Dr. Elżbieta Lewańska is an Assistant Professor at the Department of Information Systems , Poznań University of Economics and Business. Her work focuses on artificial intelligence, blockchain technology, and semantic systems for data analysis and business transformation. Research Interests : AI-driven data visualization (LumiViz, 2025) Blockchain alignment in public registers (2025) Business-IT integration frameworks Publications Trends : 2025: Generative AI for business analytics 2024-2025: Blockchain-enabled business models and adversarial text generation 2020-2022: Supply chain optimization and open data efficiency Awards : Won first place in an international competition for information credibility (2024) Professional Engagement : Active in conferences like BIS 2025, CLEF 2024, ICEIS 2011 Contributed to DIS website development (since 1998)
Emanuele Rodolà is a Full Professor of Computer Science at Sapienza University of Rome, leading the GLADIA group focused on Geometry, Learning, and Applied AI. He holds an ERC Grant and a Google Research Award. Previously, he was an Assistant and Associate Professor at Sapienza (2017-2020), a postdoc at USI Lugano, a Humboldt Fellow at TU Munich, and a JSPS Research Fellow at The University of Tokyo. He is an ELLIS fellow and member of the Young Academy of Europe, with multiple research awards. His work bridges representation learning, geometry processing, graph/geometric deep learning, and audio/visual applications. Recent research includes unlearning mechanisms, neural model dynamics, and 3D shape reconstruction. He actively serves on top-tier conference committees and organizes workshops in AI and computer vision. Education History: JSPS Research Fellowship, The University of Tokyo (2013) Humboldt Fellowship, TU Munich (2013-2016) Postdoc, USI Lugano (2016-2017) Research focuses on foundational AI models, scalable latent space techniques, and interdisciplinary applications like music generation from EEG signals. His 150+ papers span geometric deep learning, diffusion models, and multimodal systems. Awards include ELLIS fellowship and Young Academy membership.
Tom Griffiths is a Professor of Psychology and Cognitive Science at Princeton University, directing the Computational Cognitive Science Lab and co-leading the Princeton Laboratory for Artificial Intelligence . His research spans computational models of human cognition, Bayesian statistics, and AI systems. Key research themes: mathematical foundations of human intelligence, resource-rational analysis of decision-making, cultural evolution, and AI-human alignment Awards: National Science Foundation, Sloan Foundation, American Psychological Association, Psychonomic Society Recent publications focus on large language models, cognitive resource optimization, and cross-disciplinary insights from psychology, computer science, and neuroscience. He is also co-author of the popular science book Algorithms to Live By .
Sinisa Todorovic is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University (OSU). He holds a Ph.D. (2005) and M.S. (2002) from the University of Florida, and a B.S./M.S. from the University of Belgrade (1994). Before joining OSU, he was a postdoc at the Beckman Institute, University of Illinois, and a software engineer at Siemens (1998–2001). His research focuses on computer vision, machine learning, and AI, particularly semantic/instance segmentation, action segmentation in videos, few-shot learning, weakly-supervised learning, and cross-domain adaptation. He leads projects like fruit orchard segmentation datasets and transformer-based cross-domain semantic segmentation. Key contributions include the Volleyball dataset for group activity analysis, Hough Forest Random Fields for object segmentation, and boundary flow estimation. His work bridges theory and applications, including robotics, medical imaging, and sports video analysis. Todorovic advises over 20 graduate students and collaborates on grants involving AI ethics, explainable systems, and agricultural automation. He is affiliated with OSU's Data Science and Engineering and AI/Robotics research groups.
Andrew Perfors is a Professor of Psychology at the University of Melbourne, leading the Complex Human Data Hub and the Computational Cognitive Science Lab within the Melbourne School of Psychological Sciences . His research focuses on applying quantitative methods to understand higher-order cognition, including concepts, language, decision-making, and misinformation dynamics. He employs computational models and experimental approaches to investigate how cognitive constraints and social environments shape human behavior at individual and group levels. Education: PhD in Brain & Cognitive Sciences (MIT, 2008), MA in Linguistics (Stanford, 2000), BSc in Symbolic Systems (Stanford, 1999). Research Themes: Cognitive modeling of decision-making, cultural evolution, language acquisition, and misinformation spread. His work bridges computational methods with psychological experimentation, exploring topics like sampling assumptions in reasoning, trust in information sources, and the cognitive basis of gender categorization. Key Projects: Active grants include Understanding Information and Trust (2018–2025), Bridging the Meaning Gap (2023–2027), and Anti-trans Disinformation Campaigns analysis. Recent work addresses misinformation mitigation strategies and the cognitive naturalness of trans-inclusive gender categories. Grants & Collaborations: Funded by ARC and NHMRC. Collaborations include work on contact-tracing technologies during the pandemic and cross-cultural studies on privacy calculus. Labs & Teams: Directs the Complex Human Data Hub, focusing on societal challenges like misinformation and cultural dynamics. Leads interdisciplinary teams in the Computational Cognitive Science Lab, integrating psychology, computer science, and linguistics.
