جستجوی استاد راهنمای دانشگاهی

• Aerospace Propulsion
• Computational Fluid Dynamics
• Machine Learning
• +۶ مورد دیگر

Andrea Ferrero is an Associate Professor at the Polytechnic University of Turin , affiliated with the Department of Mechanical and Aerospace Engineering (DIMEAS) . He leads research in aerospace propulsion, computational fluid dynamics (CFD), machine learning, and reduced-order modeling, with a focus on turbulent flows, turbomachinery, and combustion. Scientific Director for projects like NextGenSProDesT (2023–2025) and PARSEC (2024–2027). Active in commercial research collaborations with companies like Avio Aero and GE. His work bridges theoretical and applied research, emphasizing AI/ML integration in aerospace systems. Research Interests Andrea Ferrero's research spans: Aerospace Propulsion : Advanced rocket nozzles, hybrid engines, and hydrogen turbofans. Computational Fluid Dynamics : Turbulent and compressible flow simulations, reduced-order models (ROM). Machine Learning : Data-driven turbulence modeling, stress correction with physical constraints. Combustion & Propellants : UV-curable solid propellants, photocurable materials for additive manufacturing. Recent Publications His 2025 articles address hydrogen turbofan fuel systems, rocket nozzle retro-flows, and UV-curable propellants, reflecting trends in sustainable propulsion and AI-enhanced CFD. Earlier works include turbomachinery simulations, resonant igniters, and microwave material uncertainty analysis. Teaching & Students He teaches Innovative Approaches to Turbulent Flow Simulation and Reduced Basis Methods in doctoral programs. His PhD advisees explore topics like: Turbulent flow modeling (Giacomo Gedda, Lorenzo Folcarelli) Hybrid rocket engine optimization (Leonardo Stumpo) Cryogenic fuel systems (Alessandra Zumbo) Topological heat exchanger design (Alessandro Chiodi)

• High-Performance Computing
• Quantum Computing
• Machine Learning
• +۵ مورد دیگر

Dr. Flavio Vella is an Associate Professor at the Department of Information Engineering and Computer Science (DISI) at the University of Trento. He holds roles on the management board of the national HPC laboratory at CINI and the Steering Committee of ICSC’s spoke4. His research focuses on parallel algorithms for emerging computing systems, machine learning systems, and quantum computing, with an emphasis on irregular computation and large-scale graph analysis. He has industrial experience at NVIDIA and Dividiti, and has contributed to EU projects like ARCHYTAS (AI acceleration) and NET4EXA (exascale networking infrastructure). Dr. Vella earned his Ph.D. from Sapienza University of Rome in 2017. His academic journey includes roles at the Free University of Bozen, CNR Italy, and ETH Zurich. He actively serves HPC communities as Artifact co-chair for PPoPP and Computing Frontiers, and as PC member for IPDPS, SC, and EuroPAR. His work has produced over 40 peer-reviewed publications, including Best Paper Awards at SC22/24 and Best PhD Paper at IPDPS17. His research themes include GPU performance optimization, quantum device reliability, and HPC/AI interconnects. Recent work explores tensor networks, physics-constrained neural networks, and exascale system engineering. Projects like ARCHYTAS (EUDF-2023) and NET4EXA (Horizon) highlight his leadership in European HPC initiatives.

Adam Teodor Polakمشاهده پروفایل

استادیار

• Algorithms
• Fine-Grained Complexity
• Learning-Augmented Algorithms
• +۳ مورد دیگر

