جست‌وجوی استاد و پژوهشگر

• Mental Health
• Primary Care
• Depression
• +۷ مورد دیگر

Professor Irwin Nazareth serves as Professor of Primary Care and Population Science at University College London's Department of Primary Care & Population Health. He previously held leadership roles including Head of Department (2006-2015) and Director of the MRC General Practice Research Framework (2005-2012), and currently directs the PRIMENT Clinical Trials Unit. His work bridges clinical practice, research, and education in primary care settings. His research spans mental health in primary care , with specific focus on depression, anxiety, psychosis, addiction disorders, and unexplained medical symptoms. Methodologically, he specializes in designing complex intervention trials and analyzing big data from clinical databases. His work extends internationally across Europe, South America, Africa, and South Asia, with significant projects in India. Nazareth's recent publications reveal strong trends in functional neurological disorders (particularly Physio4FMD trials), epilepsy dietary interventions (KIWE trials), and digital health solutions for tobacco cessation. His work consistently combines clinical neurology, mental health, and innovative trial methodologies with practical primary care applications. As an educator, he supervises doctoral students in Primary Care and Mental Health, teaches clinical medicine to medical students, and provides continuing education for GPs. He also leads Commonwealth Fellowship programs training healthcare professionals from Africa, India, Southeast Asia, and the Caribbean. Secured major funding from MRC, Wellcome Trust, Cancer Research UK, European Commission, and NIHR Extensive international collaborations across multiple continents Focus on implementation of research findings into clinical practice Expertise in complex intervention trials and big data analysis

Dong Liمشاهده پروفایل

دانشیار

• High performance computing
• Non-volatile memory
• Memory heterogeneity
• +۲ مورد دیگر

Dong Li is an Associate Professor at the University of California, Merced , where he directs the Parallel Architecture, System, and Algorithm Lab (PASA) and co-directs the High Performance Computing Systems and Architecture Group . He co-founded Yotta Labs Inc. and previously held research roles at Oak Ridge National Laboratory (2011-2014) and a PhD from Virginia Tech. Research Interests: Dong's work focuses on High performance computing (HPC) Memory heterogeneity and non-volatile memory Systems for machine learning and AI Fault tolerance in large-scale systems His innovations include heterogeneous memory optimization for recommendation models and GNNs, CXL memory integration, and persistent memory debugging tools. Recent Publications highlight advancements in CXL-based inter-node communication Memory tiering for laminography reconstruction ML-guided memory optimization for DLRM and GNN Fault tolerance benchmarks and error analysis Awards & Recognition: NSF CAREER Award (2016) Oracle Research Award (2022) ASPLOS Distinguished Artifact Award (2021) Virginia Tech Early Career Alumni Award (2023) Advising & Funding: Dong has mentored 22 students (8 PhD, 6 Master’s, 8 undergraduates) and secured grants from NSF, NVIDIA, Meta, and national labs (Argonne, Lawrence Berkeley, Lawrence Livermore). Collaborations include Microsoft (DeepSpeed, Intel PMDK), AMD, SK Hynix, and Intel/MICRON hardware donations.

Matt Koslovskyمشاهده پروفایل

استادیار

• Bayesian modeling
• Variable selection
• Graphical models
• +۱۰ مورد دیگر

Matt Koslovsky is an Assistant Professor of Statistics at Colorado State University. He completed his PhD in Biostatistics at The University of Texas Health Science Center School of Public Health (UTHealth) in 2016 and served as a Post-Doctoral Research Associate at Rice University's Marina Vannucci lab from 2018-2020. Prior to joining CSU in 2020, he worked as a statistical consultant at Johnson Space Center's Biostatistics Lab. PhD, Biostatistics (2016), UTHealth School of Public Health Post-Doctoral Research Associate (2018-2020), Rice University Assistant Professor (2020-Present), Colorado State University His research spans Bayesian methodology and its applications across diverse domains: Theory: Bayesian modeling, variable selection, graphical models, nonparametric Bayes Applications: Cancer prevention, mental health, microbiome analysis, space health, ecological momentary assessment Recent publications demonstrate methodological advancements in: Bayesian variable selection for rare variants Integrated population modeling Compositional data analysis Continuous-time hidden Markov models mHealth data processing Microbiome mediation effects Current advisees include: Hyungjoon Kim (PhD candidate) Brody Erlandson (PhD candidate) Suppapat Korsurat (PhD candidate)

