Hamid R. Eghbalnia Dept. of Biochemistry and Mathematics Email: eghbalni@nmrfam.wisc.edu Personal web page: http://www.nmrfam.wisc.edu/~eghbalni Phone: (608)-262-8528 Ph.D. Mathematics     2000       University of Wisconsin-Madison

My research is at the interface of computation, mathematics, and biology. In solving problems that require the analysis of data, informatics and physical principles offer complimentary points of view. The top-down view includes building models that elucidate the physical principles and the biology of the problem. I use tools and ideas from geometry, statistics, and information theory in the construction of the top-down view. In the bottom-up view, informatics and computation add to the top-down approach by giving insight into the structure of the data and optimal ways to organize and compute with the data.

One of the current applications of this research is focused on the development of adaptive and probabilistic methods. We have concentrated on data collected using NMR spectroscopy and Mass spectroscopy. In the field of biomolecular NMR, and toward rapid determination of protein structure and function, we have developed a set of web-accessible tools (see sidebar on this page). The robustness of these tools has led to community-wide use – more than 1000 users have analyzed their data with these tools in 2006 alone. These tools use experimental data, informatics, and relevant physical and mathematical theories, encoded into a computational model, in order to arrive at probabilistic explanations of structure, function, or other properties. One of the current applications of this research is  

A more recent application has been in the emerging area of system biology and metabolomics. Using metabolite profile data from NMR and mass spectrometry, we have been successful in uncovering the pattern of coherent dynamic biomarker fluxes that provide a unique predictive model for response to agents that cause inflammatory response.

My research is supported through an award from the NIH-NLM.
My earlier research was supported by an NIH-NLM fellowship in bioinformatics.

I am a mathematician by training with an extensive background in computational methods.  
Find a current CV and a list of publications (in PDF)  by clicking here.           PDF reader is free at (Adobe Acrobat PDF)

      Recent Publications

Eghbalnia, H.R., Wang, L., Bahrami, A., Assadi, A., &Markley, J.L.,  Protein Energetic Conformational Analysis from NMR chemical shifts: determination of protein secondary structure from the sequence and assigned chemical shifts,  Journal of Biomolecular NMR 2005 May;32(1):71-81.

Eghbalnia, H.R. , Bahrami, A., Wang, L., Assadi, A., &Markley, J.L.,  Probabilistic Identification of Spin Systems and their Assignments including Coil-Helix Inference as Output (PISTACHIO),  Journal of Biomolecular NMR Jul;32(3):219-33.

Eghbalnia, H.R., Bahrami, A., Tonelli, M., Hallenga K., & Markley, J.L.,  High Resolution Iterative Frequency Identification for NMR.  J Am Chem Soc. 2005 Sep 14;127(36):12528-36.

Wang, L., Eghbalnia, H.R., & Markley, J.L.,  Probabilistic Approach to Determining Unbiased Random-Coil Carbon-13 Chemical shift values from the protein chemical shift database.  Journal of Biomolecular NMR In Press (2006).

Wang, L., Eghbalnia, H.R., Bahrami, A., & Markley, J.L.,  Linear analysis of carbon-13 chemical shift differences and its application to the detection and correction of errors in referencing and spin system identification.  Journal of Biomolecular NMR 2005 May;32(1):13-22.

Eghbalnia H and Assadi A. Geometric Models in Object Recognition. Invited book chapter, Computational Geometry for Pattern Recognition, Computer Vision, Neurocomputing and Robotics. Springer Verlag, Editor: Eduardo Baryo-Corrochanona, 2004.

Assadi A and Eghbalnia H. A Geometric Method for Investigating the Nonlinear Dynamics of the Human Brain from Analysis of Functional MRI Data. Neurocomputing, in press.

Eghbalnia H and Assadi AH. A Computational Model of Visual Perception of Surfaces. To appear in IEEEE IJCNN 2003, based on invited lecture in IEEE special session on Geometric Neurocomputing.

Eghbalnia H and Assadi AH. An Application of Support Vector Machines and Symmetry to Computational Modeling of Perception Through Visual Attention. J. of Neurocomputing, 38-40: 1193-1201, 2001.

Assadi A and Eghbalnia H. Recurrent Probabilistic Dynamics: Applications to Face Recognition. J. of Neurocomputing, Vol. 38-40: 1067-1075, 2001.

Assadi A, Eghbalnia H and Palmer S. A Learning Theoretic Approach to Perceptual Geometry in Natural Scenes. J. of Neurocomputing, Vol. 38-40: 1077-1085, 2001.

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