Find Academic Supervisors

Masoumeh EbrahimiView profile

Associate Professor

• Computer Architecture
• Machine Learning
• Embedded Systems
• +6 more

Masoumeh Ebrahimi is an Associate Professor at KTH Royal Institute of Technology, Division of Electronics and Embedded Systems, and holds an Adjunct Professor position at the University of Turku, Finland. She leads research in hardware acceleration, neural architecture search, and fault-tolerant systems. Her work bridges machine learning, embedded systems, and network-on-chip (NoC) design. Research Interests: Hardware-Accelerated Machine Learning 6G Network Architectures Fault-Tolerant Computing High-Performance GPU Systems Network-on-Chip (NoC) Design Federated Learning Key Projects: Co-supervisor of Hui Chen’s postdoc project Generalizing hardware acceleration for nonlinear functions . Active in Digital Futures, a cross-disciplinary center focusing on societal challenges using digital tech. Collaborates on edge computing, 6G networks, and resilient embedded systems. Labs & Teams: Core member of KTH’s Digital Futures initiative, advancing AI accelerators and next-gen communication systems. Engaged in EU-funded projects on NoC reliability and federated learning frameworks.

Wong Kok SheikView profile

Associate Professor

• Multimedia Signal Processing
• Cybersecurity
• Digital Health
• +3 more

Associate Professor Wong Kok Sheik is the Deputy Head (Research) at the School of Information Technology, Monash University Malaysia. He holds a Doctor of Engineering from Shinshu University (Japan) and advanced degrees in Computer Science and Mathematics from Utah State University (USA). His research focuses on multimedia signal processing, cybersecurity, and digital health, with contributions to data hiding, encryption, and smart grid security. He leads a EU-funded WAge project on post-pandemic workplace health interventions. Education: PhD in Engineering, Shinshu University, Japan (2009) Masters in Computer Science & Mathematics, Utah State University, USA (2005-2004) Bachelor of Science, Utah State University, USA (2002) Professional Roles: Associate Editor, IEEE Signal Processing Letters Member, IEEE Signal Processing Society’s IFS Technical Committee Board Member, APSIPA Multimedia Security and Forensics (MSF) Committee His research interests span multimedia forensics, encrypted domain processing, and cybersecurity frameworks. Notable projects include recovery of missing coefficients in compression standards and HDR imaging enhancement. Recent work integrates digital health, addressing workplace mental/physical health in post-pandemic environments. Publications reflect expertise in watermarking, encryption, and biometric security. Key contributions include encryption-resistant data embedding and secure smart grid analytics. Awards include IEEE CES Service Awards (2015, 2016) and Best Paper recognitions. He supervises over 20 PhD/MSc students, focusing on topics like biometric recognition, data hiding, and anomaly detection. Grants include a €2M EU Horizon 2020 project (IDENTITY) and a RM146k smart grid initiative. His teaching emphasizes foundational IT research methods and theoretical computer science.

• Artificial Intelligence
• Machine Learning
• Social Network Analysis
• +4 more

Andreas Kaltenbrunner is a Part-Time Lecturer at the Universitat Pompeu Fabra (UPF) in Barcelona, specializing in Data Driven Social Analytics and Information Retrieval. He serves as Director of Research on AI and Data Science at IN3 - UOC R&I and is an Affiliate Researcher at ISI Turin, Italy. His research focuses on computational social science, user behavior analysis, and applications of AI/ML in social media and network dynamics. Key interests include large language models (LLMs), personality assessment simulations, and digital epidemiology. His work bridges theoretical frameworks with practical applications, such as predicting urban mobility patterns and evaluating AI systems’ performance in political contexts. He collaborates with institutions like UNICEF and the ISI Foundation, contributing to projects on data-driven decision-making and social impact. His interdisciplinary approach integrates network science, computational methods, and socio-cultural analysis. Recent publications highlight innovations in LLM validation, cross-lingual Wikipedia reliability modeling, and climate-related forecasting. While no explicit awards are listed, his prolific output reflects sustained contributions to computational social science and AI ethics.

