Dr. Majid Nabi is an Assistant Professor in the Electronic Systems group of the Electrical Engineering Department at Eindhoven University of Technology. His research focuses on efficient and reliable networked embedded systems, wireless sensor networks, and IoT technologies. He received his PhD in Electrical and Computer Engineering from TU/e in 2013. His work addresses challenges in dependable low-power embedded networks through protocol stack optimization, network modeling, and power-efficient baseband processor design. Applications span healthcare, automotive, and environmental monitoring systems. Recent publications include performance evaluations of Bluetooth mesh networks and energy efficiency mechanisms.
Anne-Marie Rickmann is a Research Fellow in the Department of Radiology & Biomedical Imaging at Yale University's Yale School of Medicine. Her research focuses on advancing medical imaging techniques through deep learning, with a particular emphasis on image segmentation, cortical surface reconstruction, and domain adaptation in healthcare contexts. Her work integrates cutting-edge methodologies such as neural deformation fields, diffeomorphic mesh deformations, and foundation models to address challenges in MRI/CT analysis, neuroimaging, and abdominal organ segmentation. She explores applications in pre-clinical cardiology, longitudinal cortex analysis, and post-surgical imaging, emphasizing faithful AI explanations and adaptive learning frameworks. Notable contributions include the development of tools like AbdomenNet, V2C-Long, and STRUDEL, which enhance precision in medical imaging while addressing issues like hallucination-free segmentation and uncertainty quantification. Her publications reflect a strong focus on interdisciplinary approaches at the intersection of AI, biomedical engineering, and clinical practice. No scientific awards or grants are explicitly mentioned in the provided materials. She collaborates within the Department of Radiology & Biomedical Imaging, contributing to projects that bridge computational methods with clinical diagnostics and epidemiologic studies.
Jun-Yan Zhu is the Michael B. Donohue Assistant Professor of Computer Science and Robotics at Carnegie Mellon University's School of Computer Science. He holds affiliated faculty roles in the Computer Science Department and Machine Learning Department. His research focuses on generative models, computer vision, graphics, and computational photography. Education: Ph.D., UC Berkeley (2017), advised by Alexei A. Efros B.E., Tsinghua University (2012), advised by Zhuowen Tu, Shi-Min Hu, and Eric Chang Postdoc, MIT CSAIL (2017–2018), with William T. Freeman, Josh Tenenbaum, and Antonio Torralba Research Interests: Zhu's work explores the synergy between human creators and generative models. Key areas include: - Controllable Visual Synthesis : Developing algorithms for precise image/video editing and generation. - Model Customization : Enabling users to adapt models for new tasks/concepts with minimal input. - Data Attribution : Addressing ethical challenges in synthetic data usage. - 3D/Neural Rendering : Advancing techniques for 3D object synthesis and tactile integration. His lab, Generative Intelligence Lab , emphasizes human-AI collaboration and practical applications like NVIDIA Canvas and Adobe Firefly. Articles Trends: Recent work spans 3D object generation (LEGO designs), efficient diffusion models (SVDQuant), tactile-aware 3D synthesis, and ethical data attribution systems. His research balances technical innovation with user-centric design, often bridging theory and industry applications. Awards: ACM SIGGRAPH Outstanding Doctoral Dissertation (2017) CVPR Best Paper Finalist (2022), ICRA Best Paper (2024) NVIDIA GTC Best in Show (2019) for GauGAN Advising & Grants: Supervises 10+ PhD students across CMU Robotics (RI), Machine Learning (MLD), and Computer Science (CSD). Active in NSF grants and industry collaborations (Adobe, NVIDIA). His lab hosts the Generative Intelligence Lab , part of CMU Graphics Lab and Computer Vision Group.
Lars Lundell is a Professor at Karolinska Institutet's Department of Clinical Science, Intervention and Technology within the Faculty of Medicine. His extensive research focuses on upper gastrointestinal surgery, particularly esophageal diseases, gastroesophageal reflux disease (GERD), and surgical oncology. With over 100 publications spanning three decades, he has made significant contributions to surgical guidelines and clinical practice in his field. His research interests encompass a wide range of topics in upper gastrointestinal surgery including antireflux procedures, fundoplication techniques, esophageal cancer treatment, laparoscopic approaches, and management of Barrett's esophagus. Lundell has been instrumental in numerous randomized clinical trials that have shaped current surgical practices. His work often involves multicenter collaborations across Europe, contributing to international consensus guidelines. Analysis of his 15 most recent publications reveals a continued focus on comparative effectiveness research, long-term surgical outcomes, and optimization of treatment protocols for esophageal and gastric conditions. His work spans both technical surgical innovations and health economic evaluations, demonstrating a comprehensive approach to improving patient care. Lundell has contributed to major guidelines including the ICARUS guidelines for antireflux surgery patient selection and the SAGES guidelines for peroral endoscopic myotomy. His research has appeared in top surgical and gastroenterological journals including JAMA Surgery, Annals of Surgery, and Gut. He has been involved in numerous multicenter studies examining minimally invasive versus open surgical approaches, with particular attention to long-term outcomes and quality of life measures. His work has helped establish evidence-based practices for esophageal and gastric cancer surgery, hiatal hernia repair, and management of complex GERD cases.
