جست‌وجوی استاد و پژوهشگر

Seokjun Younمشاهده پروفایل

استادیار

• Healthcare Operations Management
• Service and Retail Operations
• Health IT Management
• +۹ مورد دیگر

Seokjun Youn is an Assistant Professor in the Department of Management Information Systems at the Eller College of Management, University of Arizona . His research focuses on improving healthcare delivery systems through technological innovations and operational strategies, with additional expertise in supply chain logistics and platform business models. Education: PhD in Operations and Supply Chain Management from Texas A&M University; MS and BS in Industrial Engineering from Texas A&M University and Seoul National University, respectively. Research Areas: Healthcare operational efficiency, patient flow, health IT, human-AI collaboration, and supply chain management. Scientific Awards: DSI Best Theory-Driven Empirical Research Paper Award (2024) CMIH Grant (2020, 2021, 2023, 2024, 2025) POMS Emerging Scholars Program (2021, 2023) Professional Service: Editorial board member for Production and Operations Management and Decision Sciences journals; track chair for conferences like CHITA and DSI. His work combines econometric methods and optimization techniques to address challenges in healthcare systems, with findings published in top journals including Manufacturing & Service Operations Management and Journal of Operations Management .

Matthias Nnadiمشاهده پروفایل

مدرس ارشد

• Accounting in Emerging Economies
• International Financial Reporting Standards (IFRS)
• International Public Sector Accounting Standards (IPSAS)
• +۵ مورد دیگر

Dr. Matthias Nnadi serves as a Senior Lecturer in Accounting at Cranfield School of Management, Cranfield University, within the Department of Economics, Finance and Business Data Analytics. His academic career spans both theoretical research and practical industry applications in accounting and finance. His educational background includes a Bachelor's degree in Accounting (BSc), Master in Business Education (MEd), MBA, and PhD, complemented by Postgraduate certificates in Academic Practice and Business Enterprise. Professional memberships span multiple accounting bodies, reflecting his deep engagement with the field. Dr. Nnadi's research focuses on accounting in emerging economies, particularly examining the relevance of International Financial Reporting Standards (IFRS) in developing contexts, corporate mergers and acquisitions, and earnings management practices. His work bridges academic theory and practical application through extensive industry engagement. His scholarly output demonstrates consistent contributions to top accounting journals, with recent publications examining IFRS implementation effects, corporate sustainability impacts, and M&A performance metrics. The research shows particular strength in African and Asian emerging markets contexts. Chartered Institute of Taxation research grant recipient Seedcorn funding awardee for academic research Editorial Board member of Journal of Bonds and Derivatives As an educator, Dr. Nnadi developed the Individualised Accounting Technique (IAQ), a teaching and assessment model widely adopted in business schools for large accounting classes. His industry contributions include executive training programs on IFRS and IPSAS for both private and public sector organizations, drawing on his practical experience as former budget manager at a telecommunications company and stock accountant at a multinational corporation.

Qi Longمشاهده پروفایل

استاد

• Genomics
• Computational Biology
• Biostatistics
• +۱۰ مورد دیگر

Qi Long is a Professor at the University of Pennsylvania, holding joint appointments in the Department of Biostatistics, Epidemiology and Informatics (Perelman School of Medicine), Department of Computer and Information Science (School of Engineering and Applied Science), and Department of Statistics and Data Science (The Wharton School). He serves as Founding Director of the Center for Cancer Data Science, Associate Director of the Penn Institute for Biomedical Informatics, and Associate Director for Quantitative Data Science at the Abramson Cancer Center. His research bridges statistical and machine learning (ML/AI) method development with biomedical applications, focusing on precision medicine and population health. Education : Ph.D. (2005) and M.S. (2003) in Biostatistics from University of Michigan; B.S. (1998) in Computer Science from University of Science and Technology of China. Research interests include: Robust statistical and ML/AI methods for big health data (-omics, EHRs, imaging, mHealth) Multimodal data integration and subgroup heterogeneity analysis Missing data, causal inference, Bayesian methods, and clinical trials Data privacy, algorithmic fairness, and responsible AI in healthcare Foundation models and agentic AI for biomedicine His publications focus on privacy-preserving AI, fairness-aware ML, and integrative models for multi-omics and EHRs. Recent work explores LLMs and watermark detection in hybrid human-AI settings. Scientific Awards : Elected fellow: AAAS, ASA, IMS, ISI, AMIA He leads large NIH- and ARPA-H-funded initiatives, directing statistical coordinating centers for national clinical trials. His lab trains numerous PhD/Master’s students and postdocs, many of whom hold prestigious academic or industry positions.