James CaverleeView profile

Professor

• Machine Learning
• Recommender Systems
• Information Retrieval
• +7 more

James Caverlee is a Professor in the Department of Computer Science and Engineering at Texas A&M University, and a Visiting Researcher at Google DeepMind. He holds a Ph.D. from Georgia Institute of Technology (2007), and degrees from Stanford University and Duke University. His research focuses on trustworthy, efficient, and personalized machine learning systems, including Recommender Systems, Large Language Models (LLMs), and bias mitigation. Recent work emphasizes fairness, efficiency, and transparency in AI, with applications in conversational systems, speech, and medical QA. He has been recognized with an NSF CAREER Award, AFOSR Young Investigator Award, and DARPA Young Faculty Award. His group has published extensively at top venues like KDD, EMNLP, WWW, SIGIR, and WSDM. Education: Ph.D., Computer Science, Georgia Tech, 2007 M.S., Computer Science & Engineering-Economic Systems, Stanford University, 2001–2000 B.A., Economics, Duke University, 1996 Research Interests: Machine Learning, Recommender Systems, NLP, LLMs Bias Mitigation, Efficiency, Personalization Social Computing, Information Retrieval Awards: NSF CAREER Award (2012) AFOSR Young Investigator (2012) SIGIR Test of Time Honorable Mention (2022) Grants: Supported by NSF, AFOSR, DARPA, and industry (Google, Amazon). Labs/Teams: InfLab research group at Texas A&M, collaborating with Google DeepMind. His publications address topics like adversarial attacks on LMs, gender bias in podcasts, and efficient recommendation algorithms. Recent trends show focus on generative AI for personalization and ethical AI challenges.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +3 more

Dr. Jiayan Qiu is a Lecturer (Assistant Professor) at the University of Leicester's College of Computing and Mathematical Science. Previously, he was a postdoctoral research fellow collaborating with Prof. Zhou Wang at the University of Waterloo's Department of Electrical & Computer Engineering. He holds a Ph.D. from the University of Sydney (USYD), advised by Prof. Dacheng Tao, and completed his MPhil and Honorable B.S. at the Australian National University (ANU). His research focuses on computer vision, machine learning, and artificial intelligence, with notable contributions to visual relationship modeling, image outpainting, depth estimation, and generative models. His work has been published in top-tier venues like IEEE TPAMI, CVPR, ECCV, and ACM KDD. Professional service activities include serving as a reviewer for prestigious journals (e.g., IEEE T-PAMI, T-IP) and conferences (CVPR, ICCV, NeurIPS), as well as a member of program committees for leading AI conferences. He also contributes to academic leadership as a Guest Editor for Frontiers in Signal Processing and MDPI-Electronics .

Krishna Teja Chitty-VenkataView profile

Researcher

• Deep Learning
• Systems and Machine Learning
• Neural Network Optimization
• +7 more

Krishna Teja Chitty-Venkata is a Postdoctoral Researcher at the Argonne Leadership Computing Facility (ALCF), Argonne National Laboratory, USA, where he works in the AI/ML team (formerly Data Science group). His research lies at the intersection of systems and machine learning, focusing on optimizing neural network training, finetuning, and inference on general-purpose and AI-specific hardware platforms. He is actively involved in AI for science applications and high-performance computing for AI (HPC for AI). Education: PhD in Computer Engineering, Iowa State University, 2017–2023 Bachelor of Engineering in Electronics and Communication Engineering, University College of Engineering, Osmania University, Hyderabad, India, 2013–2017 Research Interests: Krishna's research spans hardware-aware inference optimization of deep neural networks, enhancing training and finetuning of large language models (LLMs) and vision-language models (VLMs), neural architecture search (AutoML), pruning and quantization techniques, performance modeling, and AI for science. He is particularly interested in efficient adaptation methods such as LoRA-NAS integration, structured pruning (e.g., WActiGrad), and KV cache optimization (e.g., Paged Compression). Publication Trends: His recent work emphasizes benchmarking and optimization of LLMs on diverse AI accelerators (e.g., LLM-Inference-Bench), developing scalable frameworks for CNN and ViT evaluation (ConVision Benchmark), and advancing structured pruning and mixed-precision search methods. His publications span high-impact journals and conferences in computer science, AI, and systems, reflecting a strong focus on practical, hardware-aware solutions for deep learning efficiency. Scientific Contributions: Developed LLM-Inference-Bench for evaluating LLM performance across hardware and frameworks. Created ConVision Benchmark for standardized evaluation of CNNs and Vision Transformers. Proposed WActiGrad, a structured pruning method for efficient LLM finetuning and inference. Introduced Paged Compression for efficient KV cache management in vLLM. Designed LangVision-LoRA-NAS for optimizing VLMs via NAS-integrated adapters. Professional Experience and Advising: Krishna has been mentored by Prof. Arun K. Somani (Iowa State) and supervisors Murali Emani and Venkatram Vishwanath at Argonne. He has interned at AMD, Intel, and Argonne, contributing to deep learning optimization projects. While no formal students are listed, he has co-authored multiple papers with researchers and students, indicating collaborative advising. He has no publicly listed grants, but his work at Argonne is likely supported by institutional and DOE funding. Labs and Teams: He is part of the AI/ML team within the Argonne Leadership Computing Facility, a premier HPC and AI research division. His work involves close collaboration with teams developing AI accelerators and scientific applications, positioning him at the forefront of AI for science initiatives.

