Shafaq Khan is an Assistant Professor in the School of Computer Science at the University of Windsor. She holds a PhD in Computer Science from the University of Salford (2017). Her research spans machine learning, deep learning, data analytics, and database systems with applications in healthcare informatics, agricultural technology, educational systems, and blockchain. Recent work focuses on AI-driven healthcare transformation, privacy-preserving data methods, federated learning for disease prediction, and computer vision applications in agriculture. Additional interests include educational technology for addressing disparities, blockchain implementations in government services, and open-source search engine development. Her work demonstrates consistent integration of cutting-edge computing techniques with practical domain applications.
Paul Cohen is a Professor of Computer Science at the University of Pittsburgh's School of Computing and Information (SCI), where he also directs the Modeling and Managing Complicated Systems Institute (MOMACS). Previously, he served as the founding Dean of SCI from 2017 to 2020. Before joining Pitt, he was a Program Manager at DARPA (2013–2017), leading initiatives like Big Mechanism and Communicating with Computers. Earlier roles include founding director of the University of Arizona’s School of Information: Science, Technology and Arts (SISTA), and professor at the University of Southern California’s Information Sciences Institute and the University of Massachusetts. Education: PhD in Computer Science and Psychology (Stanford University), MS in Psychology (UCLA), BS in Psychology (UC San Diego). Research Interests: Focuses on artificial intelligence, machine learning, natural language processing, and modeling complex systems like cell signaling pathways and socio-environmental interactions. His work emphasizes explainable AI, human-computer communication, and interdisciplinary problem-solving. Key Contributions: Authored Empirical Methods for Artificial Intelligence and over 200 peer-reviewed articles. His research spans robotics, education technology (e.g., the AnimalWatch tutoring system), and collaborative analysis tools like COLAB. He has won a Telly Award for his video on systemic challenges and a Best Paper award for spatial language learning frameworks. Awards & Recognition: Elected Fellow of the AAAI, recipient of the Telly Award, and winner of the Best Paper Award at the IEEE Conference on Development and Learning. Leadership & Outreach: Advocates for polymathy in education to address global challenges. His work includes developing curricula for complex systems thinking and promoting diversity in STEM through initiatives like AnimalWatch.
Dr. Michele Kennerly is an Associate Professor at Pennsylvania State University, holding joint appointments in the Department of Communication Arts and Sciences and the Department of Classics and Ancient Mediterranean Studies. She is affiliated with the College of the Liberal Arts. In addition to her teaching and research, she has held significant leadership roles, including President of the American Society for the History of Rhetoric (2019–2021) and Secretary General of the International Society for the History of Rhetoric (2020–2025). She is also a member of the Leadership Board for the Colloquium for Ancient Rhetoric and the Advisory Board of Brill's book series International Studies in the History of Rhetoric . Alongside Damien Smith Pfister and Casey Boyle, she co-founded and co-edits the University of Alabama Press series Rhetoric + Digitality . Her work bridges classical rhetoric with contemporary digital and media studies. Education: Michele Kennerly received her B.A. from Austin College in 2004, followed by an M.A. and Ph.D. from the University of Pittsburgh in 2006 and 2010, respectively. Her academic journey reflects a deep engagement with classical and rhetorical studies, culminating in her current interdisciplinary research. Research Interests: Dr. Kennerly’s research focuses on rhetorical theory and its historical reception, particularly in the ancient Mediterranean world and their modern resistive interpretations. She explores how classical texts and concepts influence contemporary discourse, including their roles in automation discussions, queer film adaptations, and medieval rhetorical practices. Her current projects include analyzing invocations of ancient Athens in automation discourse and examining the rhetorical strategies in Jane Campion’s film adaptation of The Power of the Dog and 13th-century Italian notary Brunetto Latini’s works. She emphasizes interdisciplinary approaches, integrating rhetoric with poetics, informatics, and media studies. Her scholarly contributions span classical rhetoric, digital humanities, and media studies. Recent work highlights the intersection of ancient rhetorical theories with modern technologies and cultural phenomena, such as automation, digital networks, and film. She critically examines how historical concepts are reinterpreted in contemporary contexts, reflecting her commitment to linking past and present rhetorical practices. Outstanding Teaching Award for Tenure-Track Faculty (2021) At Penn State, she directed the graduation-required communication course for six years and the undergraduate program in Communication Arts and Sciences (CAS) for three years. She mentors students in collaborative and independent research on the history of rhetoric. Her grants and leadership roles underscore her pedagogical and scholarly impact. Labs/Teams: She contributes to projects like Ancient Rhetoric + Digital Networks and collaborates on the Rhetoric + Digitality book series. She actively engages with academic societies and editorial boards to advance interdisciplinary rhetoric studies.
