جستجوی استاد راهنمای دانشگاهی

Anna Gottardمشاهده پروفایل

دانشیار

• Multivariate Statistical Models
• Graphical Models
• Statistical Machine Learning
• +۵ مورد دیگر

Anna Gottard is an Associate Professor of Statistics at the University of Florence, where she leads the Department of Statistics, Computer Science, and Applications. She directs the Florence Center for Data Science (FDS) and participates in the Technical Scientific Committee of the Tuscan Center for Big Data, Data Science, and AI (CBDAI). Her research focuses on multivariate statistical models, particularly graphical models, and extends to statistical machine learning, fair models, and directional data analysis. She is an Associate Editor for the Journal of the Royal Statistical Society Series A (JRSSA) and Statistical Methods & Applications (SMA). Her recent work includes Bayesian approaches for mixed graphical models, uncertainty-aware classification trees, and methodological advancements in latent uncertainty models. Her contributions span theoretical developments and applied research in interdisciplinary areas like biostatistics and sustainability. Her research interests emphasize bridging statistical theory with practical applications, including fairness in machine learning, interpretable models, and tree-based methodologies. She has actively contributed to open-source software, notably the Mix3Trees R package for mixed-effect tree models. Her work addresses challenges in variable selection, graphical model inference, and ethical AI practices. Current projects explore Bayesian frameworks for complex data structures and methodological improvements in graphical model interpretability. Anna has advised on interdisciplinary collaborations, such as studies on GDPR compliance in biobanking and epidemiological modeling of the SARS-CoV-2 pandemic in Tuscany. She collaborates with institutions like the CBDAI to advance data science applications in regional policy and healthcare. Her research trajectory reflects a commitment to both foundational statistical theory and real-world problem-solving across diverse domains.

• Battery simulation
• Cyber physical system
• Design simulation
• +۱۲ مورد دیگر

Sara Vinco is an Associate Professor at the Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, Italy. She specializes in battery simulation, digital twins, and energy-efficient design automation for heterogeneous embedded systems, aligning with Industrial and Information Engineering (Area 0009) and ERC sectors including Computer Architecture and Machine Learning . Her research focuses on advancing cyber-physical systems through simulation frameworks like SystemC-AMS, enabling holistic modeling of analog, digital, and thermal domains. Key projects include data-driven digital twins for EV batteries and low-area digital circuits in industrial/medical applications, supported by commercial contracts such as C-based virtual prototyping. Her recent publications (2022-2023) emphasize machine learning for battery SOH/SOC estimation , energy monitoring in production lines , and multi-domain fault modeling . These works span journals like IEEE Transactions and conferences including DATE and ISLPED. Awarded the FFABR 2017 grant and IEEE FDL Best Paper Award 2011 , she also chairs editorial boards for IEEE Transactions on CAD and DATE Conference. She supervises PhD students Giovanni Pollo (Digital Circuits) and Khaled Alamin (EV Battery Twins), reflecting her leadership in smart systems design.

• Digital Hardware Design
• Reliability Engineering
• Reconfigurable Devices
• +۳ مورد دیگر

Eleonora Vacca is a PhD student and Research Fellow in the Department of Automatic Control and Computer Science (DAUIN) at the Polytechnic University of Turin. She holds a B.S. in Electronic Engineering from the University of Palermo (2018) and an M.S. in Electronic Engineering-Embedded Systems from Politecnico di Torino (2021). Her research focuses on digital hardware design, reliability engineering, reconfigurable devices, and AI applications in aerospace and safety-critical systems. She is a member of the Aerospace and Safety Computing Lab and the CAD - Electronic CAD & Reliability Group (DAUIN). Her work addresses challenges such as radiation effects mitigation in space missions, fault-tolerant AI accelerators, and real-time anomaly detection in satellite telemetry. She has contributed to projects like the RAMSES CubeSat-1 Development (2025-2026), funded by commercial contracts. In 2024, she won the Best Student Paper Award at the NEWCAS Conference for her research on radiation effects in space missions. Vacca collaborates on teaching, including assisting in the course 'Electronic Calculators' for Computer Engineering students. Her recent publications explore AI resilience in RISC-V ecosystems, radiation environment analysis for space missions, and gesture recognition systems for smart cities. She actively contributes to conferences such as the ACM International Conference on Computing Frontiers and the IEEE International Smart Cities Conference.