Anjalie Fieldمشاهده پروفایل

استادیار

• Natural language processing
• Computational social science
• AI ethics
• +۳ مورد دیگر

Anjalie Field is an Assistant Professor in the Computer Science Department at the Whiting School of Engineering, Johns Hopkins University. She is also affiliated with the Data Science and AI Institute and the Center for Language and Speech Processing (CLSP). Dr. Field completed her PhD at the Language Technologies Institute at Carnegie Mellon University under Yulia Tsvetkov, where she was a member of TsvetShop. Prior to joining Johns Hopkins, she was a postdoctoral researcher in the Stanford NLP Group and at the Stanford Data Science Institute, working with Dan Jurafsky and Jennifer Eberhardt. She also spent time as a visiting student at the University of Washington from 2021 to 2022. Her research focuses on the ethics and social science aspects of natural language processing, developing computational models to address societal issues like discrimination and propaganda while critically assessing and improving privacy, transparency, and fairness in AI pipelines. Her work spans social media analysis, bias detection in multilingual content, police accountability systems, and ethical considerations in large language model applications. Dr. Field's publications demonstrate a consistent focus on identifying and addressing biases in language technologies, with particular attention to racial, gender, and cultural dimensions. Her recent work has expanded into domain-specific applications of NLP in astronomy, healthcare, and social justice contexts, showing the breadth of impact that ethical AI considerations can have across disciplines. Among her notable recognitions are: AI2050 Fellow by Schmidt Sciences 2022 Wikimedia Foundation Research Award of the Year Best Paper nomination at Socinfo (2020) Dr. Field teaches AI Ethics and Social Impact (Fall 2023; Fall 2024) and NLP for Computational Social Science (Spring 2024; Spring 2025). She plans to take PhD students for the 2024-2025 admissions cycle. Her work bridges technical NLP research with important social considerations, making significant contributions to both the technical community and broader societal discourse around AI ethics. She is an active member of the Center for Language and Speech Processing, where she collaborates with researchers working at the intersection of language technologies and real-world applications. Her research focuses on developing methods that not only advance NLP capabilities but also ensure these technologies serve diverse communities equitably.

• Biostatistics
• Longitudinal Studies
• Public Health
• +۴ مورد دیگر

Denise Esserman is a Professor of Biostatistics at the Yale School of Public Health, where she joined the faculty in 2014. She is a member of the Yale Center for Analytical Sciences and collaborates with multiple departments at the Yale School of Medicine, including the Clinical and Translational Science Award Program, Patient-Centered Outcomes Research Institute, and the Cancer Center. Her research focuses on methodological aspects of clustered randomized trials and sample size calculations. Education: PhD in Biostatistics from Columbia University (2006) MS in Statistics from University of Georgia (2001) Dr. Esserman's research interests span several critical areas in biostatistics and public health methodology. She specializes in clustered randomized trials, with particular expertise in understanding how intraclass correlation coefficients (ICC) and other factors impact sample size calculations. Her work extends to longitudinal studies methodology, randomized controlled trial design, and sampling techniques. She has contributed significantly to statistical methods for clinical trials, healthcare data analysis, and public health research. Her interdisciplinary approach bridges theoretical statistics with practical applications in healthcare settings. Analysis of Dr. Esserman's recent publications reveals a strong focus on methodological innovations in clinical trial design and analysis, particularly for cluster-randomized trials. Her work spans healthcare applications including fall injury prevention in elderly populations, opioid use disorder treatment in international settings, pain management for hemodialysis patients, and validation of medical coding algorithms. She frequently employs advanced statistical techniques including Bayesian methods, mediation analysis, and methods for handling clustered data. Her research demonstrates a consistent commitment to improving the rigor and applicability of statistical methods in public health and clinical research. Dr. Esserman serves as a reviewer for several prestigious journals including the American Journal of Epidemiology, Arteriosclerosis, Thrombosis and Vascular Biology; Statistics in Biopharmaceutical Research; Clinical Trials; and Obesity. As a member of the Yale Center for Analytical Sciences, Dr. Esserman collaborates with numerous researchers across Yale University. Her current projects include the EQuIP trial (HIC ID 2000033355), where she serves as Sub Investigator with primary completion date of 08/31/2027, focusing on mental health and behavioral research for sexual minority women.

