Find Academic Supervisors

Manlu LiuView profile

Professor

• AI and Business Intelligence
• Big Data
• Accounting and Finance Analytics
• +3 more

Manlu Liu is a Professor in the Department of MIS, Marketing, and Analytics at the Rochester Institute of Technology (RIT) 's Saunders College of Business , where she serves as Program Director for the MS in Business Analytics. She holds a Ph.D. in Management Information Systems from the University of Arizona and an MBA in Accounting and Finance from the Hong Kong University of Science & Technology. Specializes in AI/Business Intelligence, Blockchain Technology, and Accounting Analytics Co-initiator of RIT's Advanced Certificate in Accounting and Financial Analytics Represents RIT on the university-wide AI Task Force Her research spans community source software development, sustainability reporting, and health informatics. Publications appear in Accounting, Organizations and Society , Journal of Strategic Information Systems , and Information Systems Journal . She has served on editorial boards for Electronic Commerce Research and International Journal of Electronic Finance . Recent work focuses on blockchain applications in accounting, AI-generated NFT markets, and sustainability dynamics in auditing. She has held leadership roles in international conferences including ICIS and SIGBPS , and co-chaired the Workshop on E-Business (WeB) 2024 .

Primo ZingarettiView profile

Professor

• Artificial Intelligence
• Computer Vision
• Neural Rendering
• +5 more

Primo Zingaretti serves as a Full Professor in the Department of Information Engineering at Marche Polytechnic University's School of Engineering. His academic position focuses on information processing systems within the IINF-05/A scientific sector, maintaining an active research profile with publications extending into 2025. Contact information shows his office is located at via Brecce Bianche in Ancona, Italy, with professional correspondence handled through p.zingaretti@univpm.it. Zingaretti's research spans multiple cutting-edge domains where artificial intelligence intersects with practical applications. His primary focus areas include neural rendering for cultural heritage preservation and fashion technology, augmented reality systems development, and computer vision applications in retail environments. His work demonstrates strong interdisciplinary connections between engineering, cultural studies, and commercial applications. The research portfolio shows particular emphasis on ethical considerations in AI deployment, especially for heritage preservation and creative industries. Analysis of his recent publications reveals a clear trajectory toward practical AI implementations. His 2024-2025 work shows increasing focus on real-time rendering efficiency, ethical frameworks for extended AI, and specialized applications in retail monitoring and cultural heritage protection. The publications demonstrate consistent methodological rigor with strong emphasis on neural network optimization and practical system deployment, particularly in the Italian context including Marche region heritage sites. Zingaretti has developed significant research infrastructure including the open-source DeepReality framework for AI-based AR applications and the SIGNIFICANCE platform for combating cultural heritage trafficking. His work bridges academic research with commercial applications, particularly in retail technology where his shelf monitoring systems and shopper behavior analysis tools show direct industry relevance. Current projects indicate active development in neural rendering systems for fashion design and context-aware generative AI for XR environments.

Witold AbramowiczView profile

Professor

• Artificial Intelligence
• Blockchain Technology
• Disinformation Campaigns
• +7 more

Prof. Witold Abramowicz , currently affiliated with the Department of Economic Informatics at Poznan University of Economics and Business, has made significant contributions to artificial intelligence, blockchain technology, and disinformation campaigns. His work bridges economic informatics with cutting-edge digital transformation, focusing on Fake news detection systems Blockchain-based business models Wikipedia multilingual analysis Recent publications highlight trends in generative AI , quantum computing applications, and maritime transport analysis . He leads the OpenFact project, achieving international recognition in fact-checking competitions. Awards include the Knight's Cross of the Order of Polonia Restituta (2019) and multiple honors for doctoral supervision. Active in academic debates since the 1990s, he continues to shape research through seminars, keynote speeches, and European cybersecurity initiatives.

Piotr StolarskiView profile

Assistant Professor

• Fintech
• Blockchain Technology
• Artificial Intelligence in Finance
• +3 more

Dr. Piotr Stolarski is an Assistant Professor at the University of Economics in Poznan , affiliated with the Department of Economic Informatics . His work focuses on modern technology applications in finance and information integrity. Email: piotr.stolarski@ue.poznan.pl Phone: +48 (61) 639-27-95 Research Interests : Distributed ledger systems and cryptocurrencies Smart contract development Regulatory technology (RegTech) Insurtech and Fintech innovation Data-driven business transformation Information system security Recent Publications (2019–2025) analyze: Quantum computing impacts on cryptocurrency security Blockchain architecture for public registers AI in fake news detection Crypto exchange analysis via open data RSI indicator effectiveness in crypto markets Generative AI risks and mitigation Awards : 1st place - Information Credibility Competition 2024 1st place - CLEF-2023 CheckThat! Lab OpenFact Project Excellence 2023 GSK Economic Five Recognition 2025 Academic Contributions : Organizer of SKN Data Science seminars Chair of BSCT International Workshops (2018–2021) Active in European Researchers' Night events Collaborator with FU Berlin researchers

