Dr. Jason J. Corso is a Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan . His research focuses on high-level computer vision , video understanding , and the intersection with human language and robotics . His work emphasizes Bayesian approaches to segmentation and recognition, with applications spanning biomedicine and recreational video analysis . He is particularly known for contributions to video object segmentation , activity recognition , and vision-language frameworks . Scientific awards include: NSF CAREER award (2009) ARO Young Investigator award (2010) Google Faculty Research Award (2015) DARPA CSSG grant He also leads major projects like YouCook2 dataset , Video2Text.net , and LIBSVX framework.
Erik G. Larsson is a Professor and Head of the Division for Communication Systems within the Department of Electrical Engineering (ISY) at Linköping University (LiU), Sweden. He joined LiU in September 2007 and has previously held academic and research positions at the Royal Institute of Technology (KTH), University of Florida, George Washington University, and Ericsson Research. Research Interests: Enabling technologies for 6G wireless communication Statistical inference and signal processing Network science and complex networks Decentralized and federated machine learning over networks Physical layer security and privacy Energy-efficient digital signal processing His research group, active in areas like RadioWeaves and massive MIMO, focuses on robust, efficient, and secure wireless connectivity. Recent publications highlight trends in decentralized learning, resource allocation in wireless networks, and the integration of AI into mobile networks, particularly through projects like 'Turning the Air into an AI Computer' funded by the Knut and Alice Wallenberg Foundation. Scientific Awards and Honors: IEEE Signal Processing Magazine Best Column Award (2012, 2014) IEEE ComSoc Stephen O. Rice Prize (2015) IEEE ComSoc Leonard G. Abraham Prize (2017) IEEE ComSoc Best Tutorial Paper Award (2018) IEEE ComSoc Fred W. Ellersick Prize (2019) IEEE SPS Donald G. Fink Overview Paper Award (2023) IEEE Fellow Member, Royal Swedish Academy of Sciences (KVA) Gyllene Moroten Best Teacher Award (2021) Advising and Grants: He has supervised numerous Ph.D. and Licentiate students, many of whom now hold positions at leading industry and academic institutions. His research is currently funded by major organizations including the Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research (SSF), ELLIIT, Security-Link, Swedish Research Council (VR), and EU Horizon 2020 (H2020-SNS-6GTandem). Previous sponsors include VR, KVA, NSF, ORAU, and multiple EU FP7 and H2020 projects (e.g., MAMMOET, REINDEER, 5G-Wireless). Leadership and Service: He has served as Associate Editor for IEEE Transactions on Communications and IEEE Transactions on Signal Processing, chaired technical committees and steering committees in IEEE Signal Processing Society, and held leadership roles in major conferences such as the Asilomar Conference on Signals, Systems and Computers. He was a Visiting Fellow at Princeton University in 2015.
Hannu Toivonen is a Full Professor of Computer Science at the University of Helsinki, affiliated with the Faculty of Science and the Department of Computer Science. He leads the Discovery Research Group and is part of the Helsinki Institute for Information Technology (HIIT) and the Finnish Center for Artificial Intelligence (FCAI). He obtained his PhD in Computer Science from the University of Helsinki in 1996 and has held his professorship since 2002. His research spans Artificial Intelligence , Data Science , Computational Creativity , and Data Mining , with applications in generative art, automated journalism, and bioinformatics. His research focuses on: Developing AI systems for creative tasks (e.g., poetry, music, and art generation) Cross-lingual natural language processing and unsupervised learning Ethical and societal implications of AI in media and labor Analysis of his 15 most recent publications (2021–2025) reveals a strong emphasis on computational creativity and AI-driven content generation , with interdisciplinary applications in digital humanities, journalism, and biology. Trends include ethical AI frameworks, human-AI collaboration, and innovative evaluation methods for generative systems. Scientific Awards & Honors: Knight, First Class, Order of the White Rose of Finland (2019) Best Applied Research Award, IEEE ICDM (1998) Honorary Member of TKO-äly (2010) Member of Finnish Academy of Science and Letters He has supervised over 20 doctoral theses and secured ~7 MEUR in grants (e.g., EU projects: Embeddia , Newseye ). He leads the Discovery Research Group and collaborates with institutions like VUB (Belgium) as a Francqui International Professor (2025).
