جست‌وجوی استاد و پژوهشگر

Jian Liمشاهده پروفایل

استاد

• Quantitative Statistical Methods
• Hierarchical Linear Modeling
• Structural Equation Modeling
• +۴ مورد دیگر

Dr. Jian Li is a Professor in the Department of Research, Measurement and Statistics at Kent State University's College of Education, Health, and Human Services. She holds a Ph.D. in Quantitative Research, Evaluation, and Measurement from The Ohio State University. Her research focuses on methodological issues in quantitative statistics, including hierarchical linear modeling, structural equation modeling, and experimental designs applied to health behavior and rehabilitation contexts. Research Interests: Dr. Li specializes in developing and applying advanced statistical methods to understand how physical and psychological health influence behavior, particularly in vulnerable populations. Her work integrates quantitative techniques with substantive research in rehabilitation counseling and educational outcomes. Teaching: She teaches doctoral courses in quantitative methods including Hierarchical Linear Modeling, Structural Equation Modeling, and Program Evaluation. Her teaching focuses on developing research competence in graduate students, with three advisees receiving national dissertation awards. Awards: Outstanding Research Award (2021) President’s Faculty Excellence Award (2018) Multiple best paper awards in rehabilitation research Grants & Projects: Dr. Li has served as Co-PI on projects funded by National Institutes, including studies on multiple sclerosis and disability employment retention. She leads initiatives integrating statistical methodology with rehabilitation science.

• Survival Analysis
• Cure Rate Modeling
• Machine Learning
• +۲ مورد دیگر

Dr. Suvra Pal is an Associate Professor of Statistics at the University of Texas at Arlington. He holds a PhD from McMaster University and specializes in survival analysis, cure rate modeling, and machine learning applications in biostatistics. His NIH-funded research develops novel statistical methods for cancer prognosis and wound healing. Dr. Pal received the College of Science Outstanding Teaching Award (2022) and was nominated twice for UTA's President’s Teaching Excellence Award. Key research areas include: Machine learning-enhanced cure models Interval-censored survival data Statistical methods for oncology Optimization algorithms His publications demonstrate consistent innovation in survival analysis, with recent focus on SVM-based cure models and Fokker-Planck frameworks for cancer therapy optimization. Articles appear in Statistics in Medicine and Nature Communications. Honors include: College of Science Teaching Award (2022) NSA Travel Award (2019) Best Paper Award, International Indian Statistical Association (2013) Dr. Pal chairs dissertation committees for PhD students in statistics and serves as Associate Editor for multiple statistical journals. He secured over $1.2M in federal grants for projects integrating machine learning with biomedical research.

Anatoly Zhigljavskyمشاهده پروفایل

استاد

• Time Series Analysis
• Multivariate Statistical Analysis
• Statistical Modelling in Market Research
• +۶ مورد دیگر

Professor Anatoly Zhigljavsky serves as Chair in Statistics and Honorary Professor at Cardiff University's School of Mathematics. He holds multiple administrative positions including membership in the Senior Management Committee, School Research Committee, School Management Board, School Learning and Teaching Committee, Board of Studies, and Subject panel. University: Cardiff University School: School of Mathematics Position: Chair in Statistics, Honorary Professor Professor Zhigljavsky earned his MSc from the University of St.Petersburg, Russia in 1976, followed by his PhD in 1981 and Habilitation in 1987, all from the same institution. His academic credentials reflect a strong foundation in mathematical statistics and theoretical probability. His research spans several interconnected domains in statistics and optimization. He is particularly renowned for his contributions to Time Series Analysis, where he has advanced Singular Spectrum Analysis (SSA) into a powerful technique for time series analysis, forecasting, and change-point detection. His work in Statistical Modelling in Market Research has resulted in numerous industry collaborations, while his research in Stochastic Global Optimization has provided theoretical insights into random search algorithms, especially in high-dimensional spaces. His investigations into Probabilistic Methods in Search and Number Theory have yielded novel approaches to discrete search problems including group testing with lies. Professor Zhigljavsky has also pioneered Dynamical system approaches for studying convergence of search algorithms, bridging continuous and discrete optimization methodologies. Analysis of Professor Zhigljavsky's recent publications (2021-2025) reveals an evolving research trajectory with increasing focus on high-dimensional statistical challenges, quantization theory, and the intersection of optimization with time series analysis. His work consistently demonstrates mathematical rigor combined with practical relevance, addressing computational challenges in large-scale data analysis. His collaborations span multiple institutions with researchers including Luc Pronzato, Jack Noonan, and Anatoly Pepelyshev. Scientific recognition includes: Constantin Caratheodory Prize in France (2019) Professor Zhigljavsky has secured substantial external funding including projects with Procter and Gamble on statistical modelling in Market Research (totaling approximately £200,000), projects with AcNielsen/BASES on consumer behaviour modeling (£40,000), and projects with GlaxoSmithKline on biopharmaceutical studies (£15,000) and environmental science (£10,000). His research has consistently demonstrated practical applications across multiple industries. As an active member of Cardiff University's Statistics research group, Centre for Optimisation and Its Applications, and Statistical Modelling Unit, Professor Zhigljavsky continues to influence both theoretical developments and practical applications in statistics and optimization.

