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Yunlei LiView profile

Assistant Professor

• Machine Learning
• Bioinformatics
• Cancer Research
• +4 more

Dr. Yunlei Li is an Assistant Professor at the Erasmus Medical Center , leading the machine learning/artificial intelligence research line in the Department of Pathology & Clinical Bioinformatics. Her work spans translational cancer research and clinical data analysis, focusing on integrating multi-dimensional patient data (DNA, RNA, proteins, images, clinical) for biomarker discovery and personalized treatment strategies. Education PhD in Bioinformatics, Delft University of Technology (2010) MSc in Data Mining & Bioinformatics, Delft University of Technology (2004) Research interests center on applying novel AI/ML methods to clinical oncology challenges, including neural network modeling , NLP/LLM for clinical data, and deep learning in histopathology. Recent trends include multi-omics integration for melanoma metastasis prediction and transfer learning in glioblastoma drug response modeling. Her work aligns with UN Sustainable Development Goals, particularly SDG 3: Good Health and Well-being . Collaborations span Netherlands Cancer Institute, Delft University of Technology, and international institutions.

Balasubramaniam RameshView profile

Professor

• Agile Software Development
• Software Engineering
• Requirements Engineering
• +5 more

Dr. Balasubramaniam Ramesh is a Professor in the Department of Computer Science at Georgia State University. His research focuses on knowledge-intensive processes in software development, agile methodologies, and IT governance, with contributions to industry tools and over 20 research grants from institutions like NSF and DARPA. He has received numerous awards for teaching and research, including the Regents’ Teaching Excellence Award (2008), the top faculty award in the University System of Georgia. Education: Ph.D., New York University M.Phil., New York University P.G.D.M. (M.B.A.), Indian Institute of Management B.Engg., University of Madras, India Research Interests: Dr. Ramesh’s work bridges theory and practice in software engineering, exploring agile practices, requirements traceability, and ambidexterity in distributed development. He emphasizes the interplay between organizational processes and IT innovation, with recent work on AI ethics, blockchain applications, and cloud computing. Grants & Awards: Over 20 grants from NSF, ONR, Air Force Research Lab, and industry partners. Notable awards include the GSU Outstanding Faculty Achievement Award (2002), RCB Faculty Achievement Awards (Teaching, Research, Service), and multiple Top Professor recognitions (2014–2016). Labs & Teams: While specific lab names are not mentioned, his research collaborates with industry and academic partners to develop tools and frameworks for software process improvement and IT governance.

• Recommender Systems
• Machine Learning
• Natural Language Processing
• +4 more

Julian J. McAuley is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego. His research focuses on advancing the fields of Artificial Intelligence, Machine Learning, and Natural Language Processing, with a particular emphasis on Recommender Systems and Large Language Models (LLMs). His work addresses challenges in ethical AI, generative models, and the integration of knowledge-driven approaches into recommendation systems. Key research interests include responsible AI, lifelong learning, and the development of agentic recommender systems. He explores techniques such as retrieval-augmented generation, causal reasoning, and multi-objective alignment in LLMs. His contributions span foundational theories and practical applications, including the design of scalable systems and frameworks for evaluating generative models. McAuley’s publications highlight advancements in areas like music generation, chain-of-thought reasoning, and knowledge transfer in recommendation systems. His work often bridges theory and practice, addressing real-world challenges in personalized systems and ethical AI deployment.

