference of disease mechanisms in patient-derived neuronal networks 2025: Expert-level deep neural network for interictal discharge detection 2024: Grassmann manifold methods for invariant EEG/MEG feature extraction Scientific recognition includes: 2011: NVvTG Congres Prize 2012: Tripartite prize for Near Infrared Spectroscopy and EEG research His datasets on EEG analysis and cortical network modeling are publicly available through Zenodo, and he has presented 17 oral presentations on topics including: Post-ictal brain recovery mechanisms Mathematical models of peripheral axons Ischemic cerebral damage pathways
Faiza Allah Bukhsh is an Associate Professor specializing in Artificial Intelligence, Data Mining, Process Mining, Health Informatics, Cybersecurity, and Ethical AI. Her work bridges technical innovation with societal impact, particularly in healthcare systems analysis, telecommunications resilience, and ethical data governance. Digital Society Institute TechMed Centre Datamanagement & Biometrics Her research focuses on Explainable AI , Process Mining , and Privacy Assurance in healthcare systems, with recent work on AI music perception, sepsis treatment analysis, and privacy-utility trade-offs. Key article trends include: AI in music and creative domains Process Mining for healthcare insights Explainable Machine Learning workflows Privacy-preserving analytics Telcom infrastructure resilience
