Michele Ripplinger is a Berkeley Lecturer and Ph.D. candidate in English and Medieval Studies at UC Berkeley. Her research focuses on late medieval literature, particularly Chaucer and the reception of classical texts, with emphasis on gender studies, representational ethics, and women’s reading practices. She teaches across historical periods, integrating cultural studies and poetry analysis. Education: Pursuing a Ph.D. in English and Medieval Studies at UC Berkeley (dissertation on Chaucer and the Moralized Ovid). Research Interests: Medieval and early modern classics reception, fictionality, history of reading, women’s/gender/sexuality studies, and intersections between premodern and modern literary thought. Her publications and conference presentations highlight work on Chaucer, Hoccleve, and medieval literary methodologies. She has presented at major venues including the New Chaucer Society Congress and the International Congress on Medieval Studies. No scientific awards are listed. Advising and grants details are not provided. Office hours are by appointment.
Jackie Chit Kit Cheung is an Associate Professor in the School of Computer Science at McGill University, where he co-directs the Reasoning and Learning Lab. He holds the Canada CIFAR AI Chair and serves as an Associate Scientific Co-Director at the Mila Quebec AI Institute. He is also a consulting researcher at Microsoft Research Montreal. Academic Background: Ph.D. in Computer Science, University of Toronto (2010–2014) M.Sc. in Computer Science, University of Toronto (2008–2010) B.Sc. (Honours) in Computer Science, minors in Linguistics and German, University of British Columbia (2004–2008) Research Interests: Jackie Cheung's research lies at the intersection of natural language processing, machine learning, and cognitive science. He focuses on natural language generation , automatic summarization , commonsense and pragmatic reasoning , and the evaluation of NLP systems . His work aims to build language models that reflect real-world structure and reasoning, with applications in health, education, and language revitalization. He is particularly interested in how implicit meaning is processed in context, a core concern in linguistic pragmatics. Publication Trends: His recent publications (2024–2025) show a strong emphasis on evaluation methodologies (e.g., COSMIC, ECBD), hallucination and factuality in language models, coreference and reasoning , and long-context modeling . He frequently collaborates across institutions and integrates insights from linguistics and psychology into NLP system design and analysis. Scientific Awards: Best Paper Award, ACL 2018 Outstanding Paper Award, NAACL 2018 Student Research Workshop SAC Award, ACL 2024 (for COSMIC) Best Poster Award, CMDO AI-Health Symposium 2024 Advising and Grants: He advises a large and diverse group of graduate students, including PhD and Master’s candidates, often in co-supervision with other faculty. His group has received support from major AI and health research initiatives, including CIFAR and Mila. He has trained alumni who have gone on to faculty and industry research positions. He has served in leadership roles in top NLP conferences, including as Senior Area Chair, Workshop Chair, and Program Chair of Canadian AI 2018. Labs and Teams: He co-directs the Reasoning and Learning Lab at McGill and is deeply involved with Mila – Quebec AI Institute . He founded the NLP Reading Group at McGill, which brings together researchers from computer science, linguistics, and information studies to discuss theoretical and applied NLP topics.