He LiView profile

Assistant Professor

• Multiscale Computational Modeling
• Artificial Intelligence in Medicine
• Biomedical Engineering
• +3 more

Dr. He Li is an Assistant Professor at the University of Georgia, specializing in computational biomedical engineering. His research focuses on developing AI-driven multiscale models to bridge microscopic and macroscopic biological processes, with applications in disease modeling, medical imaging, and healthcare innovation. He leads the Computational Biomedical Engineering Lab, exploring interdisciplinary solutions in biomedical science using advanced computational methods. His work integrates physics-based modeling (e.g., molecular dynamics, spectral element methods) with AI techniques to assimilate diverse biological data sources. Recent research emphasizes predictive AI models for digital health, including radiology analysis, proton therapy optimization, and Alzheimer’s disease infodemiology. He has published extensively on large language models (LLMs) tailored for medical domains like radiology, oncology, and ophthalmology, addressing challenges in specialized clinical NLP tasks. Key contributions include frameworks like Radiology-GPT and AD-Autogpt, which advance AI applications in diagnostics and disease surveillance. His lab collaborates across disciplines to enhance clinical decision-making through AGI (Artificial General Intelligence) and multimodal data fusion. No awards or grants are explicitly listed in the provided texts. Labs/Teams: Computational Biomedical Engineering Lab @ UGA focuses on AI-driven biomedical innovation, with projects spanning medical imaging, disease modeling, and healthcare technology development.

Sangmi Lee PallickaraView profile

Professor

• Big Data
• Data Science
• Data Engineering
• +13 more

Sangmi Lee Pallickara is a Professor of Computer Science and the Clare Booth Luce Professor at Colorado State University. She is affiliated with the Department of Computer Science in the College of Natural Sciences. Her research is supported by major agencies including the National Science Foundation, Department of Homeland Security, ARPA-E, and NIFA, with ongoing projects in AI Institutes, Cyberinfrastructure, and CyberPhysical Systems. Research Interests: Her work focuses on Big Data systems for scientific applications, including scalable storage, retrieval, metadata management, predictive analytics, and interactive visualization. She applies these to domains such as agriculture, atmospheric science, environmental monitoring, and epidemiology. Her research integrates data science, distributed systems, and deep learning to enable scalable knowledge extraction from high-velocity, voluminous datasets. Publication Trends: Her recent publications emphasize scalable solutions for geospatial and spatiotemporal data, including efficient storage (e.g., ATLAS), visualization (e.g., Glance, Iris), and deep learning (e.g., Argus, CloudNet). There is a strong focus on real-world applications such as wildfire prediction, satellite data imputation, and precision agriculture, leveraging generative models, embeddings, and ensemble methods. Scientific Awards: NSF CAREER Award IEEE TCSC Award for Excellence in Scalable Computing Best Paper Award at IEEE/ACM UCC 2019 Best Paper Award at IEEE CLUSTER 2019 Best Paper Award at IEEE/ACM UCC 2014 Finalist for Best Paper Award at IEEE BDCloud 2018 Advising and Grants: She advises numerous Ph.D. and Master’s students, many of whom have gone on to careers in industry and academia. Her research is funded by the NSF (AI Institutes, CPS), DHS, ARPA-E, NIFA, and the Environmental Defense Fund. She also leads the SWiFT outreach program for K–12 STEM education. Labs and Teams: She leads a vibrant research group focused on Big Data systems, with students working on distributed storage, deep learning for satellite imagery, spatiotemporal analytics, and interactive visualization. The team collaborates with domain scientists in agriculture, climate, and public health.

