Find Academic Supervisors

Eelke BosView profile

Researcher

• Neurosurgery
• Neuro-oncology
• Meningioma
• +7 more

Eelke Bos is a researcher in the Department of Neurosurgery at Erasmus MC, actively contributing to clinical and translational neuro-oncology research. His work focuses on improving outcomes in meningioma and glioblastoma patients through surgical innovation, molecular classification, and functional assessment. Research Interests: His primary fields include neurosurgery, neuro-oncology, meningioma, glioblastoma, molecular tumor classification, and functional neurosurgery. He investigates how surgical techniques and adjuvant therapies impact patient outcomes, particularly in eloquent brain areas. The recent publications reveal a strong trend in using multicenter data and molecular markers to guide treatment decisions in brain tumors. His research emphasizes evidence-based surgical practices, functional preservation, and precision oncology in neurosurgical care. No scientific awards mentioned in the text. Advising and Grants: While specific grants are not detailed, his involvement in large consortia such as the International Consortium on Meningiomas (ICOM) and the Dutch Brain Tumour Registry suggests collaborative funding and national/international research support. He has supervised at least one junior researcher, indicating an emerging mentoring role. Labs and Teams: Eelke Bos is embedded in a multidisciplinary neurosurgical and neuro-oncology team at Erasmus MC, collaborating with experts in neuroimaging, radiation oncology, and molecular pathology. He contributes to both national registries and global research networks focused on brain tumor outcomes.

Alexander Miserlis HoyleView profile

Researcher

• Computational Social Science
• Natural Language Processing
• Machine Learning
• +6 more

Alexander Hoyle is a Researcher at the ETH Zürich AI Center, concurrently contributing to natural language processing/machine learning and social science groups. He holds a PhD in Computer Science from the University of Maryland (advised by Philip Resnik) and a Master's in Computational Statistics from University College London (advised by Sebastian Riedel and Jeff Mitchell). His research focuses on computational social science, emphasizing methods for latent construct identification (e.g., topic models, ideal point models) and evaluation frameworks grounded in validity. Key areas include bias/fairness in AI, political science applications, and mental health constructs like suicidality. He pioneered frameworks like PairScale (attitude measurement via pairwise comparisons) and TopicGPT (prompt-based topic modeling). Education: PhD in Computer Science, University of Maryland (2020-2023) MS in Computational Statistics & Machine Learning, University College London (2018) Bachelor's degree (pre-PhD details omitted) Research Interests: Combining NLP with social science needs, particularly in evaluation rigor and interpretability. Active in interdisciplinary work between NLP and computational social science (e.g., measuring attitude evolution on Reddit, improving topic model validity). Advocates for human-in-the-loop approaches to address LLM limitations in tasks like document clustering and sentiment analysis. Grants & Projects: Contributed to a landmark $2.2B DOJ settlement on NYC public housing via econometric modeling at The Brattle Group. Active in graduate labor advocacy (Maryland state legislature testimony) and mentorship (Científico Latino's mentorship program). Labs & Teams: Leads initiatives at the ETH Zürich AI Center, collaborating with groups like Microsoft Research (FATE) and AI2's AllenNLP. Involved in multi-university projects (e.g., University of Maryland's Computational Linguistics lab).