Alexei A. Efros is a Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at UC Berkeley, where he holds the Howard Friesen Professorship and is affiliated with the Berkeley Artificial Intelligence Research (BAIR) Lab. He previously served on the faculty at the Robotics Institute of Carnegie Mellon University (CMU) and completed a postdoctoral fellowship at the University of Oxford. His research spans data-driven computer vision, self-supervised learning, computational photography, and applications to computer graphics and robotics. His research interests include: Data-Driven Computer Vision Self-Supervised and Unsupervised Learning Generative Models and Image Synthesis Visual Representation Learning Applications in Robotics and Human-Computer Interaction Intersections with Human Vision and the Humanities The recent publications highlight a strong trend toward self-supervised learning, visual reasoning, and generative modeling, particularly diffusion models and 3D scene understanding. His work increasingly bridges computer vision with language, robotics, and cognitive science, emphasizing interpretability and real-world applicability. There is a clear focus on leveraging unlabeled data and developing methods for robust, generalizable AI systems. His scientific awards and recognitions include: Berkeley Fellowship Google Fellowship Soros Fellowship NSF Fellowship SIGGRAPH Outstanding Doctoral Dissertation Award Facebook Fellowship Adobe Fellowship CMU School of Computer Science Distinguished Dissertation Award ACM Doctoral Dissertation Honorable Mention Alexei Efros has advised numerous PhD students and postdocs, many of whom have gone on to faculty positions at top institutions including CMU, Stanford, MIT, Columbia, NYU, and Georgia Tech. His lab has received research funding from major tech companies and federal agencies, though specific grants are not detailed in the text. He teaches core computer vision and machine learning courses at both undergraduate and graduate levels at UC Berkeley. His research group is highly active, with ongoing projects in 3D perception, generative modeling, and vision-language systems. He leads a vibrant research lab at UC Berkeley, part of the BAIR consortium, collaborating with leading researchers such as Jitendra Malik, Trevor Darrell, Pieter Abbeel, and Angjoo Kanazawa. His lab fosters strong interdisciplinary connections with institutions worldwide, including Oxford, INRIA, and École Normale Supérieure.
Tim Althoff is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington, specializing in Artificial Intelligence and Human-Centered Computing. His research focuses on behavioral data science, combining Data Science Natural Language Processing Social Computing Human-Centered AI Ethics & Fairness to extract insights about health and well-being. Recent publications highlight advancements in: Mental health support through AI Wearable sensor health monitoring Online community analysis Reproducibility in data science Public health interventions with notable papers in ACL, Nature Machine Intelligence, and NeurIPS. Scientific recognition includes: ACL 2023 Outstanding Paper Award WWW 2021 Best Paper Award Double ICWSM 2021 Best Paper Awards SIGKDD Dissertation Award 2019 Fulbright Scholarship German National Merit Foundation Actively mentoring postdoctoral researchers and seeking PhD students in areas like neural representation learning, NLP applications to psychology, and mobile health technologies through his Behavioral Data Science Group .
Prof. Dr. Claudine Moulin is a Full Professor of Historical Linguistics at the Department of German Studies, University of Trier. She co-directs the Trier Center for Digital Humanities and serves as Vertrauensdozentin der Deutschen Forschungsgemeinschaft. With academic roots in Brussels and Bamberg, she completed her PhD in 1990 and Habilitation in 1999 at the University of Bamberg. Her research spans medieval and early modern languages, manuscript studies, digital humanities, and Luxembourgish linguistics. As a leading scholar in historical linguistics, Moulin's work explores urban language history, phraseology, and cultural knowledge transmission through manuscripts. She has directed major digital humanities projects like LexicoLux and Cartul@rium, while pioneering digital codicology methods for medieval texts. Her research combines traditional philology with computational approaches. Her academic accolades include the Akademiepreis Rheinland-Pfalz (2010), Landesverdienstorden (2014), and the Verdienstkreuz am Bande (2025). She has held visiting professorships at Sorbonne, EHESS Paris, and EPHE, and received fellowships from the Humboldt Foundation and European Science Foundation. Notable Academic Roles: Co-founder and Chair of DHd (Digital Humanities in German-speaking countries) Founding member of Trier Center for Language and Communication Member of German Historical Institute Paris scientific board Advisory positions at Austrian Center for Digital Humanities and Herzog August Bibliothek Her editorial leadership includes chief editorship of Sprachwissenschaft journal and co-editorship of Germanistische Bibliothek monograph series. She has mentored over 40 graduate students in topics ranging from medieval marginalia to urban language studies.