Remus Teodorescu is a Professor at AAU Energy , Aalborg University , specializing in Power Electronics System Integration and Materials . His work bridges Lithium-Ion Batteries , Modular Multilevel Converters , and Smart Battery Systems . Education : Not explicitly mentioned in the text. Research Interests focus on Battery Management Systems , AI-Driven Energy Optimization , and Power Electronics for renewable energy integration. Key projects include Digital Twin for Lithium-Ion Batteries and BMS-DC for Data Centers . Recent Publications (2025) emphasize Finite Set MPC , Gradient Descent Optimization , and AI in Battery Parameter Estimation . His 2024 work explores Physics-Informed Neural Networks and Fault-Tolerant Converters . Scientific Awards : Villum Foundation Grant (313 million kroner, 2021) Named world's best in electrical engineering (2023) Advising includes supervising PhD projects on AI-Accelerated Battery Twins and Data-Driven SOH Estimation . Collaborations span Energy Cluster Denmark and Villum Fonden .
Dr. Sarah Loos is an independent postdoctoral researcher at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, and a Research Fellow at Corpus Christi College. Her work focuses on theoretical descriptions of nonequilibrium systems, integrating statistical physics, thermodynamics, control theory, and nonlinear dynamics. PhD in Statistical Physics (TU Berlin, 2020) Marie-Curie and DFG Walter-Benjamin Fellow Key research areas: stochastic thermodynamics, non-Markovian processes, active matter, nonreciprocal systems Her research explores entropy production, time-reversal asymmetry, and collective dynamics in active matter. She has received multiple awards, including the Royal Society of Chemistry's Early Career Award (2024) and Springer Thesis Award (2020). Recent publications emphasize nonreciprocal interactions, optimal control protocols, and memory effects in complex systems. Scientific Awards: Early Career Award (2024) Marie-Skłodowska-Curie Fellowship (2022) DFG Walter-Benjamin Fellowship (2022) SKM Dissertation Prize (2021) Carl Ramsauer Preis (2020) EPL Poster Prize (2019) Her work has been supported by the Joachim-Herz Stiftung (2022) and Studienstiftung des Deutschen Volkes (2013-2015). Collaborations span institutions like ICTP Trieste, Leipzig University, and Duke University.
Dr. Hima Lakkaraju is an Assistant Professor at Harvard University with joint appointments in the School of Engineering and Applied Sciences and Business School , focusing on the algorithmic foundations and societal implications of trustworthy AI. She also serves as a Senior Staff Research Scientist (part-time) at Google. Her research spans machine learning, optimization, human-subject studies, and AI policy , with applications in healthcare, law, and business. Education : PhD in Computer Science, Stanford University Prior Roles : Microsoft Research, IBM Research, Adobe, Fiddler AI Dr. Lakkaraju's work emphasizes safe, fair, and interpretable AI , addressing critical questions about human-AI collaboration, model robustness, and regulatory compliance. She leads the AI4LIFE research group and co-founded the Trustworthy ML Initiative to democratize access to responsible AI research. Her research is supported by NSF, Sloan Foundation, Schmidt Sciences, Google, OpenAI, Amazon, JP Morgan, Adobe, Bayer, Harvard Data Science Initiative, and D^3 Institute . Recent publications (2025) explore reward hacking in LLMs, unified attribution frameworks, memory systems in AI agents, and science-based AI policy . Earlier works (2024) focus on medical safety benchmarks, CLIP interpretation, and generalization complexity . Her work has been featured in major media outlets including New York Times, TIME, MIT Tech Review, and Fortune . Scientific Awards : Alfred P. Sloan Fellow (2025), NSF CAREER Award (2023), MIT Tech Review 35 Innovators (2019), Google Anita Borg Fellowship (2015) Grants & Funding : NSF, Google, Amazon, JP Morgan, Adobe, Schmidt Sciences Dr. Lakkaraju advises a diverse team of postdocs, PhD, and master's students working on foundational and applied aspects of trustworthy machine learning. She teaches courses like Introduction to Data Science and Explainable AI at Harvard and Stanford.
Jiawei Zhang is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin–Madison since May 2025. His research focuses on optimization algorithms for machine learning, adversarial training, reinforcement learning, and distributed systems. Ph.D. in Computer and Information Engineering, Chinese University of Hong Kong, Shenzhen (2021) B.Sc. in Mathematics (Hua Loo-Keng Talent Program), University of Science and Technology of China His work spans nonconvex optimization , robust machine learning , and data-driven decision-making , with applications to AI and sustainable energy systems. Recent publications at ICML 2025 address stochastic primal-dual methods and contextual optimization robustness, while earlier works explore bilevel optimization, reward learning, and distributed consensus algorithms. Scientific awards include: MIT Postdoctoral Fellowship For Engineering Excellence (2023) CUHK-Shenzhen Presidential Award for Outstanding Doctoral Students (2021) SRIBD PhD Fellowship (2020-2021) CUHK-Shenzhen Outstanding Teaching Assistant (2023) He supervises undergraduate researchers and seeks graduate students with strong mathematical or algorithmic backgrounds for 2025 admission, emphasizing optimization and AI-driven applications.
