Dr. Stavros Nousias is a researcher at the Chair of Computing in Civil and Building Engineering at the Technical University of Munich , focusing on applications of Artificial Intelligence in the Built Environment . His work bridges Knowledge Representation and Reasoning , Geometry Processing , and Machine Learning to advance construction informatics and digital twinning. Research Interests: AI for building evacuation prediction, technical drawing segmentation, BIM optimization, and respiratory disease modeling. Publications: 15+ peer-reviewed articles on topics including graph neural networks for construction simulations, pulmonary airflow analysis, and heritage site monitoring. Supervised Theses: Guided projects on AI-based BIM command prediction and robotized construction simulation . Labs: Active in the BIM-Lab and Robotic Fabrication Lab . Teaching: Co-instructor for courses like Artificial Intelligence in Engineering and Computation in Engineering 1 .
Felix Heide is a Professor of Computer Science at Princeton University , where he leads the Princeton Computational Imaging Lab . He also serves as Head of AI at Torc Robotics , focusing on full autonomy stacks for self-driving trucks. His research sits at the intersection of optics , machine learning , and computer vision , addressing imaging challenges in harsh environments like dense fog, ultra-low/high illumination, and scattering media. Ph.D. in Computer Science from the University of British Columbia Postdoctoral research at Stanford University His work on computational imaging spans physics-based vision, non-line-of-sight imaging , end-to-end camera design , and robust sensor fusion . He has pioneered techniques for inverse neural rendering , nanophotonic optics , and light-speed AI through optical computing. His recent papers in Nature Machine Intelligence , Science Advances , and top conferences ( SIGGRAPH , CVPR , ICCV ) focus on: Adverse weather imaging (fog, snow, rain) Multi-sensor fusion (LiDAR, radar, gated cameras) Light transport through scattering media Optical metasurfaces and diffractive optics End-to-end optimization of imaging pipelines Event-based vision and polarization cues He has received prestigious awards including the SIGGRAPH Significant New Researcher Award , Sloan Research Fellowship , and Packard Fellowship . His lab's open-source code and datasets enable real-world applications in autonomous driving, microscopy, and augmented reality.
Anne C. Elster is a Professor and Director of the Heterogeneous and Parallel Computing Lab (HPC-Lab) at NTNU's Department of Computer Science, with additional roles as HPC Leader at the Center for Geophysical Forecasting and Senior Research Fellow at the Oden Institute. She holds board positions at NTNU and its Faculty of Information Technology. Her research spans: High-Performance Computing : GPU acceleration, auto-tuning, and heterogeneous systems Machine Learning : Applied to optimization and computational geosciences Parallel Algorithms : For scientific computing and real-time simulations Her recent publications (2021-2024) focus on GPU auto-tuning, quantum-HPC integration, distributed systems, and ML-driven geophysical data analysis, with strong emphasis on performance optimization across architectures. Awards and honors: IEEE Computer Society Distinguished Contributor (2021) IEEE Distinguished Speaker (2019-2022) IEEE Senior Member (2000) She has supervised 100+ master's students, 15+ PhDs, and secured major grants including EU H2020 projects. Current Post Docs focus on HPC acceleration and AI applications. Her HPC-Lab collaborates with CERN, Equinor, and international universities, specializing in GPU-accelerated scientific computing and tools for performance portability.
Prof. Dr. Klaus R. Pawelzik is a Professor at the University of Bremen's Institute for Theoretical Physics, where he leads the Theoretical Bio- and Neurophysics research group. His research bridges theoretical physics and neuroscience, with laboratories located in the Cognium building on the university campus. His primary research interests include: Computational models of neural dynamics and information processing Neurophysics and mechanisms of cortical computation Brain-computer interfaces and neuroprosthetic systems Dynamical systems approaches to causality and network analysis Biologically plausible learning algorithms for spiking neural networks Attention mechanisms and sensory processing in primate brains Recent publications (2015-2020) demonstrate strong focus on attention mechanisms in visual processing, causal inference methods for dynamical systems, hardware implementations for neuroprosthetics, and biologically inspired learning algorithms. The work consistently integrates mathematical rigor with experimental neuroscience, featuring collaborations with neurophysiology labs and engineering groups. Prof. Pawelzik leads an active research team developing both theoretical frameworks and experimental platforms for neuroscience research. The lab specializes in open-source neurotechnology solutions, including wireless implantable devices for electrocorticography and FPGA-based processing systems for real-time neural signal analysis.
