Francesca Dalia Faraci is a Senior Lecturer and Researcher at the Department of Innovative Technologies (DTI) of SUPSI. She holds a PhD in Electronics from the University of York (UK) and a Laurea in Physics from the University of Genoa (Italy). Her research focuses on biomedical signal processing, statistical data analysis, and wearable technology applications in healthcare. She leads projects such as Cardio MIPA (arrhythmia monitoring) and AutoPlay_DD (child development analysis via smart toys). Key contributions include developing algorithms for sleep staging, arrhythmia detection, and synthetic ECG generation. Awards include the GOSPEL and DoE School Certificates. She coordinates research projects and teaches courses in Physics and Applied Statistics. Education: PhD in Electronics (University of York, 2006), Laurea in Physics (University of Genoa, 2002) Roles: Senior Lecturer, Researcher, Project Coordinator Key Projects: Tackling teleWorking Impacts, my Doctor's Lifestyle (lifestyle medicine), Telemonitoring Breast Cancer Her research bridges AI and clinical practice, emphasizing ethical frameworks and complementarity between clinicians and AI systems. Recent work includes causal inference for sleep dynamics and bias quantification in healthcare algorithms.
Haihua Chen is an Assistant Professor of Data Science in the Department of Information Science at the University of North Texas (UNT), with a co-affiliation in Health Informatics. They lead the Intelligent Data Engineering and Analytics (IDEA) Lab, focusing on interdisciplinary research in artificial intelligence, data science, and informatics. Chen earned a Ph.D. in Information Science (concentrating in Data Science) from UNT in 2022, an M.S. in Information Science from Wuhan University, and dual B.S. degrees in Information Science and English Literature from Central China Normal University. Research interests span applied machine learning, data quality evaluation, NLP, and informatics applications in legal and healthcare domains. Notable work includes developing frameworks for measuring scientific novelty, constructing high-quality legal and biomedical datasets, and leveraging AI for precision medicine and disaster response. Chen has secured over $499K in external grants, including NSF REU and HSI projects, and $20K+ in internal grants. Their work has been published in top journals like Journal of Informetrics , IEEE Transactions on Reliability , and Scientometrics , with a strong focus on innovation measurement and data-centric AI. Teaching includes courses on computational methods, data analysis, and AI in healthcare. Professional leadership roles include chairing ASIS&T SIG-STI and editorial roles for Journal of the Association for Information Science and Technology , Knowledge and Information Systems , and others. Awards include UNT’s Great Grads Award and the Linda Schamber Writing Award.
Intae Moon, PhD is a Research Fellow in Biomedical Informatics at Harvard Medical School, working within the Department of Biomedical Informatics under the Zitnik Lab. His research sits at the intersection of medicine and artificial intelligence, focusing on developing evidence-based AI decision support tools to improve cancer patient care while addressing AI bias concerns. Dr. Moon completed his PhD in Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT) and earned his B.S. in Electrical and Computer Engineering from the University of Illinois, Urbana-Champaign. His academic background bridges engineering and medical applications, positioning him uniquely in the biomedical informatics field. His research interests span artificial intelligence applications in healthcare, particularly in cancer diagnostics and treatment prediction. He specializes in leveraging clinical data and machine learning to address challenges in cancer of unknown primary (CUP) classification, immunotherapy outcome prediction, and cardiovascular disease detection. His work emphasizes developing generalizable models that maintain accuracy across diverse patient populations while mitigating algorithmic bias. Analysis of his publication record reveals a strong focus on applying machine learning to cancer of unknown primary classification and treatment response prediction, with recent expansion into immunotherapy outcome modeling across multiple cancer types. His research demonstrates increasing sophistication in handling complex clinical data and addressing the generalizability challenges that often plague medical AI systems. 2022 Charles J. Epstein Trainee Awards for Excellence in Human Genetics Research 2022 Carlton E. Tucker Award for teaching excellence Dr. Moon's research has gained significant attention, with his work featured in MIT News, DFCI News, and Nature Portfolio. His publications have been cited extensively, with his 2023 Nature Medicine paper on cancer of unknown primary receiving over 70 citations. His research has been picked up by 33 news outlets, blogged about 3 times, posted on X by 280 users, mentioned on 3 Facebook pages, and discussed on Reddit. He collaborates extensively within Harvard's biomedical research ecosystem, particularly with researchers in the Zitnik Lab and other faculty in the Department of Biomedical Informatics including Alexander Gusev, Marinka Zitnik, Jaclyn LoPiccolo, and Kenneth Kehl. His work spans multiple domains including cancer research, cardiovascular diagnostics, and AI methodology development.