Nan Chenمشاهده پروفایل

دانشیار

• Applied Mathematics
• Uncertainty Quantification
• Data Assimilation
• +۷ مورد دیگر

Nan Chen is an Associate Professor at the Department of Mathematics, University of Wisconsin-Madison, and a faculty affiliate of the Institute for Foundations of Data Science (IFDS), a multi-University TRIPODS Phase II Initiative. His research spans applied mathematics with applications in atmosphere-ocean science, climate dynamics, and data science. Education: PhD from Courant Institute of Mathematical Sciences (CIMS) and Center of Atmosphere and Ocean Science (CAOS), New York University (NYU), May 2016 Postdoc research associate at CIMS, NYU (June 2016-May 2018) Master's degree from School of Mathematical Sciences, Fudan University, Shanghai Undergraduate in Mechanical Engineering, Fudan University, Shanghai Visited Department of Scientific Computing at Florida State University working with Dr. Max Gunzburger and Dr. Xiaoming Wang Nan Chen's research focuses on contemporary applied mathematics, particularly modeling complex systems, stochastic methods, numerical algorithms, and data science. He specializes in uncertainty quantification (UQ), data assimilation, and developing statistically accurate algorithms to address the curse of dimensionality in large-dimensional complex dynamical systems with strong non-Gaussian features. His work has significant applications in atmosphere-ocean science, including predicting phenomena such as the Madden-Julian Oscillation (MJO), monsoons, El Niño Southern Oscillation (ENSO), and sea ice dynamics. He has also extended his research to material science, neuroscience, and other complex systems. His recent publications demonstrate expertise in inverse problems, wave equations, numerical methods, and data compression techniques that blend mathematical theory with practical applications. Dr. Chen has authored a book titled "Stochastic Methods for Modeling and Predicting Complex Dynamical Systems --- Uncertainty Quantification, State Estimation, and Reduced-Order Models" published by Springer, and a tutorial paper "Taming Uncertainty in a Complex World: The Rise of Uncertainty Quantification — A Tutorial for Beginners" in the Notices of the AMS. Professional Activities: Organizing "Data Meets Dynamics: Workshop on Data Assimilation for Complex Systems and Applications" (August 21-22, 2025) Author of two articles in Elsevier's Reference Module in Earth Systems and Environmental Sciences Participant in Wisconsin Science and Computing Emerging Research Stars (WISCERS) program Judge for Outstanding Student Paper Award (OSPA) program at American Geophysical Union (AGU) fall meetings Involved in Madison Experimental Mathematics Lab (MXM Lab) Dr. Chen actively mentors undergraduate students for research during semesters and summers, encouraging them to present at the UW undergraduate symposium. He also offers reading and independent study courses for interested undergraduates. He is currently seeking highly motivated PhD students to join his research group with possible Research Assistantship support.

• Explainable Artificial Intelligence
• Industrial Machine Learning
• Business Process Management
• +۶ مورد دیگر

Adeel AHMAD is an active Associate Professor (Maître de Conférences) conducting cutting-edge research at the intersection of artificial intelligence, industrial applications, and business process management. His academic work demonstrates strong interdisciplinary connections between computer science, industrial engineering, and business informatics. Dr. AHMAD's research interests span Explainable Artificial Intelligence (XAI), Industrial Machine Learning, Business Process Management, Ontology-Based Reasoning, and Logistics Optimization. His work focuses on developing practical AI solutions for industrial contexts, particularly in Industry 4.0 environments where human-AI collaboration is essential. He has made significant contributions to meta-learning approaches for automated algorithm selection and configuration, with particular emphasis on making these systems transparent and interpretable for domain experts. His publication record shows a clear trajectory toward integrating explainability into industrial AI systems, with recent work focusing on conversational recommendation systems for cyber-physical environments. The research demonstrates consistent evolution from foundational work in business process analysis toward sophisticated AI applications in industrial settings. Active research leadership in Explainable AI for industrial applications Significant contributions to meta-learning frameworks for automated machine learning Interdisciplinary approach bridging computer science, industrial engineering, and business processes Strong publication record in top-tier conferences and journals Dr. AHMAD demonstrates strong collaborative research patterns, frequently working with colleagues including Mourad Bouneffa, Moncef Garouani, and other researchers in the French academic community. His work shows particular relevance to manufacturing, logistics, and cyber-physical systems where AI must work alongside human domain experts.

