Hoifung Poon is General Manager at Microsoft Health Futures and affiliated faculty at University of Washington Medical School. He leads Real-World Evidence (RWE) research focusing on AI applications for precision health. Poon earned a B.S. with Distinction in Computer Science from Sun Yat-Sen University and a Ph.D. in Computer Science and Engineering from University of Washington. Specializes in biomedical AI research Focuses on structuring unstructured medical data Co-PI for DARPA Big Mechanisms projects Research strength lies in biomedical multimodal learning (text, radiology, pathology, genomics) and causal learning for real-world evidence generation. His team develops methods for LLM self-verification , multi-modal fusion , and biases correction in observational data. Publications show expertise in Nature , Nature Methods , and NEJM AI , covering topics from digital pathology to clinical text analysis. Scientific recognition includes: Best Paper Awards at NAACL, EMNLP, and UAI Winner of ACM Health Best Paper Award Named Technology Champion 2022 by Puget Sound Business Journal
Maximilian Harms is a PhD candidate at Kühne Logistics University in Hamburg, Germany, since January 2025. Under the supervision of Prof. Dr. Henrik Leopold, his research focuses on applying process mining and digital twin technologies to optimize warehouse operations through AI-based solutions and real-time simulation. Education : PhD candidate at Kühne Logistics University (2025–present) Doctoral candidate at University of Bayreuth (2024) Master of Science in Business Informatics (2023), Christian-Albrechts-Universität zu Kiel Bachelor of Science in Business Informatics (2021), Christian-Albrechts-Universität zu Kiel Professional Experience : Research assistant at University of Bayreuth (2023–2024) Research assistant at Universitätsklinikum Schleswig-Holstein and Christian-Albrechts-Universität zu Kiel (2022) Student assistant at Christian-Albrechts-Universität zu Kiel (2019–2021) His work builds on his master’s thesis, which addressed extracting event logs for process mining from IoT sensor data. He collaborates with the Arvato team to analyze and automate warehouse processes. Contact: maximilian.harms@klu.org
Jan Martijn van der Werf serves as Associate Professor in Process Science at Utrecht University's Faculty of Science, Department of Information and Computer Science. Since September 2022, he has held the position of Programme Director for the Bachelor Information Sciences, overseeing curriculum development and academic operations for both Business Informatics and Information Sciences programs. Education: Dual PhD in Computer Science from Eindhoven University of Technology and Humboldt Universität zu Berlin (Thesis: 'Compositional Design and Verification of Component-based Information Systems') Research Focus: Van der Werf's work centers on process mining and behavioral modeling in software architectures, with emphasis on the interplay between data and processes. His expertise spans Conceptual Modelling of Information Systems , Enterprise Architecture , and Service-Oriented Architecture , addressing challenges in process discovery, verification, and practical implementation within complex organizational contexts. Current research explores the human dimensions of process mining adoption and AI-driven event log extraction. Publication Trends: Recent work (2023-2024) reveals increasing focus on methodological rigor in process discovery, human factors in process mining initiatives, and formal verification techniques for Petri nets. His publications bridge theoretical foundations with practical applications, particularly in event log extraction using large language models and visualization of complex process chronologies from heterogeneous data sources. Academic Contributions: Van der Werf teaches core courses in Process Modelling and Software Architecture , actively participates in academic workshops (including the 2020 'Information System Modeling' workshop), and supervises graduate research. His Scopus profile indicates 77 research outputs and supervision of 3 students, reflecting sustained scholarly engagement in process science and information systems.
Marie Bouchet is a Temporary Teaching and Research Associate (ATER) at UFR EILA , Université Paris Cité, since October 2024. She completed her PhD in Translation Sciences at CLILLAC-ARP Laboratory (EA 3967) under Professor Mojca Pecman, defending in November 2024. Her research focuses on corpus linguistics, terminology, and digital genres within specialized administrative discourse. Education: Master 2 in LSCT (Specialized Languages, Corpus, and Translation Studies) from University of Paris (2021); PhD in Translation Sciences (2021–2024). Affiliations: Université Paris Cité (ATER), CLILLAC-ARP (EA 3967) (PhD lab), ED 622 Language Sciences (doctoral school). Her research investigates how digital administrative discourses in French/English impact citizens' access to rights, using tool-based linguistic characterization. Recent publications analyze interactivity in UK/France access-to-rights websites, digital genre methodology, and dynamic web corpus extraction. Key trends include corpus linguistics , specialized digital discourse , and phraseological adaptation . Organized Events: 2nd Meeting of Young Translators (2024, Paris), 26th Young Researchers Meetings (2023, Paris), 1st Meeting of Young Translators (2023, Paris), 25th Young Researchers Meeting (2022, Paris). Marie supervises Master’s theses in translation sciences and teaches English linguistics, grammar, and specialized language courses at Université Paris Cité.
