جست‌وجوی استاد و پژوهشگر

• Computer Architecture
• Computer Systems
• Approximate Computing
• +۱۰ مورد دیگر

Pejman Lotfi-Kamran is an Associate Professor at the School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, where he also serves as the head of the school and director of Turin Cloud Services. His research focuses on computer architecture, systems, approximate computing, and cloud computing, with an emphasis on performance and energy efficiency for big-data applications. His educational background includes: Ph.D. in Computer Science, EPFL (2013) M.Sc. in Electrical and Computer Engineering, University of Tehran (2005) B.Sc. in Electrical and Computer Engineering, University of Tehran (2002) Lotfi-Kamran's research spans computer architecture innovations, including data and instruction prefetching, networks-on-chip, coherence protocols, and many-core processor design. He has pioneered work on scale-out processors, neural acceleration for GPUs, and approximate computing frameworks. His publications appear in top venues such as ISCA, HPCA, MICRO, and IEEE/ACM journals. His recent articles reflect a strong trend in improving system performance through intelligent prefetching, efficient NoC designs, and energy-aware architectures. Key themes include reducing frontend bottlenecks, optimizing cache behavior, and enhancing data delivery in large-scale systems. His work often combines cross-stack insights with hardware-software co-design for real-world impact. Scientific awards and recognitions include: 2017 CADS Best Paper Award 2016 Young Faculty Award from Iran's National Elites Foundation 2012-2013 Intel Ph.D. Fellowship 2012 and 2011 HiPEAC Paper Awards 2011 HPCA Best Student Paper Finalist Multiple academic honors from University of Tehran He has advised several graduate students including Paria Darbani, Ali Ansari, Mohammad Bakhshalipour, and Farid Samandi, many of whom have co-authored significant papers. His teaching spans institutions like Sharif University of Technology, Iran University of Science and Technology, and EPFL, covering advanced computer architecture and multiprocessor systems. He has led research projects such as AxBench and CloudSuite on Simics, and contributed to national initiatives like Iran’s National Grid. He is actively involved in tool development and continues to shape research in next-generation computing systems. He leads the Turin Cloud Services initiative at IPM and is deeply engaged in both theoretical and applied aspects of computer systems research, with ongoing work in neural acceleration, approximate computing, and scalable architectures.

Fabio Nobileمشاهده پروفایل

استاد

• Scientific Computing
• Uncertainty Quantification
• Numerical Analysis
• +۵ مورد دیگر

Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.

Daniel Kressnerمشاهده پروفایل

استاد

• Numerical Linear Algebra
• High-Performance Computing
• Matrix Equations
• +۴ مورد دیگر

Prof. Daniel Kressner is a Professor at the École Polytechnique Fédérale de Lausanne (EPFL), holding positions in the School of Basic Sciences (SB), Mathematics Institute (MATH), and the Numerical Algorithms and High-Performance Computing (ANCHP) group. He also leads the SMA-ENS unit within the SB-SMA division. His research focuses on numerical linear algebra, high-performance computing, and tensor approximation methods, with applications in scientific computing and data science. Education details are not explicitly listed, but his career at EPFL includes leadership in key research groups and doctoral programs. He supervises multiple doctoral students, including Alice Cortinovis, Peter Effenberger, and others. Research interests emphasize low-rank methods, matrix equations, and efficient algorithms for large-scale problems. Recent work includes advancements in randomized algorithms, tensor networks, and preconditioning techniques for eigenvalue problems. His publications span high-impact journals like Siam Journal on Matrix Analysis and Applications and Numerical Linear Algebra with Applications, addressing topics such as compressed sensing, multigrid methods, and distributed signal processing. Prof. Kressner advises doctoral candidates and contributes to the Program doctoral Mathématiques (EDMA-GE) committee. His lab, ANCHP, develops software tools for hierarchical matrices and tensor computations, such as the hm-toolbox for HODLR and HSS matrices.