David R. Kauchak is a Professor of Computer Science at Pomona College, part of The Claremont Colleges. He has been affiliated with the institution since 2014. His academic background includes a Ph.D. (2006) and M.A. (2006) in Computer Science from the University of California, San Diego, and a B.S. (2005) in Computer Science from the University of Utah. His research focuses on Natural Language Processing (NLP) , particularly text simplification , aiming to reduce text complexity while preserving content. Applications include improving health literacy through simplified medical text and enhancing accessibility of technical documents. He has also contributed to machine learning methodologies and information retrieval systems. Key awards include the Distinguished Poster Award at AMIA 2016 and the Best Undergraduate Paper Award at SocalNLP 2018. His work frequently addresses interdisciplinary challenges in healthcare communication, education technology, and software tool development. Teaching highlights include courses in Artificial Intelligence , Algorithms , Natural Language Processing , and foundational computer science modules. He has advised numerous projects in computational linguistics and health informatics, though specific student names are not explicitly listed in the provided materials. His collaborations span academic and medical domains, including joint research with institutions like the University of Utah and projects funded through initiatives like the German Climate Modeling Initiative (though this appears unrelated to his primary work). He maintains an active presence in academic conferences, publishing in venues such as ACL, AMIA, and IEEE journals.
Dr. Indratmo is an Associate Professor and Chair of the Department of Computer Science at MacEwan University. He holds a PhD from the University of Saskatchewan, an M.Sc. from the University of Manitoba, and a B.Eng. from Petra Christian University. His research focuses on information visualization, human-computer interaction, and social computing, with a particular emphasis on developing tools for analyzing social media data. He has contributed to projects like a visual analytical tool for sentiment analysis in Edmonton's traffic-related social media data and studies on multimedia content effectiveness in communication strategies. Indratmo teaches a range of computer science courses, emphasizing student engagement through transparent pedagogical practices. His work bridges technical innovation with social impact, aiming to enhance communication strategies for organizations through data-driven insights. He has published extensively in journals like Big Data Research and Visual Informatics , and his research spans topics from educational visualization tools to smart mirror applications and geospatial heritage systems. Outside academia, he enjoys outdoor activities in the Canadian Rockies. Notable collaborations include work on stacked bar chart efficacy, web-based course registration models, and exploratory browsing frameworks. His research portfolio demonstrates a commitment to both theoretical advancement and practical applications in computing.
Larissa Gomes Franca is a Research Fellow at the University of Cambridge's Department of Materials Science & Metallurgy, affiliated with the Photoactive Materials Group. She holds a PhD from Durham University as a Marie Skłodowska-Curie Early-Stage Researcher and earned her BSc and MSc in Physics at the Federal University of Santa Catarina, Brazil. Her research focuses on energy-efficient materials for optoelectronic applications like OLEDs , solar cells , and photon upconversion . She explores stimuli-responsive liquid crystal hosts for spectral conversion systems and investigates photophysical processes in organic materials using optical spectroscopy techniques. Key publication trends include advancements in thermally activated delayed fluorescence (TADF) , room temperature phosphorescence , and triplet-triplet annihilation for energy upconversion. Her work bridges materials informatics with liquid crystal engineering to enhance solar energy harvesting. Scientific awards: Royal Commission for the Exhibition of 1851 Research Fellowship (2023) Contact: lg735@cam.ac.uk | Personal Webpage
Richard E. Pattis is a Professor of Teaching at the University of California, Irvine , affiliated with both the Department of Computer Science and the Department of Informatics within the Donald Bren School of Information and Computer Sciences . His primary role focuses on undergraduate education, emphasizing innovative teaching methods and curriculum design. He teaches courses such as ICS 33 (Intermediate Programming), ICS 193 (Tutoring in ICS), and has developed educational materials like the EBNF chapter for programming syntax. His work integrates pedagogical approaches with practical programming concepts, prioritizing student engagement and critical thinking. Pattis actively curates education-related video clips and maintains a repository of quotations relevant to learning and programming. His research interests include formal language theory in education, the application of EBNF in introductory courses, and fostering effective debugging practices. He emphasizes the importance of clear communication and problem-solving in computer science education.