Seth Freyمشاهده پروفایل

دانشیار

• Computational Social Science
• Governance of Commons
• Complex Systems
• +۵ مورد دیگر

Seth Frey is an Associate Professor in the Department of Communication at the University of California, Davis, with affiliate status at Indiana University's Ostrom Workshop and as Research Director at Metagov. His research focuses on computational social science approaches to understanding self-governance in complex social systems, particularly through the lens of online communities as model institutions. Education: Ph.D. in Cognitive Science and Informatics (complex systems), Indiana University, 2013 B.A. in Cognitive Science, UC Berkeley, 2004 Research Interests: Frey specializes in computational approaches to institutional analysis and the cognitive science of strategic behavior . His work examines how communities design governance systems to overcome collective action problems, with emphasis on: Emergent institutional structures in digital commons Policy-as-data through NLP and institutional grammar frameworks Cognitive mechanisms underlying cooperative behavior Design principles for participatory change in online platforms His methodology integrates large-scale data analysis, web-based experiments, and computational modeling across diverse contexts including Minecraft, Reddit, and professional sports ecosystems. Publication Trends: Recent publications (2023-2025) demonstrate a cohesive trajectory toward computational institutional analysis, with increasing focus on NLP-driven policy analysis (e.g., NLP4Gov), decentralized governance architectures (DAOs, multi-level platform governance), and the cognitive foundations of collective action. His work consistently bridges theoretical institutional analysis with practical applications in digital community design, showing particular growth in translating Ostrom's design principles into computational frameworks. Awards: Honorable Mention Award for Best Paper at ACM CSCW 2019 Advising and Grants: Frey mentors students interested in data science applications at the intersection of communication, cognition, and complex systems, emphasizing resourcefulness and intellectual curiosity. His research has secured substantial funding from: National Science Foundation (NSF) NASA Ford Foundation Google Open Source Foundation He actively encourages aspiring graduate students with strong self-directed research skills to explore computational approaches to social phenomena. Labs and Teams: He leads the Computational Communication Lab at UC Davis and co-directs the Institutional Grammar Research Initiative. Through Metagov, he develops the 'Governance API' framework for modular community governance. His past affiliations include Disney Research (Walt Disney Imagineering) where he applied complexity science to theme park systems, and the New England Complex Systems Institute (NECSI). Current collaborations span Ethereum governance, Minecraft server ecosystems, and Colorado's cannabis monitoring infrastructure.

Nicole Novielliمشاهده پروفایل

دانشیار

• Affective Computing
• Software Engineering
• Natural Language Processing
• +۵ مورد دیگر

Nicole Novielli, Ph.D., is Associate Professor at the University of Bari “A. Moro” , Italy, where she conducts research on affective computing applied to software engineering and human-computer interaction. She leads the Collaborative Development Group and coordinates national projects investigating emotions in software teams, AI quality and IoT ecosystems. Education: Ph.D. in Computer Science, University of Bari, 2010 – thesis on “Lexical Semantics of Dialogue Acts” M.Sc. in Computer Science (Knowledge & Software Engineering), University of Bari, 2006 – summa cum laude B.Sc. in Computer Science, University of Bari, 2004 – summa cum laude Visiting researcher at USC-ICT, University of Aberdeen, FBK-irst (Trento) Research interests revolve around recognizing and exploiting affective and cognitive states in computer-mediated cooperative work. She studies sentiment and emotion mining in developers’ textual communication, multimodal emotion recognition via low-cost biometric sensors, and natural-language dialogue simulation for intelligent interfaces. Her work couples software engineering with natural language processing , social media analytics and human-computer interaction . Recent articles (2021-2025) reveal a clear trend: integrating deep learning and large language models into software engineering tasks—automated issue labelling, sentiment classification, technical-debt detection—while validating these techniques through rigorous empirical studies and biometric experiments . A parallel stream explores developer experience , measuring how emotions and cognitive load influence productivity, code quality and collaboration. Scientific awards include the 2020 Apex Award for Publication Excellence , multiple Distinguished Reviewer Awards at flagship venues (ESEC/FSE, ICSME, MSR), the Best Paper Award SANER 2019 and the Best Student Paper Award ACII 2009 . She currently teaches “Sentiment Analysis” in the Data-Science MSc and “Computer Networks” in the ITPS programme. She has advised numerous B.Sc., M.Sc. and PhD projects and is PI or Co-PI of four ongoing grants: EmoQuest (SIR), EMPATHY (PRIN), FAIR-Spoke 6 (PnRR), and QualAI (PRIN 2022). Dr. Novielli serves on the editorial boards of Empirical Software Engineering and Journal of Systems and Software , has guest-edited special issues on affect awareness in SE, and has chaired tracks at ICSE, SANER, MSR, ICSME and SSBSE. She co-leads the Collaborative Development Group and actively releases datasets and open-source tools for the community.

