My research is varied and revolves around the subject of complex systems. Unfortunately 'complex systems' is hard to define, and not well defined at this time. For example the first line from the Wikipedia article (Jan 28, 2019) for this subject is: "A complex system is a system composed of many components which may interact with each other". This definition applies also to an ideal gas which is typically not considered a "complex system". For now, the best definition is probably: you know it when you see it. I started this path with a keen interest in non-linear dynamics and chaotic systems as an undergraduate and graduate student. At UT Austin I did research at the Center for Non-Linear Dynamics with Harry Swinney, and completed my Ph.D. with Linda Reichl from the Center for Statistical Physics and Complex Systems, on modeling thin ferromagnetic films. A problem which appeared to show conflicting experimental results due to nonlinear scaling. My dissertation and following work with Jim Erskine helped resolve this issue.

After completing my Ph.D., I realized that as rich and complex as artificial physics systems can be, they are no match to the inherent complexity of even the most basic biological systems. This led me to pursue my first post-doc on the topic of absorption of laser radiation by the retina. In this case, working in Miami, Florida, with Bernard Gerstman, I was able to construct and solve robust discrete equations of motion for a melanosome bombarded by laser radiation and studied the acoustic and explosive vaporization effects. Our work has also led to characterization of chaotic behavior in this system and illuminate a well defined general rule for the transition between linear to non-linear regimes, as observed by deviations from the law of superposition.

During my time in Miami I also started thinking and working on the problem of biological cell division. At it's most basic, my claim is that since cell division is inherently common to all living systems, and would most probably be available to the first living systems, the underlying mechanism responsible for cell division must be relatively simple and result from interactions between inanimate objects. Indeed, careful examination of Debye screening shows that for a symmetrical two charge system in an ionic solution, there is more than enough electrostatic force between the charged division nucleation sites to accommodate both cell body and DNA spatial separation. I plan to devote some of my time for further studies in cell division, including some wet lab experiments on the growth of cells under electromagnetic fields.

By far the most complex system is that us humans use to write research summaries such as this, or countless other manifestations of our thoughts and ideas. It is the realm of intelligence. Maybe the final frontier: if one day we are able to speed up thought the way the electronic calculator/computer sped up numerical calculations, one can imagine such a construction may shed quick light on many of our physics questions. My research into artificial intelligence started with my first job in Indianapolis, Indiana. I was an associate at Yaoqi Zhou's lab, trying to better teach computers to determine the structure of a protein from its genetic sequence. For this I built a deep neural network based machine learner. My work in 2008-2010 is one of the originators of deep learning in the protein structure problem in particular and in general as a revolutionary approach to machine learning.

I am currently devoting most of my time to machine learning based personalized medicine prediction. The promise of medicine tailored to one's specific genetic makeup is very exciting but would require tools to transform the vast amount of genetic information into actionable clinical decisions. In other words, have a patient's own DNA help their doctors determine the best course of treatment for their conditions. Some specific applications are already in use, however, to apply this to a general patient much more understanding between genotype and phenotype will be needed. Because of the vast number of different genotypes and phenotypes, machine learning will prove essential.

Finally, recently I have started to think of the way matter is built on the level of the quarks and nucleons. I have constructed a model which attempts to unite all our observations at this level using only electromagnetic interaction, with mass an outcome of charge confinement. I plan to devote part of my future research time to this topic.

More information is available from my lab page at <a href="http://www.mamiris.com">www.mamiris.com</a>

- 1993-1996 B.Sc. Physics, Mathematics, Hebrew University, Jerusalem, Israel

- 1996-2003 Ph.D. Ferromagnetism, Physics Dept., University of Texas at Austin, Austin, TX, USA

- 2003-2007 Post-doc, Biological Physics, Florida International University, Miami, FL, USA

- 2007-2012 Post-doc, Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA

A full list of publications is available <a href="https://scholar.google.com/scholar?hl=en&q=eshel+faraggi"> here. </a>

Partial publication list:

** Faraggi, Eshel, et al. "Reoptimized UNRES potential for protein model quality assessment." Genes 9.12 (2018): 601.

** Faraggi, Eshel, et al. "Comparing NMR and X-ray protein structure: Lindemann-like parameters and NMR disorder." Journal of Biomolecular Structure and Dynamics 36.9 (2018): 2331-2341.

** Faraggi, Eshel, Bernard S. Gerstman, and Andrzej Kloczkowski. "A superposition test for the emergence of nonlinearities in a laser irradiated spherical absorber." arXiv preprint arXiv:1711.02986 (2017).