Justin Sirignano is a Professor of Mathematics at the University of Oxford, affiliated with the Mathematical Institute. His research bridges Applied Mathematics, Machine Learning, and Financial Mathematics, developing novel mathematical frameworks and computational methods. Education: B.A. in Mathematics, Princeton University PhD in Mathematics, Stanford University Chapman Fellow, Imperial College London His research focuses on theoretical and applied aspects of machine learning, particularly in mean-field analysis of neural networks , deep learning for PDEs/SDEs , and scientific machine learning . He has pioneered methods for solving complex financial and scientific problems using data-driven approaches. His recent publications emphasize recurrent neural networks, reinforcement learning, and PDE closure models with applications in turbulence simulation and hypersonic flows. These works span numerical methods, optimization, and stochastic processes. Scientific Awards: 2014 SIAM Financial Mathematics and Engineering Conference Paper Prize Grants & Collaborations: He has secured over $16.5 million in funding from agencies like ONR, NSF-EPSRC, and DoE. His PhD students hold positions at J.P. Morgan, Bank of America, and other institutions. Labs & Teams: He leads research groups in Machine Learning and Mathematical Finance at Oxford, collaborating with institutions like Notre Dame, Boston University, and UIUC.
Dr. April Nowell is a Professor of Anthropology in the Department of Anthropology at the University of Victoria's Faculty of Social Sciences, specializing in Paleolithic archaeology, cognitive archaeology, and the archaeology of children. Currently on leave, she leads internationally recognized research projects across Europe, the Levant, Australia, and Africa while actively accepting graduate students. She earned her PhD from the University of Pennsylvania and has developed expertise in Neanderthal lifeways, Paleolithic art, and hominin life histories. Her academic journey reflects deep engagement with both theoretical frameworks and fieldwork across diverse geographical contexts. Nowell's research examines how prehistoric societies structured knowledge transmission, childhood development, and symbolic expression. Her groundbreaking work on finger flutings in Australian caves reveals children's roles in Paleolithic storytelling traditions, while her analysis of Levantine wetland ecosystems demonstrates how Pleistocene humans adapted to environmental shifts. She challenges conventional narratives about Neanderthal cognition and has pioneered methodologies for reconstructing prehistoric childhood experiences through skeletal and material evidence. Her recent publications show consistent innovation in archaeological methodology, particularly in digital documentation of rock art and interdisciplinary approaches to human development. Key trends include integrating bioarchaeological data with cognitive models, examining material culture as evidence of social learning, and analyzing environmental archives to understand human dispersal patterns. Her scientific recognition includes: 2023 EAA Book Prize for Growing Up in the Ice Age: Fossil and Archaeological Evidence of the Lived Lives of Plio-Pleistocene Children Nowell secures major research funding including Social Sciences and Humanities Research Council grants supporting her Azraq Basin project in Jordan and Koonalda Cave research in Australia. She mentors graduate students in Paleolithic theory while collaborating with Indigenous communities and international scholars on field projects spanning five continents. Her work bridges academic research and public engagement through TEDx talks and media appearances examining science communication. She directs field programs at Jordan's Azraq Basin wetlands and Australia's Koonalda Cave, working with multidisciplinary teams including geochronologists, bioarchaeologists, and Traditional Owners to investigate Pleistocene human adaptation and cultural transmission.
