جست‌وجوی استاد و پژوهشگر

• Pharmacology
• Biochemistry
• Drug Design
• +۳ مورد دیگر

Emanuela Berrino is a Researcher at Link Campus University in Rome. Her work focuses on drug design, enzymology, and biochemistry, particularly targeting carbonic anhydrase inhibitors and CO-releasing molecules. She investigates their applications in diseases like rheumatoid arthritis, neurodegeneration, and cancer. Recent studies include pH-sensitive prodrugs, superacid-synthesized compounds, and multitarget therapeutics. Her research combines experimental and computational methods, emphasizing enzyme inhibition mechanisms and therapeutic efficacy. Education details and lab affiliations are not explicitly mentioned in the provided text. Her articles highlight collaborations in pharmacological profiling, molecular modeling, and in vivo/in vitro testing. She has explored novel inhibitors for carbonic anhydrase isoforms IX/XII and angiotensin-converting enzyme, with applications in glaucoma, epilepsy, and obesity management. Her work also involves advanced synthesis techniques like microwave-assisted methods and the development of drug delivery systems such as thermosensitive gels. Future directions likely include optimizing CO-releasing hybrids and exploring enzyme mechanisms in diatoms and bacteria.

• Machine Learning
• Deep Learning
• Edge Artificial Intelligence
• +۵ مورد دیگر

Daniele Berardini is a Research Fellow at the Department of Information Engineering (DII) of Università Politecnica delle Marche in Ancona, Italy. He is also a PhD student under Prof. Emanuele Frontoni, focusing on Edge Artificial Intelligence and real-time human behavior analysis through video surveillance. His work emphasizes lightweight Deep Learning methods for resource-constrained devices, with applications in healthcare monitoring, security systems, and environmental surveillance. Education: Bachelor's degree (cum laude) in Computer Science (2017), University of L'Aquila Master's degree (cum laude) in Computer Science (Network and Data Science) (2019), University of L'Aquila PhD in progress at Università Politecnica delle Marche (since 2020) Research Interests: His research integrates Machine Learning and Deep Learning techniques for developing efficient monitoring systems. Key areas include Edge AI for real-time analysis, human behavior detection in indoor/outdoor environments, and ethical considerations in AI deployment for healthcare and surveillance. His methodologies often combine optimization frameworks with neural networks to enhance computational efficiency. Labs/Teams: He is part of the Vision, Robotics and Artificial Intelligence (VRAI) research group at DII, focusing on interdisciplinary projects such as neonatal care monitoring, weapon detection in video surveillance, and sustainable AI for medical applications. Grants/Advising: No specific grants or advising roles explicitly mentioned in the text.

• Deep Learning for Medical Imaging
• Human Action Recognition
• Medical Record Analysis
• +۳ مورد دیگر

Lucia Migliorelli is a Research Fellow at the Polytechnic University of the Marche, Italy. She completed her Master of Science in Biomedical Engineering (cum laude) in 2018 and has been pursuing a PhD since 2018 under Prof. Emanuele Frontoni. Her research focuses on AI-driven monitoring systems for healthcare applications, leveraging multimedia data analysis without wearable sensors. Key areas include human action recognition, medical record analysis, and non-invasive patient monitoring for populations like preterm infants and elderly patients. Education highlights include a Master’s thesis on machine learning for diabetes management and ongoing PhD work on intelligent spaces for automated behavior analysis. Current projects involve federated learning for medical imaging (e.g., MRI-to-CT synthesis) and edge AI for real-time surveillance systems. She is based at the Polytechnic University of the Marche, contributing to interdisciplinary research at the intersection of biomedical engineering and artificial intelligence. Her publications span medical image analysis, AI in digital humanities, and ethical considerations in healthcare AI. She actively develops prototypes for neonatal intensive care units, emergency vehicle detection, and groundwater level prediction. No scientific awards are explicitly listed, though her work reflects strong contributions to AI-driven healthcare innovation. Current advising and grants are not detailed in the text, but her research aligns with lab efforts in environmental AI, rehabilitation robotics, and telemonitoring systems for neurological disorders. She collaborates with multidisciplinary teams to advance ethical, sustainable AI solutions for clinical and societal challenges.