Jana LevisonView profile

Associate Professor

• Groundwater flow
• Contaminant transport
• Fractured geologic media
• +5 more

Jana Levison is an Associate Professor in the School of Engineering at the University of Guelph, specializing in Water Resources Engineering. She holds the inaugural Doody Family Chair for Women in Engineering (2022-2027) and is affiliated with the Morwick G360 Groundwater Research Institute. Her research focuses on groundwater quality and quantity, particularly examining agricultural and climate change impacts on hydrological systems. Dr. Levison earned her Bachelor's and Ph.D. in Civil Engineering from Queen's University, with her doctoral research focusing on anthropogenic impacts on fractured bedrock aquifers. Prior to her position at Guelph, she completed a postdoctoral fellowship at the Université du Québec à Montréal, worked with the Cataraqui Region Conservation Authority on Drinking Water Source Protection, and served as Junior Fellow and Acting Executive Director of the Ontario Centre for Engineering and Public Policy. Her research program addresses critical water resource challenges through multiple funded projects examining agricultural nutrient transport, groundwater-surface water interactions, climate change impacts, and source water protection for Indigenous communities. Dr. Levison's work spans field investigations, advanced modeling techniques, and policy-relevant analysis to protect water resources in the Great Lakes Basin and beyond. Analysis of Dr. Levison's recent publications reveals a strong focus on nutrient transport dynamics in agricultural watersheds, particularly examining nitrogen and phosphorus movement through various hydrological pathways. Her research increasingly incorporates machine learning approaches for water quality prediction and integrates climate change scenarios into groundwater modeling. A significant portion of her work addresses source water protection challenges for First Nations communities and examines the impacts of road salt and neonicotinoids on groundwater quality. Dr. Levison has received notable recognition for her contributions to hydrogeology: IAH-CNC Early Career Hydrogeologist Award (2020) Dr. Levison has supervised over 20 graduate students and numerous undergraduate researchers, with current projects funded by MECP, OMAFRA, and NSERC. Her research group conducts field investigations in southern Ontario watersheds to understand nutrient transport mechanisms, groundwater-surface water interactions, and climate change impacts on water resources. She has established collaborative projects with multiple conservation authorities, government agencies, and Indigenous communities to address practical water resource challenges. As part of the Morwick G360 Groundwater Research Institute, Dr. Levison's team operates advanced hydrogeological monitoring equipment and employs innovative techniques for groundwater quality assessment. Her research group maintains instrumented field sites in agricultural watersheds throughout southern Ontario, with particular focus on clay plain systems in the Great Lakes Basin.

Kang ZhaoView profile

Professor

• Data Science
• Social Network Analysis
• Business Analytics
• +12 more

Kang Zhao is the Leonard A. Hadley Professor in Business Analytics and Senior Associate Dean at the Tippie College of Business, University of Iowa. He holds a faculty appointment in the Department of Business Analytics and is affiliated with the Interdisciplinary Graduate Program in Informatics. He previously served as Department Executive Officer (2023–2025) and Faculty Director of the Master of Business Analytics program (2020–2023). His educational background includes: PhD in Information Sciences and Technology, Pennsylvania State University MS in Computer Science, Eastern Michigan University BE in Electrical and Information Engineering, Beijing Institute of Technology Dr. Zhao's research lies at the intersection of data science and network analytics, focusing on social and business networks. His work explores social influence, engagement, and health outcomes in online health communities; develops advanced recommendation systems for social media and live streaming platforms; applies 'people analytics' to scholarly and sports data; models information and behavior diffusion; and analyzes supply chain network resilience. His research has been featured in prominent media outlets such as MIT Technology Review, Forbes, BBC, and The Washington Post. The 15 most recent publications reflect a strong trend in applying graph neural networks, dynamic representation learning, and deep learning to model complex networked systems in health, finance, and social media. Key themes include user engagement prediction, misinformation diffusion, stock movement forecasting, and personalized recommendations using temporal and structural network data. His scientific achievements have been recognized with numerous awards: Top Cited Article, Journal of the Association for Information Science and Technology (2024, 2023) Journal of Operations Management Ambassador Award (2024) Charles A. Taff Research Excellence Award, Tippie College of Business (2023) Best Paper Award, INFORMS Workshop on Data Science (2017) Early Career Faculty Research Award, Tippie College of Business (2016) Dr. Zhao has successfully led multiple research grants as Principal or Co-Principal Investigator from the National Science Foundation (NSF), National Institutes of Health (NIH), and the Institute of Museum and Library Services. His projects span topics including gender gaps in academic science, smoking cessation networks, misinformation in health communities, and disaster resilience in public libraries. He actively mentors a cohort of PhD and master’s students, many of whom have secured positions in academia and leading industry firms. He also contributes to the academic community through editorial roles at top journals such as Decision Support Systems , Electronic Commerce Research and Applications , and Journal of the Association for Information Science and Technology . He leads the Data and Network Analytics Research Group at the University of Iowa, which focuses on cutting-edge research in network science, machine learning, and data mining applied to real-world social and business problems.