Lisa Wu Willsمشاهده پروفایل

استادیار

• Computer Architecture
• Hardware-Software Co-design
• Hardware Accelerators
• +۶ مورد دیگر

Lisa Wu Wills is an Assistant Professor in the Department of Computer Science and Electrical and Computer Engineering at Duke University, leading the APEX Lab (Application-driven Programmable Efficient Accelerated Systems Lab). Her research focuses on hardware acceleration for big data analytics in genomics, graphs, and databases to advance healthcare and natural sciences. Education: Ph.D. in Computer Science, Columbia University, 2014 Research Interests: Dr. Wills pioneers computer architecture and hardware-software co-design to create efficient accelerators for emerging applications. Her work targets genomics , graph analytics , and database systems , emphasizing simplified hardware deployment and energy efficiency for scientific breakthroughs in healthcare and AI. Publication Trends: Her 2022-2025 publications reveal a strong focus on open-source frameworks (Beethoven, PyTFHE) for accelerator development, hardware acceleration in privacy-preserving computing, and optimization for large language models. Key themes include transfer learning for EDA, domain-specific architectures for genomics, and energy-efficient image processing. Scientific Awards: Google ML and Systems Junior Faculty Award (2025) Advising and Grants: Dr. Wills mentors three PhD students: Chris Kjellqvist (Beethoven framework architect), Mason Ma (PyTFHE lead for FHE applications), and Mansi Choudhary (COCOSSim simulator creator). Her 2025 Google award funds research on accelerating vector databases and retrieval-augmented generation for LLMs. Labs and Teams: She directs the APEX Lab at Duke, developing tools like Beethoven (open-source accelerator composer) and PyTFHE for hardware-software integration, enabling domain scientists to leverage custom acceleration with minimal hardware expertise.

Eldan Cohenمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Optimization
• +۶ مورد دیگر

Eldan Cohen serves as an Assistant Professor of Industrial Engineering within the Department of Mechanical & Industrial Engineering at the University of Toronto's Faculty of Applied Science and Engineering. His academic journey includes a PhD from the same department followed by a postdoctoral fellowship in Computer Science at the University of Toronto and the Vector Institute for Artificial Intelligence. His educational background is detailed as follows: PhD in Mechanical & Industrial Engineering, University of Toronto Postdoctoral Fellowship in Computer Science, University of Toronto and Vector Institute for Artificial Intelligence Dr. Cohen's research centers on machine learning, deep learning, heuristic search, and optimization with strong emphasis on interpretable and human-compatible AI systems. His work bridges theoretical advancements with practical applications in healthcare (e.g., patient-physician interaction analysis, surgical safety diagnostics), automated planning, natural language processing, and software engineering. Recent projects develop interpretable clustering methods for medical data and optimization techniques for constrained sequence generation. Analysis of his 2023-2025 publications reveals a concentrated focus on healthcare AI applications, particularly using large language models for clinical text analysis and diagnostic support systems. Significant work also addresses interpretable machine learning for medical imaging, diverse plan selection in optimization, and constrained sequence generation in domains like vehicle routing. No major scientific awards or fellowships are documented in the available information. As an academic advisor, Dr. Cohen mentors graduate students in mechanical and industrial engineering, guiding research in optimization and machine learning. His OptiMaL research group fosters collaboration between computer science and industrial engineering to solve real-world decision-making challenges through human-centered AI approaches. The Optimization and Machine Learning (OptiMaL) research group, led by Dr. Cohen, serves as the primary hub for developing scalable, interpretable AI solutions for complex healthcare, planning, and engineering problems, with active projects in medical diagnostics and automated planning systems.

