جستجوی استاد راهنمای دانشگاهی

Terry C. Lansdownمشاهده پروفایل

دانشیار

• Human Factors
• Transport Safety
• Driver Behavior
• +۷ مورد دیگر

Terry C. Lansdown is an Associate Professor in the School of Social Sciences and Department of Psychology at Heriot-Watt University. His expertise lies in Human Factors, particularly focusing on driver safety, workload management, and attentional distraction in transportation contexts. Prior to his academic role, he worked as a senior researcher at the Transport Research Laboratory, contributing to numerous EU-funded and government-sponsored projects. His research integrates ergonomics principles with real-world applications, addressing issues like automated driving systems, social distractions in vehicles, and occupational health in construction industries. Key research interests include: Driver behavior and road safety Human factors in automated systems Workload and attentional demands in complex tasks Health and safety in small-to-medium enterprises (SMEs) He has led studies on pandemic response strategies, such as the CHARIS project analyzing behavioral adherence during the COVID-19 crisis. Lansdown’s work frequently bridges psychological theory with practical interventions, including text messaging campaigns to improve construction worker safety and experimental designs to evaluate automation failure responses. Collaborations span academic and industrial sectors, with publications addressing topics ranging from UV exposure in outdoor workers to the cognitive impacts of in-vehicle distractions. His contributions align with UN Sustainable Development Goals related to health (SDG 3) and sustainable cities (SDG 11).

• Data Mining
• Database Systems
• Explainable AI
• +۵ مورد دیگر

Elena Maria Baralis is a Full Professor at the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin. She serves as Pro-Rector, member of the Board of Directors (without voting rights), member of the Academic Senate (without voting rights), and coordinator of the University's Permanent Observatory for Monitoring the Academic Sector. She chairs the Control and Computer Engineering Department and previously chaired the Computer Engineering School from October 2012 to October 2018. Her research interests focus on database systems and data mining, specifically explainable AI, bias detection in data analytics, and machine learning algorithms for big data. Her work spans various application domains including predictive maintenance, Industry 4.0, and healthcare. Recent publications demonstrate her expertise in speech processing, bias mitigation, and innovative neural network architectures like Kolmogorov-Arnold Networks. Her research output shows a clear trend toward addressing fairness and explainability in AI systems while exploring novel approaches to speech and language understanding. Professor Baralis has received significant recognition including becoming a Fellow of the Academy of Sciences of Turin in 2017. She has served as Editor-in-Chief for IEEE Internet of Things Journal (2016-2019) and Knowledge and Information Systems (2014-present). She actively mentors doctoral students including Claudio Savelli (researching Machine Unlearning), Eleonora Poeta, Giuseppe Gallipoli, Alkis Koudounas, and others. Her research is supported by numerous projects including AI4CTI (Artificial Intelligence for Cyber Threat Intelligence, 2025-2028), Smart manufacturing driven by Machine Learning in Industry 4.0 (2019-2020), and I-REACT (2016-2019).

Lyle Ungarمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Natural Language Processing
• +۱۵ مورد دیگر

Lyle Ungar is a Professor at the Department of Computer and Information Science at the University of Pennsylvania . He is affiliated with multiple graduate groups, including Genomics and Computational Biology in the School of Medicine , Operations, Information and Decisions in the Wharton School , and Psychology in the School of Arts and Sciences . His research focuses on explainable machine learning , deep learning , and natural language processing for psychology and medical research , analyzing social media and sensor data to understand well-being, empathy, and stress. His work spans bioinformatics , applied economics , and group decision-making . Recent publications examine LLM-based tutoring , cross-cultural translation , and AI in palliative care , showing trends in reinforcement learning , mobile health , and health data analytics . He has contributed to Google Scholar , PubMed , and DBLP with over 15 papers since 2023. Scientific Awards : 2019 Alan I. Leshner Leadership Institute Public Engagement Fellow His students include Vitoria Aquino Guardieiro , Yihao Li , and co-advised researchers like Shreya Havaldar with Eric Wong. He leads projects at interdisciplinary centers such as the Annenberg Public Policy Center , Center for Cognitive Neuroscience , and Institute for Translational Medicine .