• Signal and Information Processing
• Energy Disaggregation
• Non-Intrusive Load Monitoring (NILM)
• +15 more

Professor Vladimir Stankovic is a leading academic in the Department of Electronic and Electrical Engineering within the Faculty of Engineering at the University of Strathclyde, UK. His research focuses on signal and information processing to tackle global sustainability challenges such as energy efficiency, net-zero transition, electrification of transport, and landslide monitoring. He is actively involved in multiple high-impact research initiatives and holds leadership roles in major AI and engineering conferences. PhD (Dr-Ing), University of Leipzig, Germany, 2003 MEng (Dipl-Ing), Electrical Engineering, University of Belgrade, 2000 His research interests center on applying signal processing and AI to smart homes, micro-seismic analysis, health monitoring, remote sensing, and computer vision. He has developed innovative methods in energy disaggregation, graph signal processing, and interpretable machine learning. His work bridges engineering with sustainability, contributing to UN Sustainable Development Goals. He teaches core courses including image/video processing, communication systems, signals and systems, and information theory. The recent publications highlight a strong trend in applying AI and machine learning to energy analytics in buildings and agriculture (e.g., dairy farms), landslide monitoring using seismic data, and equitable EV charging planning. These works leverage advanced techniques such as graph neural networks, transformers, and human-in-the-loop deep learning, demonstrating a consistent focus on real-world impact, interpretability, and sustainability. He has received notable scientific awards: Finalist of the World first 10K Best paper Award (Top 3%) IEEE Multimedia Signal Processing Workshop (MMSP) top 10% best paper award 2010 Professor Stankovic has been a Principal or Co-Investigator on numerous research and knowledge exchange projects, including JED-AIs (Justice, Energy, Demand flexibility and AI for Sustainability), REMINDA, and multiple Digital Dairy Chain Innovation Vouchers. These projects are funded by EPSRC and other UKRI councils, focusing on AI-driven energy analytics, community energy sharing, and sustainable farming. He serves in key editorial roles, including Area Editor for IEEE Transactions on Communications and Associate Editor for Nature Scientific Data, and has organized major conferences such as IEEE MMSP 2017 and IJCNN 2025. He is actively involved in research teams and collaborative networks focused on AI for sustainability, energy systems, and environmental monitoring. His lab works on datasets such as the Plegma Dataset and REFIT, supporting open science in energy and seismic analytics.

Stefan ĆirkovićView profile

Lecturer

• Cybersecurity
• Artificial Intelligence
• Computer Vision
• +3 more

Stefan Ćirković is a Teaching Associate in the Department of Information Technologies at the Faculty of Technical Sciences in Čačak, University of Kragujevac. His office is located at Saint Sava 65, 32102 Čačak, Serbia. He is currently pursuing doctoral studies in Information Technology (2023–present) at the same institution. Education: Doctoral Academic Studies – Information Technology, University of Kragujevac (2023–present) Master of Academic Studies – Information Technology, University of Kragujevac (2022–2023) Basic Academic Studies – Information Technology, University of Kragujevac (2018–2022) Specialized Program – Cybersecurity, University of Kragujevac (2024) Research Focus: Ćirković specializes in applied artificial intelligence with significant work in cybersecurity, medical imaging, and network systems. His research demonstrates strong emphasis on practical AI implementations including real-time object detection (YOLO algorithm), anomaly detection in networks, federated learning for healthcare applications, and cybersecurity techniques using large language models. His work frequently intersects with medical informatics, particularly in diagnostic applications. Publication Trends: Recent publications (2024–2025) show predominant focus on AI/ML applications across multiple domains. Cybersecurity remains a core theme with novel approaches to web application security and network anomaly detection. Medical applications feature prominently, especially in diagnostic imaging (kidney stones, skin cancer) and hemodialysis optimization. Methodologically, there's consistent exploration of deep learning architectures and real-time systems. Laboratory Affiliations: Associated with the Computer Science Laboratory at the Faculty of Technical Sciences in Čačak. Utilizes institutional resources including the Moodle e-learning platform and Microsoft 365/Teams infrastructure for academic activities.