Thomas Streinzمشاهده پروفایل

استاد

• Global governance
• Law and technology
• Data regulation
• +۱۶ مورد دیگر

Thomas Streinz serves as Full-time Professor - Joint Chair at the European University Institute's Department of Law and Robert Schuman Centre for Advanced Studies since January 2025. His interdisciplinary work bridges law, technology studies, and global governance frameworks. His research focuses on digital governance and global law, particularly examining regulatory frameworks for data economies, digital infrastructures, and the interplay between European law and global tech regulation. Key projects investigate software regulation, AI/cloud computing governance, and infrastructure-as-regulation mechanisms. He integrates perspectives from science and technology studies, infrastructure studies, and critical data studies to analyze socio-techno-legal systems across public/private and jurisdictional boundaries. Professor Streinz previously served as Adjunct Professor of Law at New York University School of Law, where he directed the Guarini Global Law and Tech initiative and participated in MegaReg/InfraReg research projects through NYU's Institute for International Law and Justice. Supervises PhD candidates on global law and technology topics Active in International Thinking and Planetary Futures research cluster Administrative support provided by Alice Pineschi

Andrea Santilliمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Representation Learning
• Machine Intelligence
• +۱۰ مورد دیگر

Andrea Santilli is a Research Scientist at Nous Research and holds a PhD in Computer Science from GLADIA at Sapienza University of Rome. His research focuses on large language models (LLMs), robustness, reliability, and multimodal learning. He previously worked at Apple MLR, Hugging Face’s BigScience, and Pi School. He earned his MSc and BSc in Computer Science from Tor Vergata University and Sapienza. Education: PhD in Computer Science, Sapienza University of Rome (2024) MSc in Computer Science, University of Roma Tor Vergata (2020) BSc in Computer Science, University of Roma Tor Vergata (2018) Research Interests: Santilli’s work spans LLM robustness , mechanistic interpretability , multimodal neural databases , and instruction-tuning . He introduced Parallel Jacobi Decoding and contributed to projects like BLOOM, Camoscio, and Fauno. His research bridges syntax-aware NLP, privacy-preserving LLMs, and cross-modal alignment. Publications: His work includes advancements in 3D-text latent space alignment (CVPR 2025), evolutionary merging (ICML 2025), and efficient decoding (ACL 2023). Over 15+ peer-reviewed papers span venues like ACL, CVPR, and ICLR. Awards: Received the Emanuele Pianta Award for his MSc thesis on continual language learning with syntax-based episodic memory. Grants & Projects: Winner of ‘Machine Learning Algorithms for Translation’ grant (2022), developing Parallel Decoding Co-PI for ‘Multimodal AI for 3D Analysis’ (2021) with Ecole Polytechnique Labs & Teams: Active in GLADIA (Sapienza), Apple MLR, and Hugging Face’s BigScience initiative. Core contributor to open-source projects like PromptSource and BLOOM.

• High-Performance Computing
• GPU Acceleration
• Computational Geoscience
• +۴ مورد دیگر

Ludovic Räss is a computational geoscientist at the University of Lausanne and lecturer at ETH Zurich's Glaciology Lab. His research intersects high-performance computing (HPC), geophysics, and applied mathematics, with specialization in GPU-accelerated scientific computing and supercomputing applications. He leads the GPU4GEO initiative developing multi-physics solvers and pioneers differentiable modeling techniques for geophysical simulations using Julia. Research focuses include: Portable HPC software development Ice dynamics and porous media deformation GPU-optimized computational methods Scalable simulation architectures Differentiable programming for geophysics He designed and teaches Solving partial differential equations in parallel on GPUs at ETH Zurich, providing hands-on training in GPU programming and Julia-based scientific computing. Contributes significantly to Julia's open-source ecosystem through JuliaGPU and JuliaParallel projects.