• Decision science
• Data science
• Causal machine learning
• +6 more

Wouter Verbeke is Professor of Decision Science at KU Leuven's Faculty of Economics and Business (FEB), where he directs the POC Doctoraatsprogramma Economie en Bedrijfswetenschappen and chairs the Doctoral Committee. He is a core member of Leuven.AI and leads the Information Systems Engineering Research Group (LIRIS), focusing on data-driven business decision-making. His research spans causal machine learning, uplift modeling, and cost-sensitive learning with applications in fraud detection, credit risk, operations management, and pricing. Verbeke pioneers decision-centric frameworks that optimize business outcomes rather than purely predictive accuracy, emphasizing causal inference for resource allocation and fairness in algorithmic decision systems. Analysis of his recent publications reveals a strong trend toward graph-based anti-fraud systems (particularly for AML/smurfing detection), interpretable causal machine learning in finance, and HR analytics using process mining. His work increasingly integrates fairness considerations into operational decision systems while advancing theoretical foundations of treatment effect estimation under network interference. Scientific awards: EURO award for best article in European Journal of Operational Research (Innovative Applications of O.R.) (2014) Verbeke actively supervises doctoral candidates including S. De Vos and M. Reusens, with research funded through major grants like ANUBIS (Aligned oNline and multilevel User and entity Behavior) and prescriptive analytics projects for intelligent organizations. He serves on editorial boards of Decision Support Systems, Business Information Systems Engineering, and INFORMS Journal on Data Science. As leader of the LIRIS research group within FEB, he drives interdisciplinary collaborations between data science and business domains, maintaining strong industry partnerships for real-world validation of fraud analytics, credit risk, and operational decision systems.

• Artificial Intelligence
• Data Management
• Machine Learning
• +5 more

Dr. Jonathan Fürst is a Researcher at the ZHAW School of Engineering, part of the Intelligent Information Systems research focus at ZHAW Zurich University of Applied Sciences. His work spans AI-driven data systems, ontology matching, IoT applications, and cross-domain data integration. Key research interests include: AI applications in healthcare and education Machine learning for data integration and systems Multi-modal data exploration and natural language interfaces IoT networks and smart building technologies His projects include: AI-based Quality Management of BIM Models (project leader) DataGEMS : A data discovery platform for generalized exploratory search Multi-lingual data exploration systems Digital Health Zurich: Patient-centered clinical innovation Recent research trends emphasize explainable AI, weakly supervised learning, and real-world system robustness. His work bridges theoretical advancements with practical implementations in domains like healthcare, education, and smart infrastructure. No scientific awards are explicitly listed, though his active role in Horizon Europe projects reflects collaborative excellence. He contributes to interdisciplinary teams across data science, engineering, and healthcare.

• Artificial Intelligence
• Bioinformatics
• Natural Language Processing
• +3 more

Dr. Ahmad Aghaebrahimian is a researcher at the ZHAW School of Life Sciences and Facility Management, affiliated with the Institute of Computational Life Sciences. He specializes in computational methods applied to healthcare, natural language processing (NLP), and bioinformatics. His work integrates deep learning, ontology-based systems, and signal processing to address challenges in healthcare informatics, biomedical research, and security systems. Research Projects: Project Leader: Advancing Information Accessibility in Hospitals (LLMs) Project Leader: Multi-document Patient Records Summarization Project Leader: Plant Cell Cultures with Deep Learning Deputy Leader: Automatic Supply Chain Monitoring Research Interests: His research focuses on AI-driven solutions for healthcare, including ontology-aware relation extraction, medical text mining, and robust signal processing systems. He also explores NLP applications in question answering, entity disambiguation, and parallel corpus creation. Recent work includes drone detection using CNNs in low SNR environments and computational methods for natural products discovery. Publications Trends: Over the past decade, his publications emphasize interdisciplinary approaches combining machine learning with bioinformatics and medical informatics. Key themes include deep learning model optimization, biomedical knowledge graph construction, and practical applications of NLP in healthcare systems. Grants & Collaboration: Leads research initiatives on AI in colorectal cancer classification and supply chain monitoring, demonstrating expertise in securing project leadership roles within academic-industry collaborations.

