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Fan ZhouView profile

Researcher

• Fuel Cell Technology
• Hydrogen and Electro-fuels
• Thermal Engineering
• +3 more

Fan Zhou is a Researcher at Aalborg University's Department of Thermal Engineering within the Faculty of Engineering and Science, specializing in hydrogen and electro-fuels. His work focuses on high-temperature proton exchange membrane (HT-PEM) fuel cells and solid oxide electrolysis systems, with applications in power-to-X and micro combined heat and power (micro-CHP) solutions. He holds a PhD awarded in February 2016 and maintains an active research profile with 27 documented publications. His research interests center on Fuel Cell Technology , Hydrogen and Electro-fuels , and Thermal Engineering , with specific expertise in performance degradation mechanisms, fault diagnosis using electrochemical impedance spectroscopy (EIS) and machine learning, thermal management, and dynamic operation of energy conversion systems. Current investigations address real-time monitoring challenges in residential microgrids and power-to-X applications, examining how operational parameters like temperature, pressure, and gas composition affect system efficiency and durability. Analysis of his 15 most recent publications (2021-2025) reveals a strong emphasis on data-driven approaches for fault detection in fuel cells, dynamic operation effects on electrolysis cells, and integration of clean energy systems. Key trends include the application of convolutional neural networks for online diagnostics, investigation of AC/DC frequency effects in solid oxide electrolysis, and development of control systems for micro-CHP applications using HT-PEM fuel cells. Dr. Zhou currently participates in two major research projects: Robust And Dynamic Electrolysis for Power-to-X (2024-2027), focusing on advanced electrolysis technologies for power-to-X applications, and FC-COGEN (2023-2025), developing micro combined heat and power systems. Both projects are funded by the Energy Technology Development and Demonstration Program (EUDP) and involve collaboration with industry partners and researchers including Søren H. Jensen and Simon L. Sahlin from AAU's Power Electronics and Drives group.

• Artificial Intelligence
• Neural Networks
• Quantization
• +3 more

Hang Li is a Researcher in the Department of Molecular Biophysics and Biochemistry at Yale University’s Yale School of Medicine. Their work focuses on advancing neural network architectures, quantization techniques, and spiking neural networks (SNNs). They are affiliated with the Molecular Biophysics and Biochemistry department and contribute to interdisciplinary research in artificial intelligence and computational neuroscience. Research interests include optimizing neural networks for efficiency through quantization, exploring spiking neural networks for low-power computing, and developing methods like hybrid SNN designs, post-training calibration, and neuromorphic architectures. Their recent work addresses challenges in extreme low-bit quantization, data augmentation for object detection, and temporal coding in SNNs. Publications highlight innovations in quantization methods (e.g., TesseraQ, GenQ), spiking transformer architectures, and workload-balanced pruning strategies. While no awards are explicitly listed, their contributions to model efficiency and neuromorphic computing are notable in the field. Hang Li collaborates on projects involving neuromorphic hardware, system inconsistency benchmarking (SysNoise), and data-driven spatio-temporal analysis. Their research bridges theoretical advancements and practical applications in AI and biomedical informatics.

Timothy HobbsView profile

Academic

• Particle Physics
• Nuclear Physics
• Quantum Chromodynamics (QCD)
• +5 more

Timothy Hobbs is a Research Assistant Professor of Physics at the Lewis College of Science and Letters, Illinois Institute of Technology. His research focuses on particle and nuclear theory, particularly quantum chromodynamics (QCD), nonperturbative hadronic systems, and their implications for collider phenomenology, neutrino scattering, and beyond Standard Model (BSM) physics. He is actively involved in planning for the Electron-Ion Collider (EIC) and leverages advanced computation, machine learning, and quantum information techniques in his work. Education: B.A., Physics and Mathematics, The University of Chicago M.S., Physics, Indiana University Ph.D., Physics, Indiana University Research Interests: Dr. Hobbs explores QCD's nonperturbative structure, nuclear physics, and the interplay between theory and emerging technologies like AI. His work supports the EIC's goals to study proton/nuclei structure and BSM physics. Key topics include parton distribution functions (PDFs), neutrino scattering, and quantum entanglement in QCD. Awards: Fellow, EIC Center at Jefferson Lab (2018–2020) Advising & Collaboration: His contributions include theoretical tools for neutrino experiments, EIC planning, and global QCD analyses (e.g., CTEQ-TEA framework). He collaborates on event generators and uncertainty quantification in high-energy physics. Labs & Affiliations: Member of the American Physical Society (APS) and APS Group on Hadronic Physics.

