Abdus S. Wahed is a Professor of Biostatistics and Associate Chair in the Department of Biostatistics and Computational Biology at the University of Rochester. His research focuses on developing statistical methods for dynamic treatment regimes (DTRs), particularly in Sequential Multiple Assignment Randomized Trials (SMARTs), and their applications in hepatitis B, cancer, and obesity. He also works on longitudinal data analysis, survival analysis, and causal inference. Key Roles: Associate Chair, Professor of Biostatistics Affiliations: University of Rochester Medical Center His research interests include: Personalized medicine and adaptive treatment strategies Methodologies for analyzing longitudinal and time-to-event data Clinical collaborations in hepatitis B, liver diseases, and cancer treatment Recent work emphasizes statistical innovations in SMART trials, including interim monitoring and sample size adjustments. His collaborations span virology, oncology, and public health, addressing issues like hepatitis B treatment discontinuation and arsenic exposure effects. Awards: No specific awards listed in provided text. Advising & Grants: Active in mentoring graduate students and leading research grants focused on biostatistical methodologies. Collaborates with organizations like the Hepatitis B Research Network and the Longitudinal Assessment of Bariatric Surgery study. Labs/Teams: Engaged in multidisciplinary teams addressing chronic diseases and statistical trial design.
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۱۶-۳۰ از ۱۴۰ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
- Dynamic Treatment Regimes
- SMART (Sequential Multiple Assignment Randomized Trials)
- Causal Inference
- +۶ مورد دیگر
Yu Cheng is a Professor and Chair of the Department of Statistics at the University of Pittsburgh, affiliated with the Dietrich School. She holds secondary appointments in Biostatistics and Psychiatry. Her research focuses on dynamic treatment regimes, SMART trials, causal inference, and multiple endpoints analysis. She leads a PCORI-funded method grant on SMART studies and co-investigates projects on cardiovascular disease and maternal/child health outcomes. Her awards include ASA Fellow (2024) and Pittsburgh Chapter Statistician of the Year (2020). Dr. Cheng earned her PhD in Statistics from the University of Wisconsin-Madison (2006), following MS (National University of Singapore, 2001) and BS (University of Science and Technology of China, 1999) degrees. She has held leadership roles including ASA LiDS Section Treasurer (2021–2023), Department Chair (2014–present), and Director of Graduate Studies (2016). Her research integrates methodological innovation with clinical collaboration, addressing topics like HIV, smoking cessation, lupus, depression, and cystic fibrosis. Her 150+ publications span journals like Biometrics , Journal of the American Statistical Association , and Biostatistics . Teaching includes courses in Survival Analysis, Probability, and Statistical Consulting. She actively mentors students through supervised consulting programs.
Hüseyin Ataseven is a full-time Lecturer at Karabuk University's School of Foreign Languages, specializing in Foreign Language Education. He earned his PhD in Measurement and Evaluation in Education (2023) from Ankara University, following a Master's degree (2020) and BA (2005) in the same field. PhD: Measurement and Evaluation in Education, Ankara University (2023) MSc: Measurement and Evaluation in Education, Ankara University (2020) BA: Foreign Language Education, Gazi University (2005) His research focuses on educational assessment, language testing, and statistical analysis in education. Recent studies examine configural frequency analysis in test patterns, rubric reliability, and the impact of AI perceptions on career choices. Develops placement test evaluation methodologies Analyzes university branding and institutional identity Investigates inter-rater reliability in assessment Collaborating with scholars from Ankara University and Recep Tayyip Erdoğan University, his work appears in international social sciences conferences (e.g., ICER, EGE, AICHSS). Despite active publication, no scientific awards or citations are recorded in YÖKSİS metrics.