Haijun Xia is an Assistant Professor at the University of California, San Diego (UCSD), where he directs the Foundation Interface Lab and contributes to the Cognitive Science and Design Lab. His work focuses on Human-Computer Interaction (HCI) and Human-AI Collaboration , emphasizing the development of dynamic, malleable interfaces that integrate human cognition with intelligent systems to enhance thinking and working paradigms. Education: Bachelor's, Tsinghua University Master's and PhD, University of Toronto His research spans generative AI , interface design , and creative collaboration , with applications in data visualization, programming, and scholarly work. He advocates for treating information as a malleable material that can be flexibly manipulated by users and AI agents. Recent publications highlight advancements in: 2025 CHI Conference - Generative user interfaces, malleable overview-detail designs, and compositional structures for human-AI co-creation 2024 CHI Conference - Structured design space exploration with LLMs, ASCII diagram analysis, and adaptive presentation systems 2023 CHI/UIST Conferences - Data particle visualization, contextual logging tools, and metaphor generation for science communication Scientific recognitions include: Multiple Best Paper Awards and Honorable Mentions at CHI and UIST Hellman Fellowship and grants from NSF , Microsoft , Google , Meta , Apple , and Adobe He actively recruits undergraduate and graduate interns and has an upcoming postdoc position in early 2025 for researchers interested in foundational interface development.
Lior Wolf is a Professor at the School of Computer Science, Tel Aviv University. Previously, he was a postdoctoral researcher at MIT's Center for Biological and Computational Learning (CBCL) under Prof. Tomaso Poggio and earned his PhD from Hebrew University of Jerusalem with Prof. Amnon Shashua. His educational background includes: PhD in Computer Science, Hebrew University of Jerusalem Postdoctoral Research, MIT CBCL Prof. Wolf's research centers on artificial intelligence with seminal contributions to deep learning, computer vision, and natural language processing. His work bridges theoretical foundations (e.g., attention mechanisms, transformer analysis) with practical applications in medical imaging, speech processing, and sign language technology. He pioneered methods for neural network interpretability, efficient sequence modeling, and multimodal fusion. Analysis of his 2023-2025 publications reveals dominant trends in large language model optimization (neuron pruning, attention analysis), efficient video generation, and cross-modal learning. His work increasingly integrates medical applications (fMRI/EEG analysis) while maintaining theoretical rigor in model architecture design. His scientific achievements include: Best paper award at EMNLP 2024 for 'Backward Lens: Projecting Language Model Gradients into the Vocabulary Space' Best paper award at SCIA 2023 for 'Gradient Adjusting Networks for Domain Inversion' Best paper award at FG 2021 for 'Generating Master Faces for Dictionary Attacks' Prof. Wolf mentors graduate students in the School of Computer Science and leads research at the ICRC building laboratory. His team collaborates with 'the friends of TAU' on projects spanning biometric security, medical imaging, and generative AI. Current work focuses on efficient transformers, neural network interpretability, and multimodal medical diagnostics.
Asta Seirup Særkjær serves as an Instructor in the Department of Economics at the University of Copenhagen and participates in the Department of Economics Study Council, contributing to academic governance and curriculum oversight. Her research profile, inferred from departmental affiliation, spans core economic disciplines with emphasis on: Microeconomic theory and applications Macroeconomic policy analysis Quantitative econometric methods Development economics frameworks Labor market dynamics Comparative economic systems No scientific awards were documented in the source material. Student advising activities and research grant management details were not provided in the available text, though her Study Council role suggests involvement in academic mentorship structures.