• Machine Learning
• Natural Language Processing
• Healthcare Applications
• +7 more

Frank Rudzicz is a Professor at the University of Toronto with appointments in the Faculty of Medicine and Department of Computer Science. His research bridges machine learning, natural language processing, and healthcare applications, with a particular focus on developing AI solutions for medical diagnostics, surgical assistance, and patient care. He leads several research initiatives in medical AI and collaborates extensively with clinicians and healthcare institutions. Rudzicz's research interests span multiple critical areas in healthcare AI. He has pioneered work in speech-based detection of neurological conditions including Alzheimer's and Parkinson's diseases, developing algorithms that analyze linguistic patterns to identify early signs of cognitive decline. His work in surgical AI has led to systems that provide real-time decision support during operations and analyze surgical performance. He also investigates ethical AI implementation in healthcare, addressing privacy concerns and developing frameworks for trustworthy clinical AI systems. More recently, his research has expanded into large language models for medical applications, focusing on privacy preservation, fairness, and explainability in clinical contexts. His publication record shows a clear evolution from foundational work in speech processing and dysarthria recognition toward increasingly sophisticated medical AI applications. Early work focused on improving speech recognition for impaired speakers, which naturally led to applications in neurological disorder detection. Over the past decade, his research has expanded to encompass broader healthcare applications, with recent work heavily featuring transformer models, multimodal data integration, and ethical considerations in medical AI deployment. His current research demonstrates strong emphasis on practical implementation challenges including model security, privacy preservation, and clinical integration of AI systems. Rudzicz has received recognition for his work, including best paper awards at major conferences like ICLR 2025. His research has been supported by numerous grants focusing on AI in healthcare, though specific grant details aren't provided in the available text. He has mentored numerous graduate students who have gone on to contribute significantly to medical AI research. As a leader in medical AI, Rudzicz contributes to major collaborative efforts including the Delphi consensus statement for digital surgery. His work frequently appears in top-tier AI and medical journals, demonstrating his ability to bridge the technical and clinical domains. Through his research group at the University of Toronto, he continues to advance the frontier of AI applications in healthcare while addressing the practical and ethical challenges of implementing these technologies in real clinical settings.

Sina ZarrießView profile

Professor

• Computational Linguistics
• Natural Language Processing
• Dialogue Systems
• +5 more

Sina Zarrieß is a Professor of Computational Linguistics at the University of Bielefeld, affiliated with the Faculty for Linguistics and Literature Studies. His research focuses on computational models of language use, with applications in natural language generation, dialogue systems, and language-vision integration. He leads the Computational Linguistics Bielefeld group and collaborates on interdisciplinary projects in ecology, hate speech analysis, and multimodal interaction. Research interests include language model architecture optimization, pragmatic reasoning in generation, and ethical NLP. He actively contributes to workshops such as ACL, EMNLP, and NLP4Ecology, publishing on topics like character-based LMs, gaze-driven hate speech detection, and visualization-oriented dialog systems. His work bridges theoretical linguistics with applied machine learning, emphasizing interpretability and real-world impact. Notable projects include developing BabyLM character-based models, studying gender-inclusive language benchmarks (SlayQA), and creating tools like VIST5 for adaptive visualization dialog. He explores how LMs encode linguistic principles like Maximize Presupposition! and investigates zero-shot learning for novel object categories. Current research trends show a focus on model efficiency (e.g., sentence selection for classification), multimodal grounding (e.g., scene context in visual tasks), and human-AI interaction dynamics (e.g., explanation effects on user perception). Despite prolific publishing, no specific scientific awards are mentioned in the provided materials. Advising and grants are not explicitly detailed, though his extensive co-authorship network indicates active mentoring. The Computational Linguistics Bielefeld group maintains open research directions in visual question answering, poetry generation diversity, and historical hate speech evolution studies.

• Machine Learning
• Data Mining
• Graph Neural Networks
• +5 more

Hanghang Tong is a Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign. He also holds affiliations with the National Center for Supercomputing Applications (NCSA) and the Center for Digital Agriculture. His research interests span machine learning, data mining, graph theory, and ethical AI, with a focus on network analysis, fairness, and large-scale data systems. Tong has been recognized with prestigious awards including the IEEE Fellow (2022), ACM Distinguished Member (2020), and NSF CAREER Award (2019). His recent work explores topics like LLM safety, time series analysis, and network alignment through tools like PLANETALIGN. Tong actively contributes to open-source libraries and benchmarks, advancing reproducibility and ethical standards in AI. Education : Not explicitly listed in provided texts. His research emphasizes practical applications of AI in climate science, healthcare, and social networks. Tong's work often addresses challenges in model fairness, scalability, and cross-domain adaptation. He collaborates widely, contributing to both foundational theory and real-world systems. Key projects include CLIMB (class-imbalanced learning benchmark) and Saffron-1 (LLM safety assurance). Tong’s lab at NCSA and Siebel School drives innovation in AI-driven data science and computational methods.