Ting Sunمشاهده پروفایل

دانشیار

• Urban climate modelling
• Climate change impacts and adaptation
• Extreme weather events in cities
• +۵ مورد دیگر

Dr Ting Sun is an Associate Professor in Climate & Meteorological Hazard Risks at University College London , Department of Risk and Disaster Reduction. He earned his BEng (2009) and PhD in Hydrology (2013) from Tsinghua University , followed by a visiting period at Princeton University (2011–2012). After postdoctoral appointments at Tsinghua and the University of Reading , he held a NERC Independent Research Fellowship at Reading (2017–2022) before joining UCL in May 2022. Education PhD in Hydrology, Tsinghua University, 2013 BEng in Hydraulic Engineering, Tsinghua University, 2009 Visiting PhD Student, Princeton University, 2011–2012 Research Interests Dr Sun’s work converges on urban climate modelling across scales —from neighbourhood blocks to global grids—focusing on the impacts of weather and climate extremes such as heat waves and extreme rainfall in cities. He is the lead developer of the Surface Urban Energy and Water balance Scheme (SUEWS) and its Python wrapper SuPy , developed in collaboration with Prof Sue Grimmond’s micromet group. He also contributes as a core member of the Urban Multi-scale Environmental Predictor (UMEP) development team. His multidisciplinary expertise integrates hydro-climate dynamics, computational modelling, machine learning, built-environment processes, and public-health linkages . Research Trends from Recent Publications Across the 15 most recent articles, a clear trajectory emerges from high-resolution urban-process modelling toward integrated socio-environmental assessments . Studies published in 2024–2025 couple atmospheric models (WRF-SUEWS) with global building-morphology datasets (GLAMOUR) to quantify how cities alter rainfall patterns, temperature sensitivity, and heat-related mortality. Earlier works progressively refined SUEWS’s physical parameterisations and Python accessibility, while recent outputs leverage deep-learning remote-sensing tools (SHAFTS) and hybrid hydrological-neural architectures to deliver actionable insights for urban planning and climate adaptation. Scientific Awards & Fellowships NERC Independent Research Fellowship , University of Reading, 2017–2022 HEA Fellowship , University College London, 2023 Professional Service & Editorial Roles Topic Editor , Geoscientific Model Development (from 2025) Editorial Board Member , Scientific Data (from 2024) Peer review and consultancy for journals, conferences, and policy bodies Supervision of taught-course projects and research degrees External examining and mentoring Labs, Teams & Collaborations Dr Sun leads and collaborates within the UCL Department of Risk and Disaster Reduction , working closely with the micromet group at the University of Reading (Prof Sue Grimmond) on SUEWS/SuPy development. He is an active member of the UMEP consortium and maintains extensive international collaborations spanning Tsinghua University, Princeton, and numerous European research centres, underpinning a vibrant, interdisciplinary research network focused on urban climate resilience.

Germain Gauthierمشاهده پروفایل

استادیار

• Political Economy
• Public Economics
• Econometrics
• +۷ مورد دیگر

Germain Gauthier is an Assistant Professor at the Department of Social and Political Sciences, Bocconi University. His work bridges economics and political science with a focus on political economy, public economics, and machine learning methods. He develops AI tools for social scientists, particularly for analyzing unstructured data like texts and images. Ph.D. in Economics, Ecole Polytechnique (2018–2023) Postdoctoral Researcher, ETH Zürich (2023) M.Sc. in Analysis and Policy in Economics, Paris School of Economics (2017–2018) M.Sc. in Quantitative Economics, HEC Paris (2013–2017) His research spans two main areas: applied studies on digital technologies' societal consequences (e.g., X's algorithms, #MeToo's impact) and methodological innovations in machine learning for social science data. Recent publications include work on protest dynamics, inequality, and narrative extraction from texts. Key trends in his publications include: Machine learning applications to political and economic analysis Text mining for social science narratives Empirical studies on protest movements and digital governance Algorithmic bias in labor markets Economic policy evaluation using big data He has received significant funding: Swiss National Sciences Foundation Grant (300K CHF, 2023–2026) Swiss National Sciences Foundation Grant (100K CHF, 2020) Laboratoire d’Excellence Ph.D. Grant (2018–2023) He teaches courses on Public Finance (Bocconi) and Text as Data for Social Sciences (LMU München), and has developed open-source software packages like relatio and DeepLatent for text analysis and latent variable modeling.