• Data Science
• Network Science
• Computational Social Science
• +5 more

Yu-Ru Lin is an Associate Professor at the School of Computing and Information , University of Pittsburgh , and serves as Research & Academic Director at the Institute for Cyber Law, Policy and Security (Pitt Cyber) . With secondary appointments in Political Science, Computer Science, and the Intelligent Systems Program, their interdisciplinary work bridges computational methods with social science inquiry. Research interests focus on: Social and political network analysis Computational social science Data mining and visualization Digital accountability in political communication Policy diffusion analytics Trust-distrust dynamics in networked societies Key research themes include: Developing network theories for misinformation and extremism Designing visual analytics for policy diffusion and group bias detection Studying collective behavior during emergencies Creating computational tools for social data analysis Major grants include NSF awards for: Public policy diffusion analytics COVID-19 misinformation mitigation Urban mobility and smart city initiatives Understanding extremism in cyber-social spaces Scientific leadership roles: Editor-in-Chief of ICWSM Editorial board member at Frontiers in Big Data, EPJ Data Science, PLOS ONE, and Nature Scientific Reports Co-chair roles at KDD Humanitarian Mapping Workshop, WWW Web & Society Track, and multiple SBP-BRiMS graduate programs As founder of the Pitt Computational Social Dynamics Lab (PICSO LAB) , they lead research on social media analysis, network modeling, and urban informatics through projects like PittSmartLiving – a $1.4M NSF-funded initiative exploring real-time transit incentives and community engagement.

Hamed HaddadiView profile

Professor

• User-Centered Systems
• IoT
• Applied Machine Learning
• +4 more

Professor Hamed Haddadi holds the position of Professor of Human-Centred Systems at the Department of Computing, Faculty of Engineering, Imperial College London. He is also an Imperial College Research Fellow and serves as Security Science Fellow at the Institute for Security Science and Technology. In industry, he acts as Chief Scientist at Brave. His research focuses on User-Centred Systems, IoT, Applied Machine Learning, Privacy, and Human-Data Interaction. He leads the Imperial-X initiative and holds an EPSRC Open Plus Fellowship (2022-2027). His work bridges academia and industry, addressing challenges in privacy-preserving technologies, edge computing, and secure AI systems. Affiliations include the Artificial Intelligence Network, Centre for Cryptocurrency Research and Engineering, Data Science Institute, Energy Futures Lab, and Institute for Security Science and Technology. He actively supervises PhD students through programs like the President’s PhD Scholarships and Islamic Development Bank scholarships. Recent research trends emphasize privacy-preserving AI, secure edge computing, federated learning, and IoT security. His publications span topics like differential privacy, adversarial ML defense, and energy-efficient neural networks. Notable awards include the EPSRC fellowship supporting his work on Human-Centred Systems. Grants include Imperial College Research Fellowships and collaborations with industry partners. He directs the NetSys group and contributes to interdisciplinary initiatives like the Energy Futures Lab. His work often involves collaborative projects with industrial partners such as Brave Software to advance real-world applications of secure technologies.

Cem ErsoyView profile

Professor

• Wireless Networks and Mobile Applications
• Pervasive Health
• Internet of Things
• +12 more

Cem Ersoy is a full Professor at the Department of Computer Engineering, Boğaziçi University, Istanbul, Turkey. He serves as the Vice Director of TETAM and leads the Computer Networks Research Laboratory (NetLab), driving forward research in wireless networks, pervasive health, IoT, 5G, SDN, and emerging air computing paradigms. His research spans cutting-edge domains such as air computing, edge intelligence, wearable stress monitoring, indoor localization, and smart city neurocomputing challenges. By integrating deep reinforcement learning, probabilistic modeling, and IoT systems, his work aims to create unobtrusive, scalable, and adaptive computing solutions for real-life environments. Recent publications reveal a strong focus on air computing as a novel computation paradigm, leveraging multi-agent deep learning and simulation frameworks. Another major trend is wearable-based affect and stress monitoring , where semi-supervised learning and explainable AI reduce labeling burdens while enhancing real-life applicability. Additional work addresses edge computing orchestration , network slicing , and live video streaming optimization using SDN and reinforcement learning. Prof. Ersoy actively advises graduate researchers and leads funded projects in national and international arenas, although specific grant titles and student names are not listed in the source. His lab, NetLab, provides a collaborative environment for innovations in computer networks, IoT, and pervasive health technologies.