Kunle Olukotun is a Professor of Electrical Engineering and Computer Science at Stanford University's School of Engineering, where he has been faculty since 1991. He directs the Stanford Pervasive Parallelism Lab (PPL) and co-leads the Transactional Coherence and Consistency (TCC) project. His research focuses on computer architecture, parallel programming environments, and scalable parallel systems. Key areas include chip multiprocessors (CMPs), transactional memory systems, domain-specific languages (DSLs) for heterogeneous computing, and hardware-software co-design for machine learning workloads. His work bridges theoretical foundations with practical systems implementation. Notable contributions include the Stanford Hydra research project (one of the first chip multiprocessors with thread-level speculation), founding Afara Websystems (acquired by Sun Microsystems), and developing the Niagara processor architecture. His DSL frameworks like Green-Marl and Spatial enable efficient graph analysis and hardware acceleration. His publications reveal strong trends in parallel systems evolution: from foundational CMP research (2000s) to transactional memory (2004-2010), then DSLs for heterogeneous computing (2010-2015), and currently foundation model systems (2023-2025). Subfield analysis shows consistent focus on hardware-software co-design, sparse computation, and compiler techniques across decades. ACM Fellow (2006) for contributions to multiprocessors on a chip and multi-threaded processor design Best Paper Award at IEEE International Symposium on Workload Characteristics (IISWC '10) for EigenBench Olukotun actively mentors researchers through the Stanford Pervasive Parallelism Lab (PPL), which seeks to proliferate parallelism across application domains. His projects have secured significant industry partnerships, including the acquisition of his startup Afara Websystems by Sun Microsystems. Current research focuses on compiler frameworks for foundation model systems and hardware acceleration for sparse machine learning workloads, supported by collaborations with major tech companies. He leads the Stanford Pervasive Parallelism Lab (PPL), which develops compiler and runtime systems for heterogeneous architectures. The lab's work spans DSLs, hardware acceleration, and parallel programming models, with strong industry ties to companies like NVIDIA and Google. Current initiatives include the Mosaic compiler framework and Stardust architecture for sparse tensor computation.
Marie LUONG is a researcher at Université Sorbonne Paris Nord specializing in image processing and analysis. She is currently preparing her HDR (Habilitation à Diriger des Recherches), a prestigious post-doctoral qualification in the French academic system that demonstrates research independence and eligibility to supervise PhD students. Her research focuses on two major interconnected domains: Image Quality Enhancement : Developing techniques inspired by Human Visual System mechanisms to address coding artifacts, noise, and resolution limitations Image Classification : Creating innovative methods based on sparse representation in transform domains to improve classification accuracy Dr. LUONG's methodological contributions include: Four methods for addressing coding artifacts with a proposed Blockiness Visibility Measure Six innovative denoising approaches combining anisotropic filtering and machine learning Three example-based super-resolution methods, two integrating denoising in a unified optimization framework Four classification methods leveraging sparse representation in wavelet domains Her research demonstrates significant practical applications in digital cinema technology and medical image diagnosis systems , translating theoretical advances into real-world solutions. Academic leadership and mentoring: Co-supervised seven completed PhD theses Currently supervising two ongoing doctoral projects
Timothy Rogers is a Professor in the Department of Psychology at the University of Wisconsin. His research focuses on the intersection of semantic cognition , cognitive neuroscience , and artificial intelligence . Based in Madison, Wisconsin, he operates the Rogers Lab at the Discovery Building (330 N. Orchard Street), utilizing advanced neuroimaging techniques like 7T-fMRI to decode semantic representations in the brain. Education: BA in Psychology and English Literature (University of Waterloo), PhD in Psychology (Carnegie Mellon University) His work explores semantic control mechanisms , neural coding of concepts, and human-machine collaboration in creative tasks. Recent projects investigate LLM alignment with human judgment, context inference , and representational motifs in perception. Research trends show integration of multivariate decoding , sparse modeling , and collective intelligence to analyze semantic organization in cognition and neural systems. His lab applies these methods to problems in health AI , educational technology , and neurodegenerative disorders . Contact: 1.608.316.4339 , Discovery Building, Madison, WI 53715.
