Prof. Dr. Mehmet Reşit Tolun is a full-time Professor in the Department of Software Engineering at Çankaya University (Turkey) since 2022. Previously held full-time professor positions at Konya Food and Agriculture University (2020-2022), Aksaray University (2013-2017), and TED University (2011-2013), along with a part-time professorship at Başkent University (2017-2020). Specializes in Artificial Intelligence , Machine Learning , and Data Mining , with a focus on deep learning applications in aerospace, biomedical data analysis, and software process improvement. PhD in Computer Science (University of Kent, 1985) MSc in Computer Science (University of Kent, 1982) BSc in Physics and Computer Science (University of Kent, 1981) Research Interests span deep learning frameworks, hybrid expert systems, software engineering methodologies, and biomedical signal processing. Publications emphasize practical implementations in medical diagnostics, robotics, and agricultural pest detection. Scientific Awards include the IEEE Third Millenium Medal (2000). Supervised over 55 graduate students, including Burak Çetin, Uğur Özotuk, and Mahinur Doğan. Collaborated with researchers from Orta Doğu Teknik Üniversitesi , Çankaya University , and Aksaray University .
Steve Zdancewic is the Schlein Family President's Distinguished Professor and Associate Chair in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. He is a leading researcher in programming languages, formal methods, and computer security with over two decades of impactful contributions to the field. His research interests span programming languages, type theory, logic, computer security, quantum programming, and formal verification. Zdancewic has made significant contributions to information-flow security, memory safety, program synthesis, and the verification of low-level systems. His work often bridges theoretical foundations with practical applications, particularly through the development of verified systems using Coq and other proof assistants. Analysis of his recent publications reveals a strong focus on formal verification techniques, particularly using Interaction Trees and the Coq proof assistant. His research trajectory shows consistent evolution from foundational work on information-flow security toward increasingly sophisticated verification of complex systems including LLVM, quantum computing, and distributed systems. His work demonstrates a commitment to building practically useful verification tools while maintaining rigorous theoretical foundations. Distinguished Paper Award for Semantics for Noninterference with Interaction Trees (ECOOP 2023) Schlein Family President's Distinguished Professor (2021) Distinguished Paper Award for Interaction Trees (POPL 2020) Christian R. and Mary F. Lindback Foundation Award for Distinguished Teaching (2018) IEEE MICRO top picks (2013) Alfred P. Sloan Fellow (2009-2010) NSF CAREER award (2004) Zdancewic has advised numerous PhD students who have gone on to successful careers in academia and industry. His research has been supported by significant grants from NSF, including the NSF Expedition on the Science of Deep Specification. He is actively involved in multiple major research projects including Vellvm (verified LLVM), DeepSpec, and quantum programming verification. Zdancewic also co-organizes Penn's PL Club programming languages research group with Benjamin Pierce and Stephanie Weirich.
Lisa Beinborn is a Professor for Human-Centered Data Science at the University of Göttingen, leading the Human-Centered Data Science group. Her research bridges natural language processing with cognitive science, focusing on multilingual models and interpretability. PhD in Computer Science (2016), Technische Universität Darmstadt MSc in Computational Linguistics (2010), Saarland University & Bolzano, Italy BSc in Computational Linguistics (2008), Saarland University & Barcelona, Spain Her research explores cognitive plausibility in NLP, analyzing how language models process language differently from humans. Key areas include multilingual model interpretability, semantic drift, eye-tracking, and readability prediction. Recent work examines input representation stability in neural models, cross-lingual transfer of complexity, and aligning language models with human cognitive patterns. Her team has presented findings at EMNLP, CoNLL, ACL, and CoLING. VENI Grant for "Interpretability of Transfer in Multilingual Models" Early Career Partnership by Royal Dutch Academy of Science "Most Interesting Paper" Award at BabyLM Challenge "Best Project Award" by Network Institute She has taught courses like Language as Data and Advanced NLP at University of Göttingen, VU Amsterdam, and TU Darmstadt. Her group collaborates with institutions like Gemeente Amsterdam and NT2 on multilingual text simplification and learner correction.
