Our Team

We're scientists. We’ve dedicated our careers to mapping and understanding the mysteries of the most complex system known, the human brain. Now, we’re taking our expertise in machine learning and weaving seemingly unrelated strands of complex data into actionable information to inform your decisions and help your business navigate the data landscape.

Vince Calhoun, PhD

Vince Calhoun, PhD

Executive Scientific Officer

Dr. Calhoun has dedicated his career to increasing our understanding of the human brain by integrating multiple modalities of data and developing algorithms that map dynamic networks of brain function, structure and genetics. At Datalytic Solutions, Vince brings his world-class expertise in drawing meaningful and accurate conclusions from incredibly large and diverse datasets to our clients’ data challenges.

Sergey Plis, PhD

Sergey Plis, PhD

Director of Machine Learning

Dr. Plis is an expert in machine learning and has applied his expertise in the field of neuroscience. Sergey specializes in analyzing datasets from multiple sources and discerning meaningful patterns and outcomes using advanced machine learning techniques.

Eric Verner

Eric Verner

Chief Technology Officer

Eric Verner is a seasoned data scientist with extensive experience in programming, algorithm development, signal processing, machine learning, and optimization techniques.

Rogers Silva, PhD

Rogers Silva, PhD

Data Scientist

Dr. Silva is a multidisciplinary data scientist with extensive experience developing algorithms for statistical and machine learning, image analysis, and numerical optimization, mostly working with multimodal neuroimaging data from thousands of subjects. His research interests are multimodal data fusion, statistical and machine learning, neural networks and deep learning, image, video and data analysis, multiobjective, combinatorial and constrained optimization, signal processing, and neuroimaging. He has developed robust generalizations of classical latent variable models like ICA and jICA utilizing statistical independence at a deeper layer of a neural network model. He holds an M.S. in computer engineering (with minors in statistics and in mathematics) and a Ph.D. in computer engineering. He has previously worked as an Engineer, Lecturer, Consultant, and Research Assistant. At Datalytic Solutions, Dr. Silva adds a wide range of advanced statistical methods to best serve our clients' most challenging needs.

Harsh Gazula, PhD

Harsh Gazula, PhD

Data Scientist

Harsh Gazula is a post-doc fellow at the Mind Research Network. His main research area is combinatorial optimization, but he is also interested in examining the interplay of machine learning and mathematical programming to understand the desirable properties of optimization methods used for training a machine learning model and thus develop robust and scalable algorithms. The bottom line is to solve large problems very quickly and efficiently.

Bharath Anandigari

Bharath Anandigari

Full-Stack Web Developer

Bharath is a Master's degree student in computer science at the University of New Mexico. He is a full-stack web developer specializing in Django and Ruby on Rails frameworks. He is also well-experienced in programming, problem solving, and machine learning

Patents and Publications

Datalytic Solutions is dedicated to help you gain valuable insights from your data and employ business solutions using the power of our advanced analytics and machine learning capabilities. With over 30 researchers from diverse backgrounds including electrical engineering, neuroscience, computer science, physics, math and statistics, Datalytic Solutions can provide extensive expertise in knowledge discovery through the use of complex data and mathematical algorithms.

  • Nooner, K. B., Colcombe, S. J., Tobe, R. H., Mennes, M., Benedict, M. M., Moreno, A. L., ... & Milham, M. P. (2012). The NKI-Rockland sample: a model for accelerating the pace of discovery science in psychiatry. Frontiers in neuroscience, 6.

  • Scott, Adam, William Courtney, Dylan Wood, Raul De la Garza, Susan Lane, Runtang Wang, Margaret King, Jody Roberts, Jessica A. Turner, and Vince D. Calhoun. "COINS: an innovative informatics and neuroimaging tool suite built for large heterogeneous datasets." Frontiers in neuroinformatics 5 (2011): 33.

  • Bockholt, Henry Jeremy, Mark Scully, William Courtney, Srinivas Rachakonda, Adam Scott, Arvind Caprihan, Jill Fries et al. "Mining the mind research network: a novel framework for exploring large scale, heterogeneous translational neuroscience research data sources." Frontiers in neuroinformatics 3 (2010): 36.

  • Sarwate, Anand D., Sergey M. Plis, Jessica A. Turner, Mohammad R. Arbabshirani, and Vince D. Calhoun. "Sharing privacy-sensitive access to neuroimaging and genetics data: a review and preliminary validation." Frontiers in neuroinformatics 8 (2014).

  • Wood, Dylan, Margaret King, Drew Landis, William Courtney, Runtang Wang, Jessica Turner, and Vince D. Calhoun. "Harnessing modern web-application technology to create intuitive and efficient data visualization and sharing tools." Frontiers in Neuroinformatics: in press.

  • M. King, D. Wood, B. Miller, R. Kelly, W. Courtney, D. Landis, R. Wang, J. Turner, and V. D. Calhoun, "Automated collection of imaging and phenotypic data to centralized and distributed data repositories," Frontiers in Neuroinformatics, in press.

  • Scott, Adam, William Courtney, Dylan Wood, Raul De la Garza, Susan Lane, Runtang Wang, Margaret King, Jody Roberts, Jessica A. Turner, and Vince D. Calhoun. "COINS: an innovative informatics and neuroimaging tool suite built for large heterogeneous datasets." Frontiers in neuroinformatics 5 (2011): 33.

  • V. D. Calhoun and T. Adalı, "Feature-based Fusion of Medical Imaging Data," IEEE Transactions on Information Technology in Biomedicine, vol. 13, pp. 1-10, 2009, PMC2737598.