Marcella Noorman is an Assistant Professor in the Department of Neurobiology at the University of Chicago, where she leads interdisciplinary research at the intersection of computational neuroscience and biomedical engineering. Her work focuses on fundamental questions of neural representation and biological system modeling. Her research program spans three complementary domains: Computational Neuroscience : Developing theoretical frameworks for how sparse neural populations maintain accurate internal representations of continuous variables, as demonstrated in her 2024 Nature Neuroscience paper Systems Neuroscience : Investigating neural circuit mechanisms for spatial navigation and action selection using Drosophila connectomics (eLife 2021) Biomedical Modeling : Applying mathematical techniques to liver metabolism systems (Mathematical Biosciences and Engineering 2019) Dr. Noorman's publication record shows steady output in high-impact journals with increasing visibility, as evidenced by Altmetric attention including Wikipedia references and news coverage. Her collaborative approach integrates theoretical and experimental neuroscience through partnerships with major research institutions. The lab emphasizes quantitative approaches to neuroscience, with opportunities for students to develop skills in neural data analysis, computational modeling, and interdisciplinary research. As an early-career faculty member, she is actively building her research program and likely has openings for motivated graduate students with strong mathematical backgrounds.
Cynthia Diane Rudin is a Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, and Biostatistics & Bioinformatics at Duke University, where she directs the Interpretable Machine Learning Lab. Previously, she held faculty positions at MIT Sloan School of Management and research roles at Columbia University and NYU. PhD in Applied and Computational Mathematics, Princeton University BS in Mathematical Physics and Music Theory, University of Chicago Her research pioneers interpretable machine learning for high-stakes domains where transparency is non-negotiable. She challenges the accuracy-interpretability tradeoff myth, proving that transparent models can match black-box performance in healthcare, criminal justice, and power grid reliability applications. Her work has produced deployable systems like the 2HELPS2B ICU seizure predictor and NYPD's Patternizr crime detection tool. Rudin's publication record shows consistent focus on interpretable algorithms since 2007, with recent work exploring the Rashomon set of equally accurate models and causal inference frameworks. Her highly cited 2018 Nature Machine Intelligence paper fundamentally shifted industry practices toward transparent AI. Squirrel AI Award for AI Benefiting Humanity (2022) Guggenheim Fellowship (2022) Triple INFORMS Innovative Applications Award winner (2013, 2016, 2019) Fellow of AAAI, ASA, and IMS She actively mentors students through Duke's Data+ program and has advised teams winning international competitions. Her lab maintains strong industry partnerships focused on ethical AI deployment, with current grants from NSF, NIH, and DARPA supporting healthcare and criminal justice applications. Rudin serves on National Academies committees shaping federal AI policy and has testified before Congress on algorithmic transparency. The Interpretable Machine Learning Lab maintains an open-science ethos with all code publicly available. Current projects focus on sparse decision trees for medical diagnostics, causal inference frameworks for high-stakes decisions, and extending the Rashomon effect theory to new application domains.
