Mirjam Ernestus is a Professor of Psycholinguistics at the Centre for Language Studies within Radboud University's Faculty of Arts. She serves as Chair of the Editorial Board of Radboud University Press and Scientific Director of the Centre for Language Studies since 2017. Her career spans 20+ years in psycholinguistics and phonetics research. Member of Royal Netherlands Academy of Arts and Sciences Recipient of ERC Starting Grant and NWO VICI grant Specializes in speech comprehension and conversational speech Research Focus: Her work integrates psycholinguistics and phonetics to explore auditory word recognition, morphological processing, and cross-linguistic speech phenomena. She develops computational models like DIANA for speech comprehension analysis. Publication Trends: Recent studies examine exemplar-based processing differences between native/non-native speakers, phonetic-morphological interactions, speech rate dynamics, and multimodal language learning approaches. Her work frequently applies machine learning and experimental paradigms. Awards: ERC Starting Grant (2011) NWO VICI grant (2011) KNAW membership (2015) EURYI Award (2006) Leadership: She has directed major research initiatives including the gravitation project Language in Interaction and the Research Unit Spoken Morphology. Her management roles include overseeing 160+ researchers at the Centre for Language Studies.
Mark Steedman is a Professor of Cognitive Science at the School of Informatics , University of Edinburgh, and an Adjunct Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research bridges Artificial Intelligence , Cognitive Science , and Computational Linguistics , with a focus on Combinatory Categorial Grammar (CCG) , Prosody and Intonation , and Temporal Semantics . He has led the Institute for Language, Cognition, and Computation and contributed to interdisciplinary research at the Human Communications Research Center and Centre for Speech Technology Research . Research Interests : Steedman's work explores the intersection of formal grammar, computational models, and cognitive processes. He investigates how CCG parsing can enhance semantic inference, how prosodic features improve speech processing, and the role of temporal semantics in language understanding. His projects often integrate language models with entailment graphs for question answering and dialogue systems. Scientific Awards : Fellow of the American Association of Artificial Intelligence (1993) Fellow of the Royal Society of Edinburgh (2002) Fellow of the British Academy (2002) Member of Academia Europaea (2006) Best Paper Awards at ACL 2023 and AACL/IJCNLP 2023 Influential Paper Award (IFAAMAS 2017) Recent Trends in Publications : His recent work emphasizes language models for semantic inference , entailment graphs in multilingual settings, and incremental parsing for brain-language interfaces. Papers address challenges in hallucination , cross-lingual transfer , and prosody-text alignment .
Kathryn Lilley is a Professor in the Department of Biochemistry at the University of Cambridge, where she has held academic positions since 2004. She is Director of the Cambridge Centre for Proteomics and a Fellow of Jesus College, Cambridge. She also serves as a Member of the Milner Therapeutics Institute and previously led the Mass Spectrometry theme at the Rosalind Franklin Institute. Her research focuses on developing innovative technologies for spatial proteomics and transcriptomics, with particular emphasis on understanding the dynamic organization of proteins and RNA within cells. Her group creates robust open-source informatics pipelines that enable end-to-end analysis of proteomic data, making their methods widely accessible to the research community. Recent work has expanded into investigating the subcellular spatial transcriptome and its interactions with the proteome, generating significant interest in RNA biology. Lilley's publications reveal a strong focus on advancing spatial proteomics methodologies, with recent work emphasizing computational approaches, neural networks for data analysis, and novel techniques for studying RNA-protein interactions. Her research bridges biochemistry, computational biology, and cell biology, with applications in understanding cellular perturbation responses and disease mechanisms. 2020 Elected as a member of EMBO 2018 Human Proteomics Organisation, Distinguished Achievement in Proteomic Sciences Award 2017 European Proteomics Association Juan Pablo Albar Proteomics Pioneer award 2012 Fellow of the Royal Society of Biologists Lilley receives considerable funding from the Pharmaceutical Industry and collaborates widely both nationally and internationally. She heads a large research group that develops technologies for interrogating spatial distributions of the proteome and its reorganization upon cellular perturbation. Her work is highly regarded in the field, as evidenced by numerous plenary and keynote invitations at international conferences.