Renata Borovica-Gajicمشاهده پروفایل

دانشیار

• Database Systems
• Machine Learning
• Artificial Intelligence
• +۱۲ مورد دیگر

Renata Borovica-Gajic is an Associate Professor in Data Analytics and an ARC DECRA Fellow at the School of Computing and Information Systems (CIS), University of Melbourne. She also serves as Associate Dean (Diversity and Inclusion) for the Faculty of Engineering and IT, demonstrating leadership in both research and academic community development. Her research lies at the intersection of database systems, machine learning, and artificial intelligence, with a vision of creating adaptive, self-driving database engines that optimize query execution in real-time. Her work spans learned indexes, query optimization, data quality, and data-driven traffic optimization, aiming to reduce costs and improve performance in data analytics. The recent publications reflect a strong trend toward integrating machine learning into core database operations—particularly through learned indexes, bandit-based tuning, and reinforcement learning for traffic systems. These works emphasize automation, provable guarantees, and real-time adaptation, showcasing a cohesive research agenda focused on intelligent, self-optimizing data systems. Her scientific excellence is recognized by numerous awards, including: L'Oréal-UNESCO for Women in Science Fellowship (2023) Victorian Young Tall Poppy (2024) Test of Time Award at SIGMOD 2022 Multiple Research and Teaching Excellence Awards from the University of Melbourne Google Research Inclusion Award (2021) She actively mentors PhD students and leads significant research projects funded by the Australian Research Council, Google, and Telstra. Her service includes roles as Associate Editor for SIGMOD Record, conference organization (e.g., aiDM, ADC, VLDB), and leadership in diversity and inclusion initiatives. She has also contributed to influential publications such as a chapter in the 7th edition of Database System Concepts . Her research lab focuses on AI-powered databases, traffic optimization via reinforcement learning, and self-healing data systems, positioning her at the forefront of next-generation data management.

Robert Eberleinمشاهده پروفایل

مدرس ارشد

• Mechanics of polymers and lightweight structures
• Finite element analysis
• Biomechanics
• +۴ مورد دیگر

Prof. Dr. Robert Eberlein is a Senior Lecturer in Mechanics at the ZHAW School of Engineering , specifically working at the Institute of Mechanical Systems (IMES) . He has served as Director of IMES since 08/2017, following previous roles as Senior Lecturer at IMES (11/2013-07/2017) and industry leadership positions including CTO of Angst+Pfister Group (06/2006-10/2013). Dr. Eberlein holds a Dr.-Ing. (PhD) in Numerical Mechanics from Darmstadt University of Technology (1992-1997) and completed an exchange program at UC Berkeley (1991-1992). Education: Dr.-Ing. (PhD) in Numerical Mechanics, Darmstadt University of Technology (07/1992-07/1997); Exchange Student at University of California, Berkeley (07/1991-06/1992) Professional: Director of Institute IMES (08/2017-today); Senior Lecturer at IMES (11/2013-07/2017); CTO & Group Executive Committee, Angst+Pfister Group (06/2006-10/2013); Group Leader in Biomechanics, Sulzer Innotec (07/1998-04/2006) Dr. Eberlein focuses on experimental and numerical modeling of solid polymers and lightweight structures. His research spans material modeling, finite element analysis, and fatigue life prediction for materials like POM gears, TPU and vulcanizates. Recent work explores digital twin development for rubber spring elements and machine learning enhanced process simulation in additive manufacturing. His projects include Lifetime prediction of POM gears , Measurement of human soft tissue properties , and Optimization of plastic gear geometry . Scientific achievements include: Professor ZFH (Fachhochschulrat) - 12/2019 Dr.-Ing. (PhD) summa cum laude - Darmstadt University of Technology - 07/1997 Graduate Assistantship - Darmstadt University of Technology - 01/1993 His work appears in journals like International Journal of Non-Linear Mechanics , Rubber Chemistry and Technology , and Journal of Loss Prevention in the Process Industries . Publications since 2015 show a consistent focus on material characterization , finite element modeling , and fatigue analysis with applications in industrial components and biomedical systems.