Dr. Hyojin Park is an Assistant Professor in the School of Psychology at the University of Birmingham, UK. She specializes in neural oscillatory mechanisms underlying human speech and memory, with a focus on audiovisual speech processing and integration. Her research explores how brain oscillations, such as delta and theta rhythms, support attention, memory, and communication in multi-speaker environments. Prior to this role, she held positions as a Research Associate at the University of Glasgow and a Birmingham Fellow at the University of Birmingham. Dr. Park’s educational background includes a PhD in Cognitive Neuroscience from Seoul National University (South Korea) and a BA in Psychology from Kyungpook National University (South Korea). She leads the NEURECA research group, which investigates neural representations and computational models of multi-modal brain activity across the lifespan. The group’s work integrates neuroimaging techniques like fNIRS and EEG to study speech perception, cognitive control, and neural oscillations. Her research interests span neural oscillations, speech perception, and neuroimaging methods. Recent studies highlight the dissociation between prosodic and syntactic speech processing, the role of visual speech cues in multi-speaker scenarios, and the impact of face masks on speech feature reconstruction. She also explores theta phase modulation in auditory detection and delta oscillations’ role in semantic gist extraction. Dr. Park actively recruits PhD students and postdoctoral researchers, emphasizing collaborative projects in cognitive neuroscience and neuroimaging. Her work has been published in high-impact journals such as Current Biology and Journal of Cognitive Neuroscience , with ongoing contributions to conferences and preprint platforms like bioRxiv. She coordinates the NEURECA lab, hosting interdisciplinary collaborations on multimodal brain dynamics. Her research aligns with broader efforts in cognitive neuroscience, clinical neuroimaging, and computational modeling of neural systems.
Prof. Aslı Özyürek is a full Professor at Radboud University's Donders Institute for Brain, Cognition and Behavior and Director of the Multimodal Language Department at the Max Planck Institute for Psycholinguistics in Nijmegen. Her work bridges linguistics, cognitive science, and AI, focusing on multimodal language use in speech, gesture, and sign across cultures. Education: BA in Psychology from Boğaziçi University (Istanbul), PhD in Linguistics/Psychology from University of Chicago. Affiliations include the Center for Language Studies and Donders Institute. Awards include ERC Starting Grant, NWO VIDI/VICI grants, and membership in Academia Europea. Research explores cognitive foundations of language, cross-linguistic diversity, and applications in education/technology. Key projects include spatial cognition in bimodal bilinguals, gesture-speech integration in children, and emergent sign languages. Supervised over a dozen PhD students and led large-scale grants. Labs: Head of Multimodal Language and Cognition Lab (MPI, Radboud University). Coordinates the Nijmegen Gesture Center. Advocates for inclusive science practices and global accessibility in linguistics.
Dr. Benjamin George is an Assistant Professor in the Department of Information Systems and Supply Chain Management at the University of Toledo, USA. He holds a Ph.D. in Management Science from the University of North Texas, alongside an MBA and BBA in Decision-related fields. His research focuses on text mining, operational analytics, e-commerce, and business analytics applications, with a particular interest in healthcare administration and consumer behavior analysis. Education: Ph.D. in Management Science, University of North Texas (2016) MBA in Decision Technologies, University of North Texas (2011) BBA in Decision Science, University of North Texas (2009) Research Interests: Dr. George's work explores novel applications of text mining techniques in healthcare and consumer markets, operational analytics for business decision-making, and the role of e-commerce in shaping modern consumer interactions. He has pioneered frameworks for analyzing online reviews, patient education benefits, and cultural perceptions in healthcare contexts. Publications: His work spans over a dozen journals, including Information & Management and Decision Sciences , with a focus on topics like big data applications in accounting, healthcare service satisfaction, and consumer decision-making models. Recent trends in his publications emphasize blockchain applications, machine learning in e-commerce, and the intersection of analytics with societal phenomena like populism. Awards: None explicitly mentioned, though his editorial role as Associate Editor for Journal of Business Analytics highlights his peer recognition. Advising & Grants: While specific grant details are not provided, his editorial and reviewing roles across multiple journals and conferences indicate active engagement in academic governance. No advisees or formal grant data are listed in the provided text. Labs/Teams: No specific lab affiliations are mentioned, though collaborations with peers on topics like latent semantic analysis and healthcare frameworks suggest interdisciplinary teamwork.
