Find Academic Supervisors

• Software Engineering
• Deep Learning
• Human-Computer Interaction
• +7 more

Prof. Dr. Chunyang Chen is a full professor in the Department of Computer Science at the Technical University of Munich (TUM) in Heilbronn, Germany, and an adjunct professor at Monash University, Australia. He is a core member of the Munich Data Science Institute, board member of Heilbronn Data Science Center, and fellow at Fortiss. His research spans Software Engineering, Deep Learning, and Human-Computer Interaction (HCI), focusing on AI/ML, NLP, and program analysis applications. PhD from Nanyang Technological University (NTU), Singapore His research explores three primary domains: AI-Driven Mobile App Development : Focused on requirement elicitation, UI design, code generation, and GUI testing using LLMs, NLP, and lightweight program analysis. Software Repository Mining : Involves library/API migration, collaborative editing, query reformulation, and thesaurus construction. Deep Learning Security : Examines robustness and security in mobile apps, including on-device model attacks, backdoor attacks, phishing attacks, and defense mechanisms. His recent publications highlight trends in LLM applications for software testing, bug replay, accessibility, and dataset construction. Awards include Best Paper Honorable Mention at CHI 2024, DECRA, ACM SIGSOFT Early Career Award, multiple Distinguished Paper Awards, and the Chinese Government Award for Outstanding Self-financed Students Abroad. PhD students: Shengcheng Yu, Yanqi Su, Mark Huasong Meng, Wenchao Gu, Zhen Tao, Kai Huang, Sidong Feng, Yujin Huang, Jian Gu Master/Bachelor students: Dustin Minh Duc Vu, Jason Siu, Ruiqi Dong, Minh Nguyen, An-Chieh Chang, Chao Wu, Yujin Huang, Ishan Joshi, Xinyan Li Chunyang Chen has supervised numerous students across institutions including TUM, Monash University, Chinese Academy of Sciences, ANU, and others. His teaching portfolio includes courses on Generative AI, software engineering, and programming foundations. He actively contributes to academic service as a guest editor, program committee member, and reviewer for top-tier conferences and journals.

• Software Engineering
• Artificial Intelligence
• AI Trustworthiness Assessment (SE4AI)
• +5 more

Gias Uddin is an Associate Professor at York University's Lassonde School of Engineering and an Adjunct Professor at the University of Calgary . His research bridges Software Engineering (SE) and Artificial Intelligence (AI) , focusing on AI Trustworthiness Assessment (SE4AI) and AI-Driven Productivity Tools (AI4SE) . PhD in Software Engineering & AI, McGill University (2018) MSc in Software Engineering, Queen’s University (2008) BSc in Computer Science & Engineering, Bangladesh University of Engineering and Technology (2004) His research explores: Metamorphic Relations for LLM Hallucination Detection AI-Enhanced Software Documentation Foundational Models for Runtime System Modernization Developer-Centric AI Tooling Recent article trends show expertise in LLM Trustworthiness , Low-Code Platforms , and IoT Developer Communities . Awards include Distinguished Paper at FSE 2025 , multiple IBM Champion recognitions, and York Research Award . He leads the Data Intensive Software Analytics (DISA) Lab and mentors PhD students in SE-AI Intersections .

• Information Retrieval
• Crowdsourcing
• Human-in-the-loop AI
• +5 more

Dr. Gianluca Demartini is a leading researcher in Human-in-the-loop AI Systems with significant contributions to Crowdsourcing , Information Retrieval , and Generative AI applications. His work bridges Machine Learning and Human-Computer Interaction , focusing on Bias Management , Fact-Checking , and Ethical AI . Major Affiliations : L3S Research Center, ScienceWISE platform, and collaborations with institutions like University of Queensland and University of Padua Over 15 years, his research has explored Crowdsourcing Quality Control (Mechanical Cheat 2012), Entity Ranking (2008-2013), and Semantic Search . Recent work (2024-2026) focuses on Generative AI Impacts in domains like Media Literacy , Data Curation , and Visual Analytics . Scientific Recognition : Best Paper Award (Top 1.4%) at ICTIR 2023 Best Short Paper Award (Top 0.6%) at ECIR 2020 Honorable Mention (Top 2%) at CSCW 2020 Best Demo Award at ISWC 2011 3rd Best Paper at LA-WEB 2008 His 15 most recent publications (2024-2026) demonstrate expertise in LLM-based Content Moderation , Immersive Data Visualization , and Trustworthy AI Systems . He has pioneered methods for Bias Detection in Wikipedia (2013), Entity Ranking (2008-2013), and Human-AI Collaboration frameworks. His work consistently addresses ethical challenges in AI for Social Good and Responsible Data Science .

