Difference between revisions of "Ganesh Sivaraman"
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Ganesh Sivaraman
PhD student
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[[File:HybridMOS2_V1.png|500px|center|Semiconductor-Metal-Semiconductor]] | [[File:HybridMOS2_V1.png|500px|center|Semiconductor-Metal-Semiconductor]] | ||
| − | * '''Machine learning | + | * '''Sorted Coulomb matrix generator for Machine learning''' : [https://papers.nips.cc/paper/4830-learning-invariant-representations-of-molecules-for-atomization-energy-prediction.pdf coulomb matrix] has been developed as a feature to map and predict molecular properties. The python code takes in a collection of SMILE strings as inputs and returns a CSV file containing Labeled point vectors of molecules, optimized to be read by [https://spark.apache.org/docs/latest/ml-guide.html Apache Spark MLlib]. The serial version of the code can be accessed [https://github.com/pythonpanda/coulomb_matrix/tree/coulomb-matrix-generator here]. |
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<span style="font-size:200%"> '''publications''' → [https://www.researchgate.net/profile/Ganesh_Sivaraman here]</span>. | <span style="font-size:200%"> '''publications''' → [https://www.researchgate.net/profile/Ganesh_Sivaraman here]</span>. | ||
Revision as of 17:51, 27 September 2016
PhD student
| Office: | 1.080 |
|---|---|
| Phone: | +49 711 685-67721 |
| Fax: | +49 711 685-63658 |
| Email: | ganesh _at_ icp.uni-stuttgart.de |
| Address: | Ganesh Sivaraman Institute for Computational Physics Universität Stuttgart Allmandring 3 70569 Stuttgart Germany |
Research
My research involves computational modeling of solid state devices / materials for next generation label free DNA sequencing (and proteomics ) on High-performance computers. The device simulations are performed with the framework of Density functional theory (DFT) combined with Non-Equilibrium Greens Function (NEGF) Formalism. In addition, I am interest in the application of Machine learning to Nanotechnology and Materials modeling.
- Nucleobase interactions with lower diamondoids.
- The solid state device simulation involves gold electrodes embedded with diamond caged molecules (i.e. Diamondoids) for tunneling based electric DNA sequencing devices .
- Mutation and methylations detection
- Material modeling of Semiconducting (2H) / metallic (1T) phase in MoS2 monolayer for novel nanoscale bio-sensing application.
- Sorted Coulomb matrix generator for Machine learning : coulomb matrix has been developed as a feature to map and predict molecular properties. The python code takes in a collection of SMILE strings as inputs and returns a CSV file containing Labeled point vectors of molecules, optimized to be read by Apache Spark MLlib. The serial version of the code can be accessed here.
publications → here.
Master Thesis
"Effect of The Protein Electric Field on The Spectral Tuning Of A Photosynthetic System" (267 KB)
, 2012, CBBC Group, Sapienza University of Rome, Italy.