Byron Wallace is the Sy and Laurie Sternberg Interdisciplinary Associate Professor at Northeastern University's Khoury College of Computer Sciences, where he also serves as Associate Dean for Research and Director of the BS in Data Science Program. His research focuses on Natural Language Processing and Machine Learning applications in healthcare. Education Details of formal education are not explicitly provided in the available text, though he holds a PhD from Tufts University (mentioned in thesis award context). Research Interests His work centers on developing NLP and ML models for health applications, with particular emphasis on: Biomedical evidence synthesis automation Electronic Health Record processing Model interpretability and trustworthiness Human-in-the-loop systems Learning with limited supervision Research Trends Recent publications demonstrate strong focus on large language model applications in healthcare, including factuality evaluation for medical summarization, evidence extraction from clinical trials, and interpretable risk prediction models. Notable contributions include work on GPT-3 applications in medical evidence synthesis and neural methods for EHR analysis. Scientific Awards ACL Outstanding Paper Award (2022) ICLR Spotlight (top 5% acceptance) (2024) Best Student-led Paper Award at AMIA 2021 NSF CAREER Award (2018-2023) Advising and Grants Currently advises 4 PhD students and has mentored numerous others. Major funding includes: NSF CAREER Award ($500K+) NIH R01 grant for EHR summarization NSF Medium grant for healthcare summarization Support from Army Research Office, Amazon, and Seton Hospital Labs and Teams Leads the Evidence Inference project team working on automated biomedical evidence synthesis. Collaborates with Brigham and Women's Hospital, Mass General Hospital, and Reboot Rx for clinical translation of research.
Andrea Tagliasacchi is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU), where he holds the Visual Computing Research Chair. He is also a part-time (20%) Staff Research Scientist at Google DeepMind in Toronto and holds an associate professor (status only) appointment in the Department of Computer Science at the University of Toronto. Education: PhD in Computing Science – Simon Fraser University (NSERC Alexander Graham Bell Fellow) Postdoctoral Research – École Polytechnique Fédérale de Lausanne (EPFL) MSc in Computer Science – Politecnico di Milano (Gold Medalist) His research lies at the intersection of computer vision, computer graphics, and machine learning, with a focus on 3D visual perception. Key areas include neural radiance fields (NeRF), 3D Gaussian splatting, inverse rendering, and geometric deep learning, with applications in robotics, augmented reality, and autonomous systems. His work emphasizes robust and efficient scene understanding and reconstruction from visual data. His recent publications, appearing in top venues like CVPR, SIGGRAPH, NeurIPS, and ECCV, demonstrate a strong emphasis on neural fields, 3D reconstruction, and generative modeling. Trends include improving rendering efficiency, enhancing robustness to noise and distractors, and enabling controllable and 3D-aware generation. His group has made significant contributions to Gaussian splatting, NeRF optimization, and diffusion-based 3D/4D synthesis. Scientific Awards: 2024 CVPR Best Paper Award (Honorable Mention) 2020 CVPR Best Student Paper Award 2015 SGP Best Paper Award NSERC Alexander Graham Bell Canada Graduate Scholarship MITACS Best Paper Award (SIGGRAPH Asia 2009) NSF Best Poster Award (SGP 2012) He has advised numerous PhD and MSc students, many of whom are now researchers at leading institutions and companies. His research has been supported through collaborations with Google, Intel, and academic partners. He serves the community as a Senior Area Chair for CVPR 2025, Associate Editor for IEEE TPAMI (2024–2026), Guest Editor for IEEE TPAMI on 3D GenAI, and Program Chair for 3DV 2024. He leads a vibrant research lab at SFU focused on pushing the boundaries of 3D scene understanding with machine learning.
Simon King is a Professor of Speech Processing at the University of Edinburgh , affiliated with the School of Philosophy, Psychology and Language Sciences . He serves as Director of the Centre for Speech Technology Research (CSTR) and teaches courses like Speech Processing and Speech Synthesis , while directing the MSc in Speech and Language Processing . Research Interests His research focuses on: Developing new acoustic models (e.g., Linear Dynamical Models, factorial-HMMs) for speech recognition Advancing unit selection and HMM-based speech synthesis Integrating articulatory measurement data for enhanced modeling Exploring perceptual measures in synthesis criteria Building multilingual speech systems to identify universal speech building blocks Publication Trends Simon's recent work emphasizes deep learning (DNNs, LSTMs) in speech synthesis, multilingual frameworks , and articulatory-acoustic feature integration . His studies often bridge grapheme-based modeling , perceptual error reduction , and noise-robust synthesis . Scientific Awards EPSRC Advanced Research Fellowship (2005-2009) Students & Collaborations He has supervised numerous PhD students including Rasmus Dall, Tom Merritt, and Srikanth Ronanki. Current research fellows like Mirjam Wester and Zhizheng Wu contribute to projects such as Natural Speech Technology (NST) and Simple4All .