Xinyu Jiaمشاهده پروفایل

پژوهشگر ارشد

• Uncertainty Quantification
• Structural Reliability
• Risk Assessment
• +۳ مورد دیگر

Xinyu Jia is currently a Humboldt Research Fellow at the Engineering Risk Analysis Group, Technical University of Munich since June 2024, and concurrently serves as Associate Professor in the Department of Mechanical Engineering at Hebei University of Technology, China since October 2022. Her research focuses on advancing uncertainty quantification, structural reliability, and risk assessment methodologies for engineering systems. Her academic background includes: PhD in Mechanical Engineering, University of Thessaly, Greece (2018-2021) Bachelor of Engineering and Master of Science in Mechanical Engineering, Hunan University, China (2011-2018) Dr. Jia specializes in Bayesian learning frameworks for physics-based models, with particular expertise in uncertainty propagation in structural dynamics and industrial robotics applications. Her work develops hierarchical Bayesian approaches that integrate multi-level data to enhance predictive accuracy for complex engineering systems, addressing critical challenges in structural health monitoring and risk-informed decision making. Analysis of her 2022-2023 publications reveals a concentrated research trajectory in applying Bayesian inference to structural dynamics, with emphasis on hierarchical modeling techniques, variational inference schemes, and nonlinear model updating. These contributions predominantly appear in top-tier mechanical engineering journals, demonstrating methodological innovations that bridge theoretical statistics with practical engineering reliability problems. Her scientific recognition includes: Humboldt Research Fellowship (2023) Marie Curie Early Stage Researcher Fellowship (2018) No specific student advisement records are documented, though her Associate Professor role implies teaching responsibilities. Her fellowship awards represent significant research funding supporting her work in uncertainty quantification. As an active member of TUM's Engineering Risk Analysis Group, she contributes to high-impact projects including digital twins for ships, S3UQDyn, Navigating Risk, and infrastructure resilience initiatives like BIG-ROHU and INFRA.RELEARN, focusing on probabilistic risk modeling across civil and mechanical engineering domains.

Sami Äyrämöمشاهده پروفایل

دانشیار

• Computer Science
• Machine Learning
• Health Science
• +۱۱ مورد دیگر

Sami Äyrämö is an Associate Professor at the Faculty of Information Technology , University of Jyväskylä. His research bridges machine learning and health science , focusing on innovative applications in biomechanics , medical imaging , and exercise physiology . Specializes in automated scoring systems for medical diagnostics Pioneer in domain-specific transfer learning for healthcare data Develops synthetic data for wellbeing sector innovation His work spans colorectal cancer tissue analysis , ACL injury risk modeling , and dementia detection from speech , with recent studies applying cluster analysis and deep learning to sports biomechanics challenges. Current projects include the WellbeingDataLab initiative for synthetic exercise data, and collaborations with the Computational Data Science Research Group on spectral imaging and health analytics.

Christina Haagمشاهده پروفایل

پژوهشگر

• Mental Health
• Natural Language Processing
• Activity Sensing Data
• +۳ مورد دیگر

Dr. Christina Haag is a postdoctoral researcher at the Institute for Implementation Science in Health Care , affiliated with the Faculty of Medicine at the University of Zurich . She leads interdisciplinary projects at the intersection of mental health, digital health, and computational linguistics, focusing on chronic illnesses like multiple sclerosis (MS). Her work leverages free text, sensor data, and advanced analysis techniques such as hierarchical modeling and natural language processing (NLP). Doctorate from the Institute of Psychology, University of Zurich Research experience at the MRC Cognition & Brain Sciences Unit, University of Cambridge Her research explores: Daily-life mental and physical health indicators in MS Development of NLP methods for text classification and topic modeling Digital biomarker creation using wearable sensor data Mindfulness interventions for affective executive control Implementation of remote monitoring tools in healthcare Her recent publications highlight trends in applying NLP and machine learning to unstructured health data, analyzing MS activity patterns, and refining interdisciplinary research methodologies. She contributes to DSI communities including AI & Law , Health , and Ethics , and collaborates on projects like BarKA-MS and DSI-Approach . She is a core member of the UZH Digital & Mobile Health Group , working under Prof. Viktor von Wyl.