• Reconfigurable Computing
• Electronics Design Automation
• Post-Quantum Cryptography
• +۴ مورد دیگر

Dr. Andrea Guerrieri is an Associate Professor at the University of Applied Sciences and Arts Valais-Wallis - School of Engineering , specializing in Reconfigurable Computing, Electronics Design Automation (EDA), and Post-Quantum Cryptography . His work focuses on accelerating FPGA compilation through tools like DynaRapid and optimizing security protocols via Dynamatic , with technologies adopted by industry leaders including Intel, AMD-Xilinx, and CERN . He holds a BSc in Industrial Systems and a MSc in Engineering from HES-SO, and teaches courses in Digital Design and Embedded Hardware. BSc HES-SO in Industrial Systems (2019) BSc HES-SO in Computer and Communication Systems (2017) MSc HES-SO in Engineering (2021) Guerrieri’s research bridges High-Level Synthesis (HLS) and Reconfigurable Architectures to enhance FPGA performance for both terrestrial and space applications . His 2025 publications highlight advancements in heterogeneous computing , energy-efficient PQC , and rapid compilation frameworks . Notably, his 2024 work on DynaRapid achieved 20× speedup in C-to-FPGA implementation. Key scientific contributions span dataflow circuit optimization , dynamic scheduling , and automated code transformations . His 2023 book Applications Enabled by FPGA-Based Technology and 2021 textbook System-on-Chip Design with Arm established foundational references in embedded systems. Awards include Best Paper at FPL 2024 , Outstanding TPC Member at DAC 2024 , and IEEE Senior Membership (2021) . 2024: Best Paper Award (FPL), Outstanding Short Paper Award (HPEC) 2023: H-Saber publication on PQC optimization 2021: IEEE Senior Member recognition As Chair of Onboard Computing for the CHEESE-NASA SSERVI consortium, he leads international collaborations with ETH Zurich, University of Geneva, California State University , and companies like NVIDIA, Arm, and NASA . His projects include the Innosuisse-funded DyReCte initiative (2019–2021) for reconfigurable cryptoengines in nanosatellites.

• Reconfigurable Computing
• Electronics Design Automation
• FPGA Design
• +۶ مورد دیگر

Andrea Guerrieri serves as an Associate Professor at the School of Engineering, University of Applied Sciences and Arts Western Switzerland Valais (HES-SO Valais-Wallis), specializing in reconfigurable computing and electronics design automation. His research has established significant industry impact through tools like DynaRapid and Dynamatic, with technology adopted by major semiconductor companies including MIPS, Intel, and AMD-Xilinx. BSc HES-SO in Industrial Systems - System-on-Chip specialization BSC HES-SO in Computer and Communication Systems - Digital Design specialization MSc HES-SO in Engineering - Embedded Hardware and Firmware specialization Professor Guerrieri's research focuses on reconfigurable computing, electronics design automation (EDA), and security, with particular emphasis on FPGA design, high-level synthesis, and post-quantum cryptography implementations. His work bridges the gap between theoretical computer architecture and practical hardware implementations, with strong applications in space technology and embedded security systems. His recent publications demonstrate increasing focus on energy-efficient implementations for space applications and quantum-resistant cryptographic systems. Analysis of his 15 most recent publications reveals a clear research trajectory toward optimizing FPGA implementations for post-quantum cryptography and space applications. His work consistently addresses the performance bottlenecks in high-level synthesis while maintaining practical applicability for industry partners like NASA, CERN, and major semiconductor companies. The recent surge in best paper awards (three in 2024 alone) reflects growing recognition of his contributions to efficient FPGA compilation techniques and cryptographic implementations. Scientific Awards: Best Paper Award at FPL 2024 Best Paper Award at HPEC 2024 Best Paper Award at ISFPGA 2020 Outstanding Short Paper Award at IEEE HPEC 2024 Outstanding TPC Member Award at DAC 2024 IEEE Senior Member (2021) Multiple Best Paper nominations (FCCM 2022, FPL 2022, HiPEAC 2022) Professor Guerrieri actively participates in international research projects including the DyReCte project (2019-2021) on dynamically reconfigurable cryptoengines for nano-satellites. He currently chairs the Onboard Computing topic for the Swiss consortium CHEESE affiliated with NASA SSERVI and collaborates extensively with industry partners including AMD-Xilinx, NVIDIA, Arm, NASA, and CERN, as well as academic institutions like ETH Zurich and University of Geneva. His current research focuses on developing next-generation EDA tools and reconfigurable computing platforms for both terrestrial and space applications. His laboratory work centers around FPGA-based prototyping and validation, with specialized facilities for space applications testing. Professor Guerrieri leads a research team that includes Andres Upegui, Quentin Berthet, Laurent Gantel, and Gabriel Da Silva Marques, focusing on practical implementations of reconfigurable architectures for security and space applications.