Valentino SantucciView profile

Associate Professor

• Artificial Intelligence
• Evolutionary Computation
• Machine Learning
• +7 more

Valentino Santucci is an Associate Professor of Computer Engineering at the University for Foreigners of Perugia, Italy, affiliated with the Department of International Human and Social Sciences (SUSI). Since 2021, he has served as the Rector's Delegate for Technological Innovation and Information Flows, highlighting his leadership in digital transformation within the institution. His research spans key areas in Artificial Intelligence, particularly Evolutionary Computation, Natural Language Processing, Machine Learning applications in e-learning and sustainability, and digital technologies in education. He employs algebraic techniques to analyze combinatorial search spaces and evolutionary algorithm dynamics, contributing to both theoretical and applied advancements. The most recent publications reflect a strong trend in combinatorial optimization using algebraic frameworks, hybrid evolutionary-swarm algorithms, and applications in text complexity classification and educational technology. His work bridges computer science with humanities and sustainability, reflecting a multidisciplinary approach. PhD in Computer Science and Mathematics, University of Perugia (2012) He teaches courses in Artificial Intelligence, Computer Science for Humanities, Cybersecurity, and Sustainability across various degree programs. His editorial roles include contributing to WoS/Scopus-indexed journals, and he has taught at the University of Perugia and Hong Kong Baptist University. Dr. Santucci actively mentors through teaching and research supervision. While specific student names are not listed, his involvement in academic projects and publications suggests advisory roles. He has no explicitly mentioned grants, but his research output and leadership position indicate active project engagement. He is involved in institutional innovation through his role in technological advancement and has contributed to projects integrating soft skills and learning technologies. His work emphasizes practical applications of AI in education and sustainability, positioning him at the intersection of technical innovation and societal impact.

Stefania SpinaView profile

Professor

• Corpus Linguistics
• Linguistics
• Glottology
• +12 more

Stefania Spina is a Full Professor of Glottology and Linguistics at the University of Perugia for Foreigners, affiliated with the Department of Italian Language, Literature, and Arts in the World (LILAIM). She has held this position since August 1, 2020, and is actively engaged in research, teaching, and academic leadership. Her work focuses on corpus-based linguistic analysis, particularly in the context of Italian as a second language. Her research interests include corpus linguistics, second language acquisition, learner corpus development, phraseology, discourse analysis of media and academic texts, text complexity assessment, and computational processing of linguistic data. She has created several influential linguistic resources, such as the Perugia Corpus, Corpus of Spoken Italian (CIP), and learner corpora including LOCCLI, COLI, and CELI. She also developed MALT-IT2, a web tool for automated text difficulty classification aligned with CEFR levels. Her recent publications reflect a strong engagement with contemporary language trends, digital humanities, and media discourse. Topics include the lexical impact of AI, metaphor in political journalism, and the evolution of public language. A central theme across her work is the application of corpus methods to understand how language functions in educational, media, and technological contexts. She is the principal investigator of the PRIN 2022 project DICI-A, which aims to build a digital collocation dictionary for Italian L2 learners, in collaboration with computer scientists. Her teaching portfolio includes courses in sociolinguistics of new media, corpus linguistics, and digital humanities. She maintains an active public intellectual presence through her blog (now hosted on Substack) and social media. Email: stefania.spina@unistrapg.it Twitter: @sspina She supervises academic projects and leads research teams but no individual students are named in the provided texts. There are no mentions of scientific awards or grants beyond the PRIN project. She works in digital environments, utilizing tools like Python and AI for linguistic analysis, and contributes to the development of language technologies for education and research.

• Optimization methods
• Metaheuristics
• Heuristics
• +12 more

Mina Mirhosseini is a Research Fellow at the Faculty of Logistics, Molde University College, Norway. She holds a PhD in Computer Science from Shahid Beheshti University of Tehran, Iran, and has prior academic experience as a faculty member and lecturer in Iran and as a remote teaching assistant at the University of Hertfordshire, UK. Her primary research interests include Optimization Methods, Metaheuristics, Heuristics, Linear Integer Programming, Parallel Processing, Machine Learning, Artificial Intelligence, and Logistics. She has made significant contributions to solving complex computational problems such as the n-similarity problem and Mixed Integer Linear Programming (MILP) models using hybrid and parallel algorithms, particularly in the context of high-level synthesis and wireless sensor networks. The analysis of her recent publications reveals a strong focus on developing and applying advanced optimization techniques, especially quantum-inspired gravitational search algorithms and parallel genetic algorithms, to real-world engineering and computational challenges. Her work consistently emphasizes performance improvement, scalability, and load balancing in distributed and heterogeneous computing environments. Mina Mirhosseini has an extensive publication record in high-impact journals such as IEEE Transactions on Parallel and Distributed Systems, Journal of Parallel and Distributed Computing, Journal of Supercomputing, and Computers and Electrical Engineering. Her research has been published across a range of venues, reflecting interdisciplinary work at the intersection of computer science, electrical engineering, and applied optimization. She has actively contributed to the academic community through roles such as program committee member and executive committee member for conferences on fuzzy systems, swarm intelligence, and evolutionary computation. Her academic journey includes teaching and research roles in Iran, demonstrating a sustained commitment to higher education and scientific inquiry. Mina Mirhosseini is part of the research group focused on Planning, Optimization and Decision Support at Molde University College. Her current work continues to advance the state-of-the-art in parallel and metaheuristic optimization methods, with applications in logistics, synthesis, and sensor network design.