Prof. Dr. Frank T. Piller is a University Professor and Co-Leader of the Institute for Technology and Innovation Management (TIM) at RWTH Aachen University, where he also serves as Academic Director of the Executive MBA program at RWTH Business School. He leads a research team of approximately 30 doctoral students, 5 postdocs, and over 20 student researchers within the TIME Research Area of the School of Business and Economics. His educational background includes a doctoral degree in Operations Management from the University of Würzburg (1999) and a Habilitation degree from TUM Business School (2004) on "Innovation and Value Co-Creation." Prior to joining RWTH Aachen in 2007, he was a Research Fellow at MIT Sloan School of Management and faculty at TUM Business School. Prof. Piller is recognized as one of the world's leading experts in customer-centered value creation, specializing in mass customization, personalization, and customer co-creation. His current research focuses on how established companies can transform in response to disruptive business model innovations, with particular emphasis on digital transformation (Industry 4.0), AI-augmented innovation, and sustainable business models. He is particularly known for his work on innovation ecosystems, platform-based business models, and stakeholder-oriented technology development. His recent publications demonstrate a clear trajectory toward integrating artificial intelligence with traditional innovation management frameworks, exploring how AI transforms manufacturing systems, innovation processes, and business models. His work increasingly addresses the challenges of digital transformation in established industries while maintaining focus on customer co-creation and mass customization principles. His scientific achievements have been recognized with numerous awards: Co-Creation Award of the PDMA Nomination for "Innovating Innovation" Prize by Harvard Business Review and McKinsey "Lecturer of the Year" by Executive MBA students at TU Munich RWTH Aachen Rector's Prize for Excellent Teaching (since 2010) Grant for innovative "Flipping the Classroom" teaching concept ERC Synergy Grant for SAFER Grid project (2025-2031) Prof. Piller maintains an extensive research network spanning academia and industry. He collaborates with numerous corporations including 3M, Adidas, BASF, EON, J&J, P&G, Siemens, and Vodafone, as well as many technology startups across Europe and North America. As a co-founder, supervisory board member, and investor in innovative startups, he actively transfers research into practice. His research has received significant funding, most notably the prestigious ERC Synergy Grant for the SAFER Grid project. He leads the Technology and Innovation Management Group (TIM) within the TIME Research Area at RWTH Aachen, which comprises over 100 senior and junior researchers working at the intersection of innovation, technology management, marketing, and entrepreneurship. The institute is a leading European research institution for strategic, behavioral, and computer-supported technology and innovation management.
Victor Vianu is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego, within the Jacobs School of Engineering. His work focuses on the intersection of database theory and verification techniques, particularly in the context of data-driven business processes and workflows. Research Interests Professor Vianu's primary research interests span database theory, verification of database-driven systems, and computational logic. His current work focuses on automatic verification of interactive data-driven web services and business processes, exploring how to provide customized workflow views for different stakeholders in organizational settings. His research addresses significant technical challenges at the intersection of data management and process modeling, requiring novel approaches that go beyond traditional relational algebra to handle both data and process aspects simultaneously. His work on data-driven business processes investigates how to specify, analyze, and synthesize views of workflows that expose only information relevant to specific user roles. This research has important applications in e-commerce, digital government, healthcare, and scientific infrastructure, where different stakeholders require varying levels of workflow abstraction and detail. Research Contributions and Trends Professor Vianu's recent publications demonstrate a consistent focus on the integration of data management and workflow processes. His work has evolved from foundational database theory to increasingly practical applications in business process management. A key trend in his research is the development of formal frameworks for workflow views that maintain consistency while providing appropriate abstractions for different user roles. His publications reveal a progression from theoretical foundations to more applied aspects of workflow verification and integration, often in collaboration with researchers from INRIA and other institutions. Advising and Research Support Professor Vianu leads the UCSD Database Laboratory, which conducts research on database systems and theory. He currently advises graduate student Marysia Tran and has likely mentored numerous other students throughout his career. His research is supported by the National Science Foundation under grant "Views of Data-Driven Business Processes: Foundations and Applications" (NSF Project III 1815247). This project brings together techniques from logic, automata theory, complexity theory, algorithms, and automatic verification to address challenges in workflow management. Research Environment Professor Vianu is an active member of the UCSD Database Laboratory, which maintains a regular research seminar series. He has collaborated extensively with researchers including Alin Deutsch (UC San Diego), Serge Abiteboul (INRIA and ENS-Paris), Pierre Bourhis (Univ. of Lille and CNRS), and Adrien Koutsos (ENS Cachan). His foundational work includes co-authoring the influential textbook "Foundations of Databases" with S. Abiteboul and R. Hull, which remains a standard reference in database theory.
Gabriele Facciolo is a Professor at the Centre Borelli, ENS Paris-Saclay, France. He is a Senior Member of the Institut Universitaire de France (IUF) and holds an Innovation Chair (2025). His research focuses on image and video processing, remote sensing, and super-resolution techniques. Current affiliations: Centre Borelli (ENS Paris-Saclay), Institut Universitaire de France His research explores advanced algorithms for satellite stereo pipelines, real-time deblurring, denoising, and explainable AI systems for legal evidence enhancement. He coordinates projects like ANR SURECAVI (Super-resolution for visible camera systems) and ANR IMPROVED (video enhancement for judicial use), with recent work on Gaussian Splatting for Earth Observation and multi-date satellite super-resolution. Notable scientific achievements include the IGARSS 2025 Top 10 Student Paper Award and leadership in projects funded by ANR (€890k) and Prime Minister's entities (SGDSN/ANSSI). His work bridges computational imaging, defense applications, and digital forensics. Project leadership: SURECAVI, IMPROVED, BOFOR Key technologies: GPU acceleration, real-time processing, optical flow estimation, RPC refinement Gabriele actively contributes to open-source tools like S2P (Satellite Stereo Pipeline), MGM (MultiGlobal Matching), and OMNIflip. He teaches in the Master MVA program and collaborates across institutions (ENPC, UPF).