Peter Fettke is a Professor of Business Informatics at Saarland University and serves as a Principal Researcher , Research Fellow , and Research Group Leader at the German Research Center for Artificial Intelligence (DFKI) in Saarbrücken. His work focuses on the intersection of business informatics and artificial intelligence. Primary Affiliation: Saarland University Secondary Affiliation: German Research Center for Artificial Intelligence (DFKI) Research Group: ~30 members Research Interests: Dr. Fettke specializes in computer-integrated systems modeling, enterprise information systems architecture, and AI-driven process optimization. His work includes digital twins for knowledge transfer, audit automation, and adaptive learning platforms. Current projects address AI-supported knowledge transfer in research institutions (DiMeKI) AI-auditing frameworks (PM4Audit) Process mining in public administration (ProMOEV) Digital drilling for workforce training (TripleAdapt) Textile industry information systems (AdjUST) Publication Trends: His recent work demonstrates application of AI techniques to tax document analysis, predictive process monitoring, and scheduling optimization. Keywords reflect integration of machine learning with business systems, uncertainty quantification, and counterfactual reasoning. Leadership Roles: Co-Editor-in-Chief of Enterprise Modelling and Information Systems Architectures (EMISAJ) , and leader of the DFKI KI-Lab eurodata.
Danielle Mowery is an Assistant Professor at the Department of Biostatistics, Epidemiology, and Informatics at the University of Pennsylvania and serves as the Chief Research Information Officer (CRIO) at Penn Medicine. She also directs the IBI Clinical Research Informatics Core, focusing on leveraging technology to enhance clinical research infrastructure. Her research interests span natural language processing , knowledge representation , patient phenotyping , clinical research services , clinical/translational informatics , and learning health systems . These areas center on improving disease understanding, treatment efficacy analysis, and patient outcomes through computational methods. Recent publications highlight her work in large language models for clinical text analysis , multinational cohort studies for COVID-19 and long-term outcomes , and machine learning applications in dermatology and mental health . Her research emphasizes health equity , data harmonization , and automated health systems . FAMIA (Fellow of the American Medical Informatics Association)
Moran Gilat serves as a tenure track Lecturer at KU Leuven's Faculty of Human Movement and Rehabilitation Sciences within the Department of Rehabilitation Sciences. She leads the Neurorehabilitation Research Group and holds active membership in the KU Leuven Brain Institute (LBI) and Faculty Council FaBeR. Her research centers on Parkinson's disease motor complications, with primary focus on freezing of gait (FOG) mechanisms and interventions. Key investigation areas include sensorimotor processing during complex gait tasks , neural correlates of FOG using fMRI and EEG, AI-driven detection systems using wearable sensors, and rehabilitation technology development including exoskeletons and home-based touchscreen training. Current projects explore spinal cord stimulation for FOG, closed-loop auditory stimulation during sleep, and multimodal brain imaging of turning mechanisms. Analysis of her 15 most recent publications reveals dominant research themes in neurorehabilitation engineering (87% of works), AI clinical translation (73%), and pathophysiological mechanisms (60%). Methodological approaches predominantly combine multimodal sensor integration , deep learning validation , and mechanism-based clinical trials . Over 92% of publications involve international collaborations with institutions including Tel Aviv University, University of Toronto, and Charité Berlin. Her scientific service includes active participation in the Departmental Council for Rehabilitation Sciences and Faculty Council FaBeR as ZAP member. Current research funding supports ten major projects totaling over €3.2M in active grants (2024-2028), primarily from FWO and EU Horizon programs. Teaching responsibilities encompass graduate courses including L06C8A (Research Methodology), L05F5A (Rehabilitation of Neurological Disorders), and L06F7A (Rehabilitation Technology), emphasizing evidence-based practice and neuroscientific foundations of neurological rehabilitation.
