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Kalyan VeeramachaneniView profile

Researcher

• Big Data Science
• Machine Learning
• Artificial Intelligence Applications
• +2 more

Kalyan Veeramachaneni is a Principal Research Scientist at the Laboratory for Information and Decision Systems (LIDS) at the Massachusetts Institute of Technology (MIT). He leads the Data-to-AI group, focusing on developing AI systems to address societal challenges through big data science and machine learning. His research emphasizes statistical modeling, automated feature engineering, and human-data interaction for impactful applications. His work spans synthetic data generation, anomaly detection, adversarial machine learning, and explainable AI frameworks. Key contributions include the Synthetic Data Vault, Cardea, and Ballet collaboration tools. He collaborates across industries to deploy interpretable ML systems in healthcare, energy, and cybersecurity. Notable projects include OrionBench (time series benchmarking), Explingo (AI explanations via LLMs), and frameworks like Pyreal and ATM for automated machine learning. His research bridges theoretical advancements with practical usability, emphasizing end-user priorities in system design. Current affiliations: LIDS MIT | MIT Institute for Data, Systems, and Society (IDSS). Office: 32-D714, Cambridge, MA.

• Knowledge Representation
• Semantic Web
• Artificial Intelligence
• +4 more

Angelo A. Salatino is a researcher affiliated with the Knowledge Media Institute (KMi) at the Open University. His work focuses on advancing knowledge representation, semantic technologies, and AI applications in scholarly communication. He has contributed to over 70 publications since 2013, with recent work emphasizing large language models (LLMs), knowledge graphs, and their integration in research and industry contexts. Education: PhD from the Open University (KMi). Research interests include ontology engineering, scientific question answering, and AI-driven literature review methods. He has co-edited conference proceedings and contributed to projects like the Computer Science Ontology (CSO) and the AIDA knowledge graph. Key areas of research include: (1) knowledge injection strategies for LLMs in academia, (2) semantic analysis of ESG discourse in news, (3) health informatics applications of knowledge graphs, and (4) optimizing tourism data through NLP and graph technologies. His work bridges computational methods with interdisciplinary research challenges. Co-authored over 60 papers with collaborators like Enrico Motta and Francesco Osborne. His research often addresses practical challenges in organizing scholarly knowledge, improving metadata quality, and enhancing accessibility through neuro-symbolic AI approaches.

• Artificial Intelligence
• Planning and Decision Making
• Human-Aware AI Systems
• +4 more

Subbarao Kambhampati is a Professor of Computer Science at Arizona State University (ASU), affiliated with the School of Computing and Augmented Intelligence and the Global Futures Scientists and Scholars initiative. He specializes in artificial intelligence, focusing on planning, decision-making, and human-aware AI systems. His research explores challenges in automated planning, human-robot interaction, and the societal impacts of AI advancements. Education: Ph.D. and M.S. in Computer Science from the University of Maryland-College Park (1989), and B.Tech. from Indian Institute of Technology-Madras. Research Interests: Dr. Kambhampati's work centers on foundational AI problems, including reasoning, planning, and decision-making under uncertainty. He emphasizes human-centric AI, investigating trust dynamics, cooperative human-AI teaming, and ethical implications of AI systems. His contributions include frameworks like RADAR for user-adaptive decision support and QUIC for handling imprecise data in autonomous systems. Recent Publications: His 2025 work critiques deep learning's limitations, analyzes LLM planning capabilities, and explores trust models in human-robot interactions. Key themes include AI explainability, robust planning with LLMs, and cooperative behavior evaluation in human-AI collaborations. Scientific Awards: Fellow of AAAI, AAAS, and ACM NSF Young Investigator Award Grants and Projects: Multi-year funding from NSF, ONR, and DARPA for planning frameworks, human-robot teaming, and AI ethics Leadership in initiatives like Digital Antiquity and Partnership on AI Labs/Teams: Active in interdisciplinary research groups at ASU, including the School of Computing and Augmented Intelligence, and collaborates globally on AI policy and societal impact.

Ihab F. IlyasView profile

Professor

• Data Quality
• Machine Learning
• Database Systems
• +4 more

Ihab F. Ilyas is a Professor and holds the Thomson Reuters–NSERC Industrial Research Chair in Data Quality at the University of Waterloo's Department of Computer Science. His research focuses on data quality, machine learning for data enrichment, probabilistic data management, and knowledge graph systems. He leads projects like Holoclean (a probabilistic data repair system) and Data Civilizer (a data unification platform). His work bridges database systems and machine learning to address challenges in data integration, error detection, and large-scale knowledge representation. Education: PhD in Computer Science from Purdue University (2004), MSc and BSc from Alexandria University, Egypt (1999 and 1995). His publications span vector search, knowledge graph construction, and data curation techniques, with recent emphasis on adaptive indexing and scalable systems. He has contributed to open-source tools and frameworks for data cleaning and machine learning integration. Research interests include probabilistic data management, machine learning applications for data quality, and scalable systems for big data. His work often addresses real-world challenges in data integration and semantic representation, with a focus on practical, deployable solutions.

