CONTACT INFORMATIONDepartment of Psychiatry
University of Illinois at Chicago
1601 West Taylor St, (M/C 912),
Room # 456,
Chicago, IL 60612
Office Phone: 312-413-4455
Guidotti, Alessandro; Dong, Erbo; Gavin, David; Veldic, Marin; Zhao, Weihan; Bhaumik, Dulal; Pandey, Subhash; Grayson, Dennis, “DNA-methylation/demethylation network expression in psychotic patients with a history of alcohol abuse" . Alcoholism: Clinical and Experimental Research, 2012 (in print).
Bhaumik, D.K., Amatya, A., Normand, S-L., Greenhouse, J., Kaizar, E., Neelon, B., and Gibbons, R.D. ``Meta-Analysis of Rare Binary Adverse Event Data”, Journal of the American Statistical Association, 555-567, 2012.
Bhaumik, D. K., Roy, A., Aryal, S., Hur, K., Duan, N., Normand, S-L, Brown, H., and Gibbons, R. D. Sample Size Determination for Studies with Repeated Continuous Outcomes, Psychiatric Annals, 38:12, 765-771, 2008.
Gibbons, R.D., Segawa, E., Karabatsos, G., Amatya, A., Bhaumik, D.K., Brown, C.H., Kapur, K., Marcus, S., Hur, K., Mann, J.J. `` Random-effect Poisson Regression Analysis of Adverse Event Reports: The Relationship Between Antidepressants and Suicide”. Statistics in Medicine, 27, 1814-1833, 2008.
Roy A, Bhaumik, D.K., Aryal S and Gibbons RD, Sample Size Determination for Hierarchical Longitudinal Mixed Effects Models with Differential Attrition Rates. Biometrics, 63, 699-707, 2007.
Gibbons RD, Bhaumik, D.K. , Cox DR, Grayson DR, Davis JM, Sharma RP. Sequentially Prediction Bounds for Identifying Differentially Expressed Genes in Replicated Microarray Experiments. Journal of Statistical Planning and Inference, 129, 19-37, 2005.
Bhaumik, D.K., Gibbons RD. An upper prediction limit for the arithmetic mean of a lognormal random variable. Technometrics, 46, 239-248, 2004.
Gibbons RD, Lazar N, Bhaumik, D.K., Sclove SN, Chen HY, Thulborn KR, Sweeney JA, Patterson D. 2004. Estimation and classification of fMRI hemodynamic response patterns, Neuroimage 22, 804-814, 2004.
Bhaumik. D.K., and Sarkar, S. ``On the Power Function of the Likelihood Ratio Test for MANOVA", Journal of Multivariate Analysis, 82, 416-421, 2002.
Heiberger RM, Bhaumik, D.K., Holland B. Optimal data augmentation strategies for additive models. Journal of the American Statistical Association 88:926-938, 1993.
Bhaumik, D.K., Whittinghill DC. Optimality and robustness to the unavailability of blocks in block designs. Journal of the Royal Statistical Society B, 53:399-407, 1991.
Dr. Dulal K. Bhaumik, PhD
Professor of Psychiatry, Biostatistics and Bioengineering
Director, Biostatistical Research Center
Dulal is a Professor of Psychiatry, Biostatistics and Bioengineering at the University of Illinois at Chicago. He received his BS in Statistics from Calcutta University, his MS in Statistics from the Indian Statistical Institute, and his PhD in the same field from the University of Maryland. Before joining UIC, he served as a Professor of Statistics in the Department of Mathematics and Statistics at University of South Alabama.
Dulal's interests include Environmental Statistics, Statistical Problems in Psychiatry, Biostatistics, Design of Experiments, and Statistical Inferences. Currently he is investigating sample size determination and power computation for fMRI data.
He has conducted research, taught and published extensively in these areas. Dulal and Professor Robert D Gibbons received the American Statistical Association's W.J. Youden Award in 2002 and 2006 for Interlaboratory Testing in recognition of the contribution to the analysis of interlaboratory calibration experiments. In 2009, one of his articles received the Outstanding Statistical Application Award from the American Statistical Association.