Heng ZhengView profile

Researcher

• Computational Argumentation
• Explainable AI
• Computational Social Science
• +3 more

Heng Zheng is a Postdoctoral Research Associate at the University of Illinois Urbana-Champaign (UIUC) School of Information Sciences, mentored by Jodi Schneider, Bertram Ludäscher, and Karen Wickett. Starting August 2025, he will join the University of Kentucky as a tenure-track Assistant Professor of Information Science. His research focuses on computational argumentation, explainable AI, legal informatics, and digital libraries, with a particular emphasis on addressing information disorder and improving scientific communication. Educational Background: PhD in Artificial Intelligence (University of Groningen, 2024; advisors: Bart Verheij and Davide Grossi), MSc in Logic (Sun Yat-sen University, 2018), and BSc in Information Management (Dalian Maritime University, 2016). Research Interests: Develops formal theories for case-based reasoning in legal and scientific contexts, explores computational methods to audit scientific publications, and investigates epistemic uncertainty in information dissemination. His work bridges AI, law, and information science to enhance transparency and reliability in knowledge systems. Grants: Principal investigator on the $40k XAI-CA project (UIUC-Cardiff Collaboration) and co-recipient of IMLS and Sloan Foundation grants. Teaches Information Modeling and delivers guest lectures on argumentation in information systems. Actively reviews for journals like Studies in Logic and conferences such as ICAIL and iConference. Key Achievements: Published 15+ peer-reviewed papers (including JCDL 2024 and iConference 2024), developed datasets on scientific retractions and legal reasoning, and contributes to open science initiatives. His work on computational argumentation frameworks (e.g., AF-Xray) and entity alignment using LLMs showcases innovative applications of AI in complex domains.

• Artificial Intelligence
• Programming Languages
• Formal Methods
• +3 more

Gagandeep Singh is an Assistant Professor in the Department of Computer Science at the University of Illinois. His research focuses on trustworthy AI, programming languages, and formal methods, with a particular emphasis on ensuring safety, robustness, and ethical alignment in machine learning systems. He holds a Ph.D. from ETH Zurich and has received multiple awards, including the ACM SIGPLAN Doctoral Dissertation Award. His work spans neural network verification, adversarial robustness, and federated learning, with applications in security, reliability, and ethical AI. He has contributed to journal editorships, conferences, and has taught courses on formal methods and trustworthy AI systems. Education: Ph.D. in Computer Science, ETH Zurich (2014–2020) Masters in Computer Science, ETH Zurich (2012–2014) Bachelor's in Computer Science and Engineering, IIT Patna (2008–2012) Research Interests: Artificial Intelligence Safety & Ethics Formal Verification of Neural Networks Adversarial Machine Learning Federated Learning Programming Languages & Static Analysis Recent Articles: His most recent work addresses challenges in AI alignment, robustness certification, and federated learning optimization, reflecting a focus on both theoretical foundations and practical applications. Awards & Honors: ACM SIGPLAN John C. Reynolds Doctoral Dissertation Award (2021) ETH Medal for Best Master's Thesis (2014) Teaching & Advising: He has taught courses such as 'Formal Software Development Methods' and 'Trustworthy AI Systems', and his research group explores topics like LLM robustness and certified neural networks.