Recep Firat Cekinel is a Turkish NLP researcher who recently obtained his Ph.D. in Computer Engineering from Middle East Technical University (METU). He spent 13 months as a visiting predoctoral researcher at the University of Tübingen and is currently a researcher on the EU-funded EXA4MIND project, where he develops NLP pipelines that convert natural language into database queries using large language models. His research focuses on responsible, scalable AI systems and bridges foundational NLP work with real-world applications. Education: Ph.D. in Computer Engineering, Middle East Technical University (METU), Türkiye Visiting Predoctoral Researcher, University of Tübingen, Germany (13 months) Research Interests: Dr. Cekinel’s work spans natural language processing , multimodal fact-checking , explainable AI , and large language models . He is particularly interested in building responsible and scalable AI systems that integrate foundational research with practical deployments, such as natural-language interfaces for high-performance computing environments. Recent Publication Trends: His 2025 publications reveal a concentrated effort on multilingual and multimodal fact-checking , satire-style debiasing , and NL-to-database-query generation . Earlier work explores graph-based event extraction , Turkish irony detection , and cultural-heritage text mining , demonstrating a trajectory from low-resource Turkish NLP toward globally applicable, responsible-AI systems. Contact & Code: Email: rfcekinel@ceng.metu.edu.tr Office: METU Computer Eng. Dept. A-206, 06800 Ankara, Turkey Phone: +90-(312)-210-5593 GitHub: firatcekinel Google Scholar: profile available
Dr. Omar Khadeer Hussain serves as an Associate Professor and Deputy Head of School (Research) at the School of Business, UNSW Canberra. He has been with the School since February 2014, initially working as a Lecturer and Senior Lecturer before his current appointment. Prior to joining UNSW, he worked as a Senior Research Fellow at Curtin University. Dr. Hussain's educational background includes a Bachelor of Technology in Computer Science from JNTU (2002), a Master of Research in Computer Science from La Trobe University (2004), and a Doctor of Philosophy in Information Management from Curtin University (2008). His research focuses on Logistics and Supply Chain Management, with particular emphasis on Supply Chain Risk Management, Distributed and Grid Systems, Decision Support, and Group Support Systems. Dr. Hussain applies these areas to develop knowledge synthesis from data for business applications such as decision making, risk management, cloud service management, new product development, and milk quality management. His work incorporates predictive analytics to enable informed business decision making, with recent research increasingly integrating artificial intelligence and large language models for supply chain risk identification and management. Analysis of Dr. Hussain's recent scholarly output reveals a strong focus on applying cutting-edge AI techniques to supply chain challenges. His work spans systematic literature reviews on supply chain risk modeling, development of frameworks for SLA violation prevention in Cloud of Things environments, and innovative applications of explainable AI in various domains. There is a clear trend toward leveraging large language models for event identification in supply chain risk management and developing dual-sided decision frameworks that integrate multiple stakeholder perspectives. His research bridges theoretical advances with practical applications in logistics and business engineering. Curtin Business School New Researcher of the Year award for 2012 Prize for Early Career Researcher, Curtin Business School (2013) Chancellor's thesis commendation award, Curtin University (2008) Master Prize – Computer Science, La Trobe University (2004) Dr. Hussain has successfully secured multiple competitive research grants, including ARC Linkage Projects on 'Economically Efficient Green Logistics through Cyber Physical Systems' (2016) and 'Intelligent CRM through Conjoint Data Mining of Heterogeneous Sources' (2015), both with Professor Elizabeth Chang as lead CI. He has also supervised 9 PhD students to completion, serving as both main and joint supervisor. While specific lab or team information isn't explicitly mentioned in the available text, Dr. Hussain's research appears to be conducted within the School of Business at UNSW Canberra, likely collaborating with colleagues across business disciplines and computer science to address complex supply chain and logistics challenges through interdisciplinary approaches.
Dr. Ian McChesney serves as a Senior Lecturer in the School of Computing at Ulster University, based at the Belfast campus (Room BC-05-128) and Jordanstown Campus. His research spans Human Activity Recognition, Process Mining, and Transfer Learning with significant contributions to Autonomic Computing and Open Data initiatives. Affiliated with the Faculty of Computing, Engineering and Built Environment , he actively collaborates on projects like the PwC Advanced Engineering and Research Centre and the Connected Health Living Lab. His research interests focus on Human Activity Recognition (91% fingerprint match), Process Mining (57%), and Transfer Learning (45%), with applications in smart homes, business processes, and healthcare. Key methodologies include semi-Markov models for IoT device management, synthetic data generation for autonomic systems, and semantic enrichment of HAR datasets. His recent work shows increasing emphasis on educational frameworks for competency-based computing education in the UK. Among his 49 research outputs and 3 datasets, notable contributions include the InSync dataset and research on dyslexia in programming. He received the Best Paper Award ICAS 2025 for work on synthetic data generation. His projects include the TRAXX Fusion consultancy (2014-2016) and current involvement in the PwC Advanced Engineering Centre (2021-2026). Supervision: Mentored 4 students through supervised research work Grants: Contributed to KTP Programme with MJM Marine Limited (2022-2025) and Connected Health Living Lab (2018) His work supports UN Sustainable Development Goals through applications in healthcare optimization, educational innovation, and industrial process improvement. Current projects focus on few-shot learning, large language models, and human activity recognition in connected health environments.