Novi Quadrianto is a Professor of Machine Learning at the School of Engineering and Informatics, University of Sussex, where he joined as a Lecturer in February 2014. He is currently a Principal Investigator on three active EU grants: BayesianGDPR (ERC), TANGO (EU Horizon RIA), and Act.AI (ERC Proof of Concept). He also holds an Adjunct Professor position in Data Science at Monash University, Indonesia, and serves as Strategic Lab co-Leader of the BCAM Severo Ochoa Strategic Lab on Trustworthy Machine Learning in Bilbao, Spain. His educational background includes a PhD in Machine Learning from the Australian National University (2012) and a BEng in Electrical and Electronics Engineering from Nanyang Technological University, Singapore. During his PhD, he conducted research at multiple international institutions including HIIT-Finland, Yahoo! Research-US, University of Alberta-Canada, Fraunhofer IAIS-Germany, and IST Austria. From 2012-2014, he was a Newton International Fellow of the Royal Society at the University of Cambridge. Professor Quadrianto directs the Predictive Analytics Lab (PAL) since 2017, which focuses on "Responsible AI" research developing AI models that embed fairness, accountability, transparency, and trustworthiness. His research spans algorithmic fairness, federated learning, and computer vision, with applications in sustainable development, healthcare, and finance. His work has been funded by prestigious organizations including the European Research Council, EPSRC, and HM Treasury. His publications reveal a strong focus on addressing challenges in AI fairness, robustness, and privacy, particularly in dynamic environments and heterogeneous data settings. Recent work explores performative prediction, diversity-driven learning, and efficient vision transformer inference, demonstrating his leadership in cutting-edge machine learning research. European Research Council ERC Proof of Concept Grant (2023) Guarantor Researcher for BCAM Severo Ochoa Excellence Accreditation (2023) European Lab for Learning and Intelligent Systems (ELLIS) Scholar/Fellow (2020) European Research Council ERC Starting Grant (2019) Newton International Fellowship (2012) Microsoft Research Asia Fellowship (2009) Professor Quadrianto currently supervises six PhD students and five postdoctoral researchers. He has served as Action Editor for Transactions on Machine Learning Research since 2022 and as Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence since 2016. He has also been an Area Chair for major conferences including NeurIPS, ICML, and AAAI. His PAL laboratory hosts a team of 15 members focused on inter-disciplinary AI research with domain experts across various sectors. The PAL Lab operates three innovation strands: AI for Sustainable Development (supporting UN SDGs), AI for Healthcare (transforming health outcomes), and AI for Finance (personalized loan decision-making). The lab also leads initiatives in Diversity & Inclusion in AI and offers Pro-Bono Office Hours to organizations seeking guidance on machine learning aspects.
Antoine Doucet is a Full Professor at the University of La Rochelle, where he teaches in the Computer Science department of the University Institute of Technology (IUT). He conducts his research at the Computer Science, Image and Interaction Laboratory (L3i) within the 'Images and Content' team, which he has led since 2015. He is also a member of the Franco-Vietnamese laboratory ICTLab and serves as Director of the ICT Department at the University of Science and Technology of Hanoi since 2016. His research focuses on information retrieval, natural language processing, text mining, and artificial intelligence, with emphasis on automatic analysis of text in all forms across languages. His work prioritizes generic methods that work across languages without relying on language-specific linguistic resources. This approach is particularly valuable for under-resourced languages and noisy texts from sources like social media or OCR output. As coordinator of the Horizon 2020 NewsEye project, he led efforts to improve access to European historical newspapers through semantic enrichment and advanced search capabilities. His research has practical applications in epidemic surveillance, document fraud detection, and historical content analysis. The NewsEye project involved 11 teams across Europe, including 3 national libraries and multiple research groups. Best paper award from IMIA Yearbook 2016 (among 1,272 candidates) Best paper award at HCI International with Ilona Nawrot Press coverage for ACL 2013 paper in major publications Recipient of French scientific excellence award (Prime d'Excellence Scientifique) Doucet actively supervises PhD and Master's students, with recent advisees including Chloé Artaud (Document fraud detection), Paul Martin (Photograph Time-Stamping), Ilona Nawrot (Temporal and Multilingual Text Analysis), and Gaël Lejeune (Multilingual Epidemic Surveillance). His research has been funded through multiple projects including ANR Digistory, AmeliOCR, PHC Nusantara, and USTH SWARMS. He has also coordinated significant European projects like NewsEye and Embeddia. At L3i, he leads a research group of approximately 40 persons focused on Images and Digital Content. His work bridges theoretical advances in multilingual text processing with practical applications in historical document analysis, epidemic surveillance, and document security.