• Machine Learning
• Tensor Algorithms
• Symbolic Regression
• +۲ مورد دیگر

Joachim Giesen is Professor of Theoretical Computer Science at the University of Jena, where he leads research on algorithms for machine learning and artificial intelligence. Education: PhD and Habilitation from ETH Zurich His research develops efficient algorithms for tensor computations, symbolic regression, and optimization methods. Current work focuses on tensor network optimizations and techniques for scalable symbolic regression. He has supervised 7 PhD students to completion and leads multiple industry collaborations applying tensor computation methods to real-world problems.

Sara Johnsonمشاهده پروفایل

دانشیار

• Adolescence and young adulthood
• Identity development
• Civic development and engagement
• +۴ مورد دیگر

Sara Johnson serves as Associate Professor in both the Eliot-Pearson Department of Child Study & Human Development within Tufts University's School of Arts and Sciences and at Tisch College. Her academic appointments include the Evans Family Professorship (2020-2023) and progression from Research Assistant Professor (2013-2015) to Assistant Professor (2016-2023) before achieving her current Associate Professor position. PhD in Human Development and Family Studies, University of Connecticut (2012) MA in Human Development and Family Studies, University of Connecticut (2008) BS in Family and Community Services, Michigan State University (2006) Johnson's research centers on adolescent and young adult development through two foundational premises: individuals actively contribute to their own development and societal functioning, and contemporary Western societies maintain persistent inequalities based on systems like racism and sexism. Her work specifically examines how young people engage in justice-oriented contribution behaviors that promote equity, investigating the roles of identity development, civic knowledge, and life experiences in facilitating these processes. She pays particular attention to how these dynamics differ for youth benefiting from systems of power and privilege. Analysis of her 15 most recent publications reveals a consistent focus on identity development, critical consciousness, and civic engagement using advanced quantitative methods. Her work increasingly incorporates critical race perspectives and examines racial identity development among both marginalized and privileged youth groups. The research demonstrates methodological sophistication through longitudinal designs, mixture modeling, and mixed-methods approaches. Johnson leads the Development of Identity and Community Engagement (DICE) Lab and has secured multiple research grants from the John Templeton Foundation for projects including 'Modeling pathways of character development across the first three decades of life' and 'Evaluating the Positive Coaching Alliance Model.' Her professional activities include extensive conference presentations at major developmental science venues and editorial work for journals including Child Development and Applied Developmental Science . As an educator, Johnson teaches advanced quantitative methods courses including Structural Equation Modeling and Applied Regression Analysis, while mentoring doctoral students through dissertation committees and directing research projects. Her teaching portfolio spans undergraduate honors theses to doctoral-level seminars, reflecting her dual commitment to methodological training and content expertise in developmental science.