  • R. Silva, S. M. Plis, T. Adalı, and V. D. Calhoun, "A Statistically Motivated Simulation Framework for Data Fusion Models Applied to Neuroimaging," NeuroImage, 2013

  • J. Sui, H. He, G. D. Pearlson, T. Adalı, K. A. Kiehl, Q. Yu, V. P. Clark, E. Castro, T. White, B. Mueller, B. C. Ho, N. C. Andreasen, and V. D. Calhoun, "Three-Way (N-way) Fusion of Brain Imaging Data Based on mCCA+jICA and Its Application to Discriminating Schizophrenia," NeuroImage, vol. 66, pp. 119-132, 2013

  • Plis, S. M., Weisend, M. P., Damaraju, E., Eichele, T., Mayer, A., Clark, V. P., ... & Calhoun, V. D. (2011). Effective connectivity analysis of fMRI and MEG data collected under identical paradigms. Computers in biology and medicine, 41(12), 1156-1165.

  • Biessmann, F., Plis, S., Meinecke, F. C., Eichele, T., & Muller, K. (2011). Analysis of multimodal neuroimaging data. Biomedical Engineering, IEEE Reviews in, 4, 26-58.

  • S. M. Plis, V. D. Calhoun, M. P. Weisend, and T. Lane, "MEG and fMRI fusion for nonlinear estimation of neural and BOLD signal changes," Frontiers in Neuroinformatics, vol. 4, pp. 1-17, 2010

  • J. Liu and V. Calhoun, "A review of multivariate analyses in imaging genetics," Frontiers in Neuroinformatics, vol. 8, pp. 1-11, 2014, PMC Journal - In Process.

  • J. Sui, T. Adalı, Q. Yu, and V. D. Calhoun, "A Review of Multivariate Methods for Multimodal Fusion of Brain Imaging Data," Journal of Neuroscience Methods, vol. 204, pp. 68-81, 2012, PMC3690333.

  • V. D. Calhoun and T. Adalı, "Multi-subject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery," IEEE Reviews in Biomedical Engineering, vol. 5, pp. 60-73, 2012, PMC23231989.

  • Michael, A. M., King, M. D., Ehrlich, S., Pearlson, G., White, T., Holt, D. J., ... & Calhoun, V. D. (2011). A data-driven investigation of gray matter–function correlations in schizophrenia during a working memory task. Frontiers in human neuroscience, 5.

  • T. Eichele, V. D. Calhoun, and S. Debener, "Mining EEG-fMRI using Independent component analysis," Int. J. Psych., vol. 73, pp. 53-61, 2009, PMC2693483.

  • Roy, Sushmita, Sergey Plis, Margaret Werner-Washburne, and Terran Lane. "Scalable learning of large networks." IET systems biology 3, no. 5 (2009): 404-413.

  • Altered topological properties of functional network connectivity in schizophrenia during resting state: a small-world brain network study. PloS one. 01/2011; 6(9):e25423. January 2011

  • A baseline for the multivariate comparison of resting-state networks. Frontiers in systems neuroscience. 01/2011; January 2011

  • J. Liu and V. Calhoun, "A review of multivariate analyses in imaging genetics," Frontiers in Neuroinformatics, vol. 8, pp. 1-11, 2014, PMC Journal - In Process.

  • J. Sui, T. Adalı, Q. Yu, and V. D. Calhoun, "A Review of Multivariate Methods for Multimodal Fusion of Brain Imaging Data," Journal of Neuroscience Methods, vol. 204, pp. 68-81, 2012, PMC3690333.

  • Optically Similar Reference Samples and Related Methods for Multivariate Calibration Models Used in Optical Spectroscopy, United States 6,983,176, Issued January 3, 2006 (William Gruner)

  • Apparatus for non-invasive determination of direction and rate of change of an analyte, United States 7,761,126, Issued July 20, 2010 (William Gruner)

  • Noninvasive Determination of Direction and Rate of Change of an Analyte, United States 7,016,713, Issued March 21, 2006 (William Gruner)

  • E. Castro, V. Gomez-Verdejo, M. Martinez-Ramon, K. A. Kiehl, and V. D. Calhoun, "A multiple kernel learning approach to perform classification of groups from complex-valued fMRI data analysis: application to schizophrenia," NeuroImage, vol. 87, pp. 1-17, 2014

  • M. Arbabshirani, K. A. Kiehl, G. Pearlson, and V. D. Calhoun, "Classification of schizophrenia patients based on resting-state functional network connectivity " Frontiers in Brain Imaging Methods, vol. 7, pp. 1-16, 2013

  • W. Du, V. D. Calhoun, H. Li, S. Ma, T. Eichele, K. A. Kiehl, G. D. Pearlson, and T. Adalı, "High Classification Accuracy for Schizophrenia with Rest and Task fMRI Data," Frontiers in Human Neuroscience, vol. 6, pp. 1-12, 2012.

  • J. Arribas, V. D. Calhoun, and T. Adalı, "Automatic Bayesian classification of healthy controls, bipolar disorder and schizophrenia using intrinsic connectivity maps from fMRI data," IEEE Trans Biomed Eng, vol. 57, pp. 2850-2860, 2010, PMC Pending #241486.

  • Potluru, V. K., Plis, S. M., Mørup, M., Calhoun, V. D., & Lane, T. (2009, January). Efficient Multiplicative Updates for Support Vector Machines. In SDM (pp. 1220-1231).

  • O. Demirci, V. P. Clark, V. Magnotta, N. C. Andreasen, J. Lauriello, K. A. Kiehl, G. D. Pearlson, and V. D. Calhoun, "A Review of Challenges in the use of fMRI for Disease Classification / Characterization and A Projection Pursuit Application from Multi-site fMRI Schizophrenia Study," Brain Imaging and Behavior, vol. 2, pp. 207-226, 2008, PMC2701746.