Emre Caglayan is an Assistant Professor and Program Coordinator for Film Studies at the American University of Paris (AUP). He holds a PhD in Film Studies from the University of Kent (2014) and an MA in Film and Television from Istanbul Bilgi University (2009). His research focuses on global art cinema, slow cinema aesthetics, and the intersection of cinematic form with embodied experiences like walking. Caglayan’s work explores how cinema shapes lived experiences through close analyses of film texts and their cultural contexts. Key research interests include the affective qualities of film sound, urban space representations, and ethical dimensions of political filmmaking. His monograph Poetics of Slow Cinema: Nostalgia, Absurdism, Boredom (Palgrave, 2018) is a seminal work in slow cinema studies. He currently investigates walking as an artistic practice linked to cinema, curating film seasons and designing participatory walking activities. Caglayan teaches courses on film theory, international cinema, and cinematic history at AUP. His work has been published in journals like Quarterly Review of Film and Video , Projections , and Jump Cut , with recent contributions on Turkish cinema and film/music intersections.
Prof. Dr. Alexander Stroh-Steckelberg is Professor of Political Science with a focus on African Politics & Development Policy at the University of Bayreuth since 2022. He serves as Deputy Head of the Bayreuth International Graduate School of African Studies (BIGSAS) until 2025 and has held prior roles as Junior Professor and Senior Fellow at BIGSAS (2015–2021). His academic career includes research associate positions at the GIGA German Institute of Global and Area Studies (2006–2015) and teaching roles at the Universities of Abomey-Calavi, Heidelberg, and Lüneburg. Stroh-Steckelberg’s research spans comparative democracy studies , judicial politics , electoral systems , political party dynamics , and development policy in Africa, with regional expertise in Francophone West Africa and Rwanda . His work emphasizes institutional diffusion, informal networks in courts, and the interplay between formal and informal governance structures. Recent publications reveal a focus on judicial appointment policies , electoral manipulation , and local governance challenges across Africa. He contributes to methodological advancements in comparative area studies and network analysis , particularly in non-Western democracies. Stroh-Steckelberg is actively involved in academic communities including the American Political Science Association , German Political Science Association , and Association for African Studies in Germany .
Andrew Zisserman is a Royal Society Research Professor at the University of Oxford's Department of Engineering Science, affiliated with the Visual Geometry Group (VGG). His research focuses on computer vision, artificial intelligence, and neural networks, with significant contributions to multimodal learning, video understanding, and 3D scene analysis. He leads projects exploring visual-language models, audio-visual synchronization, and clinical imaging applications. Key research areas include: Video analysis and temporal modeling Multimodal systems for sign language translation and action recognition 3D shape estimation and physical property inference Foundation models and cross-modal retrieval Recent work highlights: Developed Flamingo and Tapir models for video-language tasks Advancements in spinal MRI analysis and clinical imaging Leadership in EGO4D and VoxCeleb challenges Honors include Fellowship of the Royal Society (FRS) and the ISSLS Prize in Clinical Science 2023 for spinal analysis innovations. His lab collaborates globally, emphasizing real-world applications in healthcare and autonomous systems.
Xingang Pan is an Assistant Professor in the College of Computing and Data Science at Nanyang Technological University (NTU), leading the MMLab@NTU. His research focuses on generative AI and visual content creation, particularly in generative models, 3D vision, computer graphics, and computer vision. Prior to NTU, he was a postdoc at the Max Planck Institute for Informatics and earned his Ph.D. from the Chinese University of Hong Kong (2021) and B.Sc. from Tsinghua University (2016). His work emphasizes generative intelligence, exploring long-term world simulation, diffusion models, and multi-scale 3D generation. Notable contributions include WORLDMEM (2025), Alias-free Latent Diffusion (2025), and SAR3D (2025). His research has been published in top venues like CVPR, ICCV, and SIGGRAPH. Xingang Pan oversees the MMLab@NTU, which actively recruits students globally without nationality constraints. The lab’s projects include GAN2Shape (unsupervised 3D reconstruction from 2D GANs) and LN3Diff (scalable 3D generation).