Anna Leshinskaya is an Assistant Professor in the Department of Cognitive Sciences at the University of California, Irvine (UCI), and a Fellow at the Center for Neurobiology of Learning & Memory. She is also affiliated with the AI Objectives Institute. She holds a PhD in Cognitive Psychology from Harvard University (2015). Her research focuses on understanding human cognition, its neural basis, and its implications for artificial intelligence. Key areas include causal learning, semantic memory, and cognitive/moral alignment between humans and AI systems. Education: PhD in Cognitive Psychology, Harvard University, 2015 Research Interests: Causal schema processing in the lateral temporal cortex Relational memory formation in the brain Neural representations of moral and hedonic value in large language models Integration of event experiences into relational knowledge Her work bridges cognitive neuroscience with AI ethics, investigating how human cognitive processes can inform machine learning systems. Publications Trends: Recent work emphasizes causal reasoning mechanisms (e.g., tool selectivity linked to causal inference) and the application of cognitive principles to evaluate AI alignment. This reflects a shift toward translational research connecting brain science with technological development. Awards: No specific prizes mentioned, but her work has been published in high-impact journals like Cerebral Cortex and Cognitive Science. Grants & Labs: Her research is supported by grants related to cognitive neuroscience and AI ethics. She runs a cognitive science lab at UCI, focusing on experimental and computational methods to study human-AI interactions. Affiliations: Active collaboration with interdisciplinary teams at the AI Objectives Institute to address foundational challenges in AI safety and alignment.
Brent Cochran is a Professor at Tufts University School of Medicine in the Department of Developmental, Molecular and Chemical Biology. His research focuses on understanding how cell growth and differentiation pathways are disrupted in cancer, particularly glioblastoma stem cells. Discovered the Jak-Stat signal transduction pathway First to isolate human glioblastoma stem cell lines Developed RNAi screening methods for cancer stem cell research DIRECTOR OF CELL, MOLECULAR AND DEVELOPMENTAL BIOLOGY PROGRAM (2018-2023) EDUCATION: PhD in Molecular Biology from Harvard University (1984) BS in Biology from MIT (1978) RESEARCH HIGHLIGHTS: STAT3 signaling in cancer stem cells Protein network modeling Multi-omics data integration Dynamic causal modeling of tumor systems He teaches Endocrinology to medical and graduate students and directs the first-year journal club. His grants include funding from National Science Foundation , DARPA , and NINDS . Professional memberships span AACR , ISSCR , and Society for Neuro-Oncology .
Ashraf A. Kassim is a Professor at the Singapore University of Technology and Design (SUTD) and serves as Associate Provost for the Office of Education and Innovation. He holds a PhD from Carnegie Mellon University and previously held positions including Professor of Electrical & Computer Engineering at the National University of Singapore (NUS), where he also served as Vice-Provost (Research) and Vice-Dean of Engineering. His industry experience includes research at Texas Instruments developing machine vision systems. His research spans computer vision , medical image analysis , machine learning , and deep learning . Key applications include diagnostic radiography, generative adversarial networks (GANs) for image synthesis, facial landmark detection, and attribute-based fashion retrieval systems. His work integrates advanced neural architectures with real-world challenges in healthcare and multimedia. Publications (170+ articles) emphasize deep learning-driven solutions: recent works focus on medical imaging (cell classification, radiograph analysis), generative models (text-to-image synthesis, GANs), and computer vision applications (facial analysis, fashion retrieval). Awards and Honors: Mendaki Foundation’s Anugerah Cemerlang (Academic Excellence Award) Institution of Engineers Singapore Award (2011) Public Administration Medal (Bronze, 2012) National Day Long Service Award (2018) NUS Annual Teaching Excellence Award Administrative service includes board memberships at Singapore Science Centre, Singapore Synchrotron Light Source, and Centre for Maritime Studies. He contributes to academic committees and journal editorial boards internationally.
