Edriss S. Titi is a University Distinguished Professor and Arthur Owen Professor of Mathematics at Texas A&M University within the College of Arts & Sciences. His research focuses on nonlinear partial differential equations, applied mathematics, and geophysical fluid dynamics. He leads studies on fluid mechanics, atmospheric and oceanic dynamics, data assimilation, and control theory. His work often addresses mathematical rigor in modeling complex systems like climate dynamics and turbulent flows. Research Interests: Nonlinear PDEs and their applications Fluid dynamics and turbulence Data assimilation algorithms Climate and ocean modeling Infinite-dimensional dynamical systems Recent publications emphasize Navier-Stokes equations , primitive equations , and data assimilation in chaotic systems . His methodologies bridge theoretical analysis and computational modeling, with applications to weather prediction and geophysical flows. Collaborations include the Institute for Applied Mathematics and Computational Science (IAMCS) at Texas A&M. Notable contributions include rigorous analysis of global well-posedness for oceanic models and development of CDAnet, a physics-informed deep learning framework for fluid flow downscaling.
Daniel Müller-Gritschneder is an Adjunct Teaching Professor (Privatdozent) at the Technical University of Munich (TUM), affiliated with the Chair of Electronic Design Automation. He leads the 'Electronic System Level' research group, focusing on embedded systems, TinyML, virtual prototyping, and hardware resilience. He temporarily served as head of the Chair of Real-Time Systems (2019–2020) and holds a senior membership in IEEE. His research spans: TinyML : Optimizing neural network inference for microcontrollers. Virtual Prototyping : Fast simulation for embedded software development (e.g., ETISS simulator). Runtime Verification : Hardware monitoring for safety-critical systems. Fault Tolerance : Cross-layer resilience against soft errors. Design Automation : NoC synthesis and RISC-V toolchain optimization. His publications emphasize RISC-V-based systems, TinyML deployment, fault injection, and embedded AI. Recent works show trends toward compiler-assisted security, thermal management, and automated design-space exploration for edge devices. Awards: Best Paper Award (SiPS 2019) Habilitation Award (Bund der Freunde der TUM, 2019) 2nd Best Paper (SMACD'15) Best Paper nominations at DAC'07, DATE'10, Analog'10, NOCS'13 He advises researchers in the Electronic System Level group and contributes to EU projects (e.g., Scale4Edge). His lab develops tools like ETISS, MLonMCU, and Seal5 for RISC-V and TinyML ecosystems.
Professor Sungheon Gene Kim holds a faculty position at the Weill Cornell Medicine Graduate School of Medical Sciences within the Department of Radiology . His research focuses on quantitative MRI methodology for oncological applications , particularly in breast cancer and head and neck cancer . Kim's lab develops advanced dynamic contrast-enhanced MRI (DCE-MRI) and diffusion MRI (dMRI) techniques to assess tumor microenvironment and treatment response . Key research areas include: Tumor vascular properties via 3D UTE-GRASP MRI Cellular microstructural analysis through POMACE framework Adipose-tissue cancer interaction via MR spectroscopic imaging His lab has received continuous funding from the National Cancer Institute (R01CA219964, UG3/UH3CA228699, R01CA160620). Recent publications demonstrate technical advancements in ultrafast MRI reconstruction , deep learning-enhanced perfusion analysis , and multi-parametric tumor characterization . Collaborations with the National Institutes of Health Quantitative Imaging Network have produced novel cellular water exchange rate measurements that correlate with patient survival outcomes .