Silvia BonomiView profile

Associate Professor

• Cybersecurity
• Distributed Systems
• Blockchain
• +8 more

Dr. Silvia Bonomi serves as an Associate Professor in the Department of Computer, Control and Management Engineering at Sapienza University of Rome, where she has held academic positions since 2006. Her career progression includes Research Fellow (2010-2011), Tenure Track Assistant Professor (2016-2019), and current Associate Professor appointment since 2019. She maintains her office in Room B114 and is actively engaged in research and teaching within the university's engineering faculty. Her educational background includes: PhD in Computer Engineering, Sapienza University of Rome and Institut de Formation Supérieure en Informatique et Communication (IFSIC/IRISA), Rennes, France (completed under advisors Prof. Roberto Baldoni and Prof. Michel Raynal) Dr. Bonomi's research critically examines dynamic distributed systems where entities autonomously join and leave networks, with applications spanning VANETs, airborne networks, social networks, and distributed cloud services. She pioneers work in Byzantine fault tolerance for mobile environments, blockchain security with emphasis on smart contract vulnerability analysis, and resilient cybersecurity frameworks integrating human factors. Her investigations into publish-subscribe systems focus on quality-of-service enhancements, while her peer-to-peer systems research addresses fundamental connectivity challenges in large-scale decentralized environments. This interdisciplinary approach bridges theoretical distributed computing with practical cybersecurity applications. Analysis of her 15 most recent publications (2021-2024) reveals dominant trends in blockchain security (particularly smart contract vulnerability taxonomies and analysis tool efficacy) and fault-tolerant distributed systems (reliable communication under Byzantine faults in dynamic networks). Her work increasingly integrates human factors into cybersecurity models and develops visual analytics for business-centric risk assessment. Publications span top venues including IEEE CSR, OPODIS, SAFECOMP, and journals like Computers & Security, demonstrating consistent contributions to both theoretical foundations and practical cybersecurity implementations.

Li LiView profile

Professor

• Mobile Software Engineering
• Intelligent Software Engineering
• Software Security
• +6 more

Li Li is a Professor of Software Engineering at Beihang University , China. Previously, he served as an ARC DECRA Fellow and Senior Lecturer at Monash University , leading the SMart software Analysis and Trustworthy computing (SMAT) research lab at the Department of Software Systems and Cybersecurity. His academic journey includes a Ph.D. in Software Engineering from the University of Luxembourg (2016), supervised by IEEE Fellow Prof. Yves Le Traon and Dr. Jacques Klein. Research Interests Li's research focuses on Mobile Software Engineering (Mobile Security, Quality Assurance) and Intelligent Software Engineering (SE4AI, AI4SE). He applies static code analysis , dynamic program testing , and machine/deep learning to enhance software security and reliability. Key areas include Android API evolution, automated patch validation, and multi-language code analysis frameworks like Scalpel for Python. Scientific Recognition ARC DECRA Fellowship Rising SE Research Star Top-5 Most Impactful Early Career SE Researchers (2020, 2017) 5 Best/Distinguished Paper Awards across PLDI, WWW, ASE, MSR, and SANER Academic Contributions He has contributed to foundational Android analysis tools (e.g., AndroZoo++, DroidRA) and developed scalable systems for distributed program analysis (Seads). His work appears in top venues like ICSE, ESEC/FSE, ASE, ISSTA, POPL, and TSE.