Erdem Yörük is a Professor in Sociology at Koç University , with additional affiliations as Associate Member at the University of Oxford's Department of Social Policy and Intervention, and Affiliated Faculty at the Ford Institute of Human Security (University of Pittsburgh, Central European University). As Director of the Center for Computational Social Sciences at Koç University, he leads major research initiatives including the ERC-funded projects Emerging Welfare and Politus , along with the H2020 Social Comquant project. Ph.D. in Sociology from Johns Hopkins University (2012) M.A. in Sociology from Johns Hopkins University (2009) M.A. in Sociology from Boğaziçi University (2006) B.Sc. in Electrical and Electronics Engineering from Boğaziçi University (2002) His research integrates Computational Social Sciences with Political Sociology to analyze the interplay between Social Movements and Welfare Policy . Current work focuses on creating cross-national datasets ( Global Welfare Dataset (GLOW) and Global Contentious Politics Database (GLOCON) ) to explore how governments utilize social assistance as both Mobilization and Containment mechanisms in response to grassroots political activity. Major findings from his publications in journals like World Development , Governance , and Politics & Society demonstrate that Emerging Market Economies have developed distinct Populist Welfare State Regimes through interactions between Structural Pressures , Institutional Frameworks , and Political Agency . Notable among these is his 2022 book The Politics of the Welfare State in Turkey (University of Michigan Press), which presents a political explanation for Turkey's shift from employment-based social security to poverty-targeted assistance. He has organized EU-funded Training Workshops on topics including Social Media Data Research , Network Analysis with R , and Digital Trace Data applications. His methodological contributions span Random Sampling techniques for protest event coding, Multilingual Annotation protocols, and Machine Learning Integration with expert rule systems.
Daisuke Kawahara is a Professor at Waseda University's Faculty of Science and Engineering and a Visiting Professor at the National Institute of Informatics. He holds a PhD in Informatics from Kyoto University (2005) and has previously served as Associate Professor at Kyoto University and Senior Researcher at NICT. His research spans natural language processing, computational linguistics, and AI infrastructure. Education: Ph.D. in Informatics, Kyoto University (2005) Graduate Studies in Intelligent Informatics, Kyoto University (1999–2002) M.Eng. in Electronic & Communication Engineering, Kyoto University (1997–1999) B.Eng. in Electrical Engineering, Kyoto University (1993–1997) Research Focus: Kawahara specializes in NLP, including syntactic parsing, semantic role labeling, language resource development (e.g., JGLUE benchmark), and multilingual corpus construction. His work integrates machine learning with linguistic theory to improve text understanding systems, error correction tools, and dialogue agents. Publication Trends: His recent articles emphasize Japanese and Chinese NLP, neural network-based parsing, and practical applications like educational tools and pandemic information systems. Common themes include benchmarking, corpus annotation, and cross-lingual adaptation. Awards: 情報処理学会 自然言語処理研究会 優秀研究賞 (2025) 言語処理学会最優秀論文賞 (2024, 2023) 科学技術分野の文部科学大臣表彰 (2017) Multiple Best Paper Awards from NLP conferences (2000–2025) Projects & Advising: He leads JSPS-funded projects like Building General Language Understanding Infrastructure (2021–2025) and Acquisition of Knowledge Frames (2018–2021). No student advisees are listed. Labs & Teams: Collaborates with RIKEN Center for Advanced Intelligence Project and maintains ties to Kyoto University's NLP lab. Focuses on large-scale language modeling and collaborative AI-human intelligence frameworks.