He has conducted research, taught and published extensively in these areas. Dulal and Professor Robert D Gibbons received the American Statistical Association's W.J. Youden Award in 2002 and 2006 for Inter-laboratory Testing in recognition of the contribution to the analysis of inter-laboratory calibration experiments. In 2009, one of his articles received the Outstanding Statistical Application Award from the American Statistical Association.
In 2008, Dulal was elected a Fellow of the American Statistical Association for his outstanding contributions to the development of Optimal Designs; Construction of Prediction and Tolerance Limits for Environmental Data; Hypotheses Testing for Mental Health Research; Development of Statistical Methodology and Dissemination of Software for Analyzing Functional Magnetic Resonance Imaging (fMRI) Data.
His research has been funded by the National Institute of Mental Health. He is also a co-author of the Wiley book Statistical Methods for Ground Water Monitoring, 2nd Edition.
Environmental Statistics, Statistical Problems in Psychiatry, Biostatistics, Design of Experiments, and Statistical Inferences
• Dimensional RDoC Modeling across the Range of Negative Mood Dysfunction (PI Scott Langenecker)
The goal of this project is to use advanced modeling and stratification techniques, with our expertise in biostatistics and statistical machine learning, to devise across-diagnosis subgroups that share core dimensions of dysfunction, which will link the neurophysiological abnormalities to disease risk and potentially provide more refined treatment targets. We will pursue, an iterative strategy of (a) cross-modality scale development and consolidation for each subdomain (e.g., PCA, ICA), (b) illness vs well characterization via standard and novel techniques (e.g., support vector), (c) subtyping using scale and disease characteristics for each subdomain (cluster, ICA).
• Multimodal Fusion of Brain Imaging Data
The complex interactions between different brain structures can be studied by brain imaging methods, as the anatomical and functional properties of these structures can be measured simultaneously. Both diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) gained popularity as non-invasive imaging tools for the study of the brain and the effects of neurological diseases. The primary goal of this project is to develop statistical approaches for combining structural and functional information in the context of connectivity studies. Further we plan to use machine learning algorithms to explore the ability of the network-based measures to provide diagnostic information for neurological diseases.
• Pattern Recognition and Functional Neuroimaging
Currently there are no known biological measures that can accurately predict future development of psychiatric disorders in individual at-risk. Our goal is to investigate whether machine learning and fMRI/DTI could help to differentiate healthy people from psychiatric disorder people.
• Examining the capacity of publicly-funded after school programs to promote children's mental health in urban, poor communities (PI Stacy Frazier).
The primary goal of this study is to apply Principal Component Analysis and IRT modeling to evaluate Organizational Social Context survey items.
• Links to Learning (PI Marc Atkins)
The primary goal of this study is to see whether the experimental intervention will lead to improvement in children's academic performance, behavior at home, and behavior at school relative to the treatment-as-usual in a longitudinal study.
• Predicting and Detecting Drug Name Confusion Errors (PI Bruce Lambert)
The objective of this study is to develop three different types of prediction limits based on (i) mixed-effects logistic regression models, (ii) Poisson Distribution, and (iii) empirical Bayes estimates with a high assurance.
• Toll-like Receptors and Cytokines in Depression and Suicide Brain (PI G. Pandey).
The goal of this project is to study Toll-like receptors, cytokines and neuroimmune genes in the postmortem brain of normal controls, suicides victims and depressed subjects to determine if abnormalities of the immune function are involved in the pathophysiology of depression and suicide.
• Zero-Inflated Mixed Models for Mental Health Service Use
The overall goal of the proposed study is to develop new statistical estimation and testing procedures that reduce the considerable time, money, and subject burden now encountered in mental health services research. We hope that the new biostatistical approaches developed in this project will enable researchers to conduct higher-quality, more accurate studies that further our nation’s public health goal of recovery for significant proportions of citizens diagnosed with mental health disorders.