• Forensic Psychology
• Forensic Psychiatrics
• Criminology
• +۵ مورد دیگر

Stefan Bogaerts serves as a Full Professor in the Department of Developmental Psychology within the Tilburg School of Social and Behavioral Sciences at Tilburg University. With an extensive publication record spanning over 20 years and 243 documented works, Professor Bogaerts maintains an active research profile with numerous 2025 publications indicating current scholarly engagement. His research interests focus on the intersection of psychology and criminal behavior, with particular expertise in forensic psychology, criminology, violence typology, childhood trauma effects, and mental health within forensic contexts. Professor Bogaerts has developed significant expertise in the quadripartite violence typology model, identity development in forensic populations, and the relationship between mental health conditions and criminal behavior. His recent publication trends reveal a strong focus on empirical validation of theoretical models in forensic psychology, with articles examining aggression measurement, victimization of mental health patients, personality pathology in sexual offenders, and the mediating role of identity in trauma-criminal behavior pathways. These works demonstrate methodological sophistication through community samples, forensic patient populations, and longitudinal studies like the TRAILS project. Professor Bogaerts holds significant external positions including Research, Psychotherapy and Policy at the Fivoor Academy of Research, Innovation and Development (2020-2025) and as National Chair of the Accreditation Committee for Judicial Behavioral Interventions at the Nederlands Jeugd Instituut (since November 2018). His research contributes to UN Sustainable Development Goals, particularly those related to justice, well-being, and reduced inequalities. He teaches advanced courses including Forensic Psychopathology, Forensic Treatment and Care Programs, and Extended Assessment Methods in the Research Master in Individual Differences and Assessment program. His research projects include 'Radicalisering moslim patienten in de ggz' (Radicalization of Muslim Patients in Mental Healthcare), where he serves as Principal Investigator alongside colleagues from multiple institutions.

Shan Huangمشاهده پروفایل

استاد

• Measurement Error Modeling
• Latent Variables Models
• Model Misspecification
• +۴ مورد دیگر

Shan Huang is a Professor in the Department of Statistics at the University of South Carolina , College of Arts and Sciences. His research focuses on measurement error modeling, latent variables, and nonparametric statistics, with a particular emphasis on methodologies for non-Gaussian data and robust inference. Research Interests include: Developing inferential methods for data with measurement errors Model misspecification and diagnostic techniques Nonparametric approaches for mean, mode, and density estimation Analysis of heavy-tailed, directional, and compositional data Recent Publications highlight advancements in Bayesian networks, modal regression with mixture distributions, and directional data modeling. His work addresses challenges in group testing data, coarsened data, and error-prone nodes. Teaching includes courses such as Advanced Statistical Inference, Latent Variable Models, and Nonlinear Statistical Models. His students have contributed to multiple publications marked with asterisks.

• Aerospace Engineering
• Astrodynamics
• Space Trajectory Optimization
• +۶ مورد دیگر

Professor Sandeep Singh is an Assistant Professor in the Mechanical, Aerospace and Nuclear Engineering Department at Rensselaer Polytechnic Institute (RPI). He joined RPI in 2022 after completing his Ph.D. at Texas A&M University, where his research focused on manifold-based trajectory optimization for space missions. Prior to his doctoral studies, he worked at the Indian Space Research Organization (ISRO) as a scientist and program manager for the ASTROSAT mission's mechanical design. His research interests include astrodynamics, low-thrust trajectory design, autonomous navigation, and space mission design leveraging manifold theory and machine learning. Education: Ph.D., Texas A&M University, 2022 B. Tech., Indian Institute of Space Science and Technology, 2013 Research Interests: Optimal low-thrust trajectory design Manifold theory applications in space mission planning Autonomous navigation using CNN frameworks and probabilistic regression Orbit maintenance and exploration in celestial mechanics Spacecraft guidance and control systems Scientific Recognition: John V. Breakwell Student Award (American Astronautical Society) Multiple university fellowships during doctoral studies Professional Roles: RPI Representative to Universities Space Research Association Member of the AIAA Astrodynamics Technical Committee Referee for international journals/conferences Labs/Teams: Active involvement with the Aerospace Systems and Controls Lab (ASCLAB) at RPI.

Shaoyi Huangمشاهده پروفایل

استادیار

• Efficient machine learning algorithms
• Algorithm-system co-design for AI acceleration
• Large-scale machine learning for chip design
• +۳ مورد دیگر

Shaoyi Huang is an Assistant Professor in the Department of Computer Science at Stevens Institute of Technology. She holds a PhD in Computer Engineering from the University of Connecticut (2024), where she was advised by Professors Caiwen Ding and Omer Khan. Her research focuses on advancing AI systems through algorithm-hardware co-design, with emphasis on efficient machine learning algorithms, energy-efficient privacy-preserving techniques, and large-scale machine learning for chip design. She has received multiple accolades including the 2024 Machine Learning and Systems Rising Star award and the 2022 DAC Publicity Paper Award. Education: PhD in Computer Engineering, University of Connecticut (2024) Appointments: Assistant Professor, Stevens Institute of Technology (2024–present) Key Research Areas: AI acceleration, graph neural networks, privacy-preserving ML, and EDA applications Her work spans theoretical contributions to practical implementations, with publications in top-tier conferences like HPCA, ASPLOS, and NeurIPS. She actively contributes to the academic community through program committee roles (e.g., DAC 2025, IPDPS 2025) and serves as a Guest Editor for special issues on high-performance computing. Huang mentors PhD students and oversees a team advancing interdisciplinary AI research, including funded projects from NSF and industry partners like AWS and OpenAI. Awards highlight her impact: Marion & Frederick Buckman Engineering Fellowship (2024), multiple fellowships from UConn, and recognition at WISE conferences for promoting women in hardware security. Her lab emphasizes scalable solutions for real-world challenges in AI and hardware systems.