• Entrepreneurship
• Innovation Management
• Sustainable Tourism
• +3 more

Roberta Kontošić Pamić is an Assistant Professor at the Faculty of Economics and Tourism of the University of Primorska , based in Pula, Croatia. She holds the position of Lecturer in charge for undergraduate courses such as Basics of Organisation , The Basics of Entrepreneurship , and graduate courses like Innovation management and lean startup . Her academic activities focus on entrepreneurship, innovation, sustainable tourism, and SME development. Her research interests include the analysis of business performance in Croatian regions, innovation-driven wage dynamics, and sustainable tourism models such as indoor camping in heritage sites. She has explored topics like environmental reporting gaps in Croatian enterprises, contributions of SMEs to EU employment, and the economic impacts of entrepreneurial zones. Her publications reflect a strong emphasis on practical applications, including the valorization of cultural heritage sites and innovative accommodation models. She has contributed to studies on Croatian construction sectors and cross-country labor market comparisons between Croatia and Slovenia. While no scientific awards are explicitly mentioned in the text, her extensive publication record indicates significant contributions to her field. She currently advises on courses related to entrepreneurship and innovation but no specific student names are listed.

Ilker HacihalilogluView profile

Associate Professor

• Artificial Intelligence in Healthcare
• Medical Image Processing
• Machine Learning Applications
• +8 more

Dr. Ilker Hacihaliloglu is an Associate Professor in the Department of Radiology and Medicine at the University of British Columbia's Faculty of Medicine. He holds a PhD from UBC (2010) and has held faculty positions at Rutgers University (2014-2021). His research focuses on applying machine learning to medical imaging, aiming to bridge clinical and engineering research for practical healthcare solutions. Key areas include AI-driven diagnostics, ultrasound imaging, and personalized medicine. Education: B.Sc. and M.Sc., Istanbul Technical University (2001, 2004) Ph.D., University of British Columbia (2010) Postdoctoral Fellow, Vancouver General Hospital (2010-2013) Research Interests: Artificial Intelligence in Medical Imaging Ultrasound-Based Diagnostics Image-Guided Surgery Computer-Aided Diagnostics for Orthopedics and Neurosurgery His work emphasizes explainable AI, standardization in machine learning metrics, and clinical translation of technologies. Scientific Contributions: Over 35 peer-reviewed articles (2019-2025) highlight innovations in medical image segmentation, AI-driven radiomics, and cross-modality imaging. Recent trends focus on synthetic data augmentation, diffusion models for medical imaging, and curriculum development in AI for healthcare. Grants and Advising: No explicit grants mentioned, but active mentorship in interdisciplinary research teams. Collaborations span clinicians, engineers, and computer scientists to address real-world healthcare challenges. Labs/Teams: Engaged in computational ultrasound and AI-driven diagnostics initiatives, though specific lab names are not provided in the text.

J. Stephen DownieView profile

Professor

• Information Retrieval
• Digital Libraries
• Digital Humanities
• +4 more

J. Stephen Downie is a Professor and Interim Executive Associate Dean at the School of Information Sciences, University of Illinois. He also serves as the Associate Dean for Research and Co-Director of the HathiTrust Research Center. His academic roles include leadership in digital libraries, digital humanities, and music information retrieval (MIREX). He holds a PhD in Library and Information Science from the University of Western Ontario. Research interests focus on information retrieval systems, multimedia MIR, political economy of communication networks, and database/web technologies. He co-founded ISMIR and directs the annual MIREX evaluation initiative. His work bridges computational analysis, cultural heritage preservation, and digital tool development. Recent scholarly output includes studies on plant systematics (Apiaceae family) and cultural heritage technologies. His projects involve collaborative research on digital libraries, data capsules, and federated repository systems. Notable organizational roles include leadership in HathiTrust and academic service in the National Center for Supercomputing Applications. Teaching responsibilities include doctoral seminars and independent studies. Active projects span computational text mining, structural analysis of music information, and accessibility initiatives for academic content.