Prof. Dan Olteanu is a full professor at the Department of Informatics, University of Zurich, leading the Data Systems and Theory (DaST) group. He holds visiting professorships at the University of Oxford and is an emeritus fellow of St Cross College. His academic journey includes a PhD from Ludwig Maximilian University (2005), postdoctoral roles at Saarland University and Cornell University, and prior faculty positions at Oxford (2007–2020). He has also worked in industry with companies like LogicBlox and RelationalAI, focusing on database systems and AI. Education: Bachelor’s in Computer Science, Politehnica University of Bucharest (2000) PhD in Computer Science, Ludwig Maximilian University (2005) Professional Roles: Full Professor, University of Zurich (since 2020) Visiting Professor, University of Oxford Emeritus Fellow, St Cross College Editorial Roles: ACM TODS, VLDBJ, SIGMOD Conference Chair: ICDT Council (since 2022) His research focuses on data systems theory, including query optimization, probabilistic databases, factorized databases, and in-database machine learning. He co-authored the seminal book Probabilistic Databases (2011) and has pioneered algorithms for efficient machine learning over relational data and incremental maintenance of analytical workloads. His work emphasizes scalable, theoretically grounded solutions for real-world data challenges. Awards: ICDT 2019 Best Paper Award ACM SIGMOD 2018 Distinguished PC Member Award ERC Consolidator Grant (2016) Oxford Outstanding Teaching Award (2009) Grants & Funding: Supported by Google, Microsoft Azure, Amazon AWS, EPSRC, and the European Commission. His research bridges academia and industry, with contributions to commercial systems like LogicBlox and RelationalAI. Labs & Teams: Heads the DaST group at Zurich, focusing on data systems theory and applications. Collaborates widely in the database and AI communities.
Dr. Jia Wu is an Associate Professor and Research Director of the Centre for Applied Artificial Intelligence at Macquarie University. He holds a PhD in Computer Science from the University of Technology Sydney (2009) and is an IEEE Senior Member. His research focuses on artificial intelligence, data mining, graph neural networks, and anomaly detection, with over 200 publications in top-tier journals/conferences like IEEE TPAMI, TKDE, and conferences like KDD, IJCAI, and NeurIPS. He has received awards including the Heidelberg Laureate Forum Fellowship (2019) and multiple best paper awards. Education: PhD in Computer Science (UTS, 2009). Current roles include Director of HDR (Higher Degree Research) and Associate Editor for IEEE TNNLS and ACM TKDD. He leads projects in AI-driven cybersecurity, personalized banking solutions, and disaster response systems. Research interests emphasize graph-based learning, fake news detection, and deep learning applications. His recent work explores hypergraph neural networks for fraud detection and brain graph analysis for neurological disorders. He has pioneered scalable semi-supervised clustering techniques and transformer-based hypergraph models for anomaly detection. Awards include CIKM'22 Best Paper Runner-Up, ICDM'21 Best Student Paper, and the 2023 Faculty of Science and Engineering Collaboration Award. His work spans 13 active research projects, including mitigating AI deepfakes in identity systems and enhancing disaster response networks through graph-based simulations. Labs/Teams: Leads teams in the Data Horizons Research Centre, Future Communications Research Centre, and Hearing Research Centre. Collaborates internationally in AI, data mining, and social network analysis.