Joachim Vothمشاهده پروفایل

استاد

• Cultural Economics
• Long-Run Growth
• Asset Market Volatility
• +۵ مورد دیگر

Joachim Voth is the UBS Foundation Professor of Economics and Scientific Director of the UBS Center for Economics in Society at the University of Zurich. His research focuses on Cultural Economics, Long-Run Growth, Asset Market Volatility, and the interplay between history and contemporary economic phenomena. He holds a Ph.D. from UCLA and has held visiting positions at institutions including Stanford and NYU. Research Interests : Voth’s work bridges historical analysis with modern economic challenges. Key themes include the impact of cultural factors on economic outcomes, the role of historical institutions, and the application of novel data sources (e.g., historical narratives, visual datasets) to understand societal trends. His recent projects analyze cultural convergence via high school yearbook portraits and the legacy of medieval anti-Semitism in Nazi-era violence. Publications : Over 50 peer-reviewed articles and three books, including Lending to the Borrower from Hell (2014) and Prometheus Shackled (2013). His work frequently combines economic theory with historical evidence, such as linking British industrialization to colonial slave wealth or explaining WWII resistance patterns through Allied bombing and radio propaganda. Grants & Awards : Supported by the UBS Foundation, European Research Council, and National Science Foundation. He is a frequent commentator in venues like VoxEU and The Economist , and his research has been featured in podcasts and documentaries. Advising : Supervised 10+ doctoral students now at top institutions like UCLA, NYU, Stanford, and the Norwegian School of Economics.

• Biophysics
• Computational Cell Biology
• Mesoscale Simulations
• +۳ مورد دیگر

Julian Charles Shillcock is a Lecturer and computational modeling specialist at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Life Sciences and the Lashuel Lab. He also serves as a Scientist in the UPDALPE group (Prof. Dal Peraro Group) and teaches in EPFL’s SSV (Teaching) and EDNE (Doctoral Education) sections. His work bridges biophysics, computational modeling, and cellular dynamics. PhD in Physics from Simon Fraser University (1996) Group Leader at Max Planck Institute of Colloids and Interfaces (2000-2005) Associate Professor at University of Southern Denmark (2005-2011) Blue Brain Project member since 2011 Shillcock’s research focuses on biomolecular condensates , membrane dynamics , and computational cell biology . He develops mesoscale simulation methods like Dissipative Particle Dynamics to study vesicle fusion , neuronal morphology , and neurodegenerative disease mechanisms . Recent work includes POETS computing platforms for accelerating simulations and Shiga toxin clustering on membranes. His publications (2022-2024) reveal trends in soft matter physics , computational neuroscience , and biomolecular condensate structure . Key collaborations include Imperial College London and University of Southampton on the POETS project. Scientific recognition includes: 2021 Polysphère prize for Best Teacher in Life Sciences He has advised PhD student Lida Kanari in computational morphology and contributed to neocortical microcircuit reconstruction . His research spans computational biophysics , toxin entry mechanisms , and novel computational platforms for life sciences education.

Umberto Michelucciمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Astrophysics
• +۵ مورد دیگر

Umberto Michelucci is a Professor of Scientific Machine Learning at Lucerne University of Applied Sciences and Arts (HSLU), Switzerland. He holds a PhD in Machine Learning applied to Physics and has over 20 years of industry experience. He is the Subject Head of Applied Data Intelligence in Continuing and Executive Education, Head of Certificates in Machine Learning/Data Engineering, and founder of TOELT LLC and the AI Center of Excellence at Helsana Versicherung AG. His research focuses on machine learning applications in science, astrophysics, uncertainty quantification, and sensor technology. Education PhD in Machine Learning applied to Physics (Portsmouth University) Master in Theoretical Physics (University of Florence) Postgraduate Certificate in Higher Education (Open University, UK) Research Interests Michelucci’s work bridges machine learning and scientific disciplines. Key areas include: Machine learning for astrophysics (INAF collaborations) Uncertainty analysis in high-stakes ML systems Deep learning for optical sensing (e.g., olive oil quality analysis) Foundational mathematical concepts for ML in science Awards & Recognition World’s Top 2% Scientists (Stanford List) Google Developer Expert in Machine Learning AI Global Ambassador (2022) TOP AI Influencer in Switzerland (2021) Grants & Collaborations He collaborates with institutions like INAF (Italy) and NVIDIA/Google, leading projects on AI for agrifood, medical imaging, and astrophysics. His work includes $multi-million industry partnerships and EU-funded research. Labs & Teams Director of the TOELT AI Lab and oversees HSLU’s Applied Data Intelligence programs. Active in open-source initiatives and global AI standardization efforts.