Mark Lewisمشاهده پروفایل

استاد

• Mathematical Biology
• Spatial Ecology
• Animal Movement Modelling
• +۷ مورد دیگر

Mark Lewis is the Kennedy Chair in Mathematical Biology at the University of Victoria, holding joint appointments in the Departments of Mathematics and Statistics and Biology. His research focuses on spatial ecology and mathematical modeling, addressing ecological challenges such as animal movement, invasive species, and disease dynamics. Lewis earned his D.Phil. in Mathematical Biology from the University of Oxford and has been elected a Fellow of the Royal Society UK. His work integrates mathematical analysis, field studies, and interdisciplinary approaches to solve ecological problems. Current projects include modeling polar bear populations, cyanobacteria dynamics, and the impact of climate change on wildlife. Lewis supervises students across both UVic and his former University of Alberta lab. Education: D.Phil. in Mathematics (Mathematical Biology), University of Oxford Awards: Royal Society Fellowship, CRM-Fields-PIMS Prize, and Okubo Prize Key Research Areas: Animal movement modeling, aquatic ecology, wildlife disease, and invasive species management Publications highlight his contributions to understanding disease spread, parasite dynamics, and ecological responses to environmental changes. Lewis collaborates widely, applying mathematical tools to real-world conservation and health challenges.

Sabrina Furtadoمشاهده پروفایل

مدرس

• Sport Management
• Sports Governance
• Public Policy in Sports
• +۶ مورد دیگر

Sabrina Furtado is a Lecturer in Sport Management and Dean’s Associate Fellow at Loughborough University’s School of Sport, Exercise and Health Sciences. She holds a BSc, MSc, and PhD. Previously, she taught at the Federal University of Paraná and State University of Santa Catarina in Brazil, focusing on sport management, sports law, and entrepreneurship in the sports sector. Her research focuses on sports governance, strategic planning, and policy development in public and private sports entities. Key areas include Olympic and Paralympic federations, sports financing, and the impact of media representation on disability inclusion. She contributes to the Sport, Business and Society and Lifestyle for Health and Wellbeing research groups, as well as the Inteligência Esportiva Research Institute . Recent work analyzes governance structures in Santa Catarina municipalities, business intelligence applications in sports management, and modernization processes in Brazilian Olympic federations. Her publications span topics like Paralympic organizational roles, media stigma in disability sports reporting, and funding mechanisms for sports initiatives. No scientific awards are mentioned. Sabrina advises master’s students on sport management and policy but no grant details are provided. Her current projects aim to strengthen sports policies and enhance stakeholders’ engagement in the sport system.

Subodha Kumarمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Blockchain
• +۷ مورد دیگر

Subodha Kumar is the Paul R. Anderson Distinguished Chair Professor of Statistics, Operations, and Data Science at Temple University’s Fox School of Business. He serves as the Founding Director of the Center for Business Analytics and Disruptive Technologies and has a secondary appointment in Information Systems. His roles include Ph.D. Program Concentration Director for Operations and Supply Chain Management. With over 240 publications, he ranks #1 globally in Information Systems Research publishing and holds a robotics patent. His research focuses on AI, blockchain, healthcare analytics, and cybersecurity, with notable contributions to digital transformation and operations management. Awards include the INFORMS ISS Distinguished Fellow and POMS Fellow. He advises major journals like Production and Operations Management and organizes conferences like POMS 2018. Media mentions span NYT, WSJ, and CBS. Education: University of Washington faculty Texas A&M University faculty Research Interests: Dr. Kumar explores AI applications, blockchain in fintech, healthcare system optimization, and supply chain analytics. He investigates cybersecurity’s operational impacts and data mining strategies. His work bridges theory and practice, addressing challenges in digital transformation and platform economies. Publications Trends: Recent work emphasizes digital platforms (e.g., blockchain adoption, telemedicine impact), organizational behavior (real-time feedback systems), and healthcare operations (personalized treatment algorithms). He frequently collaborates internationally, with studies appearing in Management Science, Information Systems Research, and Decision Sciences. Awards & Recognition: Changjiang Scholars Chair (China) ISB Visiting Professorship POMS Executive Editor Grants & Labs: Secured NIH grants for health analytics projects. Leads Temple’s analytics center fostering industry partnerships. Active in editorial roles (e.g., Production and Operations Management Journal Deputy Editor). Teaching: Offers courses in data science, optimization methods, and innovation entrepreneurship at graduate/undergraduate levels.