Professor Daniel Angus is a faculty member at Queensland University of Technology (QUT), holding the position of Professor of Digital Communication in the School of Communication and serving as Director of QUT's Digital Media Research Centre (DMRC). His research focuses on computational methods applied to communication and media studies, with a particular emphasis on AI, automation, misinformation, and digital societal impacts. He holds a PhD in computer science from Swinburne University of Technology and has extensive experience in interdisciplinary research across computer science, design, communication, linguistics, and journalism. Affiliations: ARC Centre of Excellence for Automated Decision Making & Society, ARC Centre of Excellence for the Dynamics of Language. Research Projects: Leads projects like 'Using Machine Vision to Explore Instagram’s Everyday Promotional Cultures' and 'Evaluating the Challenge of ‘Fake News’ and Other Malinformation'. Research Interests: Daniel’s work bridges technology and society, exploring AI ethics, algorithmic transparency, social media governance, and computational methodologies for analyzing communication patterns. He develops tools like Discursis and PauseCode to study discourse and conversational dynamics in healthcare, aged care, and media contexts. Grants & Awards: Principal Investigator on multiple ARC grants and collaborates with industry stakeholders to address challenges like unhealthy food advertising and platform accountability. His research has informed policy submissions to parliamentary committees on social media regulation and AI adoption. Supervision: Current PhD students focus on topics like algorithmic transparency, computational methods for meme analysis, and AI in publishing. Labs/Teams: Directs the Digital Media Research Centre, fostering interdisciplinary projects on digital culture and platform studies.
Davide Donadio is a Professor of Chemistry at the University of California, Davis. His research focuses on molecular modeling and simulations of materials, particularly in non-equilibrium processes, thermal transport, and nanostructure assembly. He leads the Naotheory Group, which develops predictive multiscale models for energy-related materials. Education : Habilitation in Materials Science, Italian Ministry for University and Research (2013) Ph.D. in Materials Science, University of Milano (2003) M.S. in Physics, University of Milano (1998) Research Interests : His work spans molecular-level understanding of energy conversion, thermal management, and nanostructure formation. Key areas include phononics, thermoelectrics, and interfacial phenomena in materials like ice surfaces, semiconductors, and clathrates. He employs machine learning and first-principles methods to bridge simulation and experiment. Awards : UC Davis Hellman Fellow (2017–2018) Young Scientist Award, Italian Institute for the Physics of Matter (1998) Grants & Labs : His funding and collaborations drive advancements in nanostructured materials and computational tools like PLUMED tutorials. The Naotheory Group actively publishes in high-impact journals and collaborates internationally on thermal transport and materials design.
Arianne Teherani, PhD, is Professor in Residence in the Department of Medicine at the University of California, San Francisco (UCSF) School of Medicine. She serves as Founding Co-Director of the UC Center for Climate, Health and Equity and Director for Program Evaluation and Education Continuous Quality Improvement at the UCSF School of Medicine. She earned her undergraduate degree in Social Ecology from the University of California, Irvine, her master's and doctorate in Education from the University of Southern California, and completed a fellowship in Higher Education at the University of British Columbia. Dr. Teherani’s research advances equity, social justice, and climate solutions in health professions education. Her work identifies and dismantles practices that perpetuate educational disparities. She leads initiatives in equitable clinical assessment, climate-health education, and sustainable healthcare training. Her scholarship focuses on educational reform, faculty development, and community-engaged learning. She has published extensively on topics including clerkship equity, professionalism, longitudinal integrated clerkships, and the integration of climate change into medical education. Her recent publications demonstrate a strong trend toward climate-health education, healthcare decarbonization, and equity in assessment. She frequently collaborates with national and UC-system-wide teams on sustainability and educational innovation. Her work emphasizes systems-level change and the translation of research into policy and practice. UCSF Faculty Sustainability Award UC Sustainability Champion Award Faculty Climate Action Champion (University of California system) Dr. Teherani mentors students, residents, fellows, and faculty in health professions education. She has led numerous educational initiatives, including the Aspiring Physicians Program and the Academic Leadership Academy, aimed at supporting underrepresented students. She has also driven continuous quality improvement in medical education through rigorous evaluation of innovative programs. Her leadership extends to national organizations including the Association of American Medical Colleges, the American Educational Research Association, and the National Academies of Science, Engineering, and Medicine. She leads the Equity and Justice in Education initiative and is deeply involved in antiracism and DEI efforts. She co-leads faculty development programs focused on sustainable healthcare and climate education across the UC system.