Maria Gendron is an Assistant Professor in the Department of Psychology at Yale University, where she directs the Affective Science and Culture Lab. Her research examines how emotions emerge through dynamic interactions between social, cognitive, and cultural systems, challenging universalist perspectives on emotion. She employs multidisciplinary methodologies including neuroimaging meta-analyses, cross-cultural fieldwork with indigenous communities, and ambulatory physiological assessments. Research Focus : Dr. Gendron investigates: The role of conceptual/semantic systems in constructing emotional experiences Cultural variation in emotion perception across diverse societies Bio-behavioral synchrony in emotion development Linguistic influences on affective processing Her work integrates frameworks from affective neuroscience and cultural psychology to understand emotion diversity. Publication Trends : Her 15 most recent articles (2010-2025) focus on deconstructing emotion universality through cross-cultural comparisons, with recurring themes of: Cultural relativity in facial/vocal emotion interpretation Language as a contextual framework for emotion Conceptual and semantic mechanisms underlying affect Methodological innovations for studying emotion diversity Lab Leadership : She directs the Affective Science and Culture Lab at Yale, advancing research on emotion perception using multimodal approaches including behavioral experiments, physiological monitoring, and fieldwork.
Weiyu Liu is an incoming Assistant Professor at the Kahlert School of Computing , University of Utah. Previously, he was a Postdoctoral Scholar at Stanford University in the CogAI group and Stanford Vision and Learning Lab (SVL), after completing his Ph.D. in Robotics at Georgia Institute of Technology under the supervision of Sonia Chernova. Ph.D. in Robotics (Georgia Tech) Bachelor's in Electrical Engineering (Georgia Tech) His research focuses on developing robots that can perceive, model, and interact with the real world through structured knowledge representations grounded in language and sensorimotor data. Key areas include language-guided manipulation , long-horizon task execution , and semantic reasoning frameworks for robotic systems. His recent work (2024) explores: Language-annotated demonstration integration (BLADE framework) 3D visual grounding with concept learners Embodied decision-making benchmarks Long-horizon inference challenges 4D instruction grounding from videos Scientific contributions include the RSS Pioneer (2023) recognition and First Place in Fetch It! Mobile Manipulation Challenge (2019) . He advocates for weekly individual mentoring , open research dissemination, and holistic student development in both academic and personal growth.
Dr. Benjamin de Haas is a vision scientist and faculty member at Justus Liebig University Giessen , Germany, within the Department of Psychology and Sports Science . He currently leads the ERC-funded Indivisual project and co-leads project C9 Factors influencing categorical face processing within the Collaborative Research Centre CRC/TRR 135. He is also a principal investigator in the NeurOscientific Workflow Assistance (NOWA) infrastructure project, dedicated to open, reproducible neuroscience. Research Focus Dr. de Haas pursues two intertwined questions: How do early and late stages of visual processing interact—from the initial registration of slanted edges to the recognition of faces? How and why do our perceptions differ from one person to the next? To answer these questions his group combines psychophysics, high-resolution eye-tracking, functional and quantitative MRI, and computational modelling, with a strong emphasis on face perception, individual differences, and naturalistic viewing conditions. Publications Overview Across more than 20 publications since 2016, Dr. de Haas has advanced understanding of individual differences in face processing, gaze control, and visual salience. His work repeatedly appears in Journal of Vision , Nature Communications , PNAS , and NeuroImage , highlighting a sustained focus on eye-movement behaviour, cortical representations of faces and scenes, and methodological best practices in neuroimaging. Current Supervision & Team Dr. de Haas currently supervises two PhD students: Elaheh Akbarifathkouhi Hilal Nizamoglu Together with Dr. Katharina Dobs (co-project leader) and affiliated post-docs and research technicians, the group forms the Indivisual laboratory at Giessen. Contact & Resources Email: Benjamin.de-Haas@psychol.uni-giessen.de Department of Psychology and Sports Science Otto-Behaghel-Str. 10F, 35394 Gießen, Germany
John M. Henderson is a Distinguished Professor at the University of California, Davis , affiliated with the Visual Cognition Lab . He holds additional roles at the Center for Mind and Brain , Center for Vision Science , Center for Neuroscience , and Plasticity and Memory Program . As an editor for Collabra: Psychology and associate editor for Journal of Experimental Psychology: General , he contributes to open science and cognitive research dissemination. Ph.D. in Cognitive Psychology, University of Massachusetts, Amherst (1988) M.S. in Cognitive Psychology, University of Massachusetts, Amherst (1986) B.S. in Psychology, University of Massachusetts, Amherst (1983) Professor Henderson’s research investigates how visual information is acquired, recognized, and integrated into cognitive systems to guide behavior. His work combines scene perception , reading processes , and visual memory using eye tracking , fMRI , brain stimulation , and computational modeling . Recent studies explore semantic guidance of attention in natural scenes, neural correlates of fixation duration, and developmental attentional patterns. His 15 most recent publications (2023-2025) reflect a focus on semantic processing in visual cognition , scene perception , and computational modeling of attention . Topics include meaning-based attentional guidance, deep learning applications in scene analysis, and neural mechanisms of memory-guided eye movements. Collaborations span cognitive neuroscience, developmental psychology, and AI-driven scene understanding. Scientific honors include: Google Scholar Classics recognition (2017) for groundbreaking 2006 paper Fellow of the Association for Psychological Science, American Psychological Association, and Psychonomic Society Grants from the National Eye Institute and National Institute on Aging support his work on visual cognition and aging. His lab trains students in cognitive methods and interdisciplinary research, bridging psychology, neuroscience, and computational modeling.