Baris Kasikci is an Associate Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Previously (2017-2023), he was a Morris Wellman Assistant Professor in the Electrical Engineering and Computer Science Department at the University of Michigan. His research focuses on building efficient and trustworthy computer systems through innovative combinations of approaches from systems, computer architecture, and programming languages. Dr. Kasikci received his PhD in Computer Science at EPFL and has held research positions at Microsoft Research Cambridge, Google, Intel, and VMware. His work addresses critical challenges in system reliability, security, and performance in increasingly complex software ecosystems. His research interests center on improving the efficiency of datacenter applications and machine learning systems, analyzing and fixing failures, and enhancing hardware security. His lab develops techniques for automated bug detection, formal verification of distributed systems, and building systems support for heterogeneous hardware architectures. Recent projects include Whisper (profile-guided branch misprediction elimination), Huron (taming false sharing), and Agamotto (automatic detection and repair of bugs in persistent memory applications). Analysis of his recent publications shows a strong trend toward optimizing large language model serving, hardware security, and performance optimization for modern heterogeneous architectures. His work bridges traditional systems research with emerging AI infrastructure needs, particularly in efficient LLM serving, security vulnerabilities in modern hardware, and performance optimization for heterogeneous computing environments. NSF CAREER award Microsoft Research Faculty Fellowship Intel Rising Star Award VMware Early Career Faculty Grant Google Faculty Award Roger Needham PhD Award (best PhD thesis in computer systems in Europe) Patrick Denantes Memorial Prize (best PhD thesis at EPFL) Best Paper Award at OSDI'18 Best Paper Award at MICRO'22 Dr. Kasikci has advised numerous PhD students who have gone on to prestigious positions in academia and industry, including Tanvir Ahmed Khan (Assistant Professor at Columbia University), Akshitha Sriraman (Assistant Professor at CMU), and Jiacheng Ma (AMD). His research has been supported by significant grants from NSF, DARPA, Intel, Google, Microsoft, VMware, and Amazon. His lab, the EfesLab, focuses on building tools and techniques that make computer systems more reliable, secure, and efficient. The EfesLab, led by Dr. Kasikci, brings together postdocs, PhD students, and undergraduate researchers to tackle fundamental challenges in systems reliability and performance. The lab has developed numerous influential tools including Whisper, Huron, and Agamotto that address critical performance and reliability issues in modern computing systems. Current research directions include efficient LLM serving, security of emerging hardware technologies, and automated debugging techniques.
Dr. Zhenghao Chen is a Lecturer in Data Science at the University of Newcastle, affiliated with the School of Information and Physical Sciences. He earned his Ph.D. from the University of Sydney in 2022, following a B.Eng. H1 degree from the same institution in 2017. Prior to his current position, Dr. Chen served as a Postdoctoral Research Fellow at the University of Sydney (2022-2024), a Research Engineer at TikTok (2024), and as a Visiting Research Scientist at Microsoft Research and Disney Research (2022-2023). Dr. Chen's educational background includes: Doctor of Philosophy, University of Sydney (2022) Bachelor of Information Technology (B.Eng. H1), University of Sydney (2017) Dr. Chen's research spans multiple domains within artificial intelligence, with particular expertise in Computer Vision, Natural Language Processing, and Machine Learning. His work in Generative AI has garnered significant recognition, with applications in both academic and industrial settings. His research interests are reflected in his Fields of Research percentages: Deep Learning (30%), Computer Vision (30%), Natural Language Processing (20%), and Multimodal Analysis and Synthesis (20%). His publications in top-tier venues like CVPR, ICCV, ECCV, and journals like IEEE TPAMI demonstrate the breadth and impact of his work. Analysis of Dr. Chen's recent publications (2022-2025) reveals a consistent focus on neural compression techniques, 3D perception, and multimodal AI systems. His work spans medical imaging (CXR bone suppression), video compression, point cloud processing, and neural surface reconstruction. A notable trend is his exploration of efficient AI systems that work well under resource constraints, as evidenced by his involvement in the EMCLR workshop. His research often bridges theoretical advances with practical applications across multiple domains. Dr. Chen has received several prestigious awards: Microsoft Research Asia StarTrack Fellowship (2025) ACM SIGMM Award for Outstanding PhD Thesis in Multimedia Computing (2024) Australia Government Research Training Program (RTP) Fellowship (2019) Google Australia Prize for Excellence in Computer Science (2017) Dr. Chen is actively involved in the academic community, serving on the Program Committee for major AI conferences including CVPR, ICCV, ECCV, SIGGRAPH, AAAI, and others. He also organizes workshops in Multimedia and ICCV conferences, and serves as a reviewer for prestigious journals. His teaching responsibilities include courses on Intelligent Visual Signal Understanding, Video Intelligence and Compression, Database and Information Management, and Computing Fundamentals at both the University of Sydney and University of Newcastle.