• Artificial intelligence
• Biomedical signal processing
• Human movement analysis
• +۱۲ مورد دیگر

Marco Ghislieri is an Assistant Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino, Italy. He is a member of the Interdepartmental Center PolitoBIOMed Lab and teaches in the Biomedical Engineering program, including courses like Neuroengineering and Design of Programmable Biomedical Devices . His research spans Artificial Intelligence, Biomedical Signal Processing, Neuroscience, and Rehabilitation Engineering . PhD in Bioengineering and Medical-Surgical Sciences (2017-2021) at Politecnico di Torino Thesis: Muscle Synergy Assessment during Cyclic and Non-Cyclic Movements His research focuses on muscle synergy analysis in Parkinson’s Disease (PD) patients post- Deep Brain Stimulation (DBS) , AI-driven gait analysis for fall prevention, and wearable sensor applications for stress-cognitive decline monitoring. He leads the S-CoDe and OMNIA-PARK projects, and contributes to PRIN as a team member. Recent publications highlight advancements in machine learning for intraoperative DBS targeting , statistical gait analysis , and neurorehabilitation tools . He serves as Associate Editor for Scientific Reports and Applied Bionics and Biomechanics , and Guest Editor for Frontiers in Neural Circuits . Awards include the Carlo J. De Luca Award (2022) , GNB Doctoral Award (2022) , and the Best Poster Award at M. Grattarola Summer School (2022) . He supervises Fabrizio Sciscenti (PhD candidate) and collaborates on neuroengineering and biomedical device design courses. His work addresses Goal 3 (Good Health) and Goal 4 (Quality Education) of the UN SDGs.

Rocco OLIVETOمشاهده پروفایل

استاد

• Software Engineering
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Prof. Rocco OLIVETO is a Full Professor at the University of Molise, affiliated with the School of Biosciences and Territory. His research spans software engineering, artificial intelligence, cybersecurity, and healthcare technology. He focuses on empirical studies of developer practices, AI-driven code analysis, vulnerability detection in smart contracts, and human-centric computing. His work also addresses challenges in game development, mobile app optimization, and wearable health monitoring systems. Notable research areas include code readability assessment, machine learning applications in healthcare diagnostics, and the effectiveness of AI tools like GitHub Copilot. He has contributed to projects like QualAI (continuous quality improvement for AI systems) and 2Vita-B (cognitive and physical rehabilitation systems). His empirical studies often bridge academic research with real-world developer workflows, emphasizing practical applicability. Prof. Oliveto's recent work explores topics such as automated gameplay analysis for game debugging, detection of engagement issues in video games, and robust methods for identifying security vulnerabilities. He has also investigated Dockerfile quality, developer frustration metrics, and the ethical implications of AI in administrative document simplification.

Marco Piconeمشاهده پروفایل

دانشیار

• Distributed Systems
• Internet of Things
• Edge Computing
• +۳ مورد دیگر

Marco Picone is an Associate Professor at the Department of Sciences and Methods for Engineering (DISMI) of the University of Modena and Reggio Emilia. He leads the Distributed and Pervasive Intelligence (DIPI) Group and holds a PhD in Information Technology from the University of Parma. His postdoctoral research at the University of Parma (2012-2015) and a visiting scholar position at the University of Cambridge (2011) further enriched his expertise. He is nationally qualified as an Associate Professor by MIUR (2020). Education: PhD in Information Technology, University of Parma (Italy) M.Sc. (cum Laude) in Computer Engineering, University of Parma Visiting Researcher, NetOS Group, University of Cambridge (UK) His research focuses on Distributed Systems , IoT , Edge Computing , and Digital Twins , emphasizing their applications in smart industries and cities. He has supervised postdocs (e.g., Matteo Martinelli) and PhD students (e.g., Enrico Rossini) on topics like Digital Twin Continuum and Edge-Cloud Systems. Teaching spans courses on Intelligent IoT , Distributed IoT Software Architectures , and Edge Computing , with a focus on lab-based learning and industry-relevant projects. He actively contributes to open-source frameworks like the White Label Digital Twin (WLDT) and collaborates on initiatives such as the Web of Digital Twins (WoDT). Research collaborations include projects on smart city data fusion, livestock waste management, and Industry 5.0 human-centric systems. His work bridges theoretical advancements with practical implementations in cyber-physical environments.