Olya Hakobyanمشاهده پروفایل
پژوهشگر
- Affective Computing
- Explainable AI
- Data Privacy
- +۴ مورد دیگر
Dr. Olya Hakobyan is a Researcher at the University of Bielefeld, working in the Faculty of Engineering within the Human-Centered Artificial Intelligence Group at the Center for Cognitive Interaction Technology (CITEC). She has been with the university since June 2021 as a research associate in the Multimodal Behavior Processing lab. Dr. Hakobyan's educational background includes: PhD in Computational Neuroscience (2016-2020) from the Institute for Neural Computation at Ruhr University Bochum Master's degree in Cognitive Science (2014-2016) from Ruhr University Bochum Bachelor's degree in Linguistics (2010-2014) from Yerevan Brusov State University of Languages and Social Sciences Her research focuses on the intersection of artificial intelligence, cognitive science, and human-computer interaction. Dr. Hakobyan specializes in affective computing, explainable AI systems, and the development of privacy-preserving data donation platforms. Her work on the Dona platform represents a significant contribution to ethical data collection methods for social interaction analysis. She investigates how AI systems can interpret human emotions through audio and visual channels while maintaining transparency in decision-making processes. Her research also extends to cognitive modeling, particularly in understanding memory processes and recognition mechanisms. Dr. Hakobyan's publication record demonstrates a strong trajectory in developing methods for analyzing social interactions through digital footprints while addressing critical privacy concerns. Her work bridges technical innovation with ethical considerations in data collection. Her scientific contributions have focused on: Privacy-preserving data donation frameworks Explainable AI for emotion recognition systems Analysis of social interactions through digital communication Cognitive modeling of memory processes Ethical considerations in AI and data science
Chad Dubéمشاهده پروفایل
دانشیار
Chad Dubé is an Associate Professor in the Department of Psychology at the University of South Florida, where he has been a faculty member since 2013, advancing from Assistant to Associate Professor in 2019. His research is centered on cognitive psychology, particularly in memory, perception, and decision-making. He completed his Ph.D. and M.S. in Cognitive Psychology at the University of Massachusetts, Amherst, under Caren Rotello, and earned his B.S. in Psychology from Eastern Michigan University. Ph.D., Cognitive Psychology, University of Massachusetts, Amherst (2011) M.S., Cognitive Psychology, University of Massachusetts, Amherst (2009) B.S., Psychology, Eastern Michigan University (2006) His research interests include recognition memory, signal detection theory, ROC analysis, visual short-term memory, alpha oscillations in attention and memory, and deductive reasoning. He employs quantitative and computational modeling approaches to investigate cognitive mechanisms underlying memory and perception. The recent publications reflect a strong focus on theoretical and empirical investigations in memory and perception, particularly using signal detection and computational modeling frameworks. Key themes include central tendency effects, serial dependency, ensemble coding, and the statistical structure of memory representations. His work bridges experimental psychology with formal modeling and information theory. Although no formal awards are listed in the provided text, his publication record in high-impact journals such as Quarterly Journal of Experimental Psychology and Journal of Memory and Language indicates scholarly recognition. Chad Dubé advises graduate and undergraduate students, as indicated by annotations (GA, UA) on his publications. He has collaborated with students such as K. Tong, J. Zepp, and M. Lowry. There is no mention of specific grants or funding sources in the provided text. He previously held a postdoctoral fellowship at Brandeis University’s Volen Center and an adjunct position at Babson College. He is affiliated with the Department of Psychology, which is part of the College of Arts and Sciences at the University of South Florida. His work contributes to the Cognitive and Neural Systems (CNS) specialty area within the department.