Simone Coniaمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Large Language Models
• +۴ مورد دیگر

Simone Conia is an Assistant Professor at Sapienza University of Rome specializing in Natural Language Processing and Machine Learning. He leads research at the intersection of Large Language Models and multilingual systems while maintaining an active research collaboration with Apple as a Research External Collaborator. His research interests focus on Large Language Models , particularly investigating their fundamental capabilities and limitations with emphasis on improving reasoning abilities and reducing hallucinations; Multilingual NLP , developing robust systems for multiple languages with attention to low-resource settings; and Retrieval Augmented Generation , enhancing language models through external knowledge integration to improve factual accuracy. His scholarly contributions include over 40 publications in top-tier conferences with significant citation impact (1,063 total citations, h-index 14). His work has been recognized with multiple awards: Outstanding Paper Award at EMNLP 2024 Outstanding Paper Award at NAACL 2021 Distinguished Program Committee member at IJCAI Honor Student at Sapienza University Dr. Conia's research demonstrates strong industry-academia collaboration, particularly through his ongoing work with Apple on multilingual language models and real-world applications. His citation trajectory shows rapidly growing impact, with citations increasing from 23 in 2020 to 399 in 2024. He actively contributes to the open-source NLP community through GitHub repositories including unify-srl, wsd-hard-benchmark, and multilabel-wsd, focusing on semantic role labeling, word sense disambiguation, and knowledge graph integration with language models.

• Control Systems
• Model Predictive Control
• Robust Control
• +۳ مورد دیگر

Sampath Kumar Mulagaleti is an Assistant Professor at the IMT School for Advanced Studies Lucca, part of the Faculty. His research focuses on control systems, with a strong emphasis on model predictive control (MPC), robust control, and linear parameter-varying (LPV) systems. He investigates invariant sets, data-driven methods, and optimization techniques to enhance system stability and performance under constraints. His work spans theoretical advancements and practical applications in control engineering, including decentralized MPC, dynamic output disturbance modeling, and robust regulation of linear systems. Notable contributions include configuration-constrained tube MPC algorithms and methods for computing safe disturbance sets using implicit RPI sets. Dr. Mulagaleti’s publications highlight a consistent focus on bridging theoretical control frameworks with real-world engineering challenges. His research often intersects with systems engineering, mathematics, and automation, addressing complex scenarios such as payload transportation by multiple vehicles and parameter-dependent control strategies. No scientific awards or grants are explicitly mentioned in the provided texts. His advising activities and student mentorship details are currently unavailable.

Nicolò Badodiمشاهده پروفایل

پژوهشگر

• Nuclear Engineering
• Computational Fluid Dynamics
• Reactor Physics
• +۳ مورد دیگر

Nicolò Badodi is affiliated with NRGroup, where he has been involved in R&D activities since 2020. His research focuses on advanced nuclear technologies, including Molten Salt Reactors (MSRs), neutronics, thermal-hydraulics, and multiphysics modeling. He has contributed to projects like the ELSMOR initiative, which involves Compact Plate Heat Exchangers for Small Modular Reactors (SMRs). His work integrates experimental and numerical analysis with simulation framework development and digital twin technologies. He supervises PhD and MSc students, overseeing theses such as 'Multiphysics Modeling Approach for Noble Metals Deposition in MSRs' (Giovanni Zullo) and 'Modelling of Gaseous/Volatile Fission Products Release from Oxide Fuel' (Stefano Passoni). His research also extends to computational fluid dynamics (CFD) applications in nuclear systems. No scientific awards are explicitly mentioned in the provided texts. His professional network includes collaboration with professors, research assistants, visiting researchers, and NRGroup alumni, indicating active academic-industrial engagement.