Aaron Sandelمشاهده پروفایل

دانشیار

• Primate Behavior
• Social Bonds
• Evolutionary Anthropology
• +۱۱ مورد دیگر

Aaron Sandel is an Associate Professor in the Department of Anthropology at the University of Texas at Austin , where he has been affiliated since at least 2012. He co-directs the Ngogo Chimpanzee Project and leads the Primate Ethology & Endocrinology (PEE) Lab . His research focuses on primate social bonds, emotions, and physiological metrics, with fieldwork based in Kibale National Park, Uganda . He teaches courses on biological anthropology, primate conservation, and the origins of friendship. Research Interests : Social bond formation across species Primate emotion quantification Life history and physiology Machine learning behavior analysis Comparative endocrinology Conservation biology Article Trends : Recent publications emphasize chimpanzee social dynamics, emotional/physiological metrics (via urine and breathing rate), and comparative evolutionary analysis. His work spans primatology , evolutionary anthropology , and machine learning applications , with increasing focus on gorilla sociality and human evolution in recent years.

Taro Mienoمشاهده پروفایل

دانشیار

• Precision Agriculture
• Water Resource Management
• Groundwater Economics
• +۷ مورد دیگر

Taro Mieno is an Associate Professor in the Department of Agricultural Economics at the University of Nebraska-Lincoln, specializing in precision agriculture and water resource economics. His work bridges agricultural policy, spatial econometrics, and environmental sustainability. University: University of Nebraska-Lincoln Department: Agricultural Economics Academic Rank: Associate Professor Research Interests: Precision agriculture and variable rate technologies Groundwater economics and drought resilience On-farm experimentation and data-driven decision making Agricultural policy and environmental externalities Machine learning applications in agronomic optimization Soil-water interactions and resource management Email: tmieno2@unl.edu

Hye-Chung Kumمشاهده پروفایل

استاد

• Health Informatics
• Data Science
• Privacy-Preserving Record Linkage
• +۴ مورد دیگر

Hye-Chung Kum is a Professor in the Department of Health Policy & Management at Texas A&M University, where she pioneers Population Informatics to transform digital data into evidence-based health policy solutions. Her work bridges computer science, public health, and social work to address critical data challenges in healthcare systems. Education: PhD, University of North Carolina at Chapel Hill (UNC-CH), 2004 MSW (Master of Social Work), UNC-CH, School of Social Work, 1998 MS, UNC-CH, Department of Computer Science, 1997 BS, Yonsei University, Seoul, Korea, Department of Computer Science, 1995 Research Vision: Dr. Kum develops privacy-preserving human-computer hybrid systems for cleaning and integrating chaotic real-world data (e.g., EHRs, administrative records). Her Population Informatics framework enables ethical large-scale analysis while addressing data genocide in marginalized communities through innovations like the Secure Decoupled Linkage (SDLink) system. Publication Trends: Recent work (2024-2025) reveals three dominant threads: (1) Mental health service utilization dynamics during/post-pandemic, (2) Racial/ethnic disparities in emergency department use and cancer outcomes, and (3) Ethical data sharing frameworks for vulnerable populations. Her Texas-focused studies on Medicaid payment models and AI-driven record linkage demonstrate practical policy applications. Research Infrastructure: As director of the Population Informatics Lab, she leads cross-disciplinary teams developing Privacy-by-Design tools that balance public health needs with data sovereignty—particularly for American Indian and Alaska Native communities. Her grant-funded work consistently addresses Medicaid transformation and healthcare cost drivers through secondary data analytics.

Rasha Kashefمشاهده پروفایل

دانشیار

• Data Science
• Machine Learning
• Big Data
• +۷ مورد دیگر

Rasha Kashef serves as Associate Professor at Toronto Metropolitan University, teaching courses including BME506 (Introduction to Software), COE628 (Operating Systems), COE691 (Software Requirements Analysis), and EE8225 (IoT Analytics). Her academic trajectory spans faculty positions at AAST Institute (2009-2011), University of Waterloo departments (2011-2016), and IVEY Business School (2016-2019), complemented by research roles at Microsoft Corp. Her educational foundation includes: Ph.D. in Electrical and Computer Engineering, University of Waterloo (2008) Bachelor’s degree, Alexandria University Faculty of Engineering (2000; Best Student Award recipient) Dr. Kashef’s research integrates advanced computational methodologies across critical domains: Core expertise in Machine Learning and Big Data analytics for complex system modeling Specialized applications in Healthcare informatics and Revenue management Technical innovation in IoT infrastructure and Distributed Computing architectures Cross-disciplinary work in Operations Research and Autonomous Systems Her publication record demonstrates evolving focus from foundational clustering techniques (2009-2010) to cooperative learning frameworks (2017) and security-oriented big data applications (2019), reflecting consistent contributions to data science scalability and robustness. Award highlights include: Dean’s SRC Award and IEEE Best Paper Awards (2024) Roger’s Cybersecure Fellowship (2023) Multiple Waterloo graduate scholarships and teaching awards No information is available regarding student advising, research grants, or laboratory leadership in the provided materials.