Rohan PadhyeView profile

Assistant Professor

• Software Engineering
• Programming Languages
• Systems
• +6 more

Rohan Padhye is an Assistant Professor at the Software and Societal Systems Department within Carnegie Mellon University's School of Computer Science . He leads the PASTA Lab and is affiliate faculty at CyLab . Ph.D. in Computer Science from UC Berkeley Master's degree in Computer Science from IIT Bombay His research spans software engineering, programming languages, systems, and security . He develops techniques for automated bug detection using dynamic program analysis and coverage-guided fuzz testing. His work includes tools like ChocoPy for compilers education and Fray for concurrency testing. Recent publications focus on: date/time bugs in Python, LLM-generated property-based tests , exception dependency analysis , and distributed system fuzzing . He has received multiple NSF grants and Amazon Research Awards . 2025: NSF Grant for Practical Controlled Concurrency Testing 2025: Amazon Research Award for property-based testing 2022: Goldwater Scholarship for John Billos (PASTA Lab member) He advises PhD students including Ao Li and Vasudev Vikram, and has mentored numerous undergraduate researchers through the REUSE program . His group's work is funded by NSF, CyLab, and Amazon .

Benjamin RisseView profile

Professor

• Biomedical Image Analysis
• Machine Learning
• Computer Vision
• +7 more

Prof. Dr. Benjamin Risse leads the Computer Vision and Machine Learning Systems (CVMLS) group at the Institute of Geoinformatics, University of Münster . His interdisciplinary research bridges biomedical image analysis , machine learning , and virtual reality (VR) applications for medical education. Develops FIMTrack for high-throughput analysis of animal locomotion Focuses on VR-based medical training systems (e.g., brain death diagnostics) Pioneers deep learning in acute myeloid leukemia (AML) diagnosis and neuroscience-inspired AI Research Interests: Biomedical image analysis, tracking algorithms, behavioral quantification, machine learning for biological systems, VR in clinical education, and interdisciplinary imaging technologies. His work addresses algorithm limitations in ecology , neuroscience , and medical diagnostics . Recent Publications (2025–2024) span coherent nanophotonic networks , VR-based medical training , and Gaussian splatting for occupancy estimation , reflecting trends in AI-driven biomedical systems and cross-disciplinary innovation . Scientific Awards: Teaching Prize of the University of Münster (2021) Lehrpreis des Fachbereichs Mathematik/Informatik (2021) AI Starter Grant on neuroscience-inspired deep learning (2022) Advising & Grants: Mentors PhD students in AI applications for 3D printing , insect monitoring , and medical education . Holds grants from BMBF , DFG , and Else Kröner-Fresenius Foundation for projects like medical tr.AI.ning and inFlame Medical Scientists College . Labs & Collaborations: Part of the Cells in Motion graduate program and Health-AI Network . Develops FIM imaging systems for Drosophila studies and insect camera traps with the Institute for Landscape Ecology . Collaborates with University Hospital Münster on AI diagnostics.

• Cardiovascular Medicine
• Stem Cell Biology
• Drug Discovery
• +4 more

Mark Mercola is Professor of Medicine (Cardiovascular) and Professor (By courtesy) in Chemical and Systems Biology at Stanford University . He leads research at the Stanford Cardiovascular Institute and previously held faculty positions at Harvard Medical School and University of California, San Diego. His work focuses on cardiogenesis, patient-specific iPSC modeling, and high-throughput drug discovery. Key affiliations: Stanford Cardiovascular Institute , Bio-X , Maternal & Child Health Research Institute Advisory roles: Vala Sciences , Regencor , Ted Rogers Centre for Heart Research Research Interests : Mercola's research bridges cardiovascular biology, stem cell engineering, and drug development. He pioneered Wnt inhibition strategies for cardiogenesis and developed iPSC-cardiomyocyte platforms for disease modeling, safety pharmacology, and target discovery in heart failure. His team explores metabolic maturation of stem cell-derived cardiomyocytes and mechanisms of arrhythmia. Scientific Awards : NIH MERIT Award for contributions to heart formation research. Students : Mentored postdoctoral researchers including Xiaozhi Gao, Hiroyuki Inoue, Adithan Kandasamy, and Sarah Sturgill. Labs & Funding : Operates at Stanford's Cardiovascular Institute and co-established the Conrad Prebys Drug Discovery Center. Funded by NIH, California Institute for Regenerative Medicine, Phospholamban Foundation, and Fondation Leducq.