John Oakey is a Professor and Graduate Coordinator in the Department of Chemical and Biomedical Engineering at the University of Wyoming, with additional affiliations to the INBRE Program, Molecular and Cellular Life Sciences Program, and Materials Science and Engineering Program. Education Postdoctoral Fellow, Center for Engineering in Medicine, Massachusetts General Hospital & Harvard Medical School (2007–2010) Ph.D. Chemical Engineering, Colorado School of Mines (2003) M.S. Chemical Engineering, Colorado School of Mines (1999) B.S. Chemical Engineering, Penn State University (1997) Research Interests Oakey’s laboratory integrates fluid dynamics, colloidal science and materials science to understand how biological systems behave under flow, on surfaces and within complex 3-D geometries. A unifying theme is the use of microfabrication and microfluidics to create new diagnostic, prognostic and therapeutic platforms. Current thrusts include: Heterogeneous biomaterials: self-assembled particulate tissue scaffolds whose mechanical and transport properties can be temporally programmed. Inertial microfluidics: exploiting lift forces for membrane-free particle sorting, enrichment and diagnostics. Multi-temporal analysis by flow cytometry: development of closed-loop, high-throughput microfluidic cytometers for longitudinal single-cell studies. Publication Trends From 2025 back to 2010, Oakey’s articles reveal a consistent trajectory that marries fundamental physics (microtubule mechanics, inertial focusing) with translational applications (cell encapsulation, tissue scaffolds, drug delivery). Recent work (2023-2025) increasingly targets injectable granular hydrogels, single-cell therapeutic delivery and sustainable carbon-sequestering living materials, demonstrating an evolution from microscale transport phenomena to macroscopic biomedical and environmental impact. Scientific Awards No named awards are listed in the supplied text. Advising & Coordination Roles As Graduate Coordinator for the Department of Chemical and Biomedical Engineering, Professor Oakey oversees graduate program development and student mentoring. While no individual students are named, his role implies active supervision of M.S. and Ph.D. advisees in chemical and biomedical engineering. Laboratory & Teams The Oakey Research Group operates from the Energy and Environmental Research Building (EERB 435A) at the University of Wyoming. The lab enjoys R1-level research infrastructure and collaborates broadly with the Wyoming INBRE network, the Molecular and Cellular Life Sciences Program, and the Materials Science and Engineering Program.
Philip S. Yu is a Distinguished Professor in the Department of Computer Science at the University of Illinois at Chicago and holds the Wexler Chair in Information Technology. Previously, he led the Software Tools and Techniques department at IBM Thomas J. Watson Research Center. Education: B.S. in Electrical Engineering, National Taiwan University M.S. and Ph.D. in Electrical Engineering, Stanford University M.B.A., New York University His research spans data mining , big data , social networks , privacy-preserving data publishing , graph/network mining , recommender systems , and deep learning . He has authored over 970 papers with 74,500+ citations and an H-index of 127. Recent work focuses on heterogeneous graph representation, quantum walks in network analysis, and federated unlearning. Scientific Honors: ACM SIGKDD 2016 Innovation Award IEEE Computer Society 2013 Technical Achievement Award IEEE ICDM 2003 Research Contributions Award IEEE Region 1 Award (1999) UIC Research of the Year (2013) IBM Master Inventor with 300+ patents AI 2000 Most Influential Scholar Honorable Mentions (2024-2025) He served as Editor-in-Chief for ACM Transactions on Knowledge Discovery from Data and IEEE Transactions on Knowledge and Data Engineering , and on steering committees for ACM KDD and IEEE Data Mining. His work bridges theoretical advances in graph neural networks , deep learning , and privacy-preserving systems with applications in healthcare, social media, and enterprise analytics.