• Political Economy
• Public Economics
• Applied Theory
• +3 more

Andrea Mattozzi is a Full Professor of Economics at the University of Bologna's Department of Economics since 2022, serving as Coordinator of the PhD Program in Economics. Previously, he held academic roles at institutions including the California Institute of Technology, Universitat Autònoma de Barcelona, and the European University Institute, where he also led the Economics Department. He earned his Ph.D. in Economics from the University of Pennsylvania and a Laurea (BA) in Economics (Summa Cum Laude) from Università Bocconi. His research focuses on Political Economy, Public Economics, and Applied Theory, with notable contributions to topics like electoral systems, policy implementation, and political competition. His work has been published in top journals such as the American Economic Review (AER) and the Journal of Economic Theory (JET), supported by grants from the NSF, Ramón y Cajal Fellowship, and EU initiatives like Horizon 2020. Mattozzi’s research demonstrates a consistent focus on institutional design, democratic accountability, and the interplay between political incentives and economic outcomes. Recent articles explore referendums, fact-checking mechanisms, and pandemic policy responses, reflecting both theoretical rigor and societal relevance. Awards: Ramón y Cajal Fellowship, NSF grants, William Polk Carey Prize (best dissertation), and CEPR Research Fellow appointment. Professional Roles: Associate Editor of the European Economic Review, RCEA Senior Fellow, and panelist for Economic Policy. His academic leadership includes directing PhD programs and coordinating interdisciplinary projects such as the European Media and Information Fund’s FacTS initiative. Mattozzi also maintains visiting professorships and collaborates internationally on policy-relevant research.

Violetta LonatiView profile

Assistant Professor

• Formal Languages and Automata
• Computer Science Education
• Theoretical Computer Science
• +2 more

Violetta Lonati is an Assistant Professor at the University of Milan 's Department of Computer Science since 2005. Her research spans Formal Languages and Automata (operator precedence languages, Wang automata, tiling systems) and Computer Science Education . She co-authored over 15 publications in theoretical computer science and education, focusing on 2D language recognition, logic characterization of automata, and pattern statistics in stochastic models. Education : PhD in Computer Science (2005) and Laurea in Mathematics (2001) from University of Milan Research Groups : ALaDDIn Lab for Didactics and Dissemination of Informatics, Bebras International Initiative Her work on Wang automata established their equivalence to tiling systems while introducing deterministic variants. In education, she designed workshops for schools and contributed to Italy's national computing curriculum proposal (2019). She held leadership roles at ACM ITiCSE (WG5 leader 2022), served as Associate Program Chair (2019-2022), and reviewed for top venues like ICER and SIGCSE TS. She received Google CS[4]HS and Informatics Europe awards for her educational contributions. Key Publications (2017-2001): Input-driven locally parsable languages (TCS 2017) Operator precedence logic characterization (SICOMP 2015) Snake-deterministic tiling systems (MFCS 2009) Graph fibrations and PageRank (RAIRO 2006) Pattern statistics in rational models (STACS 2005) Scientific awards include Google CS[4]HS (2011, 2017, 2019) and the Informatics Europe Best Practices in Education (2016). As part of ALaDDIn, she developed teacher training programs and graduate courses on computing education. Her teaching experience covers Algorithms & Data Structures (2013-2023), Computer Science Teaching (2014-2023), and courses for Biotechnology and Geological Sciences programs (2005-2007).

Michele DonatiView profile

Associate Professor

• Agricultural Economics
• Agro-environmental Policy
• Sustainable Food Systems
• +3 more

Michele Donati is an Associate Professor at the Department of Chemical, Life and Environmental Sustainability Sciences, University of Parma. With expertise in agricultural economics and agro-environmental policy, he specializes in quantitative analysis using mathematical programming models and Life Cycle Assessment (LCA) for evaluating sustainability in food systems. His teaching includes courses in Environmental Economics and Economic Modeling, and he leads research on CAP reforms and bio-economic modeling. Education: PhD in Agri-food Economics (2001–2003), Master in Environment and Natural Resource Economics (1999–2000), Degree in Economics (1992–1998) Research: Focuses on CAP policy evaluation, sustainable agri-food supply chains, bio-economic modeling, and greening strategies. Projects include Horizon2020's Strength2Food and EUROTOOLS. Teaching: Environmental Economics (2009–present), Economic Modeling (2008–present), and Rural Development (2011–2012). Awards: Received the 2016 European Association of Agricultural Economists Publication Award for policy contribution.