Hakan EmekciView profile

Assistant Professor

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

Hakan Emekci is an Assistant Professor at TED University's Graduate School of Applied Data Science. He teaches courses such as Statistical Learning, Data Analytics, and Information Retrieval in the Applied Data Science Master Program. His research focuses on AI applications in healthcare, education technology, econometrics, and data-driven methodologies. Emekci has published works on specialized language models for medical contexts, RAG systems optimization, and the integration of AI in educational frameworks. His recent studies emphasize domain-specific AI solutions and their practical implementations across diverse sectors. Emekci's academic contributions include analyzing central bank policies' impact on financial markets and developing computational frameworks for data mining. He actively teaches advanced courses at both undergraduate and graduate levels, including Data Structures and Algorithms. His work bridges theoretical data science concepts with real-world applications in healthcare, education, and finance. Notable research areas include: Healthcare informatics & AI-driven diagnostic systems Educational technology innovations Machine learning model optimization Financial econometric analysis Domain-specific large language models His publications from 2025 highlight advancements in multi-agent medical frameworks and comparative studies of retrieval-augmented generation techniques. Earlier work (2017-2018) established foundational research in econometric policy analysis and R-based data mining methodologies.

Mihai SurdeanuView profile

Associate Professor

• Natural Language Processing
• Machine Learning
• Deep Learning
• +2 more

Mihai Surdeanu is an Associate Professor in the Department of Computer Science at the University of Arizona. His academic work focuses on advancing natural language processing and machine learning techniques, with a particular emphasis on large language models, information extraction, and model efficiency. He can be reached at msurdeanu@arizona.edu. Research Interests: Natural Language Processing Machine Learning Deep Learning Information Extraction Artificial Intelligence Scientific Trends: His recent work explores critical challenges in large language models, including data contamination detection, adversarial perturbation defense, quantization methods, and reasoning robustness. Publications highlight techniques like prompt chaining, layerwise optimization, and speculative generation to improve model performance and interpretability.

• Medical Statistics
• Artificial Intelligence in Healthcare
• Machine Learning
• +3 more

Lei Clifton is a Research Fellow and Official Fellow in AI & Machine Learning at Reuben College, University of Oxford. She leads a team of researchers in the Nuffield Department of Population Health and contributes to the Pandemic Science Institute’s preparedness efforts. With a PhD in Statistical Machine Learning (2007, University of Manchester), her expertise spans large-scale clinical data analysis and healthcare AI deployment. She advises the NIHR Research Support Service on study design and collaborates with Prof David Clifton’s 'AI for Healthcare' CHI Lab. Her research focuses on medical statistics and AI applications in observational studies and electronic health records, including synthetic data generation, immune protein biomarker discovery, and scalable AI implementation in healthcare systems. She co-leads projects like the Implementation framework for AI deployment and dysregulated immune protein prediction in Multiple Myeloma. Key areas of contribution include healthcare AI ethics, cross-institutional model validation, and LLM applications in medicine. Her work bridges statistical rigor with real-world clinical challenges, emphasizing generalizability and fairness in machine learning systems.

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +3 more

Andrew Parry is a PhD Student and Tutor in the School of Computing Science at the University of Glasgow. His research focuses on advancing information retrieval (IR) systems through machine learning and large language models (LLMs), with particular expertise in ranking models, interpretability frameworks, and evaluation methodologies. He has contributed to frameworks like PyTerrier and SimIIR, and his work bridges theoretical IR advancements with practical applications in generative search and relevance assessment. Education: Pursuing PhD in Computing Science at the University of Glasgow. Research Interests: Parry explores mechanistic interpretability of IR systems, the role of LLMs in relevance assessment, and the ethical implications of neural retrieval models. His work emphasizes transparency in search algorithms and the development of robust evaluation techniques for large-scale systems. Key Publications: He has published at top venues including SIGIR, WSDM, and ECIR, with notable contributions on declarative RAG pipelines, LLM-based relevance assessment discrepancies, and corpus subsampling strategies. His 2025 work on MechIR proposes a novel interpretability framework, while recent collaborations address contamination in ranking distillation and interactive search simulations. Advising & Grants: As a Tutor, Parry supports teaching and research supervision within the School of Computing Science. His research is aligned with broader efforts in the Glasgow Information Retrieval Group, though specific grant details are not disclosed in available texts. Labs/Teams: Active member of the University of Glasgow's Terrier Team, contributing to TREC evaluations and collaborative workshops like ECIR's Collab-a-thon.

