Difference between revisions of "Ganesh Sivaraman"

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(Research)
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[[File:HybridMOS2_V1.png|500px|center|Semiconductor-Metal-Semiconductor]]
 
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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].  
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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. 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 11:28, 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. 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.