Massimo MecellaView profile

Professor

• Service-Oriented Computing
• Business Process Management
• Process Mining
• +12 more

Massimo Mecella serves as Full Professor at Sapienza University of Rome's Faculty of Information Engineering, Computer Science and Statistics, where he leads the Processes, Services and Software Management research group and participates in the CINI National Cyber Security Lab. His academic foundation stems from a PhD in Computer Engineering earned at Sapienza in 2002. His research expertise spans service-oriented computing, business process management, and cyber-physical systems with emphasis on service composition, process mining, and adaptive distributed architectures. Key technical domains include IoT integration, digital twin development, and security frameworks for complex software ecosystems. Recent publications (2024-2025) demonstrate a pronounced shift toward Large Language Model (LLM) integration in business processes and smart manufacturing. Dominant themes include LLM-driven process modeling, multimodal quality control systems, and digital twin implementations for energy management and production optimization. Methodological innovations focus on retrieval-augmented generation (RAG) for service discovery and fuzzy cognitive maturity assessments. His distinguished recognition includes: ICSOC 2013 Most Influential Paper Award (2003-2012 decade) 2017 Best Paper Award As research group leader, Mecella directs projects in smart manufacturing, process mining, and cyber-physical security, with funding typically sourced from EU initiatives and industry partnerships in aerospace, healthcare, and energy sectors. His team maintains active collaborations with aerospace manufacturers through the MICS SPOKE8 project and develops frameworks like SAMBA for human-in-the-loop manufacturing systems. The Processes, Services and Software Management group operates within Sapienza's engineering faculty infrastructure, utilizing specialized labs for CPS/IoT testing and digital twin simulation, with strong ties to the CINI Cyber Security Lab for security validation.

• 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.

• Medical Imaging
• Radiomics
• Machine Learning
• +6 more

Renato Cuocolo is an Associate Professor at the Department of Medicine, Surgery, and Dentistry "Scuola Medica Salernitana" of the University of Salerno. He actively participates in multiple European radiology societies, including chairing the ESR eHealth and Informatics Subcommittee and contributing to EuSoMII and ESUR committees. MD from University of Campania "Luigi Vanvitelli" Residency in Radiology at University of Naples "Federico II" PhD in Biomorphological and Surgical Sciences at University of Naples "Federico II" His research focuses on radiomics and machine learning applications in medical imaging, with specific emphasis on: Prostate cancer MRI analysis Musculoskeletal and genitourinary radiology Neuroradiology Systematic reviews and meta-analyses Key contributions include: Quality-checking the PROSTATEx public prostate cancer MRI database Developing classification models for cartilaginous tumors (CT/MRI) Creating radiomics pipelines for endometrial cancer risk prediction He maintains active coding skills in Python and R for medical image analysis.

Antonela TommaselView profile

Adjunct Professor

• Social Computing
• Recommender Systems
• Machine Learning
• +5 more

Dr. Antonela Tommasel is a Researcher at CONICET and an Adjunct Professor at National University of the Center of the Province of Buenos Aires (UNICEN) , working at the intersection of social computing, machine learning, and misinformation mitigation. Her work combines computational techniques to address challenges in social media analysis, bias detection, and recommender system design. PhD in Computer Sciences (2017), UNICEN Bachelor in Software Engineering (2012), UNICEN Her research focuses on recommender systems with applications to social media trustworthiness , including projects like Faking It! (fake news dataset creation) and OHARS (harm-aware recommendation frameworks). Key themes include misinformation spread analysis , gender bias detection , and machine learning applications for social good. She has led 11+ years of teaching in courses like Advanced Java Programming, Recommender Systems, and Natural Language Processing for AI specializations. Her technical certifications include NVIDIA Deep Learning Institute programs on GPU-accelerated data science and Coursera specializations in machine learning and visualization. Currently based at ISISTAN Research Institute in Tandil, Argentina, she advocates for responsible AI through tools like graphoW (graph-of-words library) and Sen4Smells (technical debt prioritization). Her work emphasizes bridging social media analysis with practical implementation frameworks.