Ayoub BagheriView profile

Associate Professor

• Natural Language Processing
• Explainable AI
• Bias Detection
• +2 more

Dr. Ayoub Bagheri is an Associate Professor of Natural Language Processing in the Department of Methodology and Statistics at Utrecht University. He coordinates the Special Interest Group in Text Mining and co-leads the Utrecht NLP Hub, a collaborative initiative supported by the Focus Areas Applied Data Science and Human-centered AI. His research focuses on explainable and trustworthy AI, personalized NLP systems, language model evaluation, multimodal language understanding, and ethical NLP applications. He develops methods to make AI transparent and aligned with human values. Bagheri teaches courses in Data Science, Text Mining, and NLP using Python and R, and coordinates the Applied Data Science master profile. His recent work examines explainability techniques for large language models and active learning approaches for systematic reviews.

Mohammad MoshirpourView profile

Professor

• Applied Software Engineering
• Artificial Intelligence
• Machine Learning
• +7 more

Professor Mohammad Moshirpour is a faculty member at the University of California, Irvine (UCI), affiliated with the School of Information and Computer Sciences and the Department of Computer Science. As Director of the Software Engineering Practice and Education (SEPE) Lab, his work spans applied software engineering, artificial intelligence, machine learning, and educational methodologies. He emphasizes practical applications, collaborating with healthcare institutions (e.g., University of Calgary nursing faculty), industry partners, and academic institutions to address real-world challenges such as pipeline monitoring, video restoration, and dementia therapy through virtual reality. His research interests include software requirements engineering, AI-driven software development, and innovative education models like project-based learning (PBL) and hackathon-based assessment. He advocates for hands-on learning, comparing software engineering education to medical training. His SEPE Lab focuses on creating scalable, maintainable software solutions with a strong emphasis on AI integration. Key collaborations include work on parent-child interaction analysis via machine learning and video super-resolution techniques. Professor Moshirpour’s recent projects highlight cross-disciplinary innovation, such as leveraging federated learning for video enhancement and applying Monte Carlo methods to pipeline monitoring. His educational contributions include frameworks for agile teaching, automated feedback systems, and curriculum modernization. While no specific awards are listed, his work reflects a commitment to bridging theoretical research with practical implementation.

ALBERTO TESTOLINView profile

Assistant Professor

• Artificial Intelligence
• Neuroscience
• Machine Learning
• +3 more

Alberto Testolin is an Assistant Professor at the University of Padua, Italy. His research focuses on artificial intelligence, computational neuroscience, and machine learning applications in healthcare and marine biology. He explores topics such as EEG seizure prediction, dolphin whistle detection, numerical cognition, and the capabilities of large language models. His work bridges theoretical models with practical challenges, including the design of robust classifiers and the analysis of neural network dynamics. Key research interests include deep generative models, computational models of cognitive disorders like dyscalculia, underwater acoustic signal processing, and the development of AI systems that emulate human judgment. His interdisciplinary approach integrates techniques from computer vision, neuroimaging, and reinforcement learning. Recent publications emphasize the challenges and advancements in EEG forecasting, the emergence of symbolic reasoning in LLMs, and the behavioral analysis of marine animals. He advocates for rigorous evaluation methodologies to ensure machine learning models generalize across diverse datasets and patient populations.

• Quantum Computing
• Software Engineering
• Artificial Intelligence
• +4 more

Dr. Aakash Ahmad is a Lecturer in Computing and Communications at Lancaster University, UK. His research focuses on quantum software systems, AI-driven software engineering practices, and microservices architecture. He leads empirical studies on developer experiences with tools like GitHub Copilot and has contributed to foundational work in quantum architecture description languages (QADL). His work bridges practical software development challenges with cutting-edge AI and quantum computing innovations. Affiliations: Department of Computing at Lancaster University, UK. Research Interests: Quantum software architecture, AI in software engineering, microservices challenges, empirical studies on developer tools, and blockchain system development. His datasets include empirical analyses of Stack Exchange and GitHub data, exploring architecture decisions in quantum systems and challenges in Copilot usage. He has received awards for impactful studies on GitHub Copilot practices and quantum software development processes. Ahmad is actively involved in organizing workshops such as the Engineering Processes and Practices for Quantum Software (PPQS’24) and presents at international conferences. Key Projects: Quantum architecture frameworks, AI ethics in development, and blockchain system design.