Tushar Sharmaمشاهده پروفایل

استادیار

• Software Engineering
• Machine Learning
• Code Quality
• +۷ مورد دیگر

Tushar Sharma is an Assistant Professor at the Faculty of Computer Science, Dalhousie University, Canada. His research focuses on software code quality , refactoring , sustainable AI , and machine learning for software engineering (ML4SE) . He holds a PhD in Software Engineering from Athens University of Economics and Business (2019) and an MS in Computer Science from IIT-Madras (India). Current affiliations: Dalhousie University, SMART Lab, IEEE Senior Member Past experience: Siemens Research (2019-2021), Siemens Corporate Technology (2008-2015) Research interests span code quality assessment, technical debt management, and sustainable AI. He founded Designite , a widely used software design quality assessment tool, and contributed to the book Refactoring for Software Design Smells . Recent work examines energy-efficient language models for code, reproducibility issues in configuration scripts, and human-guided code smell detection. Publication trends reveal expertise in code smell detection, refactoring techniques, and green AI. His articles address topics like commit message generation, model quantization, and empirical studies on code quality. Collaborative efforts include tools like DesigniteJava 2.0 and frameworks for attention mechanisms in code language models. Scientific recognition: Dean's Research Excellence Award (2025), Best Artifact Award (SCAM 2023) Grants: Mitacs Accelerate grants ($225K, $15K, $30K), NSERC Discovery Grant ($154M CFREF climate action project), DRA computing resources ($51K) He actively contributes to academic service as PC Co-chair (ICSE 2024), editorial board member (JSS), and organizer of workshops on technical debt. His media coverage highlights environmental impacts of AI and software quality challenges.

Luca Sterponeمشاهده پروفایل

استاد

• Reconfigurable computing
• Computer-aided design algorithms
• Fault tolerance and reliability
• +۷ مورد دیگر

Luca Sterpone is a Full Professor at the Department of Control and Computer Science (DAUIN), Politecnico di Torino. He serves as Head of the Control and Computer Engineering Department (2023-2027), coordinates the Aerospace and Safety Computing Lab, and is a member of the Academic Senate and Power Electronics Innovation Center (PEIC). His research spans reconfigurable computing, fault tolerance, and radiation effects analysis in electronic systems. Professor since 2021 Department Head (DAUIN) since 2023 Coordinates international collaborations with ESA, AMD Xilinx, NVIDIA, and Thales Alenia Space Develops radiation-hardened FPGA tools (SETA, VERI-Place, PyXEL) 2007 EDAA Outstanding Dissertation Award and 2005 IEEE Best Paper Award Research Focus : Designing radiation-tolerant systems for aerospace, including fault-tolerant AI accelerators, FPGA reliability, and software-based error mitigation. He investigates soft error propagation in nanoscale circuits and develops tools for radiation sensitivity analysis in VLSI. His work integrates hardware-software co-design for mission-critical applications. Awards : EDAA Outstanding Dissertation Award (2007) IEEE European Test Symposium Best Paper (2005) SMACD Best EDA Tool Award (2018) ARC Best Paper candidate (2018) Teaching : He leads courses in Reconfigurable Computing (PhD level), GPU Programming , and Operating Systems . He has formal responsibility for teaching roles across 9 bachelor's and 7 master's years, and mentors multiple PhD students. Collaborations : Coordinates with the European Space Agency (ESA), University of Bielefeld, Universidad de Sevilla, and industrial partners like AMD Xilinx, NVIDIA, and General Motors. He leads projects such as RESCHIP4EU, VEGAS, and TERRAC for radiation-hardened computing solutions.