Ying Linمشاهده پروفایل

دانشیار

• Machine Learning
• Healthcare Analytics
• Manufacturing Systems
• +۳ مورد دیگر

Dr. Ying Lin is an Associate Professor in the Department of Industrial and Systems Engineering at the University of Houston's Cullen College of Engineering. Her research focuses on machine learning applications in healthcare analytics, manufacturing systems optimization, and data-driven decision-making. She leads the Smart Health & INtelligent Engineering Systems (SHINES) Lab, which integrates AI and engineering principles to solve complex problems in healthcare and manufacturing. Dr. Lin holds a Ph.D. in Industrial and Systems Engineering from the University of Washington (2017), an M.S. in Industrial and Management Systems Engineering from the University of South Florida (2014), and a B.S. in Statistics from the University of Science and Technology of China (2012). Her research spans healthcare analytics (e.g., drug efficacy analysis, patient trajectory modeling) and manufacturing process optimization (e.g., superconductor quality control, anomaly detection). Recent work emphasizes federated learning frameworks, privacy-preserving data mining, and machine learning for medical decision support. She has published extensively on topics like predictive modeling for treatment strategies in multiple sclerosis and antipsychotic-associated weight gain in children. Dr. Lin's work bridges healthcare informatics and advanced manufacturing, with a focus on scalable solutions for real-world challenges. Her lab's projects often involve interdisciplinary collaborations and real-world data applications from electronic medical records and manufacturing processes.

Xiaojing Wangمشاهده پروفایل

دانشیار

• Bayesian modeling
• Latent variable models
• Item response models
• +۵ مورد دیگر

Xiaojing Wang is an Associate Professor in the Department of Statistics at the University of Connecticut's College of Liberal Arts and Sciences. She holds multiple affiliations including the Institute for Collaboration on Health, Intervention, and Policy (InCHIP), the Connecticut Institute for the Brain and Cognitive Sciences (IBACS), and the Center for Environmental Sciences and Engineering. Education: Ph.D. in Statistics and M.A. in Economics from Duke University (2012) M.S. in Probability and Mathematical Statistics from Chinese Academy of Sciences (2008) B.S. in Information and Computational Science from Hunan University (2005) Her research focuses on Bayesian methodologies with applications spanning neural decoding, environmental science, psychometrics, and computational biology. She develops novel statistical models for high-dimensional data including latent variable frameworks for neural spiking activity and dynamic item response theory for educational testing. Her work integrates machine learning with traditional statistical inference to address complex problems in interdisciplinary domains. Wang's publications demonstrate a consistent focus on Bayesian computation and model development, with significant contributions to Gaussian process methodologies, subgroup analysis in clinical applications, and ecological forecasting. Recent work shows increasing emphasis on neural data science and computationally intensive methods for high-dimensional inference. Awards and Honors: NSF Faculty Early Career Development (CAREER) Award (2019) Elected Member of International Statistical Institute (ISI) (2017) Jeffrey’s Excellence Prize for Best Methodological Poster (2013) As a statistical consultant, she advises through UConn's Statistical Consulting Services. Her methodological innovations support applications across health interventions, cognitive science, and environmental research through interdisciplinary collaborations.

Joseph C. Cappelleriمشاهده پروفایل

استاد مدعو

• Biostatistics
• Epidemiology
• Clinical Research
• +۴ مورد دیگر

Joseph C. Cappelleri is an Adjunct Professor of Statistics at the University of Connecticut and an Executive Director of Biostatistics at Pfizer Inc's Statistical Research and Data Science Center. He holds an MS in Statistics from City University of New York (Baruch College), a PhD in Psychometrics from Cornell University, and an MPH in Epidemiology from Harvard University. His research focuses on clinical and methodological topics including meta-analysis, patient-reported outcomes, and health measurement scales. He has authored hundreds of publications and multiple books on biostatistical methodologies. Since 2003, he has taught 'Introduction to Epidemiology' at UConn annually and delivered seminars on patient-reported outcomes and meta-analysis. Notable awards include the ISPOR Lifetime Achievement Award and ASA Fellow status. His work bridges academia and industry through collaborations with institutions like Brown University and Tufts Medical Center.