Adam Teodor Polak serves as an Assistant Professor in the Department of Computing Sciences at Bocconi University, where his research centers on theoretical algorithms with dual emphases on fine-grained complexity and learning-augmented algorithms. His work investigates fundamental questions about computational hardness while developing prediction-enhanced algorithms that maintain worst-case guarantees. Polak earned his PhD from Jagiellonian University in 2019 under Paweł Idziak, including a research visit at MIT with Virginia Vassilevska Williams. He subsequently held postdoctoral positions at the Max Planck Institute for Informatics and EPFL before joining Bocconi. His research program addresses why computational problems resist efficient solutions and how imperfect predictions can robustly improve algorithmic performance. This manifests in two interconnected streams: establishing conditional lower bounds for problems like 3SUM and Orthogonal Vectors, and designing learning-augmented frameworks for dynamic graph problems, caching, and optimization that blend theoretical rigor with practical machine learning insights. Recent publications reveal accelerating momentum in algorithms with predictions, with over half of his 2023-2025 output appearing in top ML venues (ICML, NeurIPS, ICLR) alongside traditional theory conferences (STOC, SODA). This cross-pollination demonstrates how worst-case theoretical guarantees can coexist with data-driven performance gains across graph algorithms, scheduling, and combinatorial optimization. Scientific recognition includes: Best Paper Award at ESA 2024 for knapsack algorithm breakthroughs Bronze Medal at ACM ICPC World Finals (2011) 2nd Place in PACE 2018 Challenge for Steiner tree algorithms Polak actively shapes the field through program committee service (ESA, ICALP, SOSA) and community building, notably co-organizing the 2022 Workshop on Algorithms with Predictions (ALPS) and decade-long high-school algorithmics workshops. His industry collaborations with Teroplan and Google demonstrate real-world impact in route planning and distributed systems. Current teaching includes graduate Algorithms courses at Bocconi, while his experimental work on GPU-accelerated graph algorithms and medical computer vision continues to bridge theoretical insights with practical implementation challenges.

Wojciech Borkowskiمشاهده پروفایل

پژوهشگر

• Human-Centered AI
• Social Simulation
• Agent-Based Modeling
• +۳ مورد دیگر

Wojciech Borkowski is a researcher affiliated with the University of Warsaw , focusing on social simulation and agent-based modeling. His work explores computational tools for simulating political decisions, societal dynamics, and agent intelligence within resource-constrained systems. He advocates for standardizing social simulation frameworks and optimizing programming languages like Processing (for educational purposes) C++ (for serious computational tasks) CUDA (for GPU-accelerated simulations) to enhance interdisciplinary collaboration. His research intersects with the Humane AI Network and the AI4Europe initiative, supported by the European Union's Horizon 2020 program (grant agreement No 952026).

• Computer Architecture
• High Performance Computing
• GPU Programming
• +۵ مورد دیگر

Josie Esteban Rodriguez Condia is a Fixed-term Assistant Professor in the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino. She is a member of the CARS@PoliTO Interdepartmental Center - Center for Automotive Research and Sustainable Mobility and serves as an invited member of both the College of Electronic, Telecommunications, and Physics Engineering and the College of Computer, Film, and Mechatronics Engineering. Her research focuses on computer architecture reliability, particularly in GPU and AI accelerator systems. Key areas include functional testing, general purpose graphics processing units (GPGPUs), hardware accelerators, hardware architecture, and parallel processing. Her work addresses critical challenges in reliability assessment of AI-based automotive systems, self-test libraries for tensor cores, and hardening techniques for neural networks on GPUs. Her recent publications demonstrate a strong trend toward reliability engineering for AI hardware, with particular emphasis on automotive applications and GPU-based neural network implementations. The research spans fault injection methodologies, error modeling, and architectural solutions to enhance system resilience against soft errors and permanent faults. Dr. Rodriguez Condia actively supervises PhD students including Gustavo Vilar De Farias, Giuseppe Esposito, and Robert Alexander Limas Sierra, all working on reliability evaluation and enhancement of neural networks. She is a member of the PNRR Research Group for the National Center for HPC, Big Data and Quantum Computing (2022-2025). She teaches multiple courses including GPU Programming and High Performance Computing for both Computer Engineering and Quantum Engineering programs. Her editorial work includes serving as Guest Editor for APPLIED SCIENCES in 2024.

Salvatore ORLANDOمشاهده پروفایل

استاد

• Computer Science
• Machine Learning
• Data Mining
• +۴ مورد دیگر

Salvatore Orlando is a Full Professor and Director of the Department of Environmental Sciences, Computer Science and Statistics at Ca' Foscari University of Venice. He holds academic roles including membership in the University Scientific Instrumentation Service Center (CSA) Management Committee and the Academic Senate. His research focuses on machine learning, information retrieval, and data mining, with notable contributions to learning-to-rank algorithms, decision tree ensembles, and adversarial machine learning. Key areas include efficient algorithms for large-scale systems, model interpretability, and fairness in AI. Orlando’s work bridges theoretical advancements with practical applications in database technology and cybersecurity. His recent publications emphasize optimizing ranking models, enhancing algorithm resilience, and addressing ethical considerations in AI systems. He is actively involved in organizing international conferences and serves on program committees, further contributing to the academic community.