Federico Aromoloمشاهده پروفایل

استادیار

• Real-Time Systems
• Cybersecurity
• Embedded Systems
• +۳ مورد دیگر

Federico Aromolo is an Assistant Professor, appointed since October 2023. Previously, he served as a Postdoctoral researcher (January 2023–September 2023) and completed his Ph.D. in June 2022 (October 2019–June 2022). His research focuses on real-time systems, embedded cybersecurity, and scheduling algorithms, emphasizing intrusion detection, response-time analysis, and security-performance trade-offs in embedded systems. Education: Ph.D. (2019–2022) Postdoctoral Research (2023) Research interests include: Real-Time Scheduling (e.g., EDF, Gang Tasks) Cybersecurity in Embedded/Edge Systems Software Reliability and Memory Safety His recent work explores intrusion detection via machine learning, latency optimization in edge computing, and securing real-time software without compromising temporal guarantees. His publications span IEEE Transactions and conferences like ECRTS and RTAS. He received the IEEE TCCPS Early-Career Award 2023 for contributions to Cyber-Physical Systems research. No advising or grant details are explicitly listed in the provided texts. Affiliated with research teams investigating real-time systems and embedded security, though specific lab names are unspecified.

Francesco Grossettiمشاهده پروفایل

استادیار

• Financial Disclosure Analysis
• Natural Language Processing
• Blockchain Applications
• +۳ مورد دیگر

Francesco Grossetti is Assistant Professor of Accounting Analytics and Data Science at Bocconi University, with a Ph.D. in Applied and Computational Statistics from Politecnico di Milano. His research develops computational approaches for analyzing corporate disclosures using NLP and computer vision techniques, with additional focus on blockchain implications for accounting practices. His interdisciplinary work bridges financial mathematics, stochastic modeling, and accounting innovation. Research examines volatility modeling calibration puzzles, textual characteristics in financial reports, blockchain accounting applications, and corporate governance language patterns. Methodologies include machine learning, entropy-based calibration, and sports analytics for tournament fairness. Recent projects include EXECML (analyzing executive compensation disclosures via machine learning) and multi-modal corporate communication studies. He serves on editorial boards for Finance & Stochastics, SIAM Journal on Financial Mathematics, and Quantitative Finance, reflecting his cross-disciplinary expertise.

• Computational Statistics
• Financial Data Science
• Financial Networks
• +۴ مورد دیگر

Paolo Pagnottoni is an Assistant Professor of Statistics at the Department of Economics and Management, University of Pavia (Italy), since March 2022. He holds a PhD in Applied Economics and Management from the Universities of Bergamo and Pavia (2021), a Master's in European Economics - Finance and Econometrics from the University of Tübingen (2017 with honours), and a Master's in Economics and Finance from the University of Pavia (2017 with honours). His research focuses on Computational Statistics, Financial Data Science, and Cryptocurrency Markets. Previous roles include Research Fellow positions at the University of Pavia (2019–2022), Politecnico di Milano (2020), and a Visiting Research Fellowship at University College London (2020). He also worked as a Data Analyst and Modeler at Credito Valtellinese (2017–2018) and has consulting experience in banking and institutional sectors. Teaching includes assistant lectures in Big Data Analysis (Master's, 2018/2019) and Statistica L-Z (Undergraduate, 2017/2018) at the University of Pavia, and coordination of a Data Science course for Manpower Group's Young Talent in Action Program (2018–2019). No scientific awards, grants, or advised students are explicitly listed in the provided text.

Lorenzo Rossiمشاهده پروفایل

استادیار

• Process Mining
• Robotics
• Business Process Management (BPM)
• +۵ مورد دیگر

Lorenzo Rossi is an Assistant Professor specializing in the intersection of process mining, robotics, and business process management (BPM). His research focuses on advancing methodologies for analyzing robotic systems, IoT environments, and collaborative processes through formal models like BPMN. Key areas include process discovery, digital twins, and system resilience in smart environments. Expertise: Process Mining, Multi-Robot Systems, BPMN Semantics, Cyber-Physical Systems Key Tools: BEAR (BPMN animator), MIDA (multi-instance animator), UBBA (Unity-based BPMN animator) His work emphasizes reproducibility and practical frameworks for rapid prototyping. Recent trends in his articles highlight the integration of process mining with robotics for data-driven decision-making, as well as formal verification of BPMN collaborations to ensure system correctness. Awards: None explicitly mentioned in the provided texts. Advising & Grants: No student/advisor relationships or grant details documented here. Labs/Teams: Collaboration with Unity-based simulation tools (UBBA, MIDA) and serious game platforms (PlayWithUnicam).