Zhiyi Huang is an Associate Professor of Computer Science at the University of Hong Kong, leading the Computer Science Division within the School of Computing and Data Science. He holds a PhD from the University of Pennsylvania (2013) and completed a postdoctoral fellowship at Stanford University (2013–2014). His research focuses on Theoretical Computer Science, Algorithmic Game Theory, Online Algorithms, and Differential Privacy, with notable contributions to Machine Learning and Computer Networks. Education: PhD in Computer and Information Science, University of Pennsylvania (2013) Postdoctoral Researcher, Stanford University (2013–2014) Bachelor's Degree from the Yao Class at Tsinghua University (2008) Research interests span foundational areas including algorithmic game theory, online optimization, and privacy-preserving mechanisms. He has pioneered work on revenue maximization in single-parameter settings and developed novel frameworks for analyzing price of anarchy in game theory. Key Awards: Early Career Award (Research Grant Council of Hong Kong, 2014) Best Paper Award at ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2015) Morris and Dorothy Rubinoff Dissertation Award (2013) Simons Graduate Fellowship in Theoretical Computer Science (2012–2013) Recent grants include studies on algorithmic foundations of Bayesian mechanism design (HK$675,647), online primal dual techniques (HK$496,028), and privacy-preserving mechanisms (HK$931,737). His work bridges theoretical advancements with practical applications in healthcare, autonomous systems, and cybersecurity. Notable Projects: Medical predictive systems for acute cardiopulmonary events AI-driven maritime navigation using AIS data Secure federated learning frameworks with blockchain
Michal A. Mankowski, PhD, is an Assistant Professor in the Department of Surgery at NYU Grossman School of Medicine, where he conducts impactful research in transplantation medicine. His work bridges clinical needs with data science and policy innovation, focusing on improving equity and efficiency in organ allocation systems. His research interests center on transplantation bioinformatics , HLA immunogenetics , and health equity , with a strong emphasis on kidney and liver allocation. He investigates how electronic health records and large language models can be leveraged—and secured—within clinical transplant settings. His work also explores the impact of patient comorbidities, such as delirium, on transplant access and outcomes. Recent publications reveal a strong trend toward integrating data science , machine learning , and policy modeling in organ transplantation. His research spans technical immunogenetic analysis (e.g., eplet mismatch) to ethical and operational challenges in national allocation systems. He frequently collaborates with leaders in transplant epidemiology and bioinformatics, contributing to high-impact journals such as American Journal of Transplantation and Nature Medicine . Scientific Awards : No awards are listed in the provided text. Advising and Grants : While no specific students or grants are listed, his role as an Assistant Professor and active publication record suggest involvement in mentoring graduate or medical students and securing research funding in transplantation informatics and policy. His collaborations indicate participation in multi-institutional research teams and likely grant-funded projects related to organ allocation and AI in medicine. Labs and Teams : Dr. Mankowski is part of a robust transplant research ecosystem at NYU Grossman School of Medicine, collaborating extensively with teams focused on transplant outcomes, immunogenetics, and health services research. He contributes to projects involving large databases such as the Scientific Registry of Transplant Recipients (SRTR) and Epic Cosmos, suggesting integration within data-driven clinical research networks.
Dr. Aenor Sawyer is an Associate Professor in the Department of Orthopaedic Surgery at the University of California, San Francisco (UCSF) School of Medicine. She leads the Skeletal Health Service and serves as Director of Innovator Enrichment and Manager in Strategic Alliances at UCSF Innovation Ventures while directing the UC Space Health Program. Education: MD from University of California, Davis (1993), Orthopaedic Surgery residency at Stanford University, Pediatric Orthopaedic and Sports Medicine fellowships at Boston Children's Hospital Research Focus: Combines orthopaedics, space health innovation, and digital medicine through AI-driven diagnostics, remote monitoring, and skeletal health optimization across lifespans Her scholarly output spans 2025's npj Microgravity work on AI-based bone density assessment to 2021 rural SARS-CoV-2 transmission studies, with key contributions in: Spaceflight-associated neuro-ocular syndrome monitoring Digital health applications in orthopaedics Bone health in extreme environments Leadership roles include: Co-Founder/Director, UCSF Center for Advanced 3D+ Technologies Co-Founder, UCSF Center for Digital Health Innovation Chair, Murdoch Children's Research Institute Health Technology Advisory Board
Claudia Pagliari is a Senior Lecturer in Primary Care and Informatics at the University of Edinburgh's Usher Institute, within the College of Medicine and Veterinary Medicine. She co-directs the MSc in Global eHealth and leads the NHS Digital Academy, while contributing to global health ethics advisory roles. Her work spans digital health, data science, and policy studies. Education : First-class Psychology degree (University of Ulster), PhD in Human Intelligence/Data Science (University of Edinburgh). Research Focus : Claudia directs the Interdisciplinary Research Group in eHealth, examining digital health ethics, governance, and equity. Her studies include contact-tracing apps, empathic robots, and social media misinformation, with cross-cutting interests in low- and high-income countries. Scientific Awards : Elected Honorary Fellow of the Royal College of Physicians of Edinburgh (2012). Chair of the National Expert Group in Digital Ethics (Scotland, 2020). Member of WHO Roster of Experts in Digital Health (2019). Chair of Scientific Board for NHS App Evaluation (2021). Advisory & Leadership : She advises the Scottish Government and WHO, contributes to national review boards, and serves as External Examiner for health informatics programs. Her teaching includes the Data Ethics MOOC and the Data Controversies lecture series.