Iain Murray is Professor of Machine Learning and Inference at the School of Informatics, University of Edinburgh. His research focuses on developing flexible probabilistic models applicable across diverse domains including cosmology, neuroscience, perception, speech, sports, and text. Program Chair for ICLR (2018) Publications Chair for ICML (2017, 2018) Area Chair for AISTATS, ICLR, ICML, NeurIPS, and UAI Amazon Scholar (2018-2024), first appointed in Europe Murray's research interests center on probabilistic reasoning using machine learning, with specific expertise in density estimation and Markov chain Monte Carlo methods. His work spans theoretical foundations and practical applications, with significant contributions to neural autoregressive distribution estimation (NADE), real-valued NADE (RNADE), and pseudo-marginal slice sampling techniques. His research has enabled advances in flexible probabilistic modeling across multiple domains. His publications show consistent focus on advancing probabilistic modeling techniques, with recent work emphasizing neural autoregressive models, density estimation methods, and efficient sampling algorithms. The research trajectory demonstrates progression from foundational work on NADE to increasingly sophisticated deep learning approaches for density estimation and inference. Notable Paper Award for NADE work Amazon Scholar (2018-2024) Murray has supervised numerous PhD students who have gone on to prominent positions at Google DeepMind, NYU, stability.ai, and other leading institutions. His teaching responsibilities include the Machine Learning and Pattern Recognition course and project supervision. His research group focuses on developing tractable probabilistic models with applications across multiple scientific domains.
Maks Ovsjanikov is a Professor in the Computer Science Department at École Polytechnique, France , and a Visiting Research Scientist at Google DeepMind. His research focuses on mathematically principled approaches for geometric data analysis and synthesis, including learning on surface meshes, 3D point clouds, and graphs. Key Collaborations: Google DeepMind, Sanofi, Dassault Systèmes Research Themes: Non-rigid shape matching, 3D reconstruction, transfer learning, learning on geometric data, functional maps, deep learning for scientific discovery Recent Article Trends emphasize geometric deep learning, with publications at top venues like SIGGRAPH Asia, ICCV, and CVPR. Topics include surface reconstruction, functional maps, 3D keypoint detection, and diffusion models for shape matching. Scientific Honors include: ERC Consolidator Grant (VEGA Project, 2023) ERC Starting Grant (2017) ACM SIGGRAPH 2023 Test-of-Time Award Best Paper Awards at 3DV 2021 and 3DV 2022 Student Advisees have received prestigious awards, such as the IP Paris Best PhD Thesis Award (Souhaib Attaiki, 2023) and GdR IG-RV Runner-Up (Nicolas Donati, 2024). The GeomeriX Team at École Polytechnique drives his group's research, supported by the VEGA and AIGRETTE projects.
Dongwook Yoon is an Associate Professor at the Department of Computer Science , University of British Columbia , and serves as Director of the SOCIUS Lab . He actively contributes to research in Human-Computer Interaction, Human-AI Interaction, and Virtual/Augmented Reality as a member of the Designing for People (DFP) and CAIDA research clusters. Education : PhD in Computer Science from Cornell University (2017), MS (2009) and BS (2007) in Computer Science from Seoul National University Research Focus : Designing socio-technical systems that bridge the gap between technology and human social processes, with innovations in AR/VR, multimodal interaction, and inclusive design Article Trends show his work spans: Temporal and bichronous learning environments AI self-clones and ethical implications Income inequality in virtual platforms Enhanced multimodal collaboration in VR Eyes-reduced interfaces for situational impairments Speculative participatory design for gig economy challenges Scientific Awards include: Google Academic Research Award (2024) Best Paper Award at CHI 2024 High Impact Award in Educational Technology (2024) CHCCS/SCDHM Graphics Interface Early Career Award (2023) Multiple Honorable Mentions at CHI, DIS, and CSCW Students & Collaborators range from active PhD candidates (Anika Sayara, Yuri Kim) to notable alumni (Thitaree Tanprasert, Ashish Chopra) across his SOCIUS Lab projects. His research receives funding from NSERC , KIST , Adobe , Microsoft , and Google grants.