Prof. Dr. rer. nat. Rasha Abdel Rahman is a leading figure in Neurocognitive Psychology at the Institute of Psychology, Humboldt-Universität zu Berlin . Her research bridges the domains of language production , visual perception , and semantic memory organization , with particular emphasis on electrophysiological mechanisms (EEG) and emotional influences on cognitive processing. Habilitation in Psychology (2008), Ph.D. in Psychology (summa cum laude, 2001), and M.S. in Psychology (1997) from Humboldt-Universität Since 2010: Heisenberg Professor of Neurocognitive Psychology 2009: Heisenberg Fellow Her research interests focus on: Language production mechanisms Interface between vision, semantics, and language Functional organization of semantic memory Attentional and emotional modulation of perception Face and object perception dynamics Mental imagery processes The laboratory employs behavioral and electrophysiological (EEG) methods to investigate how knowledge shapes perception and language processing. Recent studies examine: Emotional content's impact on social judgment AI-generated face perception Art perception influenced by artist morality Trustworthiness effects in visual consciousness Selected scientific awards include: Heisenberg-Fellowship (DFG, 2008) Heinz Heckhausen Junior Scientist Award (DGPs, 2002)
Assoc. Prof. Dr. Ali Şükrü Özbay currently serves as an Associate Professor at Karadeniz Technical University's Faculty of Arts, Department of English Language and Literature. He earned his PhD and MA in Applied Linguistics from Karadeniz Technical University and holds a BA in English Language and Literature from Ankara University. Education : PhD (2015) and MA (2004) from Karadeniz Technical University; BA (1996) from Ankara University Dr. Özbay's research focuses on Corpus Linguistics , Learner Corpora , Academic Writing , Translation Studies , and Language Pedagogy . His work investigates lexical bundles, support verb constructions, and pragmatic markers through computational corpus analysis. Recent publications analyze four-word recurrent expressions in educational technology contexts, collocational priming in Turkish EFL learners' mental lexicon, and semantic prosody of intensifiers in academic corpora. He supervises theses on data-driven learning applications and corpus-based teaching materials. Dr. Özbay has held managerial roles including Deputy Head of Department (2019–present) and Assistant Coordinator for Erasmus+ programs (2017–2021). He actively participates in international conferences and has published in journals like Turkish Studies , Australian Journal of Applied Linguistics , and Hacettepe Eğitim Dergisi .
Lilja Øvrelid is a Professor at the Department of Informatics, University of Oslo, leading the Language Technology Research Group. Her research focuses on syntactic and semantic text processing using machine learning techniques such as dependency parsing, negation analysis, and sentiment analysis. She teaches courses including IN1140: Introduction to Language Technology , IN5550: Neural Methods in NLP , and INF5830: Natural Language Processing . Her academic interests span natural language processing, machine learning, and computational linguistics, with a particular emphasis on Norwegian language technology. Recent publications highlight work in sentiment analysis (including patient feedback), event extraction from Norwegian news, benchmarking language models, emotion analysis for under-resourced languages (Pashto, Farsi-Dari), and bias detection in multilingual models. She actively contributes to the development of Norwegian language resources such as NorBench, NorQuAD, and NoReC. Current projects include BigMed and SIRIUS , focusing on biomedical text mining and AI infrastructure. Collaborations with colleagues like Erik Velldal, David Samuel, and Vladislav Mikhailov are frequent in her work. Despite no explicit mention of scientific awards, her contributions to NLP and computational linguistics are substantial through publications, datasets, and tool development.