• Artificial Intelligence
• Machine Learning
• Internet of Things
• +13 more

Edoardo Patti is an Associate Professor in the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, Italy. He is actively involved in research and teaching, focusing on artificial intelligence, machine learning, IoT, smart grids, energy communities, co-simulation, and digital twin technologies. He is a member of the EDA (Electronic Design Automation) research group and the Interdepartmental Center IAM@PoliTo for Integrated Additive Manufacturing. His research interests span Artificial Intelligence, Machine Learning, Internet of Things, Smart Grids, Energy Communities, Co-simulation, Distributed Software Infrastructure, Digital Twin, Physics-Informed Neural Networks, and Reinforcement Learning . His work is closely aligned with Sustainable Development Goals such as Affordable and Clean Energy (Goal 7), Sustainable Cities (Goal 11), and Climate Action (Goal 13). The latest publications reveal a strong trend in AI-driven modeling for energy systems, including digital twins for wave energy converters, urban electric mobility simulation, real-time battery health estimation, and co-simulation platforms for integrated energy systems. His work combines machine learning with physical models and large-scale distributed systems to address sustainability challenges. He supervises several PhD students, including Maria Adelaide Loffa, Matia Torlini, Rafael Natalio Fontana Crespo, Pietro Rando Mazzarino, Claudia De Vizia, and Marco Massano. He has led numerous research projects funded by EU (H2020), PNRR, regional initiatives, and corporate contracts, particularly in additive manufacturing, smart grids, and IoT platforms. He is involved in multiple research groups and centers: EDA - Electronic Design Automation (DAUIN) Interdepartmental Center IAM@PoliTo - Integrated Additive Manufacturing

• High Performance Computing
• Software Engineering for HPC
• Code Generation
• +12 more

Harald Köstler is an Associate Professor and Head of Research at the Erlangen National High Performance Computing Center (NHR@FAU) within the Department of Computer Science at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He leads the research group on HPC Software Design at the Chair of Computer Science 10 (System Simulation), focusing on software engineering for high-performance computing and data analytics. His research interests include: Software Engineering for HPC Code Generation for Numerical Solvers Performance Engineering on Hybrid Architectures Discontinuous Galerkin and Lattice Boltzmann Methods Multigrid Solvers and Parallel Algorithms Performance Portability across CPUs, GPUs, and FPGAs The recent publications highlight a strong trend in developing efficient, scalable, and portable simulation frameworks for complex physical systems. His work emphasizes code generation, performance optimization, and the integration of classical model-driven and data-driven approaches. Key application areas include computational fluid dynamics, geotechnical engineering, and climate modeling, often leveraging the waLBerla and ExaStencils frameworks. Harald Köstler has no listed scientific awards in the provided text. He advises students in the areas of high-performance computing, numerical methods, and software engineering for scientific applications. His research is supported by collaborations within the FAU HPC ecosystem and likely involves grants related to national high-performance computing initiatives. He is a key contributor to the waLBerla framework, a block-structured, high-performance software for multiphysics simulations, and is involved with the ExaStencils project, which focuses on advanced multigrid solver generation. These frameworks form the core of his research team's efforts in scalable scientific computing.

Julija ZavadlavView profile

Assistant Professor

• Multiscale Modeling
• Machine Learning
• Computational Chemistry
• +3 more

Prof. Julija Zavadlav is an Assistant Professor of Multiscale Modeling of Liquid Materials at the Technische Universität München (TUM), affiliated with the TUM School of Engineering and Design. Her research integrates physical modeling with machine learning and Bayesian techniques to develop multi-scale simulation frameworks for diverse applications in bioinformatics and engineering. Education: She earned her PhD in Physics from the University of Ljubljana (2015) and conducted postdoctoral research at ETH Zurich (2016–2019), where she received an ETH Postdoctoral Fellowship. Since 2019, she has held her current position at TUM. Research Interests: Her work focuses on advancing machine learning potentials, Bayesian uncertainty quantification, and multi-scale modeling for complex systems like ionic liquids, metal-organic frameworks, and biomolecules. Her ERC Starting Grant (2022) supports the SupraModel project, emphasizing scalable and interpretable models. Awards: Golden Teaching Award 2022 (Best Lecture), ERC Starting Grant 2022, and ETH Postdoctoral Fellowship. Her recent publications emphasize neural network potentials, transfer learning, and computational tools like JaxSGMC for Bayesian analysis. Grants and Labs: While specific lab names are not mentioned, her ERC grant underscores active funding. No formal student advisee list is provided, but her collaborative work suggests involvement in training next-generation computational scientists.