• Machine Learning
• Computer Vision
• Natural Language Processing
• +2 more

Chun-Liang Li is a Research Scientist at Apple MLR and an Affiliate Assistant Professor at the Allen School of Computer Science, University of Washington. He holds a Ph.D. in Machine Learning from Carnegie Mellon University (2014-2019) and a B.S./M.S. in Computer Science and Information Engineering from National Taiwan University (2008-2013). His research spans machine learning, computer vision, and natural language processing, with emphasis on representation learning, efficient model training, and multimodal understanding. Key focus areas include large language model optimization, contrastive learning techniques, document understanding systems, and domain adaptation methods. Recent publications demonstrate strong focus on LLM efficiency (dataset decomposition, distillation techniques), document AI (table reasoning, form extraction), and multimodal alignment (image-text retrieval, prefix conditioning). His work consistently appears in premier venues including NeurIPS, CVPR, ACL, and ICLR. Awards and Honors: IBM Ph.D. Fellowship (2018) Best student paper runner-up, IJCAI (2017) First Place in both tracks of KDD Cup (2011, 2013) He actively mentors students and collaborators, inviting contact for internship opportunities. Current industry-academic position bridges cutting-edge research with practical applications in machine learning systems.

• Image processing
• Inverse problems
• Radio interferometric imaging
• +7 more

Audrey Repetti is an Associate Professor in the Department of Actuarial Mathematics and Statistics within the School of Mathematical and Computer Sciences at Heriot-Watt University in Edinburgh, UK. She also holds a dual affiliation with the Institute of Sensors, Signals, and Systems in the School of Engineering and Physical Sciences, and is part of the Maxwell Institute for Mathematical Sciences - Edinburgh. Her research spans mathematical imaging, optimization, and computational methods with applications across astronomy, medical imaging, and optical engineering. Dr. Repetti's research focuses on developing advanced mathematical frameworks for solving imaging inverse problems. Her work centers on optimization algorithms, Bayesian uncertainty quantification, and the integration of machine learning with traditional mathematical approaches. She has made significant contributions to radio interferometric imaging, computational optical imaging with photonic lanterns, and uncertainty quantification in medical imaging. Her research bridges theoretical mathematics with practical applications in astronomy, healthcare, and engineering. Analysis of her recent publications reveals a clear trajectory toward integrating traditional mathematical imaging approaches with modern machine learning techniques. Her work increasingly focuses on 'hybrid' methodologies that combine data-driven models with optimization frameworks. Key themes include plug-and-play algorithms, uncertainty quantification in imaging, and the development of efficient computational methods for high-dimensional inverse problems. Her research demonstrates strong interdisciplinary connections between mathematics, signal processing, astronomy, and medical imaging. Dr. Repetti is actively involved in academic service, including co-organizing the 2026 ICMS Workshop on Imaging inverse problems and generating models. She has received research funding supporting her work in computational imaging and inverse problems, though specific grant details aren't listed in the provided materials. Her teaching portfolio includes advanced courses in scalable inference, deep learning, and statistics for sciences. She leads several research projects with associated software toolboxes including BUQO (Bayesian Uncertainty Quantification by Optimization), SARA-COIL (Compressive optical imaging with a photonic lantern), and CALIM (Self direction-dependent effect calibration and imaging in radio-interferometry). These projects demonstrate her commitment to developing practical computational tools that advance both theoretical understanding and real-world applications in imaging science.

Sebastián FerradaView profile

Assistant Professor

• Knowledge Graphs
• Graph Databases
• Multimedia Databases
• +3 more

Sebastián Ferrada is an Assistant Professor at the Data & Artificial Intelligence Initiative of Universidad de Chile. He also serves as Young Researcher at the Institute for Foundational Research on Data (IMFD) and Collaborating Researcher at the National Center for Artificial Intelligence Research (CENIA). His research focuses on Knowledge Graphs, with special emphasis on extraction, management, and applications for querying, browsing, and AI systems. His academic background includes: PhD in Computer Science (2021), Universidad de Chile MSc in Computer Science (2017), Universidad de Chile BEng in Computer Science (2017), Universidad de Chile Sebastián's research explores several key areas: Multimedia Databases with applications to Wikimedia Commons images Graph Databases and Knowledge Graphs construction Federated Data Management across heterogeneous RDF sources SPARQL query extensions for similarity-based operations Graph data management and compression techniques His recent publications demonstrate strong trends in knowledge graph construction, similarity-based querying, and efficient graph data management. These works combine theoretical advancements with practical implementations in real-world systems like IMGpedia and MillenniumDB. Scientific achievements include: Best Paper Award at CoopIS 2023 Best Demonstration Award runner-up at SIGMOD/PODS 2024 Best Student Paper (Resources Track) and Best Poster at ISWC 2017 First prize in CLEI 2017 for his Master's thesis Sebastián currently leads the Fondecyt project on graph data management and contributes to the U-Inicia project on AI processes in graph databases. He serves on the editorial board of Transactions on Graph Data and Knowledge.