Zoey Zhiyu ChenView profile

Assistant Professor

• Artificial Intelligence
• Natural Language Processing
• Healthcare Technology
• +3 more

Zoey Zhiyu Chen is an Assistant Professor in the Department of Computer Science at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. Her research focuses on advancing artificial intelligence, particularly in natural language processing (NLP), healthcare technology, financial technology, and dialogue systems. She explores the application of large language models (LLMs) in critical domains such as mental health therapy, cognitive behavior therapy (CBT), and finance, emphasizing robustness and ethical considerations. Key research areas include hypothesis discovery, rule learning, patient simulation for mental health training, and improving medical predictions through multimodal data analysis. Her work also addresses challenges in retrieval-augmented models, agentic search optimization, and zero-shot dialogue state tracking. Chen’s contributions span theoretical advancements in AI reasoning and practical applications in societal domains like law and healthcare. Her articles highlight trends in leveraging LLMs for clinical support, financial analysis, and improving human-AI interaction through preference learning and function calling mechanisms. She advocates for responsible AI development, as seen in studies on energy efficiency benchmarks (Hulk) and domain-specific robustness (RobustFin).

Peter L. ElkinView profile

Professor

• Biomedical Informatics
• Artificial Intelligence in Healthcare
• Natural Language Processing
• +3 more

Peter L. Elkin, MD, MACP, FACMI, FNYAM, is Professor and Chair of the Department of Biomedical Informatics and Professor of Internal Medicine at the University at Buffalo's Jacobs School of Medicine & Biomedical Sciences. He leads research in biomedical informatics, focusing on AI-driven healthcare solutions, terminology standards (e.g., SNOMED CT), NLP applications for clinical data, and analytics for substance use disorders. Affiliated with UBMD for patient care, he bridges clinical practice with translational research. Education: BS, Union College, Cum Laude (1978) MD, New York Medical College (1985) Residency, Lahey Clinic Fellowship in Biomedical Informatics, Massachusetts General Hospital (1990) Certificates: Biomedical Informatics (ACMI, 2006), ACP (2009), Internal Medicine (NYAM, 2012) His research integrates AI, NLP, and big data to address clinical challenges, including opioid/alcohol use disorders, long COVID phenotyping, and equitable ML algorithms. Recent work emphasizes semantic AI chatbots, generative AI infrastructure, and RNA editing implications in disease. Publications (2021–2025) reflect interdisciplinary trends: AI validation in medical exams (USMLE), opioid epidemiology in NY State, bias mitigation in ML, and NLP-enhanced EMR analytics. Themes include clinical AI ethics, public health informatics, and scalable terminology systems. Awards: Mastership, American College of Physicians (2009) Homer R. Warner Award (1998) Mayo Laureate Award (2005) Fellowships: ACMI (2006), NYAM (2012), Royal Danish Society (2007) He co-leads NIH/VA grants (e.g., CTSA, BD-STEP) and mentors through informatics training programs. As department chair, he oversees labs advancing NLP, clinical AI, and terminology standards.

Dongjin SongView profile

Assistant Professor

• Continual Learning
• Graph Machine Learning
• Time Series Analysis
• +2 more

Dr. Dongjin Song is an Assistant Professor in the School of Computing at the University of Connecticut, specializing in continual learning, graph representation, and time series analysis. He holds a PhD from UC San Diego (2016) and previously worked at NEC Labs America. His NSF CAREER award (2024) supports research on evolving graph learning for applications in healthcare, energy, and transportation. Recognized with the Frontiers of Science Award (2024) and UConn AAUP Excellence Award (2025), he develops algorithms addressing catastrophic forgetting and generalization in dynamic systems. Research interests include: Robust representation learning for time-series and graph data Meta-knowledge distillation for heterogeneous systems Privacy-preserving federated learning LLM-enhanced multimodal forecasting He actively contributes as Area Chair at NeurIPS and Associate Editor for Neural Networks . Educational initiatives integrate research into AI/ML courses, and outreach targets K-12 STEM engagement. Current projects explore power outage prediction, clinical time-series analysis, and cross-platform mental health monitoring.