Stan Ahaltمشاهده پروفایل

استاد

• Data Science
• High-Performance Computing
• Biomedical Informatics
• +۴ مورد دیگر

Dr. Stan Ahalt serves as the inaugural Dean of the UNC School of Data Science and Society (SDSS) and holds a professorship in the Department of Computer Science at UNC-Chapel Hill. He previously directed the Renaissance Computing Institute (RENCI) and currently acts as its executive advisor. Additionally, he is the associate director of informatics and data science at NC TraCS, the translational research institute at UNC. His academic journey includes a 22-year tenure as professor of electrical and computer engineering at Ohio State University and leadership roles at the Ohio Supercomputer Center. Ahalt’s research focuses on applying data science to societal challenges such as healthcare, climate, and judicial reform. Key initiatives include the National Consortium for Data Science and the iRODS project, which advanced enterprise data management systems. Ahalt has secured over $34.5M in grants to lead high-impact projects like the NHLBI BioData Catalyst (unifying biomedical data), the NCATS Translator (accelerating clinical discovery), and the NIH HEAL Initiative (ensuring FAIR data standards). He mentors students across doctoral and master’s programs, teaching data science fundamentals in courses like COMP 116: Introduction to Scientific Programming. His work emphasizes interdisciplinary collaboration, evidenced by roles in RENCI, NC TraCS, and the SDSS. Ahalt’s contributions span cyberinfrastructure development, educational innovation, and translational research, positioning UNC as a national leader in data science.

Jenna Wiensمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Healthcare Data Analytics
• +۵ مورد دیگر

Jenna Wiens is an Associate Professor of Computer Science and Engineering at the University of Michigan's College of Engineering. She serves as Associate Director of the Artificial Intelligence Lab and co-Director of AI & Digital Health Innovation. Leading the MLD3 research group, her work focuses on machine learning and AI applications for healthcare data. PhD from MIT (2014) under John Guttag NSF CAREER Award recipient (2016) Humboldt Foundation Carl Friedrich von Siemens Award (2024) Her research addresses four key technical thrusts: Time-series analysis for predicting clinical outcomes Robust machine learning against spurious correlations Decision-making with causal inference and offline reinforcement learning Human-AI collaboration frameworks Notable methodological contributions include the FIDDLE preprocessing pipeline for clinical time-series data, foundational work on survival analysis, and novel approaches to model selection in healthcare reinforcement learning. Her work has led to real-world AI deployments in infection prevention and patient risk stratification. Scientific honors include: Forbes 30 Under 30 (2015) MIT Tech Review 35 Innovators Under 35 (2017) Sloan Research Fellowship in Computer Science (2020) Sarah Goddard Power Award (2023) Wiens collaborates with clinicians across disciplines, emphasizing clinician-in-the-loop AI systems and ethical implementation in healthcare workflows.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence Ethics
• +۳ مورد دیگر

Anders Søgaard is a Professor at the University of Copenhagen , affiliated with both the Department of Computer Science and the Department of Communication. His research bridges Natural Language Processing and Machine Learning with a focus on AI ethics , explainability , and human-AI interaction . Primary Affiliation: Department of Computer Science, University of Copenhagen Secondary Affiliation: Department of Communication, University of Copenhagen Email: soegaard@di.ku.dk, soegaard@hum.ku.dk Research Interests His work spans Natural Language Processing , Machine Learning , and AI ethics , with recent studies addressing: Trustworthiness in AI systems Explainable AI (XAI) frameworks Multilingual model fairness and alignment Human-AI collaboration in reasoning tasks Ethical implications of social robots Mental health analytics using ML Recent Publications His 2025 output highlights trends in: AI ethics (e.g., fairness metrics, trustworthy systems) Multilingual model analysis (knowledge retention, cross-lingual transfer) Human-centric AI (gaze data, cultural considerations) Applications in healthcare and social good

• Health Informatics
• Medical Data Privacy
• Electronic Health Records
• +۴ مورد دیگر