Dr. Sebastian Michel is a researcher at the Institute of Biotechnology in Plant Production , part of the Department of Agricultural Sciences at the University of Natural Resources and Life Sciences Vienna (BOKU) . His work focuses on applying genomic and phenomic selection to improve disease resistance and climate adaptability in wheat species. Research interests include: Genomic selection for complex traits Plant disease resistance (Fusarium, Septoria, common bunt) Climate-resilient crop breeding Wheat genomics and QTL mapping Scientific contributions span numerous publications on Fusarium resistance mechanisms, deoxynivalenol detoxification, and genomic prediction models. He has supervised multiple MSc theses on transgenerational defense induction, allele prediction, and stripe rust resistance. Projects include EU-funded initiatives for: Climate Resilient Orphan Crops PhenoMix: Deep Learning in Wheat Breeding Sustainable Resistance Breeding in Ethiopia/Kenya/Zimbabwe Community engagement features keynote lectures on modern breeding methods and peer-review roles for journals like Frontiers in Plant Science and Theoretical and Applied Genetics .
Ashutosh Modi is an Associate Professor in the Computer Science and Engineering department at Indian Institute of Technology Kanpur. Previously, he conducted research at Disney Research in Pittsburgh, Los Angeles, and Zurich. His research interests span across Natural Language Processing , Machine Learning , and Artificial Intelligence , with specific focus on multi-modal Affective computing, Conversational Systems, and Natural Language Understanding. His work takes a statistical approach to developing natural language understanding models from statistical regularities in large corpora of text while leveraging linguistic theories. Dr. Modi has taught multiple courses at IIT Kanpur including: Statistical Natural Language Processing (CS779) - Fall 2020, Spring 2021, Spring 2023 Special Topics in Natural Language Processing (CS698O) - Winter 2020 Deep Reinforcement Learning (CS698R) - Fall 2021 Fundamentals of Computing (ESC101) - Spring 2022 His courses cover a comprehensive curriculum from basic linguistics fundamentals and language models to advanced topics like neural networks, word vectors, and transformer architectures including BERT. The teaching approach emphasizes both theoretical foundations and practical applications through research projects. Dr. Modi completed his PhD at Saarland University in Germany under the supervision of Prof. Dr. Manfred Pinkal and Dr. Ivan Titov. During his doctoral studies, he was associated with the Department of Computational Linguistics, MultiModal Cluster Initiative (MMCI), and Department of Computer Science.
Clemens Brunner is a Researcher at the Institute of Psychology within the Faculty of Natural Sciences at the University of Graz. His work bridges electrical/biomedical engineering and cognitive neuroscience, specializing in the neural mechanisms of arithmetic processing and numerical cognition. He actively develops open-source neuroimaging tools used globally in EEG research and sleep analysis. His research focuses on EEG oscillations, biosignal processing, and machine learning applications in cognitive neuroscience. Key interests include arithmetic fact learning, neural correlates of numerical order processing, and non-invasive brain stimulation effects on mathematical cognition. His expertise spans Python programming, statistical analysis, and contributions to major scientific libraries like scikit-learn and SciPy. Analysis of his recent publications reveals consistent emphasis on electrophysiological signatures of arithmetic processing, with growing integration of computational methods and neuromodulation techniques. His work demonstrates strong methodological innovation through open-source software development for EEG analysis and sleep staging. Brunner contributes to the University of Graz's "Brain and behavior" research network, developing tools like MNELAB, SleepECG, and XDF.jl that enhance reproducibility in neuroscience. His collaborations extend to major projects including MNE-Python and BNCI Horizon 2020, advancing brain-computer interface methodologies and neuroimaging standards.