Nizar Bouguilaمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Clustering
• +۷ مورد دیگر

Prof. Nizar Bouguila is a Professor at the Concordia Institute for Information Systems Engineering (CIISE), Concordia University, and holds the Concordia University Research Chair in Applied Artificial Intelligence. His research focuses on machine learning, data clustering, smart building systems, and energy management. He actively supervises graduate students in Computer Science, Electrical and Computer Engineering, and Information Systems Security programs. Dr. Bouguila's work integrates probabilistic models, deep learning, and domain adaptation techniques. He has pioneered methods in energy disaggregation, load forecasting, and anomaly detection in smart infrastructure. His recent projects address challenges in occupant behavior prediction, speech emotion recognition, and fake news detection using Arabic datasets. Key research themes include: Probabilistic clustering with Bayesian nonparametric mixtures Smart building analytics via IoT and sensor data Domain adaptation for cross-domain learning Explainable AI in energy systems His 2023-2025 publications emphasize: Advanced transformer networks for load forecasting Graph neural networks in transportation Multimodal data fusion for anomaly detection Topic modeling for short texts Dr. Bouguila's advising spans multiple engineering and computer science disciplines, reflecting his interdisciplinary research profile.

Xiaodong Gongمشاهده پروفایل

استاد

• Labour Economics
• Public Policy
• Econometrics
• +۷ مورد دیگر

Xiaodong Gong is a Professor at the Canberra School of Politics, Economics and Society at the University of Canberra. He joined UC’s NATSEM unit in 2011 and transitioned to his current role in 2019. His expertise spans labour supply analysis, tax-transfer policy, childcare economics, and household consumption, contributing to UN Sustainable Development Goals related to economic growth and social equity. Before joining UC, he held roles at the Australian Treasury, ANU, and Germany’s IZA. He is an Adjunct Professor at ANU’s Crawford School of Public Policy and a Fellow of IZA. His research focuses on policy evaluation, econometric modeling, and social welfare dynamics. Key research interests include the economic impacts of childcare policies, labour market dynamics, and fiscal policy effects. He collaborates internationally on projects such as regional rail development and workforce shortages in the ACT. Over 40 publications and 18 research projects reflect his interdisciplinary work in economics, public policy, and social sciences. Notable awards include the IZA Fellowship. He actively advises PhD students and leads projects like the Refreshable Panel for Research and Evaluation Services (RES Panel). His work bridges academia and policy, addressing real-world challenges in taxation, education, and public health.

K. Krishnamoorthyمشاهده پروفایل

استاد

• Multivariate Statistics
• Statistical Inference
• Censored Data Analysis
• +۵ مورد دیگر

K. Krishnamoorthy is a Professor of Statistics and holds the Philip and Jean Piccione Endowed Chair in Statistics at the University of Louisiana at Lafayette's Department of Mathematics. His expertise spans multivariate statistics, statistical tolerance regions, censored data analysis, and occupational exposure assessment. He has contributed extensively to methodologies for lognormal distributions, small sample inference, and calibration techniques. His research addresses challenges in environmental and occupational health data analysis, including exposure monitoring and statistical validation of sampling devices. Education: Ph.D. in Statistics from Indian Institute of Technology-Kanpur (1985). He has supervised over 25 Ph.D. students, many now in academic and industry roles. Notable contributions include the development of statistical tolerance regions, methodologies for data with non-detects, and software tools like StatCalc for distributional computations. Grants and Projects: Principal investigator on multiple NIOSH-funded projects (R01-OH03628 series) totaling over $2.6M, focusing on statistical methodologies for occupational exposure assessment. These include studies on lognormal distributions, equivalence testing of sampling devices, and imputation techniques for non-detect data. Publications: Over 200 peer-reviewed articles in journals like *Journal of Statistical Planning and Inference*, *Technometrics*, and *Annals of Occupational Hygiene*. Authored/co-authored three books, including *Handbook of Statistical Distributions with Applications* (CRC Press) and *Statistical Tolerance Regions* (Wiley). Labs/Teams: Leads the statistical methodologies group at UL Lafayette, collaborating with occupational hygienists and environmental scientists. Developed open-source Fortran/R programs for statistical inference in specialized distributions.