Abdus S Wahed is a Professor in the Department of Biostatistics at the University of Pittsburgh. His research specializes in statistical methods for personalized medicine, particularly dynamic treatment regimes through sequentially randomized designs, multivariate longitudinal data analysis, and survival analysis with misclassified events. His educational background includes: Ph.D., Statistics (2003), North Carolina State University, Raleigh, NC M.A., Mathematical Statistics (2000), Ball State University, Muncie, IN M.Sc., Statistics (1994), University of Dhaka, Bangladesh B.Sc., Statistics (1992, Minor: Economics and Mathematics), University of Dhaka, Bangladesh Dr. Wahed's work focuses on developing adaptive treatment strategies for personalized medicine, including methods for screening viable regimes from observational data and identifying critical variables for dynamic decision-making. His methodological interests extend to analysis of censored survival data, length-biased sampling, and joint modeling of longitudinal-time-to-event data with measurement error. Collaborative projects address depression treatment sequencing, bariatric surgery risk prediction, hepatitis clinical trials, and weight-loss interventions. Recent publications (2021-2022) demonstrate expertise in sequential multiple assignment randomized trials (SMART), prognostic accuracy for ordinal competing risks via ROC surfaces, and evaluation of parametric/nonparametric methods for effect measure modification. These contributions advance biostatistical methodology for adaptive clinical trials, causal inference, and personalized treatment optimization. As a professor, Dr. Wahed teaches graduate courses including Linear Models, Estimation Theory, Likelihood Theory, Longitudinal Data Analysis, and Missing Data in Clinical Studies. His research likely involves mentoring graduate students and securing grants in biostatistical methodology and clinical collaborations, though specific details are not provided in the source text.
Roles & Affiliations Professor at Department of Statistics and Actuarial Science , University of Waterloo since 2012. Serves as Associate Editor for Canadian Journal of Statistics and Metrika . Previously held roles at University of Alberta (2008-2011) and University of British Columbia (2008 postdoc). Education PhD in Statistics (2007) - University of Waterloo MA in Statistics (2004) - Nankai University BSc in Statistics (2001) - Nankai University Research Focus Specializes in statistical methodologies including finite mixture models, empirical likelihood, experimental design, biased sampling problems, capture-recapture data, and density ratio models. Current interests include statistical inference under constraints and analysis of non-probability survey samples. Awards CRM-SSC Prize in Statistics (2022) Institute of Mathematical Statistics Fellow (2022) Multiple awards for teaching and research excellence from University of Waterloo Students & Grants Advised over 10 PhD/Master’s students. Active grants include NSERC funding since 2009 and startup grants from University of Waterloo. Supervises postdocs in statistical methodology development.
Chris Schatschneider serves as Professor and Associate Director of the Florida Center for Reading Research at Florida State University, focusing on reading development and learning disabilities research. His educational background includes: Ph.D. from Case Western Reserve University (1995) His research spans Educational psychology , Learning disabilities , and Reading development , emphasizing early identification of at-risk readers through skill assessment. He maintains a strong focus on quantitative methodologies including hierarchical linear modeling and item response theory for educational measurement. Publication trends (2013-2024) reveal dual emphases: genetic/environmental influences on reading (via Florida Twin Project analyses) and methodological innovations in assessment design and statistical modeling for educational research. No scientific awards were documented in the source material. He mentors undergraduates through DIS programs involving Zoom-based data collection for reading experiments, though graduate recruitment is inactive for Fall 2024. Grant details were not specified. The Schatschneider Lab operates within the Florida Center for Reading Research, investigating reading skill development, disability prediction, and advanced quantitative methods through collaborative university-wide initiatives.
Andrew Bradleyمشاهده پروفایل
استاد مدعو
Adjunct Professor Andrew Bradley is affiliated with Queensland University of Technology (QUT), where he serves as the Associate Dean (Research) for the Science and Engineering Faculty. He holds positions in the Faculty of Engineering and School of Electrical Engineering & Robotics at QUT. Bradley has a PhD from the University of Queensland and extensive industry experience, including roles as Chief Technology Officer at Ausonex Pty Ltd and Principal Biomedical Engineer at Maxwell MRI. His expertise spans biomedical and electrical engineering, computing science, and communications technology. His research focuses on computer-aided diagnosis (CAD) systems in medical imaging, machine learning, medical device design, and statistical pattern recognition. He has contributed to projects involving deep brain stimulation, artificial hearts, neonatal hearing screening, and cancer diagnosis imaging. His work bridges academia and industry, emphasizing commercially oriented solutions while maintaining a strong publication record. Bradley has published over 140 peer-reviewed papers and holds 20 patents across multiple regions. His research has garnered over 8,400 citations with an h-index of 35. He has secured $6M+ in competitive grants and supported startups raising $4M in venture capital. His awards include the ARC Future Fellowship and Fellow of Engineers Australia. He has advised 21 PhD and 4 Research Masters graduates, with three current advisory roles. Bradley’s contributions span interdisciplinary collaborations and innovation in labs focusing on medical imaging, AI, and biomedical engineering applications.