Alexandros Kontogiannis is a research fellow at the University of Cambridge, Department of Engineering, specializing in fluid dynamics and applied mathematics. His work combines Bayesian inference, machine learning, and physics-informed algorithms to solve inverse problems in magnetic resonance velocimetry (MRV) and fluid-structure interaction. EPSRC National Fellow in Fluid Dynamics Member of Energy, Fluids and Turbomachinery Division Research Focus: Development of digital twin frameworks that integrate MRV data with Navier-Stokes equations to reconstruct flowfields, infer rheological parameters in non-Newtonian fluids, and estimate hidden quantities like pressure and wall shear stress. Key innovations include: Physics-informed compressed sensing for sparse MRV data Simultaneous boundary shape and flowfield estimation Bayesian turbulence model parameter learning Scientific Awards: ASME Fluids Engineering Division Graduate Student Scholar (2021) Technical Chamber of Greece (TEE) Award (2018) Limmat Foundation Academic Excellence (2017) Mentzelopoulos Scholarship for international studies (2017) Greek State Scholarships Foundation Award (2012) Key Contributions: Algorithms for 3D flow reconstruction with adaptive discretization, viscous signed distance field regularization, and multi-objective aerodynamic shape optimization. His methodologies enable 27x reductions in MRI scanning time while maintaining diagnostic accuracy.
Paolo Prandoni is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC). He serves as a Scientist in the Audiovisual Communications Laboratory (LCAV) and teaches in the SSC-ENS and SIN-ENS units, focusing on signal processing theory and practical applications in audiovisual communications. He earned his PhD from EPFL after completing all prior education there, driven by childhood fascination with long-distance telephony. His doctoral work established foundations in communication systems that continue to inform his research. Prandoni's research spans audio/image processing, machine learning for media analysis, and DSP education. Key areas include computational photography (e.g., spectral imaging, stained glass rendering), speech quality assessment via transfer learning, music information retrieval (e.g., fingering prediction), and audience analytics through his company Quividi. His work consistently bridges theoretical signal processing with real-world implementation. Recent publications reveal a strategic shift toward machine learning integration in signal processing tasks, particularly non-intrusive speech assessment and lensless imaging reconstruction. Simultaneously, he advances DSP pedagogy through MOOC development and hands-on teaching tools using off-the-shelf hardware, emphasizing accessibility and practical skill development. No scientific awards are documented in the provided materials. He has advised PhD student Thanikachalam Niranjan (thesis: Image Based Relighting of Cultural Artifacts , 2016) and teaches Communication Systems and Computer Science courses. His educational impact extends through the open-access textbook Signal Processing for Communications (2008) and tools like MultiPub for maintainable online classes. Industry engagement includes Quividi co-founding (2006) and ongoing CSO role in attention analytics. As a core LCAV laboratory member, he collaborates on interdisciplinary projects including cultural heritage digitization, embedded signal processing systems, and real-time audience measurement, leveraging EPFL's infrastructure for both academic and commercial applications.