Barry Haddow is a Senior Research Fellow at the Institute for Language, Cognition and Computation (ILCC) within the School of Informatics at The University of Edinburgh. With over 15 years of research experience, he coordinates major international projects in machine translation and natural language processing, including HPLT (Horizon Europe) and Utter (Horizon Europe). His work spans both theoretical and applied aspects of language technology with significant impact on multilingual systems. Haddow's research focuses on machine translation, particularly addressing challenges in low-resource languages, multilingual models, and speech translation. His recent work demonstrates a strong shift toward leveraging large language models for translation tasks while investigating their limitations in cross-lingual settings. He has made significant contributions to understanding how to adapt general language models for specialized translation scenarios and evaluating their performance across diverse language pairs. Analysis of Haddow's recent publications (2023-2025) reveals a clear research trajectory focusing on the intersection of large language models and machine translation. His work examines how to effectively adapt LLMs for translation tasks, investigates evaluation methodologies for multilingual systems, and develops techniques for improving translation quality across diverse language families. A notable trend is his focus on practical applications of these technologies in real-world settings, including financial language processing and speech translation systems. Haddow has coordinated multiple major European research projects including HPLT (2022-2025), Utter (2022-2025), GOURMET (2019-2022), ELITR (2019-2022), and HIML (2015-2018), securing substantial research funding from Horizon Europe and other sources. His work has established him as a leading figure in the machine translation community, regularly contributing to major shared tasks like WMT (Conference on Machine Translation). As a core member of the Institute for Language, Cognition and Computation at Edinburgh, Haddow collaborates closely with the Language Technology Group. His research directly contributes to the institute's mission of advancing computational approaches to human language processing, with particular emphasis on developing technologies that can operate effectively across the world's diverse languages.
Stephan Dilchert is an Associate Professor at the Zicklin School of Business, City University of New York (CUNY), where he serves as the Academic Director of the Executive MBA Program. He is affiliated with the Narendra Paul Loomba Department of Management. Previously, Dr. Dilchert served as Academic Director of the school's Executive Master's in Human Resource Management for seven years, demonstrating his leadership in executive education. Dr. Dilchert earned his PhD in Industrial/Organizational Psychology from the University of Minnesota in 2008. He also holds professional certifications including SPHR from the Human Resources Certification Institute (2014) and SHRM-SCP from the Society for Human Resource Management (2015), which complement his academic credentials with practical HR expertise. His research focuses on the influence of human capital characteristics such as personality and intelligence on employee job performance, including creativity and counterproductivity. Dr. Dilchert has published extensively in top journals and has co-edited the book 'Managing human resources for environmental sustainability.' His recent work has increasingly incorporated wearable sensor technology to examine physiological correlates of workplace behaviors and health outcomes, particularly through the TemPredict study. His research spans talent acquisition, people analytics, employee assessment, counterproductive work behaviors, and environmental sustainability. Dr. Dilchert's recent publications (2022-2025) reveal a strong interdisciplinary approach combining organizational psychology with digital health technologies. His work demonstrates growing interest in sex differences in physiological responses, environmental sustainability in organizations, and personality-environment alignment at work. The TemPredict study has been particularly influential in using wearable devices for early detection of health conditions including COVID-19, with significant contributions to understanding how physiological data relates to workplace behaviors and health outcomes. In his applied work, Dr. Dilchert has developed assessments and designed staffing and feedback systems for multinational corporations (P&G, MetLife, Otis, DHL), government agencies (Department of Justice, State of California), and international organizations (United Nations, Disability Rights Advocates). He has collaborated extensively with researchers including Deniz Ones and Boris Wiernik on meta-analytic studies examining personality-work relationships across cultural contexts. Dr. Dilchert teaches courses related to human resources, organizational behavior, and human capital management at both graduate and doctoral levels. His teaching portfolio includes 'HR Metrics and People Analytics,' 'Human Resources,' 'Fundamentals of Management,' and 'Human Capital & the Triple Bottom Line,' reflecting the breadth of his expertise in connecting individual differences to organizational outcomes.