Wei-keng LiaoView profile

Research Professor

• Parallel and distributed file I/O
• Storage system design
• Data mining algorithms
• +5 more

Wei-keng Liao is a Research Professor in the Department of Electrical Engineering and Computer Science at Northwestern University's McCormick School of Engineering. His research spans high-performance computing with a focus on parallel and distributed systems. Dr. Liao's research interests include parallel and distributed file I/O and storage system design, data mining algorithm design and their parallelization, data management for large-scale scientific applications, and computational model design for large-scale applications on parallel and distributed environments. He is a key contributor to the Parallel netCDF project, which provides parallel I/O capabilities for scientific applications. His recent work shows strong trends in high-performance computing infrastructure, particularly in optimizing I/O systems for scientific applications, parallel data clustering algorithms, and machine learning acceleration in distributed environments. His publications span computational science, parallel computing, and data-intensive applications across various scientific domains including materials science, astrophysics, and healthcare. Best Paper Award at IEEE International Conference on Cluster Computing (2016) for Parallel DTFE Surface Density Field Reconstruction Dr. Liao has supervised numerous research projects funded by DOE, NSF, NASA, and Argonne National Laboratory, with recent work focusing on data libraries for exascale science, machine learning-driven resilience for extreme-scale systems, and scalable data clustering for scientific computing. He leads research on the Parallel K-means Data Clustering software package and is a principal developer of Parallel netCDF. His work connects multiple research groups through the Center for Ultra-scale Computing and Information Security (CUCIS) at Northwestern University, where he collaborates with scientists across disciplines to develop scalable computing solutions for complex scientific problems.

Yu BaiView profile

Associate Professor

• Artificial Intelligence (AI)
• Computer Architecture
• Neuromorphic Computing
• +8 more

Yu Bai is an Associate Professor of Computer Engineering at California State University Fullerton and a Research Associate Professor at the Institute of Mathematical Sciences since 2023. His research spans artificial intelligence (AI), neuromorphic computing, low-power hardware design, and nano-scale computing systems. He holds a PhD in Electrical Engineering from the University of Central Florida (2016), an MS in Electrical and Computer Engineering from the University of Texas-Pan American, and a BS in Electrical Engineering from Ukraine National Aviation University. Research interests include AI applications in robotics, STEM education, and embedded hardware, alongside design automation and CMOS circuit design. His work has been supported by grants from the Army Research Office, National Science Foundation, and National Academies of Sciences. Notable contributions focus on neural network acceleration frameworks, energy-efficient architectures, and spin-based neuromorphic fabrics. He previously worked at Siemens Energy Inc. on data analysis before transitioning to academia.

Xubo YueView profile

Assistant Professor

• Federated Data Analytics
• Bayesian Optimization
• Continuous Optimization
• +5 more

Xubo Yue is an Assistant Professor in the Department of Mechanical and Industrial Engineering at Northeastern University. His research focuses on federated data analytics, Bayesian optimization, continuous optimization, Gaussian processes, and deep learning. He holds a PhD in Industrial & Operations Engineering from the University of Michigan, Ann Arbor (2023). His work bridges theoretical advancements with practical applications in advanced manufacturing, predictive maintenance, and sustainable materials discovery. Key affiliations include the Institute of Industrial and Systems Engineers (IISE), INFORMS, and the American Statistical Association (ASA). Recent research emphasizes scalable federated learning frameworks for distributed systems, causal inference in sensor networks, and sharpness-aware optimization techniques to enhance generalization. His methodologies are applied to interdisciplinary domains such as materials science, IoT systems, and renewable energy simulations. Research trends reveal a focus on: Federated learning architectures for privacy-preserving analytics Bayesian optimization for high-dimensional design spaces Integration of causal reasoning with machine learning systems Autonomous experimentation for accelerated materials discovery No scientific awards are explicitly listed in the provided information. His academic advising and grant activities are not detailed in the current data.