Carolin Vollenbergمشاهده پروفایل

پژوهشگر ارشد

• Business Process Management
• Digital Transformation
• Process Mining
• +۳ مورد دیگر

Dr. Carolin Vollenberg serves as a Post-Doctoral Researcher at the Chair of Information Systems & Transformation Management within the Faculty of Computer Science at the University of Duisburg-Essen (UDE). Her academic journey includes a PhD from the University of Muenster (2021-2025), an M.Sc. in Technical Consulting and Management from Hochschule Hamm-Lippstadt (2018-2020), and a B.Eng. in Biomedical Technology from the same institution (2014-2018). Prior to her current position, she worked as a Research Assistant at South Westphalia University of Applied Sciences and gained industry experience at Zapp Systems GmbH. PhD in Business Informatics (2021-2025), University of Muenster M.Sc. Technical Consulting and Management (2018-2020), Hochschule Hamm-Lippstadt B.Eng. Biomedical Technology (2014-2018), Hochschule Hamm-Lippstadt Research Focus: Vollenberg specializes in the governance of lightweight IT systems, digital transformation in public and healthcare sectors, and process mining applications. Her work bridges technical implementation with organizational behavior, particularly examining resistance to automation in sensitive domains like healthcare. She investigates how organizations navigate unintended consequences of technology adoption, with emphasis on RPA (Robotic Process Automation), omnichannel transformation, and data-driven process optimization. Her research methodology combines ethnographic field studies with quantitative process analysis. Publication Trends: Analysis of her 16 publications (2020-2025) reveals strong focus on healthcare IT (45% of works), public sector digitalization (30%), and foundational process management (25%). Recent output shows increasing emphasis on ethical dimensions of process mining and sustainability applications. Her collaborative work spans multiple European institutions with consistent publication in top IS conferences (ICIS, ECIS, HICSS). Best Paper nomination at HICSS-55 (2022) Associate Editor for General Track at Internationale Tagung Wirtschaftsinformatik (WI) 2025 Professional Engagement: Vollenberg actively contributes to academic discourse through editorial roles and peer review. Her industry collaborations with healthcare providers and public sector entities demonstrate applied research impact. Current projects examine virtual nursing transformations and crisis-responsive RPA implementations, reflecting her commitment to solving real-world operational challenges through information systems innovation.

• Artificial Intelligence
• Semantic Web
• Ontology Engineering
• +۲ مورد دیگر

Asunción Gómez Pérez is a Spanish computer scientist and Full Professor at the Technical University of Madrid (UPM) . She currently serves as Vice-Rector for Research, Innovation and Doctoral Studies at UPM and holds a seat at the Real Academia Española . She has authored over 300 publications and accumulated 20,000 citations. Education : PhD in Computer Science (UPM, 1993), MBA (Comillas Pontifical University) Leadership Roles : Director of the Department of Artificial Intelligence (2008–2016), Academic Director of AI Master’s/PhD programs (2009–2016), Executive Director of UPM’s Artificial Intelligence Lab (1995–1998) Her research focuses on Semantic Web and Ontology Engineering , with applications in knowledge representation, machine-machine communication, and multilingual data integration. She pioneered methods for ontology validation, metadata licensing, and AI-driven social inclusion. Key publication trends include: Ontology evaluation frameworks (e.g., OOPS!) Linked Data quality models and validation tools Multilingual and cross-lingual AI applications Interoperability solutions for smart cities and healthcare Machine Learning for social exclusion prediction Ontology-driven library and lexicography systems Scientific Awards Fellow of the European Academy of Sciences Ada Byron Prize She has led projects like the NeOn Methodology for ontology development and contributed to the European framework for linked data rights (LD Terms). Her work bridges theoretical research with practical implementations in AI and Semantic Technologies.