** Faraggi, Eshel, and Andrzej Kloczkowski. "GENN: a GEneral Neural Network for learning tabulated data with examples from protein structure prediction." Artificial Neural Networks. Springer, New York, NY, 2015. 165-178.

** Faraggi, Eshel, Yaoqi Zhou, and Andrzej Kloczkowski. "Accurate single‐sequence prediction of solvent accessible surface area using local and global features." Proteins: Structure, Function, and Bioinformatics 82.11 (2014): 3170-3176.

** Faraggi, Eshel, and Andrzej Kloczkowski. "A global machine learning based scoring function for protein structure prediction." Proteins: Structure, Function, and Bioinformatics 82.5 (2014): 752-759.

** Faraggi, Eshel, et al. "Predicting continuous local structure and the effect of its substitution for secondary structure in fragment-free protein structure prediction." Structure 17.11 (2009): 1515-1527.

** Faraggi, Eshel, et al. "SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles." Journal of computational chemistry 33.3 (2012): 259-267.

** Zhang, Tuo, et al. "SPINE-D: accurate prediction of short and long disordered regions by a single neural-network based method." Journal of Biomolecular Structure and Dynamics 29.4 (2012): 799-813.

** Xue, Bin, Eshel Faraggi, and Yaoqi Zhou. "Predicting residue-residue contact maps by a two‐layer, integrated neural‐network method." Proteins: Structure, Function, and Bioinformatics 76.1 (2009): 176-183.

** Yang, Yuedong, et al. "Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates." Bioinformatics 27.15 (2011): 2076-2082.

** Gao, Jianzhao, et al. "BEST: improved prediction of B-cell epitopes from antigen sequences." PloS one 7.6 (2012): e40104.

** Faraggi, Eshel. "An electrostatic model for biological cell division." arXiv preprint arXiv:1006.3961 (2010).

** Faraggi, Eshel. "Symmetrical charge-charge interactions in ionic solutions: implications for biological interactions." arXiv preprint arXiv:1201.0556 (2012).

** Faraggi, Eshel. "Growing E-coli in the presence of electric fields." arXiv preprint arXiv:1709.02745 (2017).

** Faraggi, Eshel, and Bernard S. Gerstman. "Acoustical resonant absorption of pulsed laser radiation by a spherical absorber." Journal of Applied Physics 102.12 (2007): 123505.

** Faraggi, Eshel, and Bernard S. Gerstman. "Resonant absorption in nanometer gold spherical particles." Optical Interactions with Tissue and Cells XVII. Vol. 6084. International Society for Optics and Photonics, 2006.

** Faraggi, Eshel, Bernard S. Gerstman, and Jinming Sun. "The emergence of chaos in a laser irradiated spherical absorber." Chaos: An Interdisciplinary Journal of Nonlinear Science 17.1 (2007): 013101.

** Faraggi, E., Gerstman, B. S., & Sun, J. (2005). Biophysical effects of pulsed lasers in the retina and other tissues containing strongly absorbing particles: shockwave and explosive bubble generation. Journal of biomedical optics, 10(6), 064029.

** Faraggi, E., Wang, S., & Gerstman, B. (2005, April). Stress confinement, shock wave formation, and laser-induced damage. In Optical Interactions with Tissue and Cells XVI (Vol. 5695, pp. 209-216). International Society for Optics and Photonics.

** Nistor, Corneliu, Eshel Faraggi, and J. L. Erskine. "Magnetic energy loss in permalloy thin films and microstructures." Physical Review B 72.1 (2005): 014404.

** Faraggi, Eshel. "Explicit solutions to phenomenological models of magnetization reversal of thin ferromagnetic films in the presence of a sawtooth magnetic field." Journal of magnetism and magnetic materials 303.1 (2006): 49-53.

** Faraggi, Eshel, Linda E. Reichl, and Daniel T. Robb. "Magnetic behavior of partially filled finite Ising surfaces." Physical Review B 74.1 (2006): 014407.

** Faraggi, Eshel, and Daniel T. Robb. "Locally converging algorithms for determining the critical temperature in Ising systems." Physical Review B 78.13 (2008): 134416.

- Helped organize and Co-Chaired several conferences in Mathematical Medicine and Protein Structure Prediction.

- Reviewer for the Physical Review, Journal of Theoretical Biology, Bioinformatics, Proteins, and many others.