Difference between revisions of "Ganesh Sivaraman"

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== Research ==
 
== Research ==
My research involves computational modeling of solid state devices / materials for  [https://en.wikipedia.org/wiki/Nanopore label free] DNA sequencing. The calculations are  performed with the framework of Density functional theory (DFT) combined with Non-Equilibrium Greens Function (NEGF) Formalism.  
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My research involves computational modeling of solid state devices / materials for  next generation [https://en.wikipedia.org/wiki/Nanopore 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.
  
* The solid state device simulation involves gold electrodes embedded with diamond caged molecules (i.e. Diamondoids) for tunneling based electrical sequencing devices (including mutation and methylations). The device simulations are performed with in non equilibrium green's function formalism combined with DFT.  
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* Nucleobase interactions with lower diamondoids.
  
[[File:tip_Rev4.png|300px|center|DIamondoid tipped gold electrodes]]
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* The solid state device simulation involves gold electrodes embedded with diamond caged molecules (i.e. Diamondoids) for tunneling based electric DNA sequencing devices .
  
* Material modeling  of Semiconducting / metallic phase in MoS<sub>2</sub>  monolayer for novel nanoscale bio-sensing application.
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* Mutation and methylations detection
  
List of  publications can be viewed  [https://www.researchgate.net/profile/Ganesh_Sivaraman here].
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[[File:tip_Rev4.png|500px|center|DIamondoid tipped gold electrodes]]
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* Material modeling  of Semiconducting (2H) / metallic (1T) phase in MoS<sub>2</sub> monolayer for novel nanoscale bio-sensing application.
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[[File:HybridMOS2_V1.png|500px|center|Semiconductor-Metal-Semiconductor]]
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* '''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''' &rarr; [https://www.researchgate.net/profile/Ganesh_Sivaraman here]</span>.
  
 
=== Master Thesis ===
 
=== Master Thesis ===
 
{{Download|MSc_thesis_abstract_sivaraman.pdf|"Effect of The Protein Electric Field on The Spectral Tuning Of A Photosynthetic System"}}, 2012, [http://bio.phys.uniroma1.it CBBC Group], Sapienza University of Rome, Italy.
 
{{Download|MSc_thesis_abstract_sivaraman.pdf|"Effect of The Protein Electric Field on The Spectral Tuning Of A Photosynthetic System"}}, 2012, [http://bio.phys.uniroma1.it CBBC Group], Sapienza University of Rome, Italy.

Latest revision as of 08:49, 13 September 2017

As Ganesh Sivaraman is not a member of our working group anymore, the information on this page might be outdated.
Ganesh sivaraman.jpg
Ganesh Sivaraman
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
DIamondoid tipped gold electrodes
  • Material modeling of Semiconducting (2H) / metallic (1T) phase in MoS2 monolayer for novel nanoscale bio-sensing application.
Semiconductor-Metal-Semiconductor
  • 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.


publicationshere.

Master Thesis

application_pdf.png"Effect of The Protein Electric Field on The Spectral Tuning Of A Photosynthetic System" (267 KB)Info circle.png, 2012, CBBC Group, Sapienza University of Rome, Italy.