Teemu Turunen-Saaresti is a Tenured Professor at the School of Energy Systems , LUT University , Lappeenranta, Finland. His research focuses on energy technology, particularly supercritical CO2 cycles, Organic Rankine Cycles (ORC), turbomachinery, and heat pump design. PhD in Energy and Environmental Technology (2004), Lappeenranta University of Technology MSc in Energy and Environmental Technology (2001), Lappeenranta University of Technology His work spans Supercritical CO2 Power Cycles , Organic Rankine Cycle Systems , Turbomachinery Design , and Non-Equilibrium Condensation Modeling . Recent studies include printed circuit heat exchangers for transcritical cycles, high-temperature ORC thermal inertia, and centrifugal compressor design for large-scale CO2 heat pumps. Publications highlight trends in sCO2 Turbines , Tip Clearance Effects , and Multiphase Flow Simulation . Funding from the Academy of Finland and Business Finland supports his research on computational/experimental condensing flows, small-scale compressors, and green shipping energy solutions. He collaborates with international teams on projects like the International Wet Steam Modeling Project , contributing to guidelines for high-temperature heat pumps (IEA HPT Annex 58) and advancements in hydrogen compression strategies.
Michalis Vazirgiannis is a Professor at LIX, École Polytechnique in France, where he leads the Data Science and Mining group (DaSciM). He holds a degree in Physics and a PhD in Informatics from Athens University (Greece), and a Master's degree in AI from Heriot Watt University, Edinburgh (UK). His academic career spans multiple prestigious institutions including Fraunhofer and Max Planck MPI in Germany, INRIA/FUTURS in Paris, AUEB in Greece, Telecom-Paristech, ENS in France, Tsinghua and Jiaotong Shanghai in China, and Deusto University in Spain. Professor Vazirgiannis's research focuses on machine and deep learning methods for graph analysis, including community detection, graph clustering, node embeddings, and influence maximization. His work in text mining encompasses Graph of Words, word embeddings with applications to web advertising and marketing, event detection, and summarization. He has active collaborations with industrial partners in analytics and machine learning for large-scale data repositories across various application domains such as recommendations, meeting summarization, influence metrics for scientific and social networks, and predictive maintenance. His recent publications demonstrate a strong emphasis on Graph Neural Networks, multilingual NLP (particularly for French and Arabic), and applications of deep learning to diverse domains including social networks, legal text, and biomedical data. There's a clear trajectory toward developing more efficient, explainable, and specialized models that address real-world challenges in data analysis. ERCIM fellowship Marie Curie EU fellowship Tencent "Rhino-Bird International Academic Expert Award" (2017) Best Paper Award at IJCAI 2018 Best Paper Award at CIKM 2013 Professor Vazirgiannis has supervised 29 completed PhD theses and has attracted significant R&D funding from national and international sources, including research agencies and industrial partners such as Google, Airbus, Huawei, Deezer, BNP, and LVMH. He leads or has led several academic research chairs including DIGITEO (2013-15), ANR/HELAS (2020-25), and AXA (2015-2018). The DaSciM research group, which he leads at École Polytechnique, has extensive experience in real-world R&D projects involving large-scale data mining. The team maintains active collaborations with major industrial partners including AIRBUS, Google, BNP, Tencent, and Tradelab, working on cutting-edge machine learning projects. The group has co-organized major conferences such as ECML PKDD 2011 and ECML/PKDD 2017 and participates in the senior organization of AI and data mining events like AAAI and IJCAI.