Gunnar Tibert is an Associate Professor at KTH Royal Institute of Technology's School of Aerospace, Moveability, and Naval Architecture. He specializes in deployable structures for aerospace applications, with a focus on bistable composites, tensegrity systems, and space deployment mechanisms. His roles include examiner and course responsible for courses like Spacecraft Dynamics and Project in Aerospace Engineering . Education: Ph.D. in Deployable Tensegrity Structures for Space Applications (KTH, 2002), Licentiate in Numerical Analyses of Cable Roof Structures (KTH, 1999). Research Interests: Structural dynamics, composite materials, space system design, and vibration suppression in deployable systems. His work spans both peer-reviewed articles and experimental studies, including sounding rocket experiments for space web deployment. Recent publications focus on planetary sunshade systems for climate engineering, metamaterials for vibration control, and additive manufacturing in satellite components. He collaborates on projects like the Suaineadh space web experiment and B2D2 composite boom deployment. Notable contributions include form-finding methods for tensegrity structures and material characterization for bistable tape springs. His research combines experimental testing, numerical simulations, and aerospace engineering principles to advance deployable space technologies.
Dominik Meidner is a Senior Lecturer at the Chair of Optimal Control at the Technical University of Munich (TUM), part of the School of Computation, Information and Technology. He leads the Department of Mathematics as Department Manager since 2025. His research focuses on optimal control of partial differential equations (PDEs), numerical methods for PDE-constrained optimization, adaptive finite element methods, and software development for scientific computing. He has authored numerous publications and contributed to software like Gascoigne and RoDoBo. His academic roles include teaching Analysis courses and supervising over 30 theses since 2009. Key awards include the 2019 Dozentenpreis and the Felix-Klein-Lehrpreis 2010. His work bridges theoretical developments with practical applications in fluid-structure interaction, fractional diffusion, and error estimation. Education: PhD (2008, Heidelberg University), Diplom (2003, Heidelberg University). Research Interests: Optimal control, numerical analysis, finite elements, PDE-constrained optimization, adaptive discretization. Software Contributions: Gascoigne (finite element toolkit), RoDoBo (optimization library). Key Activities: Co-organizer of OCIP workshops, member of the TUM-MCLQST cluster, and contributor to academic infrastructure projects.
Deepak Nadig is an Assistant Professor in the Department of Computer and Information Technology (CIT) at Purdue University and Director of the Cloud-native, Cyberinfrastructure and Networks (CYAN) Lab. He holds a Ph.D. in Computer Engineering from the University of Nebraska-Lincoln (2021). Prior to academia, he served as Director of Technology and Research at SOLUTT Corporation (India, 2009–2015), leading networking and wireless operations in 4G/LTE and multi-gigabit technologies. He is an IEEE-certified Wireless Communications Professional (2014–2019) and has received awards including the 2017 IEEE ANTS Best Paper Award and 2019 Milton E. Mohr Fellowship. Education: Ph.D. in Computer Science & Engineering, University of Nebraska-Lincoln, 2021. Master’s Thesis: Design and Deployment of DTN Architectures for Interplanetary Communication Systems, RV College of Engineering, India, 2007. Research Interests: His work focuses on computer networks, cloud-native infrastructure, software-defined networks (SDN), network virtualization, AI/ML-driven networking solutions, and cybersecurity. He leads the CYAN Lab, advancing cloud-native and edge computing architectures for data-intensive applications. Grants & Awards: NSF-funded research, Purdue Bravo Award (2021), and multiple dissertation awards. His contributions include optimizing network architectures for GridFTP transfers (SNAG framework) and scalable edge computing for agriculture (ERGO). Labs & Leadership: Director of the CYAN Lab, managing projects on network observability, SDN security, and cloud-native systems. Active in program committees for IEEE conferences and journals like IEEE/ACM Transactions on Networking and IEEE INFOCOM.