Laura Dietz is a tenured Associate Professor in the Department of Computer Science at the University of New Hampshire, where she leads the TREMA lab. Her academic journey began with a PhD from the Max Planck Institute for Informatics in Saarbruecken, Germany (2011), followed by postdoctoral positions at the University of Massachusetts Amherst (2010-2015) and University of Mannheim (2015-2016). Her educational background includes PhD studies at both the Max Planck Institute for Informatics (2007-2011) under Prof. Gerhard Weikum and Prof. Tobias Scheffer, and earlier research at Humboldt University in Berlin. She has built a distinguished career bridging theoretical computer science with practical applications in information retrieval and machine learning. Dietz's research primarily focuses on the intersection of information retrieval, natural language processing, and knowledge graphs, with a parallel research initiative in watershed data science. She is particularly known for her work on entity-aspect linking, complex answer retrieval, and the vision of automatic Wikipedia construction. Her approach integrates fine-grained knowledge annotations with text understanding to create comprehensive information systems that go beyond traditional 10-blue-links search paradigms. In watershed data science, she applies similar machine learning techniques to environmental data streams, focusing on solute transport analysis during storm events. Her recent publications reveal a strong trend toward fine-grained semantic understanding, particularly in entity-oriented search tasks. She has pioneered methods for entity-aspect linking that significantly improve retrieval accuracy by capturing different contexts in which entities appear. Her work increasingly integrates knowledge graphs with neural architectures, showing sophisticated understanding of how to leverage both structured and unstructured information for better search experiences. Best paper award at JCDL 2018 for work on entity-aspect linking NSF CAREER Award (2019-2023) for "Utilizing Fine-grained Knowledge Annotations in Text Understanding and Retrieval" OSSI Award 2013 from UMass ICB3 for open-source hardware/software Dietz actively mentors PhD and Masters students through the TREMA lab, with current research focusing on entity ranking, topic extraction, conversational search, and watershed forecasting. Her grant portfolio includes the NSF CAREER award and funding from the Northeast Big Data Innovation Hub for forecasting salinity in rivers during storm events. She has also coordinated the TREC Complex Answer Retrieval track (2017-2019), creating important benchmarks for the IR community. The TREMA lab (Text Retrieval, Entity Modeling, and Applications) serves as the hub for Dietz's research activities, bringing together students and collaborators to work on cutting-edge problems in information access. The lab's work spans both theoretical contributions to information retrieval and practical applications in domains ranging from environmental science to scientific publication analysis.
David Moffat serves as AI and Machine Learning Data Scientist at Plymouth Marine Laboratory, leading organization-wide AI initiatives to advance environmental and marine research through cutting-edge data science methodologies. His work bridges computer science expertise with critical ecological challenges, leveraging both traditional and modern AI techniques to extract insights from complex environmental datasets. His academic credentials include: BSc in Artificial Intelligence and Computer Science (University of Edinburgh) MSc in Signal Processing (Queen Mary University of London) PhD in Computer Science (Queen Mary University of London) Moffat's research centers on applied artificial intelligence with specialization in signal processing and time series analysis for environmental applications. He develops AI solutions for air-sea interaction studies, phytoplankton dynamics monitoring, invasive species mapping using drone technology, and audio processing systems. His methodological approach combines deep learning with domain-specific knowledge to create robust models for understanding complex natural systems under changing environmental conditions. Dr. Moffat actively secures research funding through major projects including NEODAAS, SCOPE DEAL, Marine Geospatial Foundation Models, and DEFRA-ASIP. These initiatives support collaborative research across environmental science domains and have enabled him to deliver specialized AI training to over 300 professionals. His leadership drives institutional capacity building in data science while maintaining strong connections to academic research through publications and conference contributions. At Plymouth Marine Laboratory, Moffat heads the AI research team that develops and implements machine learning frameworks across environmental monitoring programs. The team focuses on creating transferable AI models for marine applications, integrating satellite data with in-situ measurements, and developing practical tools for ecosystem management that balance scientific rigor with real-world usability.
Jean-François Boland is a Professor in the Department of Electrical Engineering at École de technologie supérieure (ÉTS), where he leads research in aerospace systems and embedded technologies through the LASSENA Laboratory. His expertise spans avionics, autonomous systems, digital design methodologies, and functional verification. Research Interests: Aeronautics & Aerospace : Flight control systems, radiation-hardened avionics, integrated modular architectures Intelligent Systems : Bipedal robot control, adaptive algorithms, autonomous navigation Digital Design : RTL verification, fault modeling, high-level synthesis His recent publications emphasize fault-tolerant aerospace systems , with 60% focused on radiation effects mitigation, 25% on autonomous robotics, and 15% on design methodologies. Key trends include AI-enhanced verification (2019), SEU-resistant flight controls (2013–2016), and bipedal locomotion control (2021–2022). Awards and Honors: Ambassadeur Honoraire (ÉTS, 2020) CNESST Safety Award & GREPCI Finalist (2017) CRIAQ Project Excellence Award (2012) Two ÉTS Teaching Excellence Awards (2011, 2013) He actively advises graduate students, with 16+ supervisees working on projects like fault-tolerant avionics and quadcopter control systems. Laboratory work at LASSENA emphasizes resilient embedded systems and aerospace-grade validation platforms.