• Computer Science
• Software Security
• Object-Oriented Programming
• +3 more

Arran Stewart is a Lecturer in the Department of Computer Science and Software Engineering at the School of Physics, Maths and Computing, University of Western Australia. His academic work focuses on secure software development, particularly in object-oriented programming and Java code analysis. Research Interests : Software Security and Vulnerability Detection Object-Oriented Programming Analysis Side-Effect Free Methods in Java Secure State Management Frameworks His publications between 1995 and 2017 demonstrate expertise in software engineering and legal systems, with recent work emphasizing secure coding practices and method classification in Java. While no scientific awards or student advisement details are explicitly mentioned in the provided texts, his research output spans interdisciplinary domains including computer science and legislative jurisprudence.

Joseph GlazView profile

Professor

• Applied Probability
• Geometrical Probability
• Probability Approximations
• +7 more

Joseph Glaz is a Professor in the Department of Statistics at the University of Connecticut. His work focuses on applied probability and statistical methodology, particularly in scan statistics and related inference techniques. Contact: joseph.glaz@uconn.edu Phone: (860) 486-4193 (Storrs Campus) Research highlights: Develops scan statistics for discrete , continuous , and conditional data frameworks Specializes in change-point detection for normal data mean/variance Innovates robust methods for outlier-prone datasets Extends scan statistics to network and graph structures Contributed to foundational texts like the Handbook of Scan Statistics Publication trends include: Scan statistics for genomic data (Hi-C translocation detection) Nonparametric and Bayesian extensions of scan methods Approximations and inequalities for sequential testing Applications in quality control , medical imaging , and sensor networks

Irena VodenskaView profile

Professor

• Network Theory in Finance
• Systemic Risk Modeling
• Complexity Science in Macroeconomics
• +12 more

Irena Vodenska is Professor of Finance and Director of Finance Programs at Boston University’s Metropolitan College, Department of Administrative Sciences. She holds a PhD in statistical finance and an MA in economics from Boston University, an MBA from Vanderbilt University, and a BS in computer information systems from the University of Belgrade. She is also a Chartered Financial Analyst (CFA) charter holder. Her research is at the intersection of finance, complexity science, and artificial intelligence, focusing on systemic risk modeling, ESG investments, and financial network dynamics. She has led major interdisciplinary research projects funded by the National Science Foundation, the European Commission, and the U.S. Army Research Office. PhD, Statistical Finance – Boston University MA, Economics – Boston University MBA – Owen Graduate School of Management, Vanderbilt University BS, Computer Information Systems – University of Belgrade Dr. Vodenska’s research interests include network theory in finance, systemic risk propagation, AI-powered ESG analysis, cryptocurrency price forecasting, and financial regulation. She employs big data, machine learning, and natural language processing to analyze financial news, market dynamics, and corporate sustainability. Her work investigates how climate disinformation spreads via social networks and influences public policy and governance. The recent articles highlight a consistent focus on modeling financial and economic systems using network science and AI. Trends include systemic stress testing, sentiment analysis in financial markets, cascading failures, and the interplay between macroeconomic indicators and financial networks. Her work spans econophysics, behavioral finance, public health economics, and ethical AI in fintech. National Science Foundation (NSF) research grant (2023) NSF EAGER Award (2014–2015) European Commission FET Open Grant (2012–2014) U.S. Army Research Office (ARO) Grant (2020–2021) MEXT Post-K Computer Grant, Japan (2016–2019) Alexander Hamilton Fulbright Fellowship (1994) Owen Graduate School Fellowship (1995–1996) Dr. Vodenska teaches core finance courses such as Investment Analysis and Portfolio Management, Derivatives Securities, and Financial Regulation and Ethics. She co-developed the MET AD 678 course with Professor Tamar Frankel from BU Law, emphasizing real-world case studies and ethical decision-making. Her research grants have supported innovative work in systemic risk modeling, AI for ESG, and financial network stability. She is actively involved in mentoring, conference organization, and editorial roles in leading journals. She is a key organizer of the International School and Conference on Network Science (NetSci) and the Big Data in Economics, Science, and Technology (BEST) Conference. Her lab and research team focus on complexity in financial systems, bringing together economists, physicists, computer scientists, and data analysts to study global financial stability and sustainability.

