Difference between revisions of "Ganesh Sivaraman"

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(Research)
(Research)
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== Research ==
 
== Research ==
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.  
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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.
  
 
* Nucleobase interactions with lower diamondoids.
 
* Nucleobase interactions with lower diamondoids.
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[[File:HybridMOS2_V1.png|500px|center|Semiconductor-Metal-Semiconductor]]
 
[[File:HybridMOS2_V1.png|500px|center|Semiconductor-Metal-Semiconductor]]
  
* Machine learning based molecular classifier : [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. My aim is to implement a molecular classifier based on sorted coulomb matrix as feature for molecules. Implementation is done with [https://spark.apache.org/docs/2.0.0/index.html Apache Spark] (Python API) and machine learning is performed with the Spark built in [http://spark.apache.org/docs/latest/mllib-guide.html MLlib].  
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* '''Machine learning based molecular classifier''' : [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. I aim   to implement a molecular classifier based on sorted coulomb matrix as feature for molecules. Implementation is done with [https://spark.apache.org/docs/2.0.0/index.html Apache Spark] (Python API) and machine learning is performed with the Spark built in [http://spark.apache.org/docs/latest/mllib-guide.html MLlib].  
  
 
<span style="font-size:200%"> '''publications''' &rarr; [https://www.researchgate.net/profile/Ganesh_Sivaraman here]</span>.
 
<span style="font-size:200%"> '''publications''' &rarr; [https://www.researchgate.net/profile/Ganesh_Sivaraman here]</span>.

Revision as of 11:27, 30 August 2016

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
  • Machine learning based molecular classifier : coulomb matrix has been developed as a feature to map and predict molecular properties. I aim to implement a molecular classifier based on sorted coulomb matrix as feature for molecules. Implementation is done with Apache Spark (Python API) and machine learning is performed with the Spark built in MLlib.

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.