Richard Kempter is a Professor at Charité - Universitätsmedizin Berlin's Institute of Neuroscience, where he leads research in computational neuroscience with a focus on hippocampal circuitry and memory systems. His work bridges experimental neuroscience with theoretical modeling, examining how neural networks support spatial navigation, memory formation, and consolidation processes. His research interests center on the computational principles underlying hippocampal function, particularly in memory consolidation, spatial navigation, and neural coding. Kempter investigates how hippocampal circuits generate sharp wave-ripple events, how grid cells form spatial representations, and how memory traces transform during systems consolidation. His work combines computational modeling with experimental data analysis to develop testable theories about neural mechanisms. Analyzing Kempter's recent publications reveals a strong focus on hippocampal CA3 circuitry, with particular attention to sharp wave-ripple complexes and their role in memory consolidation. His work demonstrates how specific connectivity patterns between pyramidal neuron subtypes enable memory replay, while his computational models explain how grid-like representations emerge in entorhinal cortex. The research spans multiple scales from single-cell properties to network dynamics, with applications to both rodent and human memory systems. Kempter's scientific contributions have appeared in top-tier journals including Nature , Neuron , and PNAS , reflecting the significance of his work in understanding fundamental neural mechanisms. His publications demonstrate consistent innovation in developing computational frameworks that explain experimental observations while generating new testable predictions about memory systems. His research team collaborates extensively across institutions, working with experimental neuroscientists to bridge theoretical models with empirical data. Current projects examine how neural synchrony creates functional filters during rest states, how population sparseness affects memory capacity, and how subtype-specific connectivity enables sequential activation during memory replay events.
Professor Tadashi Wadayama serves in the Department of Computer Science within the Faculty of Engineering at Nagoya Institute of Technology. He holds a full professorship position and leads research initiatives in coding theory, signal processing, and deep learning applications for next-generation communication systems. Professor Wadayama received his B.E., M.E., and D.E. degrees from Kyoto Institute of Technology in 1991, 1993, and 1997 respectively. He began his academic career at Okayama Prefectural University in 1995 as a research associate and spent 1999-2000 as a visiting researcher at Essen University in Germany. He joined Nagoya Institute of Technology as an associate professor in 2004 and was promoted to full professor in 2010. He maintains active memberships in IEEE and the Institute of Electronics, Information and Communication Engineers (IEICE). His research spans multiple interconnected domains with primary focus on Coding Theory , Signal Processing for Wireless Communications , and Deep Learning applications . Professor Wadayama has made significant contributions to LDPC codes, MIMO signal detection, and the emerging field of deep unfolding techniques that bridge neural networks with traditional signal processing algorithms. His work increasingly incorporates physics-aware modeling of communication channels governed by partial differential equations. Recent research demonstrates strong interdisciplinary integration between information theory, machine learning, and communication engineering principles. Analysis of his recent publications reveals a clear trajectory toward developing foundational technologies for post-Shannon communication architectures. His work emphasizes ultra-large-scale coding, goal-oriented communication, digital homeostasis mechanisms, physics-embedded signal processing, and dual-process learning systems that combine fast reactive processing with deliberative meta-learning using LLM orchestrators. Fundamentals Review Best Author Award, IEICE, 2022 SRC 2010 Paper Award, Storage Research Promotion Organization, 2011 Professor Wadayama has successfully led multiple competitive research grants including JSPS Grant-in-Aid projects. He currently serves as Principal Investigator for the JST CRONOS project "Digital Cybernetics: Towards Next-Generation Communication Architecture" (2025-2031), which aims to develop foundational technologies supporting autonomous, adaptive, and robust large-scale AI systems. As IEEE Information Theory Workshop General Co-chair (2020-2021) and former chair of IEICE's Information Theory Research Committee (2020-2022), he maintains active leadership roles in the academic community. He leads the Wadayama Group at Nagoya Institute of Technology, which focuses on digital cybernetics and communication physics. The group develops physics-aware signal processing implementations, dual-process learning systems, digital homeostasis mechanisms, and system integration for next-generation communication architectures. His team collaborates with researchers from Kyoto University, Hiroshima University, Institute of Science Tokyo, and Tokyo University of Science, creating a robust research ecosystem focused on post-Shannon communication frameworks.