Anurag PurwarView profile

Associate Professor

• Robotics Design
• Kinematic Analysis
• Machine Learning in Engineering
• +3 more

Dr. Anurag Purwar is an Associate Professor of Mechanical Engineering at Stony Brook University, directing the Computer-Aided Design and Innovation Lab and serving as PI for NSF I-Corps. He holds a Ph.D. from Stony Brook (2005) and a B.Tech. from IIT Kanpur (1995). His research merges rigid body kinematics with machine learning for mechanism and robot design, yielding 107+ peer-reviewed publications and patents licensed to industry. Key projects include the sit-to-stand walker assistive device (SAE Top 100 Award) and MotionGen software tools. Education: Ph.D., Stony Brook University (2005); B.Tech., IIT Kanpur (1995) Awards: A.T. Yang Award (2017), SAE Top 100 Award (2016), ASEE Distinguished Teaching Award (2021) Leadership: Program Chair (ASME 2014), Conference Co-Chair (IDETC 2016), NAI Senior Member Research focuses on machine learning-driven design of mechanisms, robotics, and assistive technologies. His labs develop algorithms for path synthesis, generative design, and kinematic analysis. He actively commercializes innovations through industry partnerships and has spun off startups like MotionGen.io.

Gabriel SosaView profile

Academic

• Art Education
• Illustration
• Public Art
• +5 more

Gabriel Sosa is the Saturday Studios Director in Art Education and a Visiting Lecturer in Illustration at Massachusetts College of Art and Design (MassArt). He is also Deputy Director of Essex Art Center in Lawrence, Massachusetts, demonstrating strong ties to community-based arts education and public engagement. Education: M.F.A., School of the Museum of Fine Arts at Tufts University, 2016 B.A., Boston University, 2007 Universidad Autónoma de Madrid, Spain, 2007–2008 His research and creative interests center on public art, language, identity, and community engagement . As a Cuban-American artist and former court interpreter, his work interrogates linguistic accessibility, cultural hybridity, and institutional critique. His projects often involve collaboration with urban communities, museums, and public spaces, reflecting a deep commitment to socially engaged art practices. Themes of visibility, communication, and spatial politics recur across his exhibitions and curatorial work. The 15 most recent articles reflect a consistent engagement with socially responsive art , blending curatorial innovation, performance, and public intervention. They span disciplines including public art, disability studies, digital culture, and political aesthetics , demonstrating a transdisciplinary approach rooted in community dialogue and critical theory. Scientific and Artistic Honors: Materia Abierta Residency, Mexico City (2021) Art & Law Program Residency, New York (2021) Urbano Project Residency, Boston (2021) Now + There Accelerator Program, Boston (2020) Lugar a dudas Residency, Colombia (2019) Gabriel Sosa actively mentors youth and emerging artists through programs like Saturday Studios and the Urbano Project. While no formal grants are listed, his residencies and public art projects suggest funding from cultural and community organizations. His curatorial and educational leadership at Essex Art Center and MassArt highlights a sustained commitment to expanding access to the arts. He leads initiatives at Saturday Studios and Essex Art Center , both focused on community-driven art education and exhibition. These labs and teams emphasize inclusivity, youth development, and cross-cultural dialogue, functioning as incubators for socially engaged artistic practice.

Diogo Ribeiro FerreiraView profile

Associate Professor

• Data Science
• Applied Machine Learning
• Deep Learning
• +6 more

Diogo Ribeiro Ferreira is an Associate Professor of Information Systems at Instituto Superior Técnico (IST) , part of the University of Lisbon . He is also a researcher at the Institute of Plasmas and Nuclear Fusion (IPFN) . His career spans teaching, research, and collaboration with institutions like JET (Joint European Torus) in nuclear fusion. His research focuses on Data Science , Applied Machine Learning , Deep Learning , and Process Mining , with applications in Nuclear Fusion (plasma diagnostics, disruption prediction) and Information Systems (workflow management, healthcare process analysis). He has taught courses like Data Administration in Information Systems and Data Analysis and Integration since 2003. His work bridges Computer Science and Physics , particularly in using Graph Neural Networks for plasma modeling and GPU Acceleration for high-performance computing. He has supervised over 20 PhD and MSc students, including André C. Santos and Fernando Szimanski, and co-authored 15+ recent publications in journals like Journal of Plasma Physics and Fusion Engineering and Design . Scientific Awards: 2012 ICGA Journal Award Best Paper Award at 9th IEEE International EDOC Conference 2005 Collaborations: Affiliated with JET Contributors and IPFN, focusing on real-time tomography and fusion reactor optimization.