Professor Kenny Mitchell is a faculty member at the School of Computing Engineering and the Built Environment at Edinburgh Napier University. With over 60 research outputs listed, he specializes in Interactive Graphics, Virtual Reality, and Augmented Reality technologies. Research Interests Mitchell's work focuses on real-time systems, motion prediction, and human-computer interaction in immersive environments. His research spans generative AI environments , 3D facial reconstruction , and light field rendering . Key themes include AI-driven animation , networked VR experiences , and haptic-visual integration . Article Trends Recent publications emphasize Transformer-based motion prediction (NeFT-Net), speech-to-VR systems (HoloJig), and low-latency avatar synchronization . His work integrates machine learning with computer graphics for applications in telepresence dance and emotionally intelligent avatars . Projects CAROUSEL+ : £929,077 funded by European Commission (2021-2024) for telepresent dance systems DISTRO : £243,804 European Commission grant for 3D graphics training (2015-2018)
Kenneth Jansen is a Professor in the Department of Aerospace Engineering Sciences at the University of Colorado, holding the Denver Business Challenge Endowed Professorship. He serves as Director of the Aerospace Mechanics Research Center (AMReC) and specializes in computational fluid dynamics (CFD), turbulence modeling, and aerodynamic performance analysis. His research focuses on: Turbulent boundary layers under pressure gradients and curvature Large eddy simulation (LES) and direct numerical simulation (DNS) of aerospace flows Data-driven turbulence closure models for CFD Hypersonic and supersonic flow analysis Active flow control for aerodynamic efficiency Mesh adaptation and high-performance computing Recent publications highlight his work on jet interactions, wind tunnel simulations, and multifidelity modeling. Awards include the Denver Business Challenge Endowed Professorship. Jansen leads projects involving the PHASTA finite element solver and contributes to exascale computing initiatives (ECP applications). His collaborations span experimental and computational studies for aerodynamic flow control and medical CFD applications.
Rafael Cossent Arín is Research Associate Professor at Comillas Pontifical University's Institute for Research in Technology, specializing in energy economics and regulation. As coordinator of the Smart and Sustainable Grids Research Unit (2016-2021) and current Co-director of the Chair for Hydrogen Studies, he focuses on renewable integration and electricity market design. Holds an Industrial Engineering degree and PhD in Electrical Engineering from Comillas. With over 50 research projects in power sector regulation, his work addresses distributed generation integration, electric mobility, and hydrogen decarbonization strategies. Visiting researcher positions include INESC Porto and Heriot-Watt University. Research covers: Distribution network planning under energy transition constraints Regulatory frameworks for flexibility markets TSO-DSO coordination mechanisms Hydrogen economy deployment challenges Recent publications analyze grid digitalization metrics, renewable hosting capacity, and regulatory sandboxes for energy innovation. Significant projects include EU Horizon initiatives (OneNet, EUniversal) and technical advisories for Spanish energy transition policies. Supervises doctoral research on DSO flexibility mechanisms and grid requirements for decarbonization.
Sandra Pieraccini is a Full Professor in the Department of Mathematical Sciences "GL Lagrange" (DISMA) at the Politecnico di Torino, where she also serves as Deputy Director of the department, Contact Person for student orientation, and Coordinator of the basic subjects for first-year engineering programs. She is a member of the University Open Access Commission and actively contributes to academic governance. Her research interests include machine learning, numerical analysis, scientific computing, uncertainty quantification, and numerical optimization . She is a key member of the research group Numerical Analysis and Scientific Computing and leads the national research project FaReX (2023–2025) on reduced-order modeling and automatic learning. Her work integrates advanced numerical methods with AI techniques, particularly in modeling discrete fracture networks and fluid dynamics. The most recent publications reflect a strong trend in combining graph-informed neural networks , explainable AI , and meshless computational methods to solve complex problems in geophysics, fluid mechanics, and data science. Her research bridges applied mathematics with real-world engineering and environmental challenges. She is an active member of the scientific community, serving on the editorial boards of Journal of Machine Learning for Modeling and Computing and GEM , and participating in steering committees of UMI groups on AI and machine learning. She has also contributed to organizing major workshops and conferences. Sandra Pieraccini teaches across multiple programs, including doctoral courses in Mathematical Sciences and Aerospace Engineering, master’s courses in Mathematical Engineering and Data Science, and bachelor’s courses such as Linear Algebra and Problem Solving Lab. She is deeply involved in curriculum development and academic leadership.