Brenden Lake is an Associate Professor of Computer Science and Psychology at Princeton University, starting Fall 2025. Previously, he was an Associate Professor of Psychology and Data Science at New York University. He is the principal investigator of the lab for Human & Machine Intelligence, which moved from NYU to Princeton in 2025 and is jointly affiliated with the Department of Computer Science and the Department of Psychology. His lab is located in Princeton's Peretsman Scully Hall, rooms 117, 120, and 121. Ph.D., Massachusetts Institute of Technology, 2014 Lake's research focuses on the intersection of human and machine intelligence, specifically examining human cognitive abilities that elude current AI systems. His work centers on few-shot learning of new concepts, learning by generating new goals, learning by asking questions, and learning by producing novel combinations of known components. He employs modern neural network modeling approaches including meta-learning, fine-tuning LLMs, neuro-symbolic modeling, and learning from child headcam videos. His research aims to advance both psychology and computer science by exploring what makes human intelligence unique and using those insights to develop more powerful AI systems. Lake's recent publications demonstrate significant trends in grounded language acquisition through child perspectives, systematic generalization in neural networks, and the intersection of developmental psychology with AI. His work has appeared in top-tier venues including Science (2024) and Nature (2023), with multiple publications exploring how insights from human cognition can improve machine learning systems. His research shows how incorporating human cognitive ingredients can make AI systems more powerful and human-like while addressing longstanding debates about neural network capabilities. Science publication (2024) on Grounded language acquisition through the eyes and ears of a single child Nature publication (2023) on Human-like systematic generalization through a meta-learning neural network Multiple publications covered by major media outlets including New York Times and Washington Post Lake advises Ph.D. students in computer science, psychology, and related fields through his lab. His research is supported by publications in top venues across computer science and cognitive science. He teaches courses including Computational Cognitive Modeling and Advancing AI through Cognitive Science, bridging the theoretical and practical aspects of his research. Lake leads the lab for Human & Machine Intelligence, which studies the ingredients of intelligence in humans and machines. The lab investigates human cognitive abilities that current AI systems cannot replicate, with the dual goal of advancing psychological understanding of human intelligence while developing more capable artificial intelligence systems. Current research focuses on few-shot concept learning, learning through goal generation, and learning by asking questions.
Craig Buckley is an Associate Professor in the Department of the History of Art and affiliated with the Yale School of Architecture. His research bridges modern architecture with visual arts and media, focusing on techniques like montage, collage, and screen-based practices. Education: BA (Trent University), MA (University of Western Ontario), PhD (Princeton University) His work examines how media such as print, film, and digital screens shape architectural culture. Key themes include cinema architecture, media theory, and the intersection of design with socio-political movements like 1968 protests. His recent articles analyze topics such as colonial-era cinema architecture, media façades, and pneumatic structures. He teaches courses on architecture’s relationship with media and print culture, often utilizing Yale’s special collections.