Tania Lombrozo serves as the Arthur W. Marks ’19 Professor at Princeton University, leading the Concepts and Cognition Lab where she investigates the psychological and philosophical dimensions of human reasoning. Her work uniquely integrates empirical methods from cognitive science with conceptual frameworks from analytic philosophy. Her academic background includes a Ph.D. from Harvard University, establishing her foundation in interdisciplinary research approaches. Lombrozo's research centers on the human drive to explain, examining why we seek explanations for certain phenomena but not others, how explanation-seeking affects learning, and whether explanatory processes serve epistemic goals or introduce reasoning errors. She explores connections between causal reasoning, moral responsibility, and intuitive theories of knowledge, drawing from cognitive, social, and developmental psychology alongside philosophy of science and moral philosophy. Her methodology emphasizes experimental rigor while addressing normative questions about ideal reasoning. Analysis of her 2024-2025 publications reveals dominant themes in explanation evaluation across scientific and moral contexts, with significant attention to jargon in science communication, simplicity principles (Ockham’s razor), and moral responsibility in collective action. Her work increasingly addresses AI-human interaction, particularly how explanations influence trust in large language models and the cognitive effects of chain-of-thought reasoning. Notable honors include: Arthur W. Marks ’19 Professorship Excellence in Mentoring Graduate Students Award Lombrozo actively mentors graduate students including Sarah Joo, Casey Lewry, and Sebastian Montesinos, with research supported by interdisciplinary grants spanning cognitive science, ethics education, and technology policy. Her Concepts and Cognition Lab functions as a collaborative hub where philosophical questions are tested through behavioral experiments, contributing to both theoretical advances and practical applications in science communication and AI design.
Peter Richtarik is a Professor at the King Abdullah University of Science and Technology (KAUST), specializing in Machine Learning, Optimization, and Federated Learning. He actively teaches courses such as Stochastic Gradient Descent Methods and mentors PhD and MS students like Konstantin Burlachenko, Kai Yi, and Lukang Sun. His research spans distributed optimization, parameter-efficient fine-tuning, and theoretical frameworks for non-convex and non-smooth problems. Recent work includes 2025 contributions to Bernoulli-LoRA (theoretical PEFT) and Gluon (LMO-based optimizers). He co-developed Thanos (block-wise pruning) and BurTorch (CPU-optimized training framework). His publications focus on communication efficiency ( ATA , LoCoDL ), differential privacy ( DP-RBCD ), and stochastic proximal methods. Richtarik received the Charles Broyden Prize for his work on quasi-Newton methods. He frequently presents at workshops like MLSS in Senegal and FLOW seminars.
Vikram Kodibagkar is a Professor in the School of Biological and Health Systems Engineering at Arizona State University, with additional affiliations to the School of Medicine and Advanced Medical Engineering. He leads the Prognostic Bioengineering (ProBE) Lab, conducting cutting-edge research in cellular and molecular imaging, magnetic resonance physics, and biomedical engineering. Education: Ph.D. in Physics, Washington University, St. Louis (2002) M.Sc. in Physics, Indian Institute of Technology-Mumbai (1997) B.Sc. in Physics, University of Mumbai, India (1995) Research Focus: Professor Kodibagkar's research centers on developing advanced imaging technologies for medical applications. His work encompasses cellular and molecular imaging , multimodality probe development , and magnetic resonance oximetry . A key focus is the development of novel contrast agents and imaging techniques for detecting hypoxia in tumors and brain injuries. His lab also works on compressed sensing accelerated magnetic resonance spectroscopic imaging (MRSI) and functional imaging of implants . The ProBE Lab emphasizes comprehensive understanding of both theory and practical techniques to train the next generation of imaging leaders. Current research activities include developing non-invasive methods for real-time monitoring of engineered cells and tissues, investigating tumor oxygenation dynamics, and creating novel MRI nanosensors for various medical applications. Research Funding and Grants: Professor Kodibagkar has secured significant funding from major organizations including: National Institutes of Health (NIH) - Multiple R01 grants National Science Foundation (NSF) - CAREER Award US Department of Defense (DOD) DARPA/BTO Flinn Foundation Texas Higher Education Coordinating Board Teaching and Mentorship: He teaches various courses including BME 350 Signals & Systems for Bioengineers, BME 465/565 Magnetic Resonance Imaging, and supervises honors theses and research projects. His teaching spans undergraduate to doctoral levels, focusing on biomedical engineering and imaging technologies. Laboratory and Team: Professor Kodibagkar directs the Prognostic Bioengineering (ProBE) Lab at Arizona State University. The lab conducts interdisciplinary research combining engineering, physics, and medicine to develop next-generation imaging technologies for clinical applications.
