Engineer

Ashwin Nair

+1-716-***-**** Email: adhykc@r.postjobfree.com https://www.linkedin.com/in/anair4142/ https://github.com/Ashwin4142 EDUCATION

M.S. in Industrial and Systems Engineering (Operations Research) Feb 2021 University at Buffalo SUNY GPA 3.67/4.00

B.E. in Mechanical Engineering July 2018

Chhattisgarh Swami Vivekanand University CGPA 7.64/10.00 EXPERIENCE

Trainee Engineer, Simplex Casting Limited June 2017 - July 2017

Trained to work with Cupola and Induction Furnace.

Trained to maintain heat and correct environment for furnace operations. Trainee Engineer, Bhilai Steel Plant May 2016 - June 2016

Worked as the Trainee Quality Engineer at the coke and coal section.

Trained to check all the safety regulations for the working of the coke and coal section of the power plant. TECHNICAL SKILLS

Programming Languages / Frameworks: Python, R, Minitab, NumPy, Pandas Microsoft Office: Excel, Word, PowerPoint, Pivot Tables, VLOOKUP Optimization Software / Statistics: Gurobi, IBM ILOG CPLEX, Minitab Database Management: PostgreSQL, MySQL

Data Visualization: Tableau, Matplotlib, Seaborn

Machine Learning: Scikit-learn, Tensorflow, SciPy

PROJECTS

Handwritten Digits Classification Python, NumPy, Pandas, TensorFlow, Scipy Oct 2020 - Nov 2020

Created a Neural Network and Performed Classification to get a Test Accuracy of 94.2 percent.

Studied Hyper-Parameter Selection for Neural Network.

Performed Comparisons between Neural Network and Deep Neural Network in terms of Running Time and Accuracy.

Used TensorFlow to create a Convoluted Neural Network and studied the results. Classification and Regression Python, Pandas, Numpy, Matplotlib, Scipy Sept 2020 - Oct 2020

Performed Linear Discriminant Analysis and Quadratic Discriminant Analysis.

Applied Linear Regression, Ridge Regression, and Ridge Regression with Gradient Descent and calculated the MSEs.

Also applied the Non-Linear Regression and Calculated the MSE with and without Regularization.

Found the least MSE occurs at Linear Regression with Intercept. APIs and Data Visualization Python, API, Pandas, NumPy, Matplotlib May 2020

Using RapidAPI collected live data for Premier League 2019-20 season.

Created Pandas Data Frame for the league standings.

Visualized the point standings of the top four teams and produced the visual evidence of Liverpool’s dominance in the league.

Compared teams trying to secure 35 points and avoid relegation. Traveling Salesman Problem Python, VeroViz, Pandas, NumPy May 2020

Using VeroViz produced an auto-generated TSP problem and visualized it on the map.

The locations were pointed on the map using VeroViz API.

Using Pandas generated a data frame containing longitudinal and latitudinal details of each point.

The heuristic methods used were Nearest Neighbour and Simulated Annealing.

Then the two methods were compared, Simulated Annealing performed better by decreasing the cost of the path by almost 100 percent.

Data Analysis and Predictive Modelling R, Microsoft Excel Sept 2019 - Jan 2020

Performed Data Visualization to find relations between Carbon Emission and multiple factors like population growth, per capita income, and real GDP per capita.

Generated a Correlation plot and found that Carbon Emission and Total Energy Consumption had a positive correlation of 0.98.

Models Implemented were Linear Regression, Logistic Regression, Ridge and Lasso, MARS, GAM, Regression Tree, Random forest, Bagging, and Boosting.

Boosting model was the best working model with the least RMSE value of 9.31.

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