Prof. Tilmann Gneiting is a Professor of Computational Statistics at Karlsruhe Institute of Technology (KIT) and Head of the Computational Statistics Working Group at the Heidelberg Institute for Theoretical Studies (HITS). His research focuses on probabilistic forecasting, statistical evaluation of predictive models, and applications in environmental science, epidemiology, and renewable energy. He has held academic roles in mathematics and statistics departments across multiple institutions. His work emphasizes rigorous statistical methodology for forecasting systems, including ensemble models, uncertainty quantification, and validation frameworks. Notable contributions include developing scoring rules for probabilistic predictions, Bayesian model averaging techniques, and geostatistical methods for weather and climate modeling. Courses taught include Forecasting: Theory and Practice and Time Series Analysis . Prof. Gneiting’s research spans diverse areas such as earthquake forecasting, solar energy prediction, and pandemic modeling. He has contributed to R packages like ensembleMOS and ProbForecastGOP , advancing computational tools for statistical analysis. His work bridges theoretical statistics with practical applications in environmental and public health domains. Professional awards and recognitions are not explicitly listed in the provided texts. His advising and grants involve collaborations in interdisciplinary projects, though specific student names or grant details are not mentioned. He leads computational statistics teams at HITS and actively publishes in top-tier statistical and applied journals.
Matthew Zawistowskiمشاهده پروفایل
دانشیار بالینی
Matthew Zawistowski is a Clinical Associate Professor in the Department of Biostatistics at the University of Michigan School of Public Health . He completed his PhD and postdoctoral training at the University of Michigan, focusing on statistical genetics and population genetics. His work bridges Electronic Health Records (EHRs) with genomic data through the Michigan Genomics Initiative (MGI) . Education: PhD in Biostatistics, University of Michigan (2011) MS in Biostatistics, University of Michigan (2007) MS in Mathematics, Lehigh University (2005) BS in Mathematics, Saint Joseph's University (2003) Zawistowski’s research focuses on statistical genetics , precision medicine , and EHR-based phenotyping . He develops methods for analyzing genetic variants in large-scale sequencing studies and explores how polygenic risk scores can inform disease prediction and treatment strategies. His publications highlight expertise in polygenic risk modeling , meta-analysis , and genomic data integration . Key collaborations include the MGI and the BRIDGES Consortium , with applications in cancer, diabetes, and psoriasis genetics. Scientific awards: 2022 SPH Excellence in Teaching award
Michael McDermott, PhD, is a Professor of Biostatistics, Neurology, and the Center for Health + Technology at the University of Rochester Medical Center. He holds an academic appointment in the Department of Biostatistics and Computational Biology. His research focuses on statistical methodology including order-restricted inference, clinical trial design, diagnostic test evaluation, and meta-analysis. Dr. McDermott has collaborated extensively on neurological disorders such as Parkinson’s, Huntington’s, and multiple sclerosis, and has a joint appointment with the Department of Neurology. Educated at the University of Rochester (PhD, 1989), his work bridges statistical theory and clinical application. He has authored influential papers on hypothesis testing under order constraints and developed methods for verification bias correction in diagnostic studies. McDermott serves on editorial boards for journals like International Statistical Review and Movement Disorders , and has held leadership roles in professional societies. His research interests span statistical theory (e.g., multivariate analysis, ROC curves) and applied biostatistics (e.g., dose-response modeling, clinical trial optimization). McDermott’s methodological contributions address challenges in medical research design, including adaptive trial phases and missing data imputation techniques. He has been recognized as a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute. Collaborative efforts include leading national/international groups studying neurological diseases and serving as an advisor for the T32 Training Grant in Biostatistics. His lab focuses on advancing statistical tools for translational medical research while maintaining active involvement in neurology-related clinical studies.