Dr. Pavlos Topalidis is a Greek cognitive neuroscientist and PhD candidate at the University of Salzburg's Centre for Cognitive Neuroscience (CCNS), working in the laboratory for Sleep and Consciousness Research under the supervision of Univ.-Prof. Dr. Manuel Schabus. Since October 2023, he has served as a Junior Lecturer at the University of Salzburg, teaching Human Cognition and Sleep at both Bachelor and Master's levels. His educational background includes: PhD student at University of Salzburg (since 2019), Centre for Cognitive Neuroscience MSc in Neuro-Cognitive Psychology from Ludwig-Maximilians-Universität Munich (2017-2019) BSc (Hons, first class) in Psychology from University of Aberdeen, UK (2012-2016) Research visit at Yale University (2022) Dr. Topalidis's research focuses on information processing during sleep, particularly predictive coding and statistical learning mechanisms. His work explores how the brain processes information across wakefulness and sleep states, with applications in sleep monitoring using wearable technology. He investigates oscillatory brain activity patterns and their relationship to cognitive functions, with additional interests in cultural variations in sleep patterns and insomnia treatments. His technical expertise spans multiple neuroimaging methods including EEG, MEG, fMRI, and actigraphy, with advanced programming skills in Matlab and R for data analysis. His scientific contributions have been recognized with multiple awards including a poster award at the Sleep, Cognition and Consciousness Winter Symposium (2023) and a project presentation award at the ESRS Sleep School (2021). His research has led to numerous publications focusing on sleep classification algorithms, cross-cultural sleep studies, and the application of physiological measures to understand mental states. February 2023: Poster award at Sleep, Cognition and Consciousness Winter Symposium November 2022: Salzburg University research stay funding at Yale University (1700€) October 2022: Shortlisted for best poster award at the 26th European Sleep Research Society Conference September 2021: ESRS 3rd Sleep School project presentation award October 2013: ERASMUS scholarship As a Junior Lecturer, Dr. Topalidis mentors undergraduate and graduate students in cognitive neuroscience and sleep research. His current projects involve developing low-cost sleep monitoring solutions using wearable technology and investigating predictive coding mechanisms during sleep. He has also completed specialized training in Cognitive Behavioral Therapy for Insomnia (2023) and Good Clinical Practice (2022). Dr. Topalidis is an active member of the Sleep and Consciousness Research laboratory at the Centre for Cognitive Neuroscience, collaborating with researchers across Europe. His work bridges basic cognitive neuroscience with clinical applications, particularly in developing accessible sleep monitoring tools and understanding sleep's role in cognitive processing and memory consolidation.
Nadiya Gandalialikhani is a researcher at the Institute of Economics within the Kurt Rothschild School of Economics and Statistics (RoSES) at Johannes Kepler University Linz. She holds an MSc degree and contributes to both research and teaching activities at the university. Her research interests focus on applied time series econometrics , behavioral macroeconomic theory , and financial economics , with particular expertise in energy markets and oil price forecasting. Dr. Gandalialikhani's work bridges traditional econometric methods with machine learning approaches to analyze complex financial and energy market dynamics. Dr. Gandalialikhani teaches Bachelor thesis seminars in Economics and Economic Psychology for the 2025 Winter semester, guiding students through research methodology and thesis development. Her office is located in the Kepler Building on the 1st floor (room K154/2C), and she is available for consultations by appointment. Her publication record demonstrates consistent contributions to energy economics, with four peer-reviewed articles published between 2013-2014 focusing on oil price volatility, neural network applications in forecasting, and energy commodity market relationships. Research focuses on advanced time series analysis in financial and energy markets Specializes in hybrid forecasting models combining econometrics with machine learning Active contributor to energy economics literature, particularly regarding oil markets Dr. Gandalialikhani collaborates with researchers across multiple institutions, as evidenced by her co-authored publications with scholars from various academic backgrounds. Her work has appeared in reputable journals including OPEC Energy Review, International Journal of Economics and Financial Issues, and International Journal of Energy Economics and Policy.
Jozsef Arato is a Research Fellow at the Vienna Cognitive Science Hub, University of Vienna, where he has worked as a senior scientist for data science since 2019. His research spans multiple interdisciplinary domains within cognitive science, with particular expertise in computational modeling of eye-movement patterns and visual statistical learning. His research interests focus on understanding the principles that guide learning and decision-making through computational modeling approaches. He investigates how eye-movement patterns can reveal learning processes, comparing gaze patterns across different observers. His work spans diverse application areas including art perception, environmental psychology, and animal communication systems. His publication record from 2020-2024 shows a strong focus on eye-tracking methodologies applied to art perception, with significant contributions to understanding how gaze patterns relate to aesthetic experiences. His research also extends to environmental conservation psychology and comparative studies of vocal learning in birds, demonstrating remarkable interdisciplinary breadth. Dr. Arato has been actively involved in academic service, having served as Program Chair of the Pattern Recognition in Neuroimaging Summer School in 2020. He organizes the CogData reading group and contributes to the academic community through collaborative research projects. As an educator, he teaches Scientific Computing in Python (TEWA 1) for the Psychology master's program at the University of Vienna. Previously, he taught Cognitive Modelling at both the University of Vienna and Eötvös University in Budapest, demonstrating his commitment to training the next generation of cognitive scientists and data analysts.