• Causal Inference
• Machine Learning
• Natural Language Processing
• +3 more

Dhanya Sridhar is an Assistant Professor at the Department of Computer Science and Operations Research (DIRO) of the University of Montreal and a core academic member of Mila - Quebec Artificial Intelligence Institute. She holds a Canada CIFAR AI Chair and co-leads the IVADO R3AI working group on safe and aligned AI. PhD from University of California Santa Cruz Postdoctoral research at Columbia University Data Science Institute Her research focuses on integrating causality and machine learning to build AI systems that are robust to distribution shifts, capable of efficient task adaptation, and aligned with human knowledge. Recent work includes causal effects of social interactions on US election participation and causal modeling in geriatric-oncology patient outcomes. Current research themes: Causal representation learning Temporal causal inference Counterfactual modeling Causal abstraction in large models Responsible AI development Scientific awards: Canada CIFAR AI Chair Major grants: NSERC Discovery Grant (PVX20965-RGP) 2023-2029 NSERC DGECR Grant 2023-2025 Multiple MITACS Acceleration Quebec grants Advising: PhD students: Philippe Brouillard, Shruti Joshi, Mizu Nishikawa-Toomey, Tom Marty, Cristian Manta

R. Iris BaharView profile

Professor

• Computer Architecture
• Low Power Design
• Machine Learning
• +5 more

R. Iris Bahar is a Professor of Computer Science and Engineering at Colorado School of Mines, where she serves as Department Head of Computer Science since 2022. Previously, she held dual appointments as Professor of Engineering and Computer Science at Brown University for 26 years. Her research focuses on energy-efficient and reliable computing across system-level to device-level domains, with applications in near-data processing, robust machine learning for robotics, and noise-immune nanoscale circuit design. Research Interests include computer architecture, low-power design, and robotic system optimization. She pioneers hybrid generative-discriminative inference techniques for robot scene perception, achieving 40% accuracy improvements in occluded environments through FPGA-based Monte-Carlo sampling implementations. Her work on subthreshold CMOS noise modeling provides 1000x simulation speedups over SPICE-based methods. Near-Data Processing Architectures Approximate Computing Hardware/Software Co-design Nanoscale Noise Modeling Robust AI for Robotics Scientific Awards include the 2024 IEEE Undergraduate Teaching Award, 2019 Marie R. Pistilli Women in Engineering Achievement Award, and NSF CAREER award. She is an IEEE Fellow and ACM Distinguished Scientist. Funded Research spans NSF grants on thermal noise effects ($360k), durable data structures ($500k), and Brown SEED grants for autonomous robotics. Her lab trains 12+ students in hardware acceleration and probabilistic computing.

Fatih BayView profile

Professor

• Neutrino Physics
• Particle Physics
• Detector Development
• +3 more

Fatih Bay serves as Full Professor and Chair of the Department of Mechanical Engineering at Antalya Bilim University since 2020, leveraging his physics expertise in an engineering leadership role. With a Ph.D. in Physics from the University of Bern (2012), he has driven major contributions to neutrino physics through international collaborations at CERN, Fermilab, and KEK, currently focusing on the DUNE experiment's quest to explain matter-antimatter asymmetry. His academic journey features: B.Sc. in Physics, Kocaeli University (2004) M.Sc. in Physics, Middle East Technical University (2008) Ph.D. in Physics, University of Bern, Switzerland (2012) Dr. Bay's research centers on experimental neutrino physics and advanced detector development, specializing in liquid argon time projection chambers, emulsion detectors, and iron-scintillator calorimeters. His current work with DUNE integrates deep learning for neutrino interaction reconstruction while exploring supernova neutrino detection capabilities. This builds upon foundational contributions to OPERA, T2K, and MicroBooNE experiments. Analysis of his 2023-2025 publications reveals dominant focus on DUNE detector systems, with emerging trends in AI-driven event reconstruction, liquid argon purity optimization, and multi-detector synergies for supernova neutrino studies. His work bridges particle physics methodology with cutting-edge computing techniques to enhance detection precision in large-scale experiments. His exceptional contributions earned recognition through: Breakthrough Prize in Fundamental Physics (2016) for T2K experiment leadership Dr. Bay secured significant research funding including a TÜBİTAK 'Returning Researchers' fellowship (2015) and leads international collaborations requiring substantial project resources. While student advising details aren't specified, his department chair role implies graduate mentorship in mechanical engineering contexts. His laboratory work spans CERN (Switzerland), KEK (Japan), and Fermilab (USA) facilities, with current focus on DUNE's vertical drift technology development and ProtoDUNE-SP calibration systems.