Paul M. Mayer is a Professor in the Department of Chemistry and Biomolecular Sciences at the University of Ottawa. He holds a PhD from the University of Ottawa (1994) and a BSc from the University of Manitoba (1990). His research focuses on gas-phase ion chemistry, including the dynamics of ion dissociation and reactivity in mass spectrometry, with particular emphasis on polycyclic aromatic hydrocarbons (PAHs) and their roles in astrochemistry and atmospheric processes. He collaborates internationally, notably with groups in France, Argentina, and Switzerland, using advanced techniques like tandem mass spectrometry and photoelectron photoion coincidence (PEPICO) spectroscopy. Key research areas include the unimolecular chemistry of PAH ions, the interstellar medium's PAH hypothesis, and atmospheric chemistry studies on reactive organic carbon (ROCs). His group employs state-of-the-art facilities such as the John L. Holmes Mass Spectrometry Facility and the Swiss Light Source synchrotron. Over 30 students and postdocs have graduated under his supervision, many entering careers in academia, government agencies (e.g., Health Canada), and industry (e.g., SCIEX Inc.). Publications span over 160 articles, emphasizing reaction mechanisms, astrochemical modeling, and analytical methodologies. Collaborations bridge disciplines, integrating experimental and computational approaches to address fundamental questions in physical and environmental chemistry.
Lisa E. Hileyمشاهده پروفایل
دانشیار
- Clinical methods and practice-based learning for clinicians
- Interdisciplinary programming and research
- Language and literacy practices for young children
- +۲ مورد دیگر
Lisa E. Hiley is an Associate Professor in the Department of Communication Sciences & Disorders at Nazareth College's York Wellness & Rehabilitation Institute. She coordinates the Developing Child and Families Minor and serves as Chair of her department. Education: Ph.D., University of Rochester M.S. in Communication Sciences and Disorders, Penn State University B.A. in Communication Sciences and Disorders, Nazareth College Research and Teaching Focus: Clinical methods, interdisciplinary programming, language/literacy practices for preschool and school-aged children (especially those with complex communication differences), program evaluation, and trauma-informed/home-visiting models. She emphasizes community-engaged learning and interprofessional collaboration. Scientific Awards: ROC the Future's Jacque Cady Annual Advocacy for Children Award (2020) for improving services for at-risk children and preparing culturally-sensitive educators
Hasnaa Zidani is a Professor in applied mathematics at INSA Rouen Normandie, affiliated with the Laboratoire de Mathématiques de l'INSA (LMI), CNRS EA-3226. She holds the Chaire d'excellence 'COPTI' (2021–2025), funded by ANR, Région Normandie, and the EU, focusing on optimal control applications in environmental modeling, transportation, and image processing. Her research emphasizes optimal control theory, Hamilton-Jacobi equations, numerical methods, and stochastic systems. Recent activities include organizing conferences like the “On the Road to New Horizons” event (June 2024) celebrating Witold Respondek and serving as a speaker at the 3rd International Conference on Variational Analysis (2024). She co-leads the Workshop on Optimal Control Theory (2023) and has contributed to the ROC-HJ solver for optimal control problems. Key research interests include multi-objective optimization, stochastic control, and applications in aerospace engineering. Her work bridges theoretical advancements with practical tools like the ROC-HJ software. Awards include the COPTI excellence chair, recognizing her contributions to applied mathematics and control theory. Grants: COPTI Project (ANR, 2021–2025) Labs/Teams: Laboratoire de Mathématiques de l'INSA (LMI), collaboration with CMM (Chile) and Thales Alenia Space