• Asymptotic statistics
• Bayesian inference
• Biostatistics
• +9 more

Enrico Bibbona is an Associate Professor at the Department of Mathematical Sciences (DISMA) at Politecnico di Torino, where he contributes to the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. His academic profile spans statistics, mathematical modeling, and data science applications across multiple engineering disciplines. His primary research interests include: Asymptotic statistics Bayesian inference Biostatistics Industrial statistics Mathematical biology and modeling in life sciences Statistical inference for stochastic processes Stochastic modeling His specific research lines focus on Reaction Networks approximation and inference, Statistical modeling of animal calls, Statistics and modeling for the SmartPad system (in collaboration with ITT), and Statistics for deterministic and stochastic dynamical models. His work bridges theoretical statistics with practical applications across various scientific domains, particularly in biological systems, industrial applications, and data science. His notable recognition includes the Elsevier/SPA Travel Award presented by Elsevier and Bernoulli Society, Denmark (2011). Professor Bibbona actively mentors PhD students across multiple programs, currently supervising Daniele Poggio and Saeed Sani in Mathematical Sciences, and has previously guided Elena Sabbioni (thesis: Statistical Methods for Complex Biological and Clinical Data) and Hiu Ching Yip (thesis: Statistical models for understanding biomedical data). His teaching spans Mathematical Engineering, Data Science and Engineering, and Communications Engineering programs, where he teaches courses including BioQuants, Statistical methods in data science, and Project: Software-defined communication systems. He is a key member of the Statistics and Data Science research group at DISMA and has contributed to practical applications through patents including "BRAKING SYSTEM TEMPERATURE ESTIMATOR" and "A method for estimating the wear of a vehicle brake element," demonstrating his ability to translate theoretical work into real-world solutions.

• Biomedical Engineering
• Mechanical Measurements
• Energy Storage Systems
• +4 more

Emanuele Rizzuto is an Associate Professor in the Department of Mechanical and Aerospace Engineering at Sapienza University of Rome. His research focuses on biomedical and industrial applications, including biomechanical measurements of muscle tissues, neuromuscular junction functionality, and energy storage systems. He leads initiatives such as the Rome Technopole 'Advanced Materials and Systems' and has secured significant grants for projects like NEST and SELFIE-CHECK. Rizzuto teaches courses in mechanical measurements, clinical diagnostics, and energy engineering. His editorial roles include guest editorships for journals like Energies and Sensors . Recent work includes innovations in hydrogen production, battery efficiency, and optical sensors for biomedical applications. Education : Industrial Engineering qualifications and National Scientific Qualification as Full Professor (2019). Research Interests : Biomedical engineering (muscle repair, neurodegenerative diseases), energy storage, and smart materials for heritage preservation. Grants : Over €500k in recent funding for energy storage, biomaterials, and ALS research. Labs/Teams : Open Lab Rome Technopole, involvement in interdisciplinary projects like CALAMITY and Counteracting neuromuscular junction dismantlement.

• Formal Methods
• Formal Verification
• Binary Decision Diagrams
• +7 more

Paolo Enrico Camurati is a Full Professor in the Department of Control and Computer Engineering (DAUIN) at the Polytechnic University of Turin, where he contributes to research and teaching in formal methods and hardware verification. He is a member of the Formal Methods (FM) research group and has been actively involved in PhD supervision in the Computer and Systems Engineering program. His research interests include formal verification, binary decision diagrams, SAT solvers, electronic CAD, and the formal verification of hardware design correctness. These areas align with theoretical computer science and cyber-physical systems, contributing to advancements in reliable computing systems. Recent publications (2022–2024) show a strong focus on model checking techniques, interpolation-based learning, and optimization of binary decision diagrams for machine learning applications, indicating active and impactful research in formal methods and EDA. He has taught core computer engineering courses such as Specification and Simulation of Digital Systems , Algorithms and Data Structures , and Programming Techniques at both bachelor's and master's levels, supporting quality education in computing disciplines. He has no listed scientific awards in the provided material. Prof. Camurati advises PhD students in the Computer and Systems Engineering program and has taught extensively across multiple academic years. While specific grants are not mentioned, his ongoing research output suggests active funding and project involvement. He is a key member of the FM - Formal Methods Group (DAUIN), which drives research in formal verification and automated reasoning. The FM - Formal Methods Group (DAUIN) serves as his primary research lab, focusing on theoretical and applied aspects of formal verification, model checking, and decision diagram technologies.