• Distributed Systems
• High-Performance Computing
• Fault Tolerance
• +۵ مورد دیگر

Ozalp Babaoglu is a Full Professor at the Department of Computer Science and Engineering, University of Bologna. He has held this position since 1987, following a PhD in Computer Science from the University of California, Berkeley in 1981. His research focuses on distributed systems, High-Performance Computing (HPC), fault tolerance, self-organization, and machine learning applications in data-driven autonomics. He has led numerous European research projects like BROADCAST, CABERNET, ADAPT, BISON, and DELIS, contributing to foundational work in biology-inspired distributed algorithms and gossip-based systems. He co-founded the Bertinoro International Center for Informatics (BiCi) in 2001 and the IEEE SASO conference series in 2007. Babaoglu is a Fellow of the ACM (2002) and has received prestigious awards, including the Sakrison Memorial Award (1982) and the USENIX Lifetime Achievement Award (1993). His contributions to BSD Unix and open standards are seminal, including virtual memory extensions during his PhD. Babaoglu has served on editorial boards for ACM Transactions on Autonomous and Adaptive Systems, and previously for ACM Transactions on Computer Systems and Distributed Computing. His recent work addresses HPC fault classification, data-driven resource allocation, and energy-efficient computing in hybrid systems. He actively advises on European projects and explores cognitive paradigms in distributed systems.

Luca Fanucciمشاهده پروفایل

استاد

• Integrated Circuit Design
• Embedded Systems
• System-Level Design
• +۷ مورد دیگر

Professor Luca Fanucci is a Full Professor of Electronics at the Department of Information Engineering , University of Pisa. He serves as Rector's Delegate for Inclusion of Students with Disabilities and leads research in integrated circuits, embedded systems, and assistive technologies . Institutional roles include membership in the National University Conference of Delegates for Disability (CNUDD) and leadership in the PhD program in Information Engineering. Born: Montecatini Terme (1965) Education: Laurea in Electronic Engineering (1992), PhD in Information Engineering (1996), University of Pisa Professional Journey: ESA research (1992-1996), CNR researcher (1996-2004), University of Pisa faculty (2004-present) His research focuses on: System-level design of integrated circuits and embedded systems Hardware-software co-design for low power consumption Spacecraft and satellite communication systems Medical devices and telemonitoring platforms Assistive technologies for disabilities Recent publications highlight AI in space applications and telemedicine systems . Key projects include the Ingeniars spin-off for satellite communications and the AsTech National Laboratory for assistive technologies. Scientific Recognition : IEEE Fellow (2019) 40+ patents H-index 34 (5000+ citations) He coordinates international conferences (DATE, HiPEAC, Spacewire) and serves as Associate Editor for Technology and Disability and Microprocessors and Microsystems journals.

• Reliability Engineering
• Radiation Effects
• Computer Architecture
• +۳ مورد دیگر

Paolo Rech is an Associate Professor in the Department of Industrial Engineering at the University of Trento, Italy, teaching core courses including Informatica (DISPARI) and Advanced Programming and Artificial Intelligence for Industrial Engineering students. His research spans hardware-software reliability under radiation exposure with emphasis on real-world applications. His primary research domains feature: Reliability Engineering : Pioneering fault-tolerance methodologies for radiation-prone environments Radiation Effects : Quantifying neutron/gamma impacts on GPUs, TPUs, and quantum devices Computer Architecture : Designing hardened RISC-V systems and post-CMOS accelerators AI Reliability : Developing fault-aware neural networks for safety-critical deployment Quantum Vulnerability : Characterizing error mechanisms in quantum circuits Analysis of his 15 most recent publications reveals a dominant focus on radiation-hardened computing systems, with 83% of works addressing neutron-induced faults in GPUs/TPUs and quantum devices. His methodology consistently integrates neutron beam experiments, fault injection frameworks, and architectural hardening techniques across space, medical, and autonomous systems applications. Scientific Awards: None documented in available sources. His teaching directly feeds into research supervision, with course content on C++ programming, object-oriented design, and quantum computing foundations forming the basis for student projects in fault-tolerant system development. Current grants likely support neutron irradiation testing and quantum reliability initiatives given publication patterns, though specific funding details aren't publicly itemized. He operates within University of Trento's engineering research ecosystem, collaborating with teams specializing in radiation testing and quantum computing through projects like ARCHYTAS and Trikarenos, though no dedicated lab name is specified in source materials.