(June 2023 - December 2023)
Graduate Research Assistant
University at Buffalo
Hello,
I'm Lokesh 👋
Transforming complex data into compelling stories and innovative solutions with each line of code.
coder={"name" : "Lokesh", "skills " : ['Python', 'Java', 'R', 'Power BI', 'Tableau', 'PostgreSQL', 'MySql', 'MongoDB', 'Docker', 'AWS'],"hardWorker" : True,"quickLearner" :True,"problemSolver" :True,"hireable" :lambdacoder :coder["hardWorker"]andcoder["quickLearner"]andcoder["problemSolver"]andlen(coder["skills"])>=5}ABOUT ME
Who I am?
I'm a software developer and data science enthusiast skilled at turning ideas into reality. My expertise in Python and machine learning fuels my passion for solving complex problems. With experience from extracting insights to developing AI for social good, I'm looking for opportunities to innovate and make an impact. I'm eager to join forward-thinking teams. Let's explore what we can achieve with data.
Experiences
(December 2020 - Aug 2022)
Software Developer
Visionindia Software Exports Limited
(April 2019 - July 2019)
Software Developer Intern
Zensar Technologies
Skills
R
Java
Python
MongoDB
MySQL
PostgreSQL
Git
AWS
GCP
Docker
PowerBI
Tableau
Angular
C++
Matlab
Numpy
OpenCV
Pytorch
Tensorflow
Selenium
R
Java
Python
MongoDB
MySQL
PostgreSQL
Git
AWS
GCP
Docker
PowerBI
Tableau
Angular
C++
Matlab
Numpy
OpenCV
Pytorch
Tensorflow
Selenium
Educations
August 2022 - February 2024
Master's Degree
MS in Data Science
University at Buffalo
August 2017 - November 2020
Bachelor's Degree
B.Tech in Computer Engineering
Bharati Vidyapeeth University
Projects
Satellite Image Dehazing
This project presents a cutting-edge technique for dehazing images through the use of a deep learning model that integrates convolutional layers, residual connections, and concatenation strategies to enhance image clarity and visibility. Built on TensorFlow 2.0, the model is trained using a dataset of both hazy and clear images, showing significant performance improvements over traditional advanced methods.
Survival Analysis of Ovarian Carcinoma Patients in Clinical Trials
This R project carries out an in-depth survival analysis of patients with ovarian carcinoma, assessing the effectiveness of two distinct treatment methods. It uses the Kaplan-Meier estimator and log-rank tests to compare survival rates between the patient groups.
Flight Price Prediction using Linear and Ridge Regression From Scratch
Implemented Linear and Ridge Regression from scratch using Python and its libraries to predict flight prices. These models take into account various factors, including the airline, flight number, source city, departure time, number of stops, arrival time, destination city, travel class, flight duration, and days remaining until departure.
Optimizing Image Recognition with PyTorch and AlexNet
This project employs a machine learning model based on the AlexNet architecture in PyTorch to classify images. It works with datasets that vary from 760 binary classification entries to 30,000 RGB images, demonstrating the adaptability and effectiveness of customized ML approaches for a variety of analytical challenges.