Arjun (Raj) Manraiمشاهده پروفایل

استادیار

• Clinical Genomics
• Machine Learning in Healthcare
• Algorithmic Bias in Medicine
• +۳ مورد دیگر

Arjun (Raj) Manrai is an Assistant Professor at Harvard Medical School and a faculty member in the Computational Health Informatics Program (CHIP) at Boston Children’s Hospital. He earned an A.B. in Physics (Harvard, Highest Honors) and a Ph.D. in Bioinformatics and Integrative Genomics from Harvard-MIT. His research focuses on improving medical decision-making through computational approaches in clinical genomics, algorithmic bias mitigation, and healthcare AI ethics. Key projects include race-free kidney function equations, genetic variant classification, and semi-supervised learning for medical imaging. Education: B.A. in Physics, Harvard University (Highest Honors) Ph.D. in Bioinformatics and Integrative Genomics, Harvard-MIT Division of Health Sciences and Technology Research interests span AI-driven diagnostics, health equity, and reproducibility in biomedical studies. His work has been featured in New England Journal of Medicine , JAMA , and highlighted by the New York Times and NPR. Notable contributions include advancing race-free diagnostic algorithms, addressing biases in clinical genomics, and advocating for transparent AI systems in healthcare. The Manrai Lab collaborates widely to translate computational methods into clinical practice.

David Hongمشاهده پروفایل

استادیار

• Big Data Analysis
• Low-Rank Matrix and Tensor Methods
• Unsupervised Learning
• +۵ مورد دیگر

David Hong is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Delaware. He holds a PhD from the University of Michigan, where he was an NSF Graduate Research Fellow, and previously served as an NSF Postdoctoral Research Fellow at the University of Pennsylvania. His research focuses on developing robust methods for analyzing heterogeneous and high-dimensional data, particularly through low-rank matrix and tensor techniques. Applications span medical imaging, radar systems, genomics, and astronomy. He emphasizes theoretical guarantees and practical algorithms for signal extraction and inverse problems. Education: PhD in Electrical Engineering and Computer Science (University of Michigan), NSF Postdoctoral Research Fellowship (University of Pennsylvania). Research Interests: Low-rank matrix/tensor methods, heterogeneous data analysis, unsupervised learning, and applications in healthcare, imaging, and sensor systems. His work addresses noise robustness, scalable algorithms, and real-world deployment challenges. Scientific Awards: Recipient of the NSF Postdoctoral Research Fellowship (2020) and NSF Graduate Research Fellowship (2015). Advising & Grants: Advisor to graduate students in machine learning and signal processing (no named advisees listed). Active NSF grant recipient for foundational and applied research in data science. Labs/Teams: Engaged in interdisciplinary collaborations through the University of Delaware's Center for Computational Research and Data Science initiatives.

• Labour Market Projections
• Cooperative Entrepreneurship
• Active Labour Market Policies
• +۲ مورد دیگر

Luc Sels has served as Rector of KU Leuven since August 1, 2017, and was re-elected for a second four-year term in May 2021. He is a full professor in the Faculty of Economics and Business (FEB), where he joined in 1996, became full professor in 2004, and served as Dean from 2008 to 2017. He concurrently holds an honorary professorship at Cardiff University and advisory roles at Friedrich-Alexander Universität Erlangen-Nürnberg, National University of Singapore, and Korea University. His research centers on labour market projections (employment, skill levels, regional forecasts), active labour market policies, and cooperative entrepreneurship. As Chairholder of the Randstand Chair on Employer Branding, VBO-FEB Chair on Belgian Business Champions, and CERA-Boerenbond Chair on Cooperative Entrepreneurship, he publishes in top journals including Journal of Management and Harvard Business Review. His work bridges academic research and policy impact through the Centre of Expertise in Labour Market Monitoring. Analysis of Sels' 2021-2025 publications reveals two dominant research streams: human resource management (particularly employer branding mechanics and line manager roles in training effectiveness) and cooperative enterprise dynamics (focusing on social capital in agri-food cooperatives). Methodological innovations in survival analysis for organizational research complement these themes, demonstrating interdisciplinary rigor across management, economics, and policy domains. Scientific Awards: No specific prizes, fellowships, or medals are mentioned in the source text. Sels actively supervises doctoral research, including S. Friedel's work on agri-food cooperative legitimacy, A. Billiet's study of member 'voice' mechanisms, K.G. Tamayo's analysis of line manager HRM involvement in Philippine call centers, and S. Ghielen's employer branding equity research. His current funded projects include Intercooperation (2024-2028) examining cross-cooperative legitimacy, Centre of Expertise for Labour Market Monitoring (2021-2025), and Relationships Matter (2019-2024) investigating social capital in agricultural cooperatives. He conducts research through KU Leuven's Department of Work and Organisation Studies and Leuven Economics of Education Research Centre (LEER), while directing the Centre of Expertise in Labour Market Monitoring for Flemish government policy advising. His cooperative entrepreneurship work operates through the CERA-Boerenbond Chair, integrating academic research with industry partnerships.