Luca Giaccone is an Associate Professor in the Department of Energy (DENERG) at Politecnico di Torino, where he is also a member of the Academic Senate and the Committee for Teaching Coordination. He is affiliated with the College of Electrical and Energy Engineering and leads the CADEMA Research Group. His academic work focuses on computational electromagnetics and human exposure to electromagnetic fields. Research Interests: Bioelectricity and human exposure to low-frequency electric and magnetic fields Computational electromagnetics and numerical dosimetry Simulation engineering, modeling, and optimal design using MATLAB and Python Electromagnetic compatibility (EMC) and safety of medical devices Wireless power transfer (WPT) systems, especially in automotive applications Recent Research Trends: His recent publications center on data-driven modeling of electric motors, surrogate modeling for fast exposure assessment, and the electromagnetic safety of medical implants near WPT systems. He frequently employs learning-based human models and investigates uncertainties in dosimetry, especially under pulsed or multi-frequency exposures. Scientific Awards and Recognition: Mentoring Polito Project (M2P), awarded by Politecnico di Torino on February 16, 2023 Advising and Grants: He supervises PhD student Junlong Liu in Electrical, Electronics, and Communications Engineering. He leads or participates in multiple funded research projects, including WPT-SAFE (2025–2027, national PRIN), SAFECHARGE (2024–2026, international), and various corporate consulting projects on electromagnetic exposure from welding devices, photovoltaic systems, and industrial cabins. He has served as Scientific Manager on over ten consulting and research contracts since 2015. Labs and Teams: He is a key member of the CADEMA Research Group (DENERG), focusing on computational electromagnetics and electromagnetic safety applications in energy, medicine, and the environment.
Dr. Todd Griffith is an Associate Professor of Mechanical Engineering at the University of Texas at Dallas (UTD), affiliated with the Erik Jonsson School of Engineering and Computer Science . He leads the Griffith Lab and focuses on advancing wind energy technologies, particularly floating offshore turbines and large-scale rotor systems. His research integrates structural dynamics, aerodynamics, and control engineering to enhance energy efficiency and reduce costs in renewable energy systems. Key areas of expertise include wind turbine design , additive manufacturing , and floating platform systems . He has secured significant grants, including a $3.3 million award from the U.S. Department of Energy's ARPA-E program to develop scalable floating offshore wind turbines. His work emphasizes bio-inspired design principles, such as mimicking natural structures (e.g., palm trees) to improve turbine resilience in extreme conditions. Dr. Griffith’s research also explores active flow control using plasma actuators and advanced materials for lightweight, durable components. His contributions span experimental validation, computational modeling, and field prototyping, with a focus on deep-water offshore wind energy systems. He has advised numerous projects funded by federal agencies and industry partners, contributing to the development of next-generation wind energy infrastructure. His lab collaborates on interdisciplinary initiatives, including turbine blade manufacturing, structural health monitoring, and cost-effective energy solutions.
Michele Ruggeri is affiliated with TU Wien's Forschungsgruppe Numerik von PDEs within the Department of Mathematics and Geoinformation. His work focuses on developing advanced numerical methods for partial differential equations, particularly in micromagnetics and materials science. Key research areas include computational modeling of magnetic materials (e.g., magnetic skyrmions), finite element methods for liquid crystals, and error estimation in stochastic Galerkin FEM. He contributes to the Commics software framework for micromagnetic simulations. Recent publications (2020-2023) address topics like: implicit-explicit time integration for Landau-Lifshitz-Gilbert equations, numerical analysis of nematic liquid crystals (Ericksen model), and optimal convergence of adaptive boundary element methods. His work bridges applied mathematics with computational materials science. Ruggeri collaborates extensively with researchers such as Dirk Praetorius, Giovanni Di Fratta, and Ricardo Nochetto. While no awards are explicitly mentioned, his active publication record indicates sustained academic engagement.
Peng Song is an Assistant Professor of Computer Science at Singapore University of Technology and Design (SUTD), affiliated with the Pillar of Information Systems Technology and Design (ISTD). His research focuses on computer graphics, geometric modeling, computational design, and fabrication, emphasizing the development of algorithms for designing functional real-world objects. He holds a PhD from Nanyang Technological University (2013) and prior research roles at EPFL, University of Science and Technology of China, and Nanyang Technological University. Education: PhD in Computer Science, Nanyang Technological University (2013) Master’s Degree, Harbin Institute of Technology (2010) Bachelor’s Degree, Harbin Institute of Technology (2007) His research interests span computational design of assemblies, 3D printing, robotics, and urban modeling. Notable projects include generative urban design, wireframe mesh modeling, and mechanisms for custom-fit medical devices. He has received awards such as the SMI 2024 Best Paper Award and SIGGRAPH 2022 Honorable Mention. Grants & Professional Roles: Leading Singapore MOE Academic Research Fund Tier 2 grants (2023–2024) Associate Editor for Computers & Graphics and Graphical Models (2024–present) Program Committee Member for SIGGRAPH Asia, Pacific Graphics, and SPM He teaches courses such as 'Graphics and Visualization' and 'Extended Reality' at SUTD. His group’s work is showcased on YouTube and his lab’s website.