Eraldo Ribeiro is an Associate Professor of Computer Science at Florida Institute of Technology (FIT), situated in the College of Engineering and Science's Department of Electrical Engineering and Computer Science. He holds a Ph.D. in Computer Vision from the University of York (2001), an M.Sc. in Computer Science from the Federal University of São Carlos (1995), and a B.Sc. in Mathematics from the Catholic University of Salvador (1992). His research focuses on computer vision, pattern recognition, and machine learning, with emphasis on 3-D shape modeling, image registration, human-motion recognition, and non-rigid deformation analysis. Current projects include underwater video mosaicing techniques and spatio-temporal crime pattern modeling. He serves as an associate editor for Machine Vision and Applications and Journal of Signal, Image, and Video Processing . Ribeiro’s recent work spans diverse applications including automated anuran call classification, pollen grain recognition via CNN-RNN hybrid models, and network-centric approaches to image annotation. His lab’s projects address challenges in medical imaging registration, social media analytics, and biofilm adhesion studies. He collaborates on interdisciplinary initiatives such as coral reef texture classification (using SVMs) and data science for urban crime analysis. Ribeiro’s contributions to computer vision education include teaching CSE4001 and CSE5683 courses at FIT.
Nikolaos Papadakis is an Associate Professor at the Department of Electrical and Computer Engineering of the Hellenic Mediterranean University (HMU). He holds additional roles including Vice President of the Department of Social Work at HMU and Head of the Telecommunication and Informatic Technology team at the same department. His academic journey includes a BSc in Computer Science from the University of Cyprus (1997), an MSc from the University of Crete (1999), and a PhD in Computer Science from the University of Crete (2004). Prior to HMU, he held visiting professorships at the University of Crete, Technical University of Crete, and Technological Educational Institute of Crete, alongside research contributions at FORTH from 1997-2006. His research focuses on Databases and Knowledge Representation Artificial Intelligence Applications Semantic Web Technologies Software Engineering Methodologies Distributed Algorithms and Communication Protocols Renewable Energy Systems (wind, solar, energy efficiency) Notable projects include the SAVE initiative for net-zero sports facilities and studies on wind turbine blade optimization. His work bridges theoretical computer science with applied engineering solutions. Recent publications emphasize sustainable energy systems, composite material analysis, and quantum optics technologies. He maintains active collaborations in interdisciplinary areas such as epidemiological data modeling and space optical systems development. His career trajectory includes tenure at the Technological Educational Institute of Crete (2009-2019), reflecting a consistent commitment to academic innovation and applied research. Current research continues to explore emerging trends in renewable energy integration and advanced material science.
Jonay Tomás Toledo Carrillo is a Full Professor at the University of La Laguna, affiliated with the Department of Computer Science and Systems Engineering within the School of Computer Science and Systems Engineering. He leads the GRULL (Robotics Group of the University of La Laguna), focusing on robotics, control systems, and autonomous systems. He earned his PhD in 2008 with a thesis on nonlinear control strategies for quadrotor helicopters, advised by Dr. Leopoldo Acosta Sánchez. His research interests span robotics, autonomous navigation, sensor fusion, and assistive technologies. Key projects include developing mobility aids for visually impaired individuals and enhancing localization systems through adaptive algorithms. Recent work emphasizes real-time sensor processing (e.g., LSTM networks for odometry), magnetic field control for accessibility, and Kalman filter variants for multi-sensor integration. Publications highlight contributions to robotics applications, including autonomous vehicles, wheelchair navigation, and BCI systems. His work bridges theoretical control methods with practical implementations in unstructured environments. Collaborations include interdisciplinary efforts in computer vision, ontology-based navigation, and educational simulators like MNEME for memory hierarchy teaching. Dr. Toledo Carrillo’s research aligns with the university’s engineering and automation priorities, with a focus on real-world impact through low-cost, high-precision solutions. His team’s innovations address challenges in both robotics engineering and accessibility technologies.
Dr. Matthew R. Boutell is Professor and Associate Department Head of Computer Science and Software Engineering at Rose-Hulman Institute of Technology. He specializes in image recognition, machine learning, and pattern recognition, with additional expertise in robotics education and mobile application development. His research explores: Computer vision applications in photography and chromatography Machine learning techniques for security systems and game AI Innovative approaches to computer science education Entrepreneurial mindset development in technical curricula Analysis of Dr. Boutell's publications reveals three primary themes: Applied computer vision systems leveraging spatiotemporal and contextual features Educational research on instructional methods in computing fields Interdisciplinary applications of machine learning in chemistry and security Dr. Boutell received the Dean's Outstanding Teaching Award (2016) and served as a Fulbright Scholar at Copperbelt University in Zambia (2011-12). He played a key role in establishing Rose-Hulman's multidisciplinary robotics minor and developed courses in Android application development. His educational innovations include integrating robotics into introductory programming courses and creating video-based learning materials.