• Sequential Data Analysis
• Medical Data Analytics
• EEG Data Analysis
• +۳ مورد دیگر

Dr Lin Yue is a Lecturer at the University of Adelaide , affiliated with the Faculty of Sciences, Engineering and Technology and the School of Computer and Mathematical Sciences . She earned her PhD from Jilin University, with part of her doctoral studies completed as a joint PhD candidate at the University of Queensland. Past affiliations: Northeast Normal University, University of Queensland, University of Newcastle Her research focuses on Sequential Data Analysis and its applications in Medical Data Analytics, EEG Data Analysis, Brain-Computer Interfaces, Social Media Data Analytics, and Sentiment Analysis . She collaborates with academia, government, and professional organizations, supported by internal and external research grants. Dr Yue is eligible to supervise Masters and PhD students as a Co-Supervisor and contributes to advancing data mining and machine learning techniques in healthcare and time series analysis.

Mehrtash Tafazzoli Harandiمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Computer Vision
• +۳ مورد دیگر

Mehrtash Tafazzoli Harandi is an Associate Professor in the Department of Electrical and Computer Systems Engineering at Monash University, part of the Faculty of Engineering. His research focuses on machine learning and computer vision, particularly visual data analysis, with contributions to geometric deep learning, continual learning, and medical imaging. He holds editorial roles at IET Computer Vision , Frontiers in Imaging , and Journal of Imaging . Education & Previous Affiliations: Prior to Monash, he worked at NICTA (Canberra & Queensland Research Labs) and CSIRO-Data61. His Erdős number is 4 via a collaboration path through Richard Hartley. Research Interests: His work spans geometric learning, diffusion models, medical image analysis, and sustainable AI applications. Key areas include unlearning mechanisms in AI, 3D reconstruction compression, and robust MRI reconstruction using contrastive learning. Grants & Projects: He leads projects funded by ARC, US Air Force, and industry collaborations, including 'Can Machines Unlearn?' (ARC, A$790k) and 'Exploiting Geometries of Learning' (ARC, A$420k). His work addresses challenges in lifelong learning, model adaptation, and trustworthy AI from limited data. Awards: Recipient of Best Recognition Paper (IEEE DICTA 2013), NICTA Impact Award (2015), and multiple outstanding reviewer recognitions at top conferences. Teaching: Teaches courses on neural networks, computer vision, and advanced data analysis at Monash University. Supervises PhD students with a focus on mathematical and computational proficiency. Labs/Teams: Collaborates with the Australian Center for Robotic Vision (ACRV) and contributes to interdisciplinary projects at CSIRO-Data61. His research group explores cutting-edge AI applications in healthcare, manufacturing, and environmental sustainability.

Tanya Tierneyمشاهده پروفایل

مدرس پزشکی

• Medical Education
• Healthcare Communication
• Simulation-Based Training
• +۳ مورد دیگر

Dr Tanya Tierney serves as Assistant Dean, Clinical Communication at the Lee Kong Chian School of Medicine, Nanyang Technological University. She is a资深 medical educator specializing in patient-centered communication and simulation-based teaching methodologies. Previously, she held roles at Imperial College London, including Course Leader for Clinical Communication and Head of Year for Graduate Entry Medicine students. Her expertise includes simulated patient (SP) training, curriculum development, and student welfare initiatives. Dr Tierney holds a PhD in Neuroendocrinology from the National Institute for Medical Research (1998) and transitioned to medical education research in 2004. Her work focuses on simulation-based learning, ethnographic approaches to non-technical skills development, and stress management in healthcare settings. Key contributions include developing assessment tools like the Imperial Stress Assessment Tool (ISAT) and advancing hybrid simulation methodologies. Her research portfolio spans over two decades, with recent emphasis on empathy development in healthcare professionals, global perspectives on equity in medical education, and optimizing simulated participant programs. She has authored numerous studies on clinical communication, surgical training, and interprofessional collaboration.