Sushil Prasadمشاهده پروفایل

استاد

• Data Intensive Computing
• Energy-Efficient Deep Learning
• Parallel Algorithms
• +۳ مورد دیگر

Sushil Prasad is a Professor of Computer Science at the University of Texas at San Antonio (UTSA), affiliated with the College of Sciences. His research focuses on data-intensive computing, energy-efficient deep learning models, parallel algorithms, and high-performance software systems. He holds a Ph.D. from the University of Central Florida, an M.S. from Washington State University, and a B.Tech. from the Indian Institute of Technology, Kharagpur. His work emphasizes integrating parallel and distributed computing into early computer science curricula. Key research interests include geospatial data analysis using ICESat-2 and Sentinel-2 imagery, edge device-optimized neural networks, and scalable polygon processing algorithms. He has contributed to frameworks like MPI-GIS and Crayons for high-performance geospatial computing. His educational initiatives include NSF-funded curriculum modernization efforts in parallel computing education. Recent work trends show a focus on climate science applications (e.g., polar sea ice classification), energy-efficient AI, and GPU/OpenMP parallelization. He has organized workshops like EduHPC and EduPar to advance HPC education strategies. Notable recognition includes the TCPP Outstanding Service Award (2012). His projects span cloud-based GIS systems, distributed ML training, and big spatial data processing. Collaborations include NSF-funded research on colocation mining, trajectory analysis, and curriculum development for undergraduate HPC education.

Jakob Eg Larsenمشاهده پروفایل

دانشیار

• Human Computer Interaction (HCI)
• Personal Informatics
• Quantified Self
• +۳ مورد دیگر

Jakob Eg Larsen is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the Department of Applied Mathematics and Computer Science (DTU Compute) within the Cognitive Systems Section. He leads the Mobile Informatics and Personal Data Laboratory (MILAB). His research focuses on Human-Computer Interaction (HCI), Personal Informatics, Quantified Self, and Information Visualization, with applications in wearable technology, mental health, and healthcare. He teaches courses in Digital Media Engineering, including user experience, mobile application prototyping, and personal informatics. Education: PhD (2005) and MSc in Computer Science (1999) from the University of Copenhagen, with additional studies in cognitive psychology. Research emphasizes wearable devices for mental health interventions (e.g., PTSD treatment), physical activity tracking in pregnancy, and personalized hearing aid systems. Over 98 publications and 12 supervised PhD projects highlight his contributions to HCI, mHealth, and data-driven healthcare solutions. Labs/Teams: MILAB focuses on mobile informatics and personal data interaction. Collaborations span interdisciplinary fields like audiology, clinical psychology, and public health.

• Journalism
• Communication Technology
• Social Media
• +۳ مورد دیگر

Yee Man (Margaret) Ng is an Associate Professor in Journalism and the Institute of Communications Research at the University of Illinois. She combines data science methodologies with communication research to study social media dynamics, digital journalism, and technology adoption patterns. Her research interests include: Computational social science Social media analytics Data visualization techniques Communication technology impacts Journalism innovation Post-adoption technology behavior Ng's methodology integrates big data analysis, machine learning algorithms, and traditional survey/experimental approaches. Current work extends diffusion of innovations theory by examining social media users' discontinuance and readoption behaviors. Academic contributions span both empirical research and educational practice. She has taught courses like: JOUR 460 - Data Storytelling JOUR 410 - Data Storytelling for Journalism JOUR 425 - Multimedia Editing and Design JOUR 199 - Hands-on Media Technology Her publications analyze topics ranging from YouTube's recommendation algorithms to digital sustainability in news media, with particular focus on: Platform migration patterns Algorithmic content moderation Political communication dynamics Media technology adoption Regional web usage differences Incivility measurement techniques