Jonathan Winawer is an Associate Professor of Psychology and Neural Science at New York University (NYU). He leads the Winawer Laboratory for Human Visual Perception and Neuroscience, which is affiliated with the NYU Psychology Department, NYU Center for Neural Science, NYU Neuroscience Institute, and collaborates with the NYU Comprehensive Epilepsy Center and Stanford University's Human Intracranial Cognitive Program. Ph.D., Massachusetts Institute of Technology (2007) M.S., City College of the City University of New York (2005) A.B., Columbia University (1995) His research focuses on the biological basis of visual perception, including computational models of the visual pathways using fMRI, EEG, ECoG, and psychophysics. Key areas include: Encoding of visual stimuli in neural pathways Perceptual memory's influence on cognition Neural circuit properties underlying imaging signals Individual differences in visual perception Publications highlight interdisciplinary approaches spanning computational neuroscience, neuroimaging, and psychophysics, with recent work on population receptive fields, spatial frequency tuning, and memory-related cortical dynamics. He has mentored numerous students and postdocs, many of whom have advanced to academic and research roles at institutions like Stanford, Columbia, and the University of Amsterdam. Department of Psychology, NYU Center for Neural Science, NYU Neuroscience Institute, NYU Comprehensive Epilepsy Center, NYU Stanford University collaborations
Shubham Tulsiani is an Assistant Professor at Carnegie Mellon University's Robotics Institute, where he leads the Computer Vision group and the Physical Perception Lab. His research focuses on inferring physically and spatially grounded representations from perceptual inputs, with applications in 3D vision, robot manipulation, and neural scene reconstruction. He directs an active research group with multiple PhD and Master's students. Research interests center on 3D scene understanding , robot learning , and generative modeling , with specific emphasis on: self-supervised perception, neural rendering, multi-view geometry, manipulation from visual inputs, and physics-based reasoning. The lab develops methods that leverage physical world constraints as supervisory signals. Recent publications demonstrate strong focus on diffusion models for 3D tasks , sparse-view reconstruction , and robotic manipulation transfer . Key trends include neural inverse rendering, view synthesis from limited observations, and translating human interactions to robot actions. Awards include: Best Student Paper Award at CVPR 2015 Advising includes supervision of 5 PhD students, 4 MS students, and undergraduates. Lab alumni hold positions at Google, Stanford, Meta, and Princeton. The Physical Perception Lab collaborates with FAIR Pittsburgh and the CMU Computer Vision group.
Laurent Bricault is a Professor of Roman History at the University of Toulouse Jean Jaurès, affiliated with the ERASMUS research team (Reception of Antiquity: Sources, Memory, Issues) under the Heritage, Literature, History (PLH) unit. His academic career spans institutions like the University of Poitiers (Master's 1986), Sorbonne (Doctorate 1994, HDR 2006), and Toulouse (since 2008). He specializes in the history of ancient religions, particularly Egyptian cults in the Roman world, provincial coinage, and Lagid/Roman Egypt. University of Poitiers - Master's in Ancient History (1986) Sorbonne - Doctorate in Egyptology (1994) Sorbonne - Habilitation to Direct Research (2006) Bricault's research explores the diffusion of Isiac cults, religious syncretism, and numismatic evidence for ancient beliefs. His recent publications focus on Mithraic studies, Egyptian deities in the Mediterranean, and domestic religious practices. He organizes international conferences and edits the Bibliotheca Isiaca series, contributing to the understanding of polytheistic traditions. Bricault co-edits the Supplements to the Bibliotheca Isiaca and collaborates on the Roman Provincial Coinage project. He curated the international exhibition Rome and the Mystery of Mithra (2021-2023) and maintains the RICIS online database for Isiac cult inscriptions. His work bridges epigraphy, numismatics, and cultural history to analyze religious networks across the Roman Empire.
Roger Boltshauser is a Full Professor and Deputy Head at the Department of Architecture, ETH Zurich. His practice and academic work emphasize tectonic building culture, proportion, and material experimentation, diverging from mainstream German-Swiss architectural trends. He combines teaching with active architectural practice, influencing younger generations at ETH and TU Wien. Key themes: Tectonic structures, grid-based order, social responsibility in architecture, critique of modernist canons Notable projects: Hochhaus H1 Zwhatt-Areal (2025), Masterplan Zoo Basel (2025), Centre for Dental Medicine Zurich (2025) His buildings prioritize mass and permanence , evident in projects like the Kopfholz School and Rauch House . Recent works at the EPFL Solar Biennale (2025) and Architecture of Memory exhibition (2025) explore sustainable design and cultural narratives. Boltshauser’s studio on Dubsstrasse exemplifies research into load-bearing structures and urban spatial relationships.