Massimo Bertozziمشاهده پروفایل

دانشیار

• Computer Vision
• Real-Time Image Processing
• Autonomous Driving
• +۱۲ مورد دیگر

Massimo Bertozzi is an Associate Professor in the Department of Engineering and Architecture at the University of Parma, Italy. He has held a permanent academic position since 1997 and is a leading researcher in computer vision and intelligent vehicles. His work spans real-time image processing, autonomous driving, pedestrian detection, and embedded systems. He earned his Dr.Eng. in Electronic Engineering from the University of Parma in 1994 and completed his PhD in Information Technologies in 1998. His academic journey progressed from PhD student to assistant researcher and eventually to Associate Professor. His research focuses on vision-based systems for autonomous vehicles, including stereo vision, CNNs, SWIR imaging, and sensor fusion. He has been instrumental in pioneering projects such as ARGO, VIAC, TerraMax, and V-Charge. His current work includes collaboration with the IMP Lab on deep learning for computer vision. His recent publications (2022–2025) demonstrate a strong trend in AI-driven image synthesis, disentangled representations, 3D reconstruction, and real-time autonomous driving algorithms. Key themes include diffusion models, GANs, transformers, and efficient neural architectures applied to vision tasks. Scientific Awards: Best Paper Award, CONTROLO 2022 Best Paper Award, VISAPP 2022 He has advised numerous researchers and co-authored with PhD students and collaborators such as Tomaso Fontanini, Leonardo Rossi, and Alex Ergasti. He has not been mentioned to have received major external grants, but his involvement in EU-funded projects (PReVENT, PEGASE, V-Charge) indicates significant collaborative funding. He teaches courses including Computer Science and Programming Laboratory, Fundamentals of Computer Vision, and Deep Learning for Engineering Applications. He is affiliated with the Computer Engineering Group and the IMP Lab at the University of Parma, contributing to a vibrant research environment focused on intelligent systems and autonomous technologies.

• Software ecosystems
• Mining software repositories
• Software evolution
• +۲ مورد دیگر

Eleni Constantinou is an Assistant Professor at the University of Cyprus (UCY) within the Software Engineering and Internet Technologies (SEIT) group. She previously served as Assistant Professor at Eindhoven University of Technology (TU/e) in the Software Engineering and Technology (SET) group (2019-2022) and as Postdoctoral Researcher at the Software Engineering Lab of the Department of Computer Science, University of Mons (2016-2019) under Prof. Dr. Tom Mens. Education : B.Sc., M.Sc., and Ph.D. from the Department of Informatics at Aristotle University of Thessaloniki Research Interests : Software ecosystems Mining software repositories Software evolution Software health Software reuse Research Trends : Her recent publications focus on software ecosystem health (2022-2025), dependency security analysis (2024-2025), bot detection in collaborative development (2022), and GitHub repository mining (2018-2022). She has also contributed to software reuse metrics (2013-2015) and developer retention studies (2017). Projects : Scientific Coordinator of SECOHealth project (2017-2019)

Rocco Tripodiمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Machine Learning
• Lexical Semantics
• +۳ مورد دیگر

