Graduate Student Researcher

• Jun 2022 - Present

Graduate Teaching Assistant - Software Engineering

• Jan 2023 - May 2023

Graduate Teaching Assistant for the Course CS5704 - Software Engineering.Responsibilities:Assisted students with assignments related to Java and Kotlin, providing guidance and support to ensure successful completion.Explained complex software engineering concepts, including design patterns, through clear and concise explanations, fostering student understanding and application.Conducted grading of assignments, ensuring fairness and accuracy in evaluating student work based on established rubrics and guidelines.Actively participated in brainstorming sessions, collaborating with course instructors to develop engaging discussions and assignments that enhanced student learning experience.Offered one-on-one consultations to address students' questions and concerns, providing personalized assistance to aid in their academic success.Maintained effective communication with students, promptly responding to inquiries and providing timely feedback to facilitate their learning process.✨ Achievements:Received positive feedback from students for exceptional teaching skills, resulting in improved course evaluations and overall student satisfaction.Collaborated with faculty members to create and implement innovative teaching strategies, incorporating real-world examples and hands-on activities to enhance student engagement and comprehension.Developed additional resources, such as coding tutorials and study guides, to supplement course materials and further support student learning.💡 Technical Skills:Programming Languages: Java, Kotlin.Tools: Github, Gitlab. Show less

Graduate Teaching Assistant - Introduction to Data Analytics and Visualization

• Aug 2022 - Jan 2023

Responsibilities:Assisted students in understanding and applying data science and machine learning concepts, including regression, classification, clustering, statistics, and natural language processing.Designed assignments that challenged students to apply their knowledge and skills in real-world scenarios, fostering critical thinking and problem-solving abilities.Graded assignments and provided constructive feedback to help students improve their understanding and implementation of data analytics and visualization techniques.Conducted weekly office hours, offering personalized guidance and clarification on complex topics related to machine learning and data science.Collaborated with course instructors to brainstorm and develop engaging discussions and activities that enhanced students' comprehension and practical skills in data analytics and visualization.✨ Achievements:Recognized for exceptional teaching skills and ability to simplify complex concepts, resulting in positive feedback from students and improved course evaluations.Contributed to the development of a comprehensive curriculum that integrated cutting-edge techniques and tools in data analytics and visualization, preparing students for real-world applications.Led workshops on machine learning algorithms and data analysis techniques, providing students with hands-on experience in utilizing popular libraries and software tools.💡 Technical Skills:Programming Languages: Python, RData Analytics Tools: Pandas, NumPy, Scikit-learn, TensorFlow, KerasData Visualization: Tableau, Matplotlib, SeabornMachine Learning Concepts: Regression, Classification, Clustering, Dimensionality Reduction, Natural Language Processing, Deep LearningStatistical Analysis: Hypothesis Testing, Descriptive Statistics, Probability Distributions Teaching Assistant for the course CS3654 - Introduction to Data Analytics and Visualization. Show less

Machine Learning Engineer

• Jun 2023 - Aug 2023

I played a pivotal role most recently at UnarLabs LLC as a Machine Learning Engineer developing a new backend system for which enhanced multisensory information access for visually impaired individuals. My responsibilities included architecting and optimizing data pipelines for tasks like data preprocessing, model training, and inference. I leveraged various frameworks such as OpenCV, PyTorch, Transformers, and FastAPI, along with technologies like Distributed Servers, Docker, GCP, and HuggingFace. This comprehensive approach allowed me to engage in full-stack data science and contribute significantly to the project's success. Show less

Machine Learning Intern

• Jun 2022 - Aug 2022

● Responsible for developing a template engine project to help Data Scientists and Machine Learning Engineers at Outreach to use templates to deploy any NLP model online inorder for them to avoid writing redundant boiler plate code. ● Delivered an Online Inference Solution with a gRPC based Microservice in Golang serving NLP based models viz. BERT, ROBERTA and DISTILBERT for topic detection, question detection, action analysis and sentiment analysis. ● Wrote Python pipelines for ingesting data, preprocessing, tokenization, prediction and postprocessing of text data. ● Wrote Bash scripts to instantiate NLP model binaries in the ONNX format on the NVIDIA Triton Inference Server and packaged the inference solution as a docker image. ● Wrote a Go based microservice to be used to communicate with the inference server via gRPC requests and responses. Dockerized the microservice solution which would be later used to communicate with the inference service. ● Wrote tests for the application service as well as the inference service via CircleCI configuration files. ● Deployed the application online via Kubernetes manifests on Outreach Staging Environment. ● Reduced Data Scientist efficiency time from 3-4 days to 2 Hours. Show less

Software Engineer

• Dec 2019 - Feb 2021

● Responsible for implementing a Facial Expression Recognition (FER) application. ● Performed Exploratory Data Analysis on the underlying data - (FER 2013 dataset) and tested classical Machine Learning models viz. Logistic Regression, Support Vector Machine as a baseline classifier. ● Implemented a Proof of Concept Convolutional Neural Network VGG-19 transfer learning model as the final classifier in Pytorch achieving an accuracy of 73% on the validation set. ● Integrated the application with a MongoDB database to store meeting metadata (timestamps, faces detected, expressions classified etc) and respective images in a GridFS format. ● Wrote a GUI script to translate the POC into a desktop application using Python Tkinter. ● Alternatively wrote a Flask application to build a web application on the underlying model and dockerized the application. ● Packaged the code in a python based executable which could be instantiated with a button click on the desktop as an application. ● Owned the application from design, development to production. ● The project is in beta testing at Volkswagen and Bajaj India. Show less