Tzu-Mao Li is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the University of California, San Diego (UCSD), affiliated with the Center for Visual Computing. His research focuses on differentiable graphics algorithms, combining classical visual computing with modern machine learning techniques. He holds a Ph.D. from MIT CSAIL under Frédo Durand and a postdoc at MIT and UC Berkeley with Jonathan Ragan-Kelley. His work spans rendering, programming languages for graphics, Monte Carlo methods, and inverse problems. Education: B.S. and M.S. from National Taiwan University (2011-2013), advised by Yung-Yu Chuang. Ph.D. from MIT CSAIL (Computer Graphics Group), advised by Frédo Durand. Postdoctoral research at MIT and UC Berkeley with Jonathan Ragan-Kelley. Research Interests: Differentiable rendering, Monte Carlo integration, programming language design for visual computing, physical simulation, adversarial machine learning, and applications in computer vision and robotics. Key areas include rendering algorithms (path tracing, bidirectional methods), optimization techniques (MCMC, gradient-based), and neural representations (SDFs, neural fields). Publications focus on advancing rendering algorithms, differentiable systems, and applications in inverse problems. Notable contributions include edge sampling for differentiable rendering, warped-area sampling, and diffusion models for BSDF sampling. Awards: ACM SIGGRAPH 2020 Outstanding Doctoral Dissertation Award, multiple Best Paper Awards at SIGGRAPH, and oral presentations at ICCV. Teaching: Courses include CSE 167 (Computer Graphics), CSE 168 (Rendering), and CSE 272 (Advanced Image Synthesis), emphasizing physically-based methods and programming.
Tim G. J. Rudner is an Assistant Professor in the Department of Statistical Sciences at the University of Toronto, a Faculty Member at the Vector Institute, and a Title A Fellow at Trinity College, University of Cambridge. He was previously an Assistant Professor and Faculty Fellow at New York University. University: University of Toronto School: Faculty of Arts and Science Department: Department of Statistical Sciences Affiliation: Vector Institute, Trinity College (Cambridge) He holds a PhD in Computer Science and an MSc in Statistics from the University of Oxford, where he was advised by Yee Whye Teh and Yarin Gal, and a BS in Applied Mathematics and Economics from Yale University. PhD: Computer Science, University of Oxford MSc: Statistics, University of Oxford BS: Applied Mathematics and Economics, Yale University His research focuses on building robust, transparent, and trustworthy machine learning systems, particularly for high-stakes applications. He develops probabilistic models that improve generalization under distribution shifts, provide reliable uncertainty estimates, and enable fair and interpretable predictions. His work spans generative models, large language models, healthcare, and biomedical discovery. The recent publications highlight a strong trend toward function-space modeling, Bayesian regularization, and AI safety. Tim's work emphasizes principled uncertainty quantification, robustness to subpopulation and semantic shifts, and the development of frameworks for AI governance and specification. His research bridges theoretical advances with real-world applications, especially in safety-critical domains like medicine and defense. Tim has received numerous accolades including being named a Rhodes Scholar, Qualcomm Innovation Fellow, and 2024 Rising Star in Generative AI. He was awarded a $700,000 Foundational Research Grant and a $30,000 Apple Seed Grant for improving LLM trustworthiness. Rhodes Scholar Qualcomm Innovation Fellow AISTATS Notable Paper Award (2024) Outstanding Paper Award, ICLR GenAI4DM Workshop (2024) Apple Seed Grant ($30,000) Foundational Research Grant ($700,000) NeurIPS Spotlight Talk 2024 Rising Star in Generative AI He actively mentors students, particularly first-generation and low-income scholars, and has contributed to major policy frameworks including the OECD AI Classification Framework and a series of CSET issue briefs on AI safety. His work demonstrates a strong commitment to responsible AI development, combining technical rigor with societal impact. Tim leads research efforts at the intersection of machine learning theory and practical deployment, with ongoing projects in generative modeling, reliable LLMs, and AI governance. His lab produces high-impact work regularly published at top-tier conferences such as NeurIPS, ICML, and AISTATS.