• High performance computing
• Computational Science and Engineering
• Scientific computing
• +۷ مورد دیگر

Olaf Schenk is a Professor at the Institute of Computing within the Faculty of Informatics at Università della Svizzera italiana (USI), Switzerland. He serves as Director of the Institute of Computing and Co-Director of the Master in Computational Science. He is also an adjunct member of the Computer Systems Institute at USI. PhD in Information Technology and Electrical Engineering, ETH Zurich (2001) Venia Legendi in Mathematics and Computer Science, University of Basel (2009) Applied Mathematics, Karlsruhe Institute of Technology (KIT), Germany His research focuses on high-performance computing , computational science and engineering , and applied algorithms for extreme-scale simulations. He bridges computer science with scientific computing needs, particularly in parallel algorithms , sparse solvers , graph analytics , and manycore architectures . His work emphasizes scalable software tools and programming models for emerging HPC systems. The 15 most recent publications reflect a consistent focus on sparse matrix computations , parallel and task-based algorithms , graph partitioning , and performance optimization for heterogeneous and manycore systems. Keywords span high-performance computing, numerical linear algebra, and large-scale data analysis, showing strong integration of theoretical algorithm design with practical implementation. Olaf Schenk has received several prestigious honors: Elected Fellow, Society for Industrial and Applied Mathematics (SIAM) Senior Member, IEEE and ACM SIAM Supercomputing Prize 2023 IBM Faculty Award Two Leadership Computing Awards from the U.S. Department of Energy He has held leadership roles as Chair, Vice Chair, and Program Director of the SIAM Activity Group on Supercomputing. He serves as Associate Editor for ACM Transactions on Mathematical Software and on the editorial board of SIAM Journal on Scientific Computing . He has participated in over 60 international program committees, including top-tier conferences such as SC, IPDPS, and IEEE CSE. He advises PhD and Master’s students in computational science and leads research projects funded by national and international agencies. He is also the Founder & Director of Panua Technologies Sagl, focusing on high-end software for simulation and optimization. His research group at USI works on next-generation computing tools for extreme-scale scientific simulations, with ongoing work in adaptive algorithms, resilience, and hybrid CPU-GPU computing. He leads collaborative projects with institutions in Europe and the U.S., aiming to develop scalable, robust, and efficient software for future exascale systems.

• Astrophysics
• Solar Physics
• Radiative Transfer
• +۳ مورد دیگر

Simone Riva is a Postdoctoral researcher affiliated with the Faculty of Informatics at the Università della Svizzera italiana . He is also associated with the Euler Institute and the Istituto ricerche solari Aldo e Cele Daccò (IRSOL) . His contact information includes simone.riva@usi.ch and offices located at East Campus, Sector D, Office D5.13 (Lugano) and Via Patocchi 57 (Locarno). His research focuses on astrophysics , particularly in the modeling of solar radiation transfer and polarization phenomena . Key areas include computational methods for solving radiative transfer equations, partial frequency redistribution effects, and high-performance computing techniques applied to stellar atmospheres. He has contributed to studies involving the Ca I 4227 Å line and the development of scalable numerical solvers for 3D transfer of polarized radiation. Recent work emphasizes the application of parallel preconditioned Krylov solvers and HPC techniques to address challenges in radiative transfer with angle-dependent partial redistribution. His research bridges computational physics with observational astrophysics, aiming to advance our understanding of solar magnetic fields and chromospheric dynamics. While no scientific awards are explicitly listed, his contributions to funded projects and collaborations with institutions like IRSOL underscore his active role in advancing solar and stellar physics research. He is affiliated with labs including the Euler Institute and IRSOL, which provide critical computational and observational infrastructure for his work.