Lars Davidson is a Professor in the Department of Fluid Dynamics at Chalmers University of Technology. His research focuses on numerical simulations of fluid flow and heat transfer, with an emphasis on turbulence modeling for Large Eddy Simulation (LES) and hybrid LES/RANS methods. He has developed computational codes CALC-BFC and CALC-LES based on finite-volume techniques, and recently integrated machine learning to enhance wall functions and turbulence models. Key projects include Hybrid LES/RANS for wall-bounded flows Machine learning applications in fluid dynamics Aeroacoustic noise reduction in automotive and aerospace systems Wind turbine load analysis in forested regions . His publications span 302 articles in journals and conferences, with recent work on Neural networks for turbulence closure Plasma actuators for drag reduction Lattice Boltzmann wall-modeled LES . Collaborations include teams at Volvo, Siemens, and international research groups.
Cornelius Faber is a University Professor in the Department of Radiology at the University of Münster, Germany, where he leads the Experimental Nuclear Magnetic Resonance research group. His work focuses on developing and implementing novel MRI techniques that extend the boundaries of magnetic resonance imaging in terms of spatial and temporal resolution, sensitivity, and specificity for physiological, structural, and molecular changes. He actively participates in the "Cells in Motion" interdisciplinary research initiative at the university. Professor Faber's research spans multiple critical areas in medical imaging and biomedical science. His primary expertise lies in MRI cell tracking , enabling visualization of cellular dynamics in vivo. He has made significant contributions to infection imaging , developing methods to detect and characterize microbial infections using MRI. His work on MR methodology development has advanced quantitative imaging techniques, while his research on multimodal integration in MR and MRI contrast mechanisms has provided deeper insights into molecular and cellular processes. His research bridges physics, engineering, and biomedical applications, with particular relevance to inflammation, cancer, neurological disorders, and cardiovascular disease. Analysis of Professor Faber's extensive publication record reveals a clear evolution from fundamental MRI technique development toward increasingly sophisticated applications in disease models. His recent work demonstrates a strong trend toward multimodal imaging approaches that combine MRI with complementary techniques such as mass spectrometry, optical imaging, and PET. This integration creates comprehensive diagnostic platforms that provide both anatomical and molecular information. A notable pattern is the focus on cellular dynamics, particularly immune cell behavior in inflammatory conditions and tumor microenvironments, with applications spanning neuroscience, oncology, and cardiology. Professor Faber leads a multidisciplinary research team of approximately 15 members, including scientists, doctoral students, technicians, and medical students. His laboratory is deeply integrated with the University of Münster's research infrastructure, particularly the Multiscale Imaging Centre. The group's work contributes significantly to advancing preclinical MRI methodologies while maintaining strong clinical relevance, with numerous publications in high-impact journals across medical imaging, neuroscience, and biomedical engineering disciplines.
Raghavendra Selvan, an Assistant Professor (Tenure Track) at the University of Copenhagen, holds joint appointments in the Machine Learning Section (Department of Computer Science), Kiehn Lab (Department of Neuroscience), and the Data Science Laboratory. His academic journey includes a PhD in Medical Image Analysis (2018), MSc in Communication Engineering (2015), and BSc in Electronics and Communication Engineering (2009). PhD - Medical Image Analysis, University of Copenhagen (2018) MSc - Communication Engineering, Chalmers University (2015) BSc - Electronics and Communication Engineering, BMS Institute of Technology, India (2009) His research focuses on Bayesian Machine Learning with emphasis on Medical Image Analysis, Graph-based Learning, Tensor Networks, Approximate Inference, and Multi-Object Tracking Theory. Recent publications highlight his contributions to environmentally sustainable AI practices, efficient deep learning in medical imaging, and novel applications of tensor networks. Key research areas: Green AI and Environmental Sustainability Medical Image Analysis Graph Neural Networks Crystal Structure Prediction Model Compression Materials Science Applications