Raquel Fernández is a Full Professor of Computational Linguistics and Dialogue Systems at the Institute for Logic, Language & Computation (ILLC), University of Amsterdam. She serves as Vice-Director for Research at ILLC and is a board member of the ELLIS Amsterdam Unit. Her research focuses on interdisciplinary approaches at the intersection of computational linguistics, cognitive science, and artificial intelligence, with emphasis on dialogue modeling, multimodal processing, and language grounding in visual/social contexts. Her work is supported by prestigious grants including the European Research Council (ERC Consolidator Grant 819455) and multiple Dutch Research Council (NWO) awards (VENI, VIDI, Aspasia). She has received scientific recognition such as the Outstanding Paper Award at EMNLP and Best Data Award at GenBench Workshop. Her recent publications analyze multimodal dialogue systems, visual storytelling evaluation consistency, and co-speech gesture modeling, reflecting trends in Linguistic-Cognitive Integration , Multimodal AI , and Contextual NLP . She leads the Dialogue Modelling Group and has been actively involved in academic leadership as co-president of SemDial, VP-Elect for SIGDAT, and ethics chair for major conferences like COLM. Scientific Awards ERC Consolidator Grant 819455 NWO VENI/VIDI/Aspasia grants Outstanding Paper Award at EMNLP 2023 Best Data Award at GenBench Workshop Elected ELLIS Fellow 2023
Prof. Dr. Frank Kammerzell is a Professor at the Department of Archaeology and Cultural History of Northeast Africa within Humboldt University of Berlin since May 2003. His academic career includes habilitation at the University of Göttingen (1999) and extensive research collaborations with Hebrew University of Jerusalem and University of Vienna. His educational background encompasses Medieval and Modern History (major 1980), Egyptology (major), Coptic Studies, General and Indo-European Linguistics, and Assyriology. He earned his Dr. phil. from Göttingen with a dissertation on 'Studies on the Language and History of the Carians in Egypt' (1990) and completed habilitation with 'Language contact and language change in ancient Egypt' (1999). Kammerzell's research spans Egyptian phonology, morphology, syntax, lexis, language history, diachronic typology, language contact, writing systems, and Carian history in Egypt. His work integrates linguistic analysis with archaeological contexts, examining language evolution through contact and typological shifts. Notable projects include studies on Egyptian-Akkadian typological developments and classifiers in ancient Egypt. His publications since 2000 demonstrate sustained focus on Egyptian linguistic structures, writing systems, and cross-linguistic influences. Key themes include verb classifiers, glossing rules for Ancient Egyptian, and lexical semantics-pragmatics interfaces, bridging Egyptology with broader linguistic theory. Graduate Scholarship from the State of Lower Saxony (Doctoral Scholarship) 1987-1989 Kammerzell has led significant research projects funded by the DFG, Fritz Thyssen Foundation, and Volkswagen Foundation. His collaborative work with institutions in Jerusalem and Göttingen highlights international partnerships. While specific advisees aren't listed, his editorial roles suggest mentorship in academic publishing. He co-founded research groups on multilingualism and script systems, contributing to journals like Lingua Aegyptia and Ägypten und Levante , underscoring team-based archaeological-linguistic research.
Charless C. Fowlkes is a Professor in the Department of Computer Science at the University of California, Irvine (UCI), and a member of the UCI Vision Group. His research focuses on computational vision, integrating visual recognition with 3D scene understanding and developing tools for biological image analysis. UCI Chancellor's Fellow (2019-2022) NSF CAREER Award recipient (2013) Helmholtz Prize winner (2015) Research Interests His work spans computational vision, image understanding, 3D scene reconstruction, and machine learning applications in biological and forensic domains. He develops methods for automated pollen classification, cardiac tissue analysis, and forensic shoeprint matching. Recent Publications His recent work includes 3D scene reconstruction with epipolar transformers, forensic shoeprint analysis, and image inpainting techniques. These show trends in integrating geometric understanding with deep learning. Scientific Awards Awarded the Marr Prize (2009), Helmholtz Prize (2015), and NSF CAREER Award (2013), he has received recognition for both theoretical and applied contributions to computer vision. Teaching & Advising He has taught graduate and undergraduate courses in computer vision since 2008 and advised numerous PhD, MS, and BS students who now work at institutions like Google, Apple, and CMU. Collaborations He collaborates with labs at UIUC (Punyasena Lab), Harvard (DePace Lab), and UCI (Cinquin Lab, Khine Lab) for biological applications of computer vision.