• Multiscale/multiphysics modeling
• Biophysics
• Cellular and molecular biomechanics
• +۶ مورد دیگر

Igor V. Pivkin is a full professor at the Institute of Computing within the Faculty of Informatics at the University of Lugano (USI). He holds a B.Sc. and M.Sc. in Mathematics from Novosibirsk State University, followed by an M.Sc. in Computer Science and a Ph.D. in Applied Mathematics from Brown University. Before joining USI, he was a Postdoctoral Associate at MIT's Department of Materials Science and Engineering. His research focuses on multiscale/multiphysics modeling , numerical methods, and large-scale simulations of biological and physical systems. Key areas include biophysics, cellular/molecular biomechanics, stochastic modeling, and coarse-grained molecular simulations. He leverages high-performance computing and particle-based methods to address complex biological phenomena. His work spans diverse applications, including cancer cell dynamics, bioleaching bacterial biofilms, and erythrocyte mechanics in human spleen circulation. He has pioneered computational tools such as the Bayesian recursive global optimizer (BaRGO) and the in-silico lab-on-a-chip framework, enabling petascale simulations of microfluidic systems at cellular resolution. Pivkin collaborates extensively with institutions like the SIB Swiss Institute of Bioinformatics and has contributed to advancing methodologies for multi-model scientific simulations. His research integrates experimental data with computational models to bridge gaps between microscopic and macroscopic biological processes.

Renato Pajarolaمشاهده پروفایل

استاد

• Computer Graphics
• Scientific Visualization
• 3D Modeling
• +۵ مورد دیگر

Prof. Dr. Renato Pajarola is the Head of the Visualization and MultiMedia Lab at the Department of Informatics, University of Zurich. His research focuses on computer graphics, scientific visualization, and geometric processing, with applications in 3D scanning, point cloud analysis, and real-time rendering. He leads a team developing advanced visualization techniques for high-dimensional data, parallel rendering frameworks, and interactive systems for complex datasets. Key research areas include: 3D reconstruction of indoor environments Tensor approximation for volume visualization Interactive ray tracing and point cloud processing Parallel rendering frameworks (e.g., Equalizer) Scientific computing and sensitivity analysis His recent publications emphasize: High-dimensional data exploration using tensor methods Efficient rendering techniques for large-scale point clouds Integration of citizen-reported weather data for environmental analysis Prof. Pajarola’s lab collaborates on projects like VIAN (visual annotation tool for film analysis) and Terrender (web-based terrain visualization). His Erdős number is 3, reflecting interdisciplinary research connections in mathematics and computer science.

Duccio Testaمشاهده پروفایل

پژوهشگر

• Plasma Physics
• Nuclear Fusion
• Mathematical Modeling
• +۴ مورد دیگر

Duccio Testa is a Researcher at the Swiss Plasma Center (SPC) within the School of Basic Sciences at École Polytechnique Fédérale de Lausanne (EPFL). He holds concurrent Lecturer positions in the School of Engineering (SB-SPH) and the School of Humanities and Social Sciences (CDH-SHS). His career spans plasma physics for nuclear fusion, mathematical modeling of complex systems, and archaeology of the Roman and pre-Roman eras. Education : High School Diploma from Liceo Classico Vincenzo Gioberti (1988); Bachelor/Master in Physics from Universita di Torino (1994); Ph.D. in Plasma Physics from Imperial College London (1998); Post-Doc at MIT (2002-2007). His research focuses on plasma diagnostics and magnetic sensor development for tokamaks like JET, TCV, and ITER. He pioneered LTCC sensors and real-time MHD instability detection algorithms (e.g., SparSpec). His work bridges fusion engineering and archaeology , with publications on Etruscan civilization and Pharaonic trade routes. Recent articles highlight advancements in photolithography-based magnetic sensors for DTT and DEMO, high-frequency diagnostics across tokamaks, and fast ion transport modeling . He has received European Community Research Training Grant , Ph.D. Fellowship , and Post-Doc Fellowship . Scientific Awards European Community Research Training Grant (1995) Ph.D. Fellowship (Imperial College London, 1995-1998) Post-Doc Fellowship (MIT, 1998-2007) Testa has mentored numerous Ph.D. students (e.g., Theodoros Panis, Christian Schlatter) and post-doctoral researchers , while contributing to MOOC courses and tutorials on plasma physics. His leadership in international collaborations (EPFL, MIT, UKAEA) underscores his role in fusion technology and diagnostic systems .