Johan Gustav Bellika is a Professor at the Department of Clinical Medicine, UiT The Arctic University of Norway, based in Tromsø. His work bridges clinical medicine with health informatics, focusing on practical applications that improve healthcare delivery and patient outcomes. Current Position: Professor, Department of Clinical Medicine Institution: UiT The Arctic University of Norway Location: Tromsø, Norway Contact: johan.gustav.bellika@uit.no Professor Bellika's research spans several critical areas in modern healthcare. His primary focus is on health informatics with particular emphasis on medical data privacy, electronic health records, and the application of artificial intelligence in clinical settings. He has made significant contributions to understanding chronic pain management through technology, primary care research networks, and patient-centered care models. His work often involves large population studies such as the Tromsø Study, examining how digital health tools impact healthcare utilization and patient outcomes. His research demonstrates a consistent thread connecting technological innovation with practical clinical applications. Bellika has been instrumental in developing privacy-preserving architectures for healthcare data analysis, which enable researchers to gain insights from sensitive health information without compromising patient privacy. His work on federated learning frameworks represents cutting-edge approaches to analyzing medical data while maintaining strict privacy controls. Health Informatics and Medical Data Privacy Chronic Pain Management through Technology Primary Care Research Networks (PraksisNett) Electronic Health Records and Clinical Decision Support Patient-Centered Digital Health Solutions Federated Learning Applications in Healthcare Professor Bellika's publication record shows a strong trend toward interdisciplinary research that combines clinical medicine, computer science, and public health. His recent work increasingly focuses on the intersection of artificial intelligence and healthcare, particularly how machine learning can be applied to chronic conditions while maintaining rigorous privacy standards. The geographical scope of his research extends across Norway with particular emphasis on Northern Norwegian populations, providing valuable insights into healthcare delivery in Arctic and remote regions. His collaborative approach is evident through numerous co-authored publications with researchers across multiple institutions and disciplines. While specific awards aren't detailed in the available information, his extensive publication record in reputable journals suggests recognition within his field. Professor Bellika has been actively involved in several major research initiatives including the Tromsø Study and PraksisNett, Norway's nationwide practice-based research network. His work on privacy-preserving architectures for healthcare data has significant implications for how medical research can be conducted while respecting patient confidentiality. He appears to be particularly focused on translating research findings into practical tools for clinicians, as evidenced by his work on audit and feedback systems for antibiotic prescribing.

Michael Moorمشاهده پروفایل

استادیار

• Medical AI
• Machine Learning in Healthcare
• Causal Learning
• +۳ مورد دیگر

Prof. Michael Moor is a tenure-track Assistant Professor for Medical AI at ETH Zurich's Department of Biosystems Science and Engineering in Basel. Previously, he conducted postdoctoral research at Stanford University under Prof. Jure Leskovec, focusing on medical foundation models. His work spans causal learning, multimodal AI, and sepsis prediction. Moor holds an MD from the University of Basel and a PhD from ETH Zurich's Machine Learning and Computational Biology Lab under Prof. Karsten Borgwardt. Research Interests: Generalist medical AI models Multimodal medical reasoning Zero-shot and few-shot learning Clinical causal inference Retrieval-augmented language models Sepsis prediction systems Key Achievements: Published foundational work in Nature (2023) on generalist medical AI Developed Med-Flamingo multimodal model (2023) Zero-shot causal learning framework accepted to NeurIPS 2023 (Spotlight) International sepsis prediction study with 156k ICU patients (2023) Lab Activities: Leads ETH's Medical Foundation Models group, collaborating with Stanford HAI and NASA Ames. Active in creating medical AI benchmarks like AgentClinic and developing retrieval-augmented systems like Almanac.

Christopher Kananمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Deep Learning
• Continual Learning
• +۵ مورد دیگر

Christopher Kanan is a tenured Associate Professor of Computer Science at the University of Rochester, leading the AI Initiative within the Hajim School of Engineering & Applied Sciences. He holds secondary appointments in Brain and Cognitive Sciences, the Goergen Institute for Data Science and AI (GIDS-AI), and the Center for Visual Science. His research focuses on deep learning systems for artificial general intelligence (AGI), including continual learning, medical computer vision, and visual question answering. Previously, he was an Associate Professor at RIT’s Carlson Center for Imaging Science and a leader at Paige.AI, contributing to the FDA-cleared Paige Prostate system. Kanan earned his PhD from UC San Diego, completed postdoctoral work at Caltech, and worked at NASA JPL. Education: PhD in Computer Science, UC San Diego MS in Computer Science, University of Southern California Bachelor’s in Philosophy and Computer Science, Oklahoma State University Research Interests: Kanan’s work spans foundational AI capabilities like continual learning, medical imaging (pathology and radiology), multi-modal reasoning, and cognitive science-inspired models. His lab develops bias-robust AI systems and applies deep learning to healthcare and fusion research. Articles Trends: His recent work emphasizes out-of-distribution generalization, foundation models in pathology, and stability in continual learning. Key themes include AI applications in healthcare, model robustness, and neuroscience-inspired algorithms. Awards: NSF CAREER Award Senior Member, AAAI and IEEE DoE and NSF grants totaling $5M+ DARPA/ARL awards Advising & Grants: Mentored over 10 PhD students, including Robik Shrestha and Usman Mahmood. Secured grants for AI in nuclear fusion and medical imaging. Led RIT’s Center for Human-aware AI (CHAI) as Associate Director. Labs & Teams: Heads the University of Rochester AI Initiative, collaborates with Paige.AI, and leads teams advancing AI in pathology and robotics. His lab’s KLab (klab.cis.rit.edu) focuses on vision and learning systems.