Elizabeth YaleView profile

Associate Professor

• History of Science
• Early Modern European History
• Book History
• +7 more

Elizabeth Yale is an Associate Professor in the Department of History at the University of Iowa, College of Liberal Arts and Sciences, where she also serves as Director of Undergraduate Studies. Her research lies at the intersection of the history of science, book history, and early modern European intellectual culture, with a focus on how knowledge was created and preserved in domestic and archival contexts. Her educational background includes a PhD in the History of Science from Harvard University (2008). She previously taught at Western Carolina University and Harvard before joining the University of Iowa. Dr. Yale's research explores the material and social practices of knowledge-making, particularly in early modern Britain. She emphasizes the role of families, manuscript culture, and archival infrastructures in the development of scientific thought. Her work spans themes such as gender, materiality, and the transmission of medical and natural knowledge. She teaches courses on early modern Europe, the history of science and medicine, book history, and gender, often incorporating hands-on analysis of texts through the University of Iowa Center for the Book. Her recent publications reveal a consistent interest in the material forms of knowledge—letters, lists, marginalia, and archives—and their role in shaping scientific epistemology. She frequently examines how women’s intellectual labor and domestic spaces contributed to scientific advancement. Her collaborative projects, including an NEH-funded initiative with Matthew Brown, aim to build innovative educational spaces for studying global print and manuscript cultures. Visiting Scholar, Max Planck Institute for the History of Science, Berlin (2016) President, Andrew W. Mellon Society of Fellows in Critical Bibliography (2021–2022) Senior Fellow, Andrew W. Mellon Society of Fellows in Critical Bibliography Dr. Yale has been active in mentoring and curriculum development, particularly through experiential learning programs like the Hawkeye History Corps. She has secured external funding, including an NEH Humanities Initiatives Grant, to support interdisciplinary teaching and research. Her advising likely extends to undergraduate and graduate students interested in history of science, gender studies, and material text analysis, though specific students are not listed. She is also engaged with public history, contributing accessible commentary to outlets like The Atlantic , Quartz , and Iowa Public Radio , bridging academic research with broader societal conversations on science, gender, and race. She is involved in collaborative scholarly communities, especially through the Rare Book School and the University of Iowa Center for the Book, where she contributes to projects that integrate book history with scientific and archival studies. These affiliations support a vibrant research and teaching environment centered on material knowledge practices.

Sunnie Sun ChungView profile

Professor

• Big Data Analytics
• Artificial Intelligence
• Natural Language Processing
• +3 more

Dr. Sunnie Sun Chung is a Professor in the Department of Electrical Engineering and Computer Science at Cleveland State University's College of Engineering. She leads the Big Data Analytics & AI Lab and has received multiple Faculty Merit Recognition Awards (2020-2024) and Golden Apple Awards (2021-2022). Research Interests : Big Data Analytics, Artificial Intelligence, Natural Language Processing, Cyber Security, Machine Learning, and Optimization in Cloud/IoT environments. Teaching : Advanced courses in Data Mining, Big Data Systems, and Deep Learning (DSA460/CIS492/593, CIS611/711, etc.) with dedicated teaching labs. Publications reflect expertise in Knowledge Graphs for Medical QA , Network Intrusion Detection with Graph Models , Privacy-Preserving Cloud Learning , and Basketball Analytics . Her work spans journals like IEEE Transactions on AI and IEEE Access. Scientific Awards : Faculty Merit Recognition Awards (4x) Golden Apple Teaching Award (2x) Students Supervised : Over 10 PhD/Master’s students including Asanka Mananayaka (NLP-based Intrusion Detection), Yixi Luo (Homomorphic Encryption), Dennis Risch (Medical QA Systems), and collaborators with Case Western Reserve University. Labs & Collaborations : Director of the Big Data Analytics & AI Lab since 2018. Hosted annual IBM Health collaborative workshops since 2018. Maintains strong industry ties with Microsoft, Amazon, Oracle, and Cleveland Clinic.