Joseph D. RomanoView profile

Assistant Professor

• translational bioinformatics
• environmental toxicology
• biomedical artificial intelligence

Joseph D. Romano serves as Assistant Professor of Informatics and Pharmacology within the Department of Biostatistics, Epidemiology and Informatics, leading a research program focused on artificial intelligence applications for predicting chemical toxicity and advancing drug discovery. His core research interests include: Translational Bioinformatics for biomedical data integration Environmental Toxicology mechanisms and prediction Biomedical Artificial Intelligence with emphasis on interpretability Romano's laboratory develops computational frameworks leveraging knowledge graphs, graph neural networks, and automated machine learning to model chemical-biological interactions. Current projects address toxicology prediction, drug-target affinity modeling, and venom-derived therapeutic discovery through multi-omics integration and semantic knowledge representation. Analysis of his 2022-2025 publications reveals consistent innovation in applying graph-based machine learning to toxicology and pharmacology challenges, with particular strengths in knowledge-guided deep learning for interpretable toxicity prediction and federated approaches for clinical data analysis. His research group maintains active development of open-source tools including ComptoxAI for automated predictive toxicology and VenomKB for computational toxinology, supporting collaborative advances in environmental health and drug safety assessment.

Yi HuView profile

Assistant Professor

• Power Systems
• Artificial Intelligence
• Data Science
• +1 more

Yi Hu is an Assistant Professor in the Department of Electrical and Computer Engineering at Michigan Technological University, appointed in August 2025 following his Ph.D. completion at North Carolina State University. His work bridges artificial intelligence and power systems engineering to advance grid digitalization and renewable energy integration. His academic credentials include: Ph.D. in Electrical Engineering, North Carolina State University (2025) M.S. in Electrical Engineering, Peking University (2018) B.S. in Electrical Engineering, Chongqing University of Posts and Telecommunications (2014) Dr. Hu's research specializes in generative AI applications for power systems , with emphasis on synthetic data generation , missing data restoration , and load profile analysis . He pioneers techniques using LLMs and GANs to reduce data requirements for grid analytics while enhancing privacy preservation and situational awareness in renewable energy integration. His publication portfolio reveals a cohesive trajectory applying cutting-edge AI to power system data challenges, particularly in load forecasting and grid resilience. The work spans computer science , electrical engineering , and data analytics , consistently targeting practical implementations for modern smart grids. Award recognitions include: Exceptional Reviewer for IEEE Transactions on Sustainable Energy (2024) E-STAR Award from Eaton Research Lab for generative AI innovation (2024) Dr. Hu actively recruits graduate researchers for industry-collaborative projects, leveraging his internship experiences at Eaton and Quanta Technology. His group emphasizes open-source development and top-tier publications in energy AI, with current funding supporting synthetic data frameworks and human-machine interfaces for grid operators. Establishing his research lab at MTU, he focuses on real-world AI deployment for grid digitalization through partnerships with Harvard University, NC State, and energy industry leaders, advancing tools for next-generation power system management.

Gustavo A. OlivaView profile

Adjunct Professor

• Programmable Blockchains
• Software Ecosystems
• Code Analytics
• +6 more

Gustavo A. Oliva is an Adjunct Professor at Queen's University in Canada, where he leads the blockchain research team at the Software Analysis and Intelligence Lab (SAIL). His research focuses on enabling cost-effective decentralized applications on programmable blockchain platforms like Ethereum, alongside empirical studies in software ecosystems, code analytics, and explainable AI. Dr. Oliva earned his PhD from the University of São Paulo (USP) in Brazil under Professor Dr. Marco Gerosa. Prior to his current role, he was a Post-Doctoral Fellow at Queen's University supervised by Professor Dr. Ahmed Hassan. His primary research spans programmable blockchains, software ecosystems (particularly npm), code analytics, and explainable AI. He employs static analysis, historical repository mining, and machine learning to investigate software evolution, dependency management, and smart contract development. Current projects address gas efficiency challenges in Ethereum, upgradeability patterns in smart contracts, and the impact of foundation models on software engineering practices. Recent publications reveal a dominant focus on blockchain systems (70% of recent work), with growing emphasis on foundation model challenges (FMware). His Ethereum research explores transaction processing, gas optimization, and technical debt, while newer work catalogs software engineering challenges in trustworthy AI-powered systems. Scientific recognition includes: Microsoft Azure for Research sponsorship Capes/CNPq scholarship for Visiting Research at Queen's University (2014) HPE scholarships for Smart Cities and Cloud Service Choreography projects European Commission FP7 funding for CHOReOS project Dr. Oliva actively mentors 8+ students across academic levels. His PhD advisees include Muhammad Ahasanuzzaman (ongoing), Amir Mohammad Ebrahimi, and Filipe Cogo (now at Huawei). Master's students Michael Pacheco and Ahmad Abdullah Zarir now work at Huawei and Amazon respectively. He also supervises visitor and undergraduate researchers in blockchain projects. His service includes program committees for ICSE, SANER, and MSR conferences, plus tutorial leadership at ASE, KDD, and FSE. As director of SAIL's blockchain research team, he manages projects on Ethereum smart contract analysis, npm dependency ecosystems, and AI-driven software engineering. Current initiatives include SPICE (automated issue labeling) and foundational work on trustworthy FMware development, with industry collaborations at Huawei and Amazon.