Jae Eun Chungمشاهده پروفایل

استاد

• New Media Technologies
• Psychosocial Impacts
• Health Communication
• +۳ مورد دیگر

Jae Eun Chung is a Professor in the Department of Communication Studies at Howard University's School of Communications. Her research focuses on new media technologies, their psychosocial impacts, and health communication, with a particular emphasis on community engagement and digital discourse. Ph.D. in Communication from the University of Southern California M.A. in Telecommunications from Indiana University at Bloomington Dr. Chung's work explores the intersection of social media and health, analyzing how digital platforms influence public health perceptions, patient-doctor interactions, and health equity. She has published extensively in journals like Journal of Communication , Computers in Human Behavior , and Health Communication , often employing community-based participatory research (CBPR) to address disparities in asthma care, maternal health, and HPV-associated cancer awareness. Recent publications highlight trends in leveraging social media for health interventions, examining misinformation dynamics during crises like the COVID-19 pandemic, and analyzing digital ecosystems. Her methodological approaches include network analysis, narrative studies, and assessments of digital engagement metrics. Howard University RCMI Grant: Enhancing recruitment of African-Americans and Hispanics in health research NSF HDR Grant: Collaborative research on data analytics for community enrichment PACRI Grant: Improving health outcomes for asthmatic children in public housing Bezos Earth Fund: Indoor environmental quality disparities in underserved communities Dr. Chung collaborates with interdisciplinary teams, including public health experts and data scientists, to advance health communication strategies. She has mentored students and researchers in projects analyzing social media's role in health promotion, digital privacy, and community-academic partnerships.

• Computational Social Science
• Political Participation
• Big Data
• +۳ مورد دیگر

Dr. Jonathan Bright is a Senior Research Fellow and Associate Professor at the Oxford Internet Institute, University of Oxford , specializing in computational approaches to the social and political sciences. His research explores how digital technologies reshape political participation and how new data sources enhance governmental decision-making. Research Interests: Focused on social media, political behavior, computational social science, and big data , Bright’s work addresses digital governance, algorithmic bureaucracy, and the dynamics of political fragmentation online. His empirical studies span open government data usage, crowdsourced geographical information, and the impact of neighborhood effects on data completeness. Scientific Contributions: Leading studies on algorithmic governance in UK local authorities (2020) Investigating geodemographic biases in crowdsourced platforms like OpenStreetMap (2018) Mapping political fragmentation in social media ecosystems (2018) Grants & Collaborations: Funded by UK taxpayers, ESRC, and Google, his work intersects with projects on vaccine misinformation, hate speech monitoring, and digital economies. Formerly held positions as Research Fellow and Research Associate at OII from 2013 to present.

• Databases
• Data Mining
• Machine Learning
• +۳ مورد دیگر

Ming-Syan Chen is a distinguished academic holding dual roles as a Distinguished Research Fellow and Director of the Research Center for Information Technology Innovation (CITI) at Academia Sinica, Taiwan, and a Distinguished Professor jointly appointed across multiple departments at National Taiwan University (NTU), including Electrical Engineering (EE), Computer Science and Information Engineering (CSIE), and the Graduate Institute of Communication Engineering (GICE). His career spans academia and industry, with prior roles as a research staff member at IBM Watson Research Center and leadership positions in Taiwan's technology sector. Education: He earned a B.S. in Electrical Engineering from National Taiwan University, followed by M.S. and Ph.D. degrees in Computer, Information, and Control Engineering from the University of Michigan, Ann Arbor. Research Interests: Chen's work focuses on databases, data mining, machine learning, multimedia networking, and cloud computing. He has authored over 350 papers and holds numerous patents, contributing to foundational advancements in query processing, data management, and networked systems. Award Highlights: Recipient of ACM and IEEE Fellowships, National Chair Professorship (lifetime honor), Teco Award, Pan Wen Yuan Distinguished Research Award, and IBM's Outstanding Innovation Award. His contributions span research, teaching, and technology commercialization. Leadership & Service: Former Dean of NTU's College of Electrical Engineering and Computer Science, CEO of Taiwan's Networked Communication Program, and Editor-in-Chief of the International Journal of Electrical Engineering. He has chaired international conferences and served on editorial boards of journals like IEEE TKDE and VLDB. Labs & Teams: Leads the Network Database Laboratory and collaborates on national initiatives in information and communication technologies. His research groups focus on data science, distributed systems, and social network analysis.