Dr. Laura McMahon is an Associate Professor in Film and Screen Studies at the Faculty of Modern and Medieval Languages and Linguistics , University of Cambridge. She is currently on sabbatical leave and will return in Easter Term 2024. Her research explores intersections between film and philosophy , with a focus on French and Francophone cinema , decolonial approaches , feminist theory , and ecocritical perspectives . Dr. McMahon’s work examines how contemporary feminist filmmakers like Onyeka Igwe and Khady Sylla engage with archives to reframe history through speculative reimagining . She also investigates ecological themes in Claire Denis’s films and the ethics of animality in global art cinema. Her monograph Animal Worlds: Film, Philosophy and Time (2019) and edited collections like Animal Life and the Moving Image (2015) highlight her contributions to debates on nonhuman representation and deconstructive aesthetics . Her recent publications analyze documentary practices , postcolonial archives , and critical repurposing in moving image work. She welcomes inquiries from MPhil and PhD students working on related topics. Her teaching and research are affiliated with the Cambridge Film and Screen initiative and the Cambridge Italian Research Network (CIRN).
Dr. Thomas Brodie is an Assistant Professor in 20th Century European History at the Department of History, University of Birmingham. His academic career spans prestigious institutions including Oxford University, University of Leeds, and Jesus College, Oxford before joining Birmingham in 2018. Education: DPhil in History (The University of Oxford, 2014) MSt in Modern British and European History (The University of Oxford, 2010) BA in History (The University of Oxford, 2009) Fellowship of the Higher Education Academy (2020) Postgraduate Certificate in Higher Education (University of Birmingham, 2020) Dr. Brodie specializes in Modern European history with particular focus on Germany during the Nazi era and post-war periods. His research examines the intersection of religion, society, and war, with special attention to Catholic communities in Germany during WWII and the subsequent evolution of mourning practices and memory culture. His work combines theological analysis with social and cultural history using previously unused archival materials from church, state, and private collections. His recent publications demonstrate a clear trajectory from examining wartime religious communities to post-war memory practices, showing his evolving scholarly interests in how societies process mass bereavement and construct collective memory. The articles primarily focus on German religious history during and after WWII, with increasing attention to spatial dimensions of mourning and commemoration. Scientific Awards: Runner-up award for the Wiener Library's Fraenkel Prize (2014) AHRC award for doctoral research Hanseatic Scholarship for archival research in Berlin and German cities Dr. Brodie teaches a range of undergraduate and postgraduate courses including 'The Rise and Fall of the Third Reich' and 'Life After Death: Culture, Memory and Power in Postwar Europe.' He supervises BA dissertations and welcomes PhD students working on Modern German history or thematic interests in religion, war, and memory in European contexts since 1800. His current research project examines German society's responses to bereavement during WWII and its aftermaths through personal diaries, letters, artistic representations, and war memorials.
Haibin Ling is the SUNY Empire Innovation Professor in the Department of Computer Science at Stony Brook University, part of the College of Engineering and Applied Sciences. His research focuses on computer vision, medical image analysis, augmented reality, and AI applications in science. He holds a Ph.D. from the University of Maryland (2006) and prior degrees from Peking University. Previously, he worked at Temple University (2008–2019) and held roles at Siemens Corporate Research, UCLA, and Microsoft Research Asia. Professor Ling's work spans biomedical imaging, AI for science, and human-computer interaction. He leads the CV Lab and collaborates with the AI Institute at Stony Brook. Awards include the NSF CAREER Award (2014), Best Student Paper (ACM UIST 2003), and IEEE Fellow (2020). He serves on editorial boards for IEEE Trans. PAMI, Pattern Recognition, and CVIU, and chairs major conferences like CVPR. His research group includes over 50 students and alumni, with active projects in tracking benchmarks (LaSOT), Leafsnap, and medical imaging tools. Notable publications address OCTA flow estimation, backdoor attacks on vision models, and topology-guided medical learning. Collaborations involve institutions like Temple University and Stony Brook's Department of Applied Mathematics and Statistics.