Jonathan R. Lyon is Professor of History of the High and Late Middle Ages at the Institute for Austrian Historical Research within the Faculty of Historical and Cultural Studies at the University of Vienna. He maintains office hours on Thursdays from 14:00-16:00 (available via Zoom by appointment) and can be contacted at jonathan.lyon@univie.ac.at or +43-1-4277-27297. His educational background includes: B.A. in History and Latin, Colgate University (1997, Summa cum laude) Exchange year at Albert-Ludwigs-Universität Freiburg (1996) M.A. in History, University of Notre Dame (1999) Ph.D. in History, University of Notre Dame (2005) Lyon's research centers on the Holy Roman Empire during the High and Late Middle Ages, European political and social structures (1000-1500), the evolution of statehood, corruption mechanisms, medieval kinship systems, and gender dynamics with particular emphasis on masculinity. His work examines how political power, social networks, and religious institutions intersected in shaping medieval governance and community life, often challenging traditional narratives about medieval state formation through comparative analysis of German and Hungarian contexts. Analysis of his 15 most recent publications reveals consistent chronological focus on the 12th-15th centuries with increasing attention to corruption, gender, and monastic institutions since 2015. His scholarship demonstrates methodological diversity through charter analysis, prosopography, and comparative institutional studies, while maintaining thematic continuity in examining power dynamics between nobility, monarchy, and religious entities across Central Europe. His major scientific awards include: 2024 Otto Gründler Book Prize for Corruption, Protection and Justice in Medieval Europe: A Thousand-Year History 2017 John Nicholas Brown Prize for Princely Brothers and Sisters: The Sibling Bond in German Politics, 1100–1250 Lyon has secured prestigious research funding including the Fulbright Fellowship (2000-2001), DAAD Research Fellowship (2007), FWF Lise Meitner Fellowship (2013-2014), and Alexander von Humboldt Research Fellowship (2017-2018). He served as Academic Director of the Berlin Consortium for German Studies (2012-2013) and currently advises the Manchester Medieval Sources Series. His ongoing projects include editing charters of Quedlinburg abbesses and authoring an introductory text on the medieval Holy Roman Empire.
Andreas Bode is a Research Fellow at the University of Wuppertal, where he is part of the group led by Sascha Orlik in the Department of Mathematics. His research focuses on D-modules on rigid analytic spaces, integrating geometric representation theory and nonarchimedean geometry. His research interests span several areas of algebraic geometry and number theory, including the study of p-adic differential operators, nonarchimedean geometry, and representation theory. He explores the interplay between D-modules and rigid analytic spaces, contributing to the understanding of holonomicity, Auslander regularity, and the application of almost mathematics in p-adic contexts. His work bridges geometric and analytic approaches to problems in nonarchimedean settings. Bode's recent publications include advancements in the theory of Auslander regularity for p-adic structures, contributions to the holonomicity of D-cap-modules on rigid analytic spaces, and explorations into locally analytic representations of p-adic groups. His work often intersects with homological algebra and functional analysis, reflecting a deep engagement with both theoretical and methodological challenges in nonarchimedean geometry. He has not been mentioned as having received any scientific awards in the provided texts. Bode's current role as a postdoctoral researcher does not involve formal academic advising of students. No grants are specifically mentioned in the provided information. He is affiliated with the Algebra and Number Theory group at the University of Wuppertal, collaborating with colleagues such as Sascha Orlik and contributing to the broader research community in nonarchimedean geometry.