Weiming XiangView profile

Associate Professor

• Cyber-Physical Systems
• Safe AI
• Formal Methods
• +6 more

Dr. Weiming Xiang is an Associate Professor at the School of Computer and Cyber Sciences of Augusta University , Georgia. He transitioned from Assistant Professor (2019–2023) to his current role (2023–present). His academic journey includes postdoctoral positions at Vanderbilt University and University of Texas at Arlington , and a research associate role at the University of Hong Kong . He earned his Ph.D. (2014) in hybrid transportation systems from Southwest Jiaotong University , an M.Sc. (2007) in automation from Nanjing University of Science and Technology , and a B.E. (2005) in electrical engineering from East China Jiaotong University . Research Interests: Dr. Xiang focuses on formal synthesis and verification techniques for cyber-physical systems (CPS) , particularly addressing safety, security, and reliability in learning-enabled CPS . His work spans hybrid systems , neural network verification , control theory , and data-driven modeling , with applications in autonomous vehicles , power systems , and transportation . He develops scalable verification frameworks using reachable set computation , interval arithmetic , and polyhedral methods . Scientific Awards: NSF CAREER Award (2022) IEEE Senior Member (2017–present) Outstanding Reviewer awards from journals like IEEE Transactions on Automatic Control, Neurocomputing, and Journal of the Franklin Institute Top 1% Reviewers in Engineering, Publons (2018) Grants & Collaborations: Dr. Xiang leads multiple NSF-funded projects, including a $498,985 CAREER Award (2022–2027) for machine-learning-intensive CPS upgrades, a $499,000 CPS Program grant with Jason Orlosky (2022–2025) for XR-assisted h-CPS modeling, and a $270,913 NSF grant with Hoang-Dung Tran (2023–2026) for safety assessment of learning-enabled systems. He also contributes to Augusta University's CyberCorps Scholarships for Service program. Labs & Teams: Dr. Xiang leads the AI-CPS Lab , which explores intersections between artificial intelligence , formal verification , and cyber-physical systems . His lab collaborates on projects involving runtime safety monitoring , neural network compression , and hybrid control architectures .

• Functional Safety
• Functional Testing
• Microprocessor Testing
• +9 more

Riccardo Cantoro is an Associate Professor in the Department of Control and Computer Science (DAUIN) at Politecnico di Torino, where he is a member of the College of Computer, Film and Mechatronics Engineering and the College of Mechanical, Aerospace and Automotive Engineering. He is affiliated with the CAD - Electronic CAD & Reliability Group and the CARS@PoliTO Interdepartmental Center for Automotive Research and Sustainable Mobility. His work bridges academic research and industrial applications through multiple commercially funded projects. Scientific Disciplinary Sector: IINF-05/A - Information Processing Systems ERC Sectors: PE7_4, PE6_2, PE6_11, PE6_12 His research focuses on functional safety, functional testing, and microprocessor testing, with a strong emphasis on embedded systems and reliability. He applies machine learning and formal methods to enhance test efficiency and system robustness, particularly in automotive and safety-critical domains. His work integrates computer-aided design, fault modeling, and resilience assessment in both hardware and AI systems. The recent publications highlight a trend toward data-efficient and intelligent testing methodologies, combining machine learning (e.g., TabPFN, active learning) with traditional electronic design automation. Topics include microcontroller performance screening, CNN resiliency, FeFET device testing, and system-level test optimization, reflecting a cohesive research agenda in trustworthy computing and hardware reliability. Scientific Awards: No awards explicitly mentioned in the provided texts. Advising and Grants: Dr. Cantoro supervises numerous PhD students in Computer and Systems Engineering, focusing on functional safety, test methodologies, and AI for CAD. He leads multiple industry-funded research projects, including collaborations with Infineon Technologies and Dana-TM4 Italia, on topics such as ATPG tools, speed monitor modeling, and power module reliability. His role as Scientific Manager/Head underscores his leadership in applied research and technology transfer. Labs and Teams: He is a core member of the CAD - Electronic CAD & Reliability Group (DAUIN) and contributes to the CARS@PoliTO center, fostering interdisciplinary research in automotive systems and sustainable mobility.