Rocco Tripodi is a Researcher at Ca' Foscari University of Venice's Department of Environmental Sciences, Computer Science and Statistics. He holds a Ph.D. in Computer Science (2015) from the same institution, focusing on game-theoretic models for Natural Language Processing (NLP). His roles include teaching courses such as Deep Learning for NLP, Introduction to Coding, and Database Systems. He has also served as an Assistant Professor at the University of Bologna (2021–2024) and collaborated with labs like Sapienza NLP and the European Centre for Living Technology (ECLT). His research spans NLP, machine learning, and computational linguistics, with a focus on lexical semantics, game theory applications, and ethical AI. Research Interests : His work addresses challenges in lexical semantics, including word sense disambiguation, semantic role labeling, and cross-lingual NLP. He explores game-theoretic frameworks for text analysis and develops tools for cultural heritage (e.g., historical text analysis). Recent projects include evaluating large language models (e.g., ChatGPT), detecting sensitive data in text, and tracing historical biases through diachronic embeddings. Publications : Tripodi's research trends focus on NLP fairness, multilingual models, and historical discourse analysis. Notable contributions include frameworks for abuse detection evaluation (AAA), multilingual semantic role labeling (UniteD-SRL), and diachronic analysis of antisemitic language in 19th-century French texts. His work bridges technical NLP advancements with societal impact, such as ethical AI and cultural heritage preservation. Labs & Collaborations : Active in interdisciplinary projects like ODYCCEUS and Polifonia, he collaborates with institutions to apply NLP to social sciences and humanities. His labs emphasize practical applications, such as transforming text into knowledge graphs (Text2AMR2FRED) and analyzing historical textiles using spectroscopic methods.

Ruaridh Clarkمشاهده پروفایل

عضو هیئت علمی

• Signal Processing
• Biomedical Engineering
• Wearable Sensors
• +۵ مورد دیگر

Ruaridh Clark is a Senior Research Fellow in the Department of Electronic and Electrical Engineering at the University of Strathclyde, United Kingdom. He is actively engaged in research, supervising PhD students, and contributing to multiple funded projects in biomedical signal processing and neurotechnology. His research interests lie at the intersection of biomedical engineering, signal processing, and human-computer interaction, with a strong focus on EEG-based systems, wearable sensors, and real-time health monitoring. His work enables advancements in brain-computer interfaces, emotion recognition, seizure detection, and cognitive state assessment. The analysis of his recent publications reveals a consistent trend in developing robust, real-time, and wearable neurophysiological systems. These works emphasize adaptive signal processing, multimodal data fusion, machine learning, and low-power embedded solutions for clinical and real-world applications. 9 prizes or awards (specific names not listed in text) Ruaridh Clark supervises several PhD students, including Agathe Bouis, Joshua Gribben, and Beth Probert. He is involved in 18 research projects, indicating active grant funding and collaborative research. His work is highly interdisciplinary, bridging engineering, neuroscience, and clinical applications. He is affiliated with the research group led by Professor Malcolm Macdonald and collaborates across disciplines, including with researchers in psychology. His lab focuses on developing wearable, real-time, and intelligent systems for neurophysiological monitoring and human performance assessment.

• Additive Manufacturing
• Laser Processing
• Welding Technology
• +۵ مورد دیگر

Nicola Contuzzi serves as an Assistant Professor in the Department of Mechanics, Mathematics & Management at the Polytechnic University of Bari, Italy. His institutional contact details include email nicola.contuzzi@poliba.it and phone +39 0805963079, with office location at Via Orabona 4, Bari. Active in advanced manufacturing research, he contributes significantly to the field through experimental and computational methodologies. His research focuses on Additive Manufacturing (especially Wire Arc Additive Manufacturing), Laser Processing (welding, marking, and milling), and Joining of Dissimilar Materials . Utilizing integrated experimental-statistical-numerical approaches, he investigates microstructure-property relationships in metallic components, with emphasis on process optimization for automotive and aerospace applications. Key areas include parameter effects on bead geometry, energy efficiency in industrial processes, and non-destructive evaluation of welded joints. Analysis of his 15 most recent publications (2024-2025) reveals dominant themes in WAAM parameter optimization (feed rate, arc power), dissimilar material welding (aluminum-steel, copper-stainless steel), and laser process modeling. The research consistently employs statistical methods and numerical simulations to predict outcomes like weld geometry, microstructure evolution, and mechanical properties, demonstrating strong industry applicability. Scientific awards: No awards mentioned in available sources. Advising and grants: Student supervision details and grant funding information are not documented in the provided text. His active publication record suggests ongoing research projects, though specific collaborations or funding sources remain unspecified.