Shaileshh Bojja Venkatakrishnan is an Assistant Professor in the Department of Computer Science and Engineering at The Ohio State University's College of Engineering. His research spans multiple domains at the intersection of networking, distributed systems, and machine learning with a particular focus on blockchain technologies and cryptocurrency networks. Educational Background: PhD in Computer Science from University of Illinois Urbana-Champaign (UIUC), 2017 Bachelor's degree from Indian Institute of Technology Madras, 2012 Dr. Bojja Venkatakrishnan's research interests center around fundamental algorithmic questions at the networking layer of Bitcoin and other cryptocurrency networks. He is particularly interested in applying machine learning techniques for developing algorithms over networks and graphs, with emphasis on 'first-principles' thinking to solve complex distributed systems problems. His work extends to scheduling algorithms for data center networks, peer-to-peer networks, and information theory. The breadth of his research is evident in his publications spanning blockchain networking, payment channel networks, decentralized applications, and machine learning applications for network optimization. Research Impact: Recipient of the Joan and Lalit Bahl fellowship at UIUC His publication record demonstrates significant contributions to the field, with papers appearing in top venues including SIGCOMM, NSDI, SIGMETRICS, and IEEE Transactions. His recent work focuses on enhancing blockchain scalability through peer sampling techniques, transaction reordering mitigation, and efficient peer-to-peer network design for decentralized applications. The trajectory of his research shows a consistent focus on addressing fundamental challenges in distributed systems with practical implications for real-world blockchain implementations and network architectures.
Günter Klambauer is a Professor at the Institute for Machine Learning , Johannes Kepler University Linz, and leads the LIT Artificial Intelligence Lab in Austria. His research bridges artificial intelligence with life sciences , focusing on deep learning applications in retinal imaging , drug discovery , and hydrological modeling . Affiliation: JKU Institute for Machine Learning & LIT Artificial Intelligence Lab Key Research Areas: Medical Imaging AI, Biological Sequence Modeling, Generative Models for Molecules, Time-Series Forecasting His recent publications highlight extended LSTM architectures (xLSTM) for biological sequence modeling, contrastive learning in retinal imaging, and in-context learning for low-data drug discovery. He has pioneered frameworks like TiRex for zero-shot forecasting and LaM-SLidE for spatial dynamical systems. Scientific Awards: Austrian Life Science Award (2012) Award of Excellence (2014) ELLIS Society Scholar (2020) Director, ELLIS Machine Learning for Molecules Discovery Program (2023) Professor Klambauer collaborates extensively on AI-driven biomedical projects , including retinal image analysis and antibody design, while advancing foundational neural network architectures for diverse domains from healthcare to climate modeling.
Dr. Michel Dumontier is a Distinguished Professor of Data Science at Maastricht University, where he serves as the founder and Director of the Institute of Data Science. He is internationally recognized as a co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles, which have transformed scientific data management globally. His academic background includes: BSc in Biochemistry from the University of Manitoba (1999) PhD in Bioinformatics from the University of Toronto (2004) Assistant/Associate Professor at Carleton University (2005-2013) Associate Professor at Stanford University (2013-2016) Distinguished Professor at Maastricht University (2017-present) Dr. Dumontier's research focuses on unlocking data potential for scientific discovery, with expertise in knowledge graphs for drug discovery and personalized medicine. His work spans FAIR data principles, generative AI, machine learning, semantic technologies, ontology, and data integration. His recent publications reveal a strong trend toward applying generative AI to healthcare data, with increasing focus on synthetic health data generation, privacy-preserving techniques, and knowledge graph applications in drug repurposing. His work bridges computer science, biomedical informatics, and clinical applications across multiple medical domains. Dr. Dumontier has secured significant research funding as a principal investigator: NWO (Dutch Research Council) Horizon Europe MCSA NIH/NCATS ARPA-H He coordinates the AIDAVA and REALM projects, leads the NCATS Biomedical Data Translator, and directs the GENIUS AI lab. As editor-in-chief of the journal Data Science, he shapes discourse in the field. Dr. Dumontier maintains active industry connections through: Minderheidsaandeelhouder at Data2Discovery Inc Scientific advisor and minority shareholder at OntoForce NV Scientific advisor, board member, and minority shareholder at Comunicare Editor-in-chief of Data Science Journal at Sage Publishing