Dr. Ekaterini Mitsiou is a Lecturer at the Department of Byzantine and Modern Greek Studies, University of Vienna. Specializing in late Byzantine history (12th-15th centuries), her research spans intellectual networks, canon law, monastic space, and mobility patterns in the Eastern Mediterranean. Key Research Themes: Byzantine educational systems and manuscript circulation Gender roles in legal and monastic contexts Transcultural interactions with Latins and Muslims Climate history and demographic shifts Digital humanities applications to Byzantine texts Publication Trends: Recent work focuses on ENCHANT project data (2023-2025) analyzing charter networks, 13th-century criminal justice in Epirus, and comparative studies of patriarchal authority. Earlier scholarship (2016-2020) examined monastic economics, women's patronage, and the Latin Empire's administrative structures. Teaching: Currently instructing "Georgios Pachymeres and the Byzantine Historiography of the 13th and 14th Centuries" (2025S) and maintaining a long history of teaching courses on Byzantine history and textual analysis.
Siavash Arjomand Bigdeli serves as an Associate Professor of Computer Vision at the Technical University of Denmark, following prior employment as a scientist at the Swiss Center for Electronics and Microtechnologies (CSEM). His research focuses on: Ante-/Post-Hoc explainability of machine learning models Integration of statistical models in learning/inference processes Philosophical methodologies in artificial intelligence development Advanced computer vision techniques for visual understanding Recent publications reveal consistent specialization in image restoration and stereo vision, employing deep learning architectures and probabilistic graphical models to solve core challenges in visual data reconstruction and temporal coherence. His work demonstrates strong interdisciplinary connections between theoretical machine learning, practical computer vision applications, and epistemological considerations in AI systems.
Md Atiqur Rahman Ahad is a Professor at the University of Dhaka (DU) and a Specially Appointed Associate Professor at Osaka University. His academic career spans multiple institutions across Bangladesh, Australia, and Japan. His educational background includes: B.Sc.(Honors) and Masters from University of Dhaka Masters from University of New South Wales PhD from Kyushu Institute of Technology Professor Ahad is a recognized expert in computer vision and activity analysis. His research focuses on: Computer Vision and Image Processing Sensor-based Activity Analysis Human-Media Interaction Gesture and Activity Recognition Imaging Technologies His recent publication trends show a strong emphasis on activity recognition using various sensor modalities and computer vision techniques. He has published extensively on packaging activity recognition, gait analysis, and hand gesture recognition, demonstrating expertise in both theoretical foundations and practical applications of vision-based activity understanding. Professor Ahad serves in editorial roles for academic journals: Associate Editor for Human-Media Interaction, Frontiers in Computer Science Guest Associate Editor for Big Data Networks, Frontiers in Big Data
Maximilian Jakob Schirl serves as a Junior Researcher at the Centre for Secure Energy Informatics within the Department of Computer Science, Faculty of Natural and Mathematical Sciences at Paris Lodron University of Salzburg. His work focuses on cybersecurity, energy informatics, and data-driven solutions for smart energy systems, contributing to multiple funded research projects including FTZ CyberSec and ECOSINT. His research spans cybersecurity in energy infrastructure, smart meter data analytics, industry 4.0 adoption, and digital readiness assessment. He employs advanced techniques like reinforcement learning and privacy-preserving algorithms to address challenges in local energy communities, supply chain resilience, and sociodemographic profiling from energy consumption patterns. Key methodologies include microaggregation for data anonymization, load profile analysis, and interoperability frameworks for Austrian energy systems. Recent publications demonstrate a strong trend toward machine learning applications in energy informatics, particularly reinforcement learning for production systems and privacy risk assessment in smart grids. His work bridges theoretical algorithms with practical industrial implementations, emphasizing data-driven optimization of low-voltage networks and secure energy community integration. Dr. Schirl actively participates in major funded projects including FTZ CyberSec (2025-2028) for cybersecurity evaluation, DAWN (2025-2026) for data-driven network optimization, ECOSINT (2021-2024) for energy community integration, and DIH West (2019-2023) supporting SME digitalization. These initiatives involve cross-institutional collaborations with researchers like G. Eibl and D. Radovanovic across Austria. As a core member of the Centre for Secure Energy Informatics, he contributes to developing secure, interoperable energy community frameworks through the ECOSINT project and advances privacy-preserving techniques for smart meter data within the FTZ CyberSec initiative.