April Yi WangView profile

Assistant Professor

• Human-Computer Interaction
• Educational Technology
• Programming Support Systems
• +4 more

April Yi Wang is a tenure-track Assistant Professor at the Department of Computer Science, ETH Zürich, leading the PEACH Lab. She holds core faculty roles at the Institute for Intelligent Interactive Systems and the ETH AI Center. Her research focuses on human-centered approaches in programming, education technology, and data science collaboration. She earned her PhD from the University of Michigan (2023) and MSc from Simon Fraser University (2018), advised by Steve Oney and Christopher Brooks. Education: PhD in Information Science, University of Michigan (2018–2023) MSc in Computer Science, Simon Fraser University (2016–2018) B.Eng in Computer Science, Zhejiang University (2013–2016) Research Interests: Human-Computer Interaction (HCI) Programming Support Systems Collaborative Data Science AI-Enhanced Education Literate Programming Accessibility in Technology Recent Work Trends: Her 2025 publications emphasize AI-driven educational tools (e.g., Math2Visual for math pedagogy, datAR for data literacy), emotion-aware moderation systems, and studies on workplace multitasking. Her work bridges HCI with computational education, focusing on intuitive programming interfaces and inclusive design. Awards: Gary M. Olson Award (2023), ACM CHI Honorable Mentions (2023/2020/2018), Rising Stars in EECS (2022). Grants: Innovedum funding for Coducate project (2025). Lab Focus: Designing expressive systems for programming and data literacy through visual/tangible interfaces, AI co-decomposition tools, and interdisciplinary metaphors. Teaching: Courses on Human-Computer Interaction, Educational Technology, and Mixed Reality at ETH Zürich.

Premtim SahitajView profile

Researcher

• Knowledge-based reasoning
• Multimodal Large Language Models
• Retrieval-Augmented Generation (RAG)
• +5 more

Premtim Sahitaj is a researcher and doctoral student affiliated with the Q&U Lab of the XplaiNLP group, focusing on computational approaches to fake news detection under the BMBF-funded news-polygraph project. He holds a B.Sc. in Business Information Systems and an M.Sc. in Data Science from the University of Trier, where he previously worked as a research assistant on the ReCAP project (DFG priority program RATIO) addressing natural language processing in argumentation domains. His research interests span knowledge-based reasoning (multimodal information extraction, RAG with LLMs, data synthesis), language modeling (multimodal LLM architectures, pre-training optimization), and applications in low-resource languages (e.g., Albanian). He actively supervises student theses and advanced projects through the Q&U Lab. Prior work includes integrating background knowledge into LLMs' argumentation processes and evaluating information representation strategies. His doctoral research aims to develop computational frameworks supporting investigative journalism through fake news detection platforms.

• Gamification
• Virtual Reality
• Extended Reality
• +5 more

Nannan Xi is an Associate Professor (tenure track) in the department of Information and Knowledge Management. Her research focuses on gamification, virtual and augmented reality technologies, and their applications in areas such as elderly well-being, consumer behavior, and ethical AI systems. She has collaborated extensively on studies involving the metaverse, service robots, and immersive technologies. Key research areas: Gamification design, extended reality (XR), AI ethics, and sustainable consumption. Notable contributions include experiments on metaverse shopping acceptance, gamification in healthcare, and ethical considerations in AI agents. Her work aligns with UN Sustainable Development Goals related to health and innovation. She has organized academic events such as workshops at the Pacific Asia Conference on Information Systems (2023) and contributed to conferences like ICIS 2024. Publications span journals like Technological Forecasting and Social Change and Internet Research , with a focus on empirical studies and systematic reviews.

• Artificial Intelligence in Education
• Generative AI and Teamwork
• Crowdfunding and Entrepreneurship
• +4 more

Ethan R. Mollick is an Assistant Professor at the University of Pennsylvania's Wharton School, specializing in the intersection of Artificial Intelligence, education, and organizational behavior. His research focuses on generative AI applications in knowledge worker productivity, teamwork, and learning, as well as crowdfunding dynamics and entrepreneurial ecosystems. Current faculty at Wharton School, University of Pennsylvania Research areas: AI in education, team innovation, crowdfunding, prompt engineering His work includes field experiments and LLM benchmarking , addressing topics like AI compliance, human-machine co-creation, and gender disparities in funding. Notable collaborations include studies with Lilach Mollick and Venkat Kuppuswamy. Recent AI Trends : Human-AI co-teaming, prompt strategy, ethical AI Entrepreneurship : Solo vs. team ventures, crowdfunding outcomes, female founder equity