Lin ChenView profile

Associate Professor

• Software Engineering
• Programming Languages
• Artificial Intelligence for Software Engineering
• +4 more

Lin Chen is an Associate Professor in the Department of Computer Science and Technology at Nanjing University, China, specializing in software engineering and programming languages with applications to AI-integrated systems. His work bridges theoretical foundations and practical tools for software analysis, testing, and ecosystem studies. Education: Ph.D. in Computer Software and Theory, Southeast University (2009) B.S. in Computer Science and Technology, Southeast University (2001) Visiting Scholar at Purdue University (2015-2016) His research spans software testing, programming language design (particularly gradual typing), and AI-enhanced software engineering. Key contributions include empirical studies of Python's dynamic features, mutation testing frameworks for AI systems, and defect prediction models. He investigates how programming language semantics impact software quality and maintenance in open-source ecosystems. Recent publications (2023-2024) reveal strong trends in applying software engineering techniques to AI systems, analyzing Python's typing evolution, and developing practical testing tools. There is significant emphasis on empirical validation, with 60% of recent work focusing on Python ecosystem analysis and 30% on AI/software integration. Scientific Awards: FSE 2016 Distinguished Artifact Award First Prize of Hubei Science and Technology Award (2015) First Prize of Jiangsu Science and Technology Award (2012) First Prize of Jiangsu Science and Technology Award (2007) Professor Chen has advised 14+ graduate students including PhD candidates Hao Ren and Wanwangying Ma, and master's students like Fan Yang and Yuanlei Han. He actively recruits self-motivated PhD and undergraduate researchers for projects in software analysis, testing, and intelligent engineering. His group collaborates with industry partners on tool development for defect prediction and type system analysis. His research team at Nanjing University focuses on four pillars: (1) Software Analysis and Testing for dynamic languages, (2) Technical Debt and Refactoring in evolving systems, (3) AI-driven defect prediction, and (4) Gradual typing semantics for multilingual ecosystems. Current projects include large-model-based test generation and knowledge graph applications for QA system validation.

Morakot ChoetkiertikulView profile

Lecturer

• AI-Driven Software Engineering
• Defect Prediction
• Machine Learning for Software Engineering
• +2 more

Morakot Choetkiertikul is a Lecturer at the Faculty of ICT, Mahidol University, Thailand, where he co-founded the SERU research group. He earned his Ph.D. in computer science from the School of Computing and Information Technology, Faculty of Engineering and Information Sciences (EIS) at the University of Wollongong (UOW), Australia, where he worked in the Decision Support Lab (DSL). His research focuses on AI-Driven Software Engineering , particularly in defect prediction, code quality assessment, and security vulnerability analysis. His work bridges artificial intelligence with practical software development challenges, creating tools that address real-world problems in software engineering processes. The SERU research group he co-founded demonstrates a strong commitment to developing practical solutions for the software industry. Morakot's publication record from 2020-2025 shows a clear evolution from foundational defect prediction research to comprehensive tool development across multiple aspects of the software lifecycle. His work spans code proficiency analysis (PyGress, jscefr), vulnerability detection (V-Achilles, DEV-EYE), and AI applications in development processes (Autorepairability studies), with increasing emphasis on empirical validation in real-world settings. Examiners' Commendation for Outstanding Thesis Best thesis award from University of Wollongong Dr. Choetkiertikul actively contributes to the software engineering community through program committee roles at major conferences including ASE, APSEC, ICSE, and ICSME. His leadership roles have expanded from committee member to track chair and early research achievements co-chair, reflecting his growing recognition in the field. His research on autorepairability, bus factor monitoring, and code proficiency assessment demonstrates a commitment to improving both software quality and developer productivity through data-driven approaches.