Update README.md

README.md

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 ## Hello, Welcome to my personal webpage.
 
-<img src="/images/tholesidhant.jpg" width="30%" height="30%">
+ <img src="/images/tholesidhant.jpg" width="30%" height="30%">
 
 # Journey till now:
 ### 1. Bachelors in Technology (Mechanical) from VIT Pune ( Pune University ) [2013 -2017]>>
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 ### 2. Masters of Science (Engineering Design) by Research from IIT Madras [2017-2020]>>
 
-<img src="/images/iitmlogo.png" width="8%" height="8%"> <img src= "https://ed.iitm.ac.in/~palramu/ADOPT.jpg">
+ <img src="/images/iitmlogo.png" width="8%" height="8%"> <img src= "https://ed.iitm.ac.in/~palramu/ADOPT.jpg">
 
- >I am a part of **ADOPT** group at IIT Madras (Advanced Design Optimization and Probabilistic Techniques). This group focuses on _optimization and probabilistic and metamodeling techniques_ for the problems where small data is available. My research was focused on developing interpretable multidimensional visualization technique which can be helpful for designer to take informed decisions. 
+I am a part of **ADOPT** group at IIT Madras (Advanced Design Optimization and Probabilistic Techniques). This group focuses on _optimization and probabilistic and metamodeling techniques_ for the problems where small data is available. My research was focused on developing interpretable multidimensional visualization technique which can be helpful for designer to take informed decisions. 
 
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 ### 3. Working as Data Science Associate at ZS Associates (Current)
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 ### Design Space Exploration and Optimization using Self Organizing Maps [Journal Paper] **[Impact Factor: 3.37]**
 ##### Structural and Multidisciplinay Optimization, Springer
->Identifying regions of interest (RoI) in the design space is extremely useful while building metamodels with limited computational budget. Self-organizing maps (SOM) are used as a visualization technique for design space exploration that permits identifying RoI. Conventional implementation of SOM is susceptible to folds or intersections that hinder visualizing the design space. This work proposes a modified SOM algorithm whose maps are interpretable and that does not fold and allows smoother input and performance space visualization. The modified algorithm enables identification of RoI and additional sampling in the identified RoI allows building accurate Kriging metamodel, which is then used for optimization. The proposed approach is demonstrated on benchmark nonlinear analytical examples and two practical engineering design examples. Results show that the proposed approach is highly efficient in identifying the RoI and in obtaining the optima with less samples.
+Identifying regions of interest (RoI) in the design space is extremely useful while building metamodels with limited computational budget. Self-organizing maps (SOM) are used as a visualization technique for design space exploration that permits identifying RoI. Conventional implementation of SOM is susceptible to folds or intersections that hinder visualizing the design space. This work proposes a modified SOM algorithm whose maps are interpretable and that does not fold and allows smoother input and performance space visualization. The modified algorithm enables identification of RoI and additional sampling in the identified RoI allows building accurate Kriging metamodel, which is then used for optimization. The proposed approach is demonstrated on benchmark nonlinear analytical examples and two practical engineering design examples. Results show that the proposed approach is highly efficient in identifying the RoI and in obtaining the optima with less samples.
 
 Here is the [link](https://link.springer.com/article/10.1007/s00158-020-02665-6#:~:text=Self%2Dorganizing%20maps%20(SOM),hinder%20visualizing%20the%20design%20space.) for the journal paper.