Prof. Joaquin GARCIA ALFARO is a Professor at Telecom SudParis, affiliated with the SCN department. His research focuses on cybersecurity, network security, quantum computing applications, and resilience engineering in cyber-physical systems. He has contributed to advancements in intrusion detection systems, blockchain integration in cellular networks, and privacy-preserving frameworks for IoT and healthcare. University: Telecom SudParis Key Research Areas: Cybersecurity, Quantum Computing, IoT Security, Resilience Engineering Labs: SAMOVAR laboratory His work emphasizes practical solutions for real-world challenges, including secure data provenance, digital twin implementations, and energy-efficient edge computing. Recent research explores quantum-resistant protocols and collaborative drone systems.
Ying Wang is an Associate Professor in English linguistics at Karlstad University since 2020, specializing in English for academic purposes, applied corpus linguistics, and second language writing. She holds a PhD from Uppsala University (2013) and has taught courses at both undergraduate and graduate levels focusing on academic writing, second language pedagogy, and corpus methodology. Her research explores rhetorical structures in disciplinary genres, evaluative language resources, and the impact of extramural English activities on L2 writing development. Notable projects include the Swedish Learner English Corpus (SLEC) initiative and analyses of predatory publishing practices in political science. She has also examined government communication strategies during the UK's COVID-19 pandemic response through corpus-assisted discourse studies. Key research contributions span formulaic language use in ELF contexts, methodological innovations in corpus linguistics, and linguistic comparisons between well-established and predatory journals. Her work bridges theoretical linguistics with practical applications in education and scholarly publishing ethics. Publications span prestigious journals like English for Specific Purposes , Text & Talk , and Journal of Second Language Writing , reflecting her interdisciplinary approach to language studies. Current projects emphasize corpus-driven research on academic communication practices and their pedagogical implications.
Jordan Boyd-Graber is a Professor in the Department of Computer Science at the University of Maryland's College of Computer, Mathematical, and Natural Sciences. He serves as a leading researcher in Natural Language Processing with significant contributions across multiple NLP subfields. His work bridges theoretical advances with practical applications requiring human-AI collaboration. His research interests span Natural Language Processing , Question Answering systems , Human-AI collaboration , Machine Translation , and Topic Modeling . He focuses on developing systems that work effectively with humans rather than replacing them, emphasizing interpretability and user-centered design. His work often involves creating evaluation frameworks that better capture real-world utility rather than just technical metrics. His publication record shows consistent leadership in the field, with numerous papers at top venues including ACL, EMNLP, and NAACL. Recent work (2023-2024) demonstrates strong engagement with LLMs, human evaluation methodologies, and practical applications in health and translation domains. His research often involves student collaborators, indicating active mentorship. ACL Fellow (2021) Program Chair for ACL 2023 Organizer of prompt hacking competition Leader in human-centered NLP evaluation Boyd-Graber has secured substantial funding for his research, particularly in projects involving human-AI collaboration and question answering systems. His work often involves interdisciplinary teams spanning computer science, linguistics, and domain-specific applications. He has mentored numerous graduate students who have gone on to successful careers in academia and industry. He leads research groups focused on developing interpretable NLP systems that work effectively with humans, particularly in high-stakes domains like healthcare and education. His lab frequently develops novel evaluation methodologies that better capture real-world utility rather than just technical metrics.