Khalil Esper is a Researcher at the Department of Computer Science, Faculty of Engineering, Friedrich-Alexander-University Erlangen-Nuremberg (FAU), where he works at the Chair of Computer Science 12 (Hardware-Software Co-Design). His research focuses on verification, energy optimization, and runtime requirement enforcement in embedded systems and MPSoCs. His educational background includes: Informatics Engineering from Aleppo University, Syria (2010-2015) European Master in Embedded Computing Systems (EMECS) from Rhineland-Palatinate University of Technology Kaiserslautern-Landau (Germany) and Norwegian University of Science and Technology (Norway) (2017-2019) Esper's research interests center around verification and model checking, energy optimization on MPSoC, real-time systems and embedded systems, and autonomic computing. His work particularly focuses on runtime requirement enforcement mechanisms for non-functional properties in multi-processor systems-on-chip, with applications extending to medical devices and human-robot interaction systems. He has developed approaches using finite state machines, reinforcement learning, and evolutionary algorithms to ensure system properties are maintained during execution. His publication record shows a strong trend toward applying formal methods and runtime enforcement techniques to increasingly complex systems, with recent work expanding into safety-critical applications like orthoses and human-robot interaction. The interdisciplinary nature of his research bridges computer science, embedded systems engineering, and biomedical applications. Esper has supervised multiple theses including: Sascha H.: Runtime Requirement Enforcement of Non-Functional Requirements on MPSoCs Using Fuzzy Logic (2022) Iana S.: Feedback-Based Control of Non-functional Program Execution Properties on Linux (2023) Philipp L.: Runtime Requirement Enforcement of Functional and Non-Functional Requirements of a Knee Orthosis Based on a Digital Twin (2024) Avinash N.: Runtime Requirement Enforcement of Safety Properties of an Ankle Orthosis Based on a Digital Twin (2024) Zhiyi T.: Generation of Environment FSMs Using Machine Learning Techniques (2025) Moustafa A.: Runtime Requirement Enforcement of Safety Properties of Human-Robot Interaction Based on a Digital Twin (2025) Florian K.: Runtime Requirement Enforcement of Safety Properties of Human-Robot Interaction (2025) He has been actively teaching courses on Approximate Computing and Embedded Systems since the 2021/2022 academic year, demonstrating his commitment to academic instruction alongside his research activities. Esper is involved in the InvasIC research project, part of the DFG Transregional Collaborative Research Center 89 on Invasive Computing, which explores novel approaches to resource management in parallel computing systems.
Laura Belli is a Researcher at the Department of Engineering and Architecture , University of Parma, with a focus on interdisciplinary projects bridging IoT, Machine Learning, and Smart Systems . Her work spans smart agriculture, vehicular networks, and urban mobility. Research Interests: Internet of Things (IoT) in agriculture and transportation Machine Learning for predictive modeling and data analysis Edge Computing and network optimization Blockchain applications in data integrity Driver health and stress monitoring Recent Publications highlight her contributions to privacy-preserving vehicular systems , smart farming datasets , and adaptive IoT protocols . She collaborates on projects like OPEVA and DistriMuse , emphasizing data-driven innovation.
David B. Larson is Professor of Radiology (Pediatric Radiology) at Stanford University School of Medicine and Director of the Stanford Radiology AI Development and Evaluation (AIDE) Lab. He is also Associate Chief Quality Officer for Improvement at Stanford Health Care and co-founder of the American College of Radiology's Learning Network. MD and MBA from Yale University (2002) Pediatric internship and radiology residency/fellowship at University of Colorado Health Sciences Center (2003-2008) Board-certified in Pediatric Radiology and Diagnostic Radiology His research focuses on sociotechnical healthcare systems , radiology quality improvement , and AI implementation . Key projects include optimizing CT radiation dose, developing EMM frameworks for AI monitoring, and establishing ACR accreditation standards for radiology AI. Recent publications analyze diagnostic certainty frameworks , clinical history completeness , and radiation dose harmonization . His work emphasizes interdisciplinary collaboration through programs like the Radiology Improvement Summit and the Stanford Medicine Center for Improvement. He actively mentors postdoctoral researchers and leads initiatives in pediatric imaging , clinical decision support systems , and medical AI ethics . Current teaching includes graduate medical education courses in radiology and AI applications.
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