Hi there, I'm Vedika Srivastava 👋

AI Engineer | Research Enthusiast | Lifelong Learner

I'm Vedika, an AI Engineer with a passion for solving real-world problems using the power of AI, ML, and NLP. My journey in technology has taken me from diving deep into Electronics and Communication Engineering to exploring the limitless possibilities within Artificial Intelligence and Data Science. I believe in leveraging technology to make a positive impact, and I'm on a mission to emerge as a thought leader in AI, driving innovation and inspiring others along the way.

📚 Education

Master of Science in Artificial Intelligence
- Boston University-Graduate School of Arts and Science
- GPA: 3.78 / 4
Bachelor of Technology in Electronics and Communication
- Dr. Vishwanath Karad MIT World Peace University
- CGPA: 9.79 / 10, Bronze Medalist

💼 Experience

AI Engineer I, Time Machine Learning Inc, Boston, USA (Since Mar 2024)
Teaching Assistant, Boston University College of Arts and Science, Boston, USA (Jan 2023 - Dec 2023)
Data Science Fellow, Institute of Global Sustainability, Boston, USA (May 2023 - Jul 2023)
SDE Research Intern, IBM Integrated Software Labs, Pune, IN (Jan 2022 - Jul 2022)
NLP Research Intern, Tech Mahindra Maker's Lab, Pune, IN (Oct 2020 - Jan 2021)

🚀 Skills

Coding: Python, Java, C++, C, SQL, MATLAB, Kotlin, HTML, JavaScript, PHP
Technologies: Data Science, Machine Learning, NLP, AI, ANN, Deep Learning, Git, Docker, AWS, GCP, TensorFlow, PyTorch, RASA, Computer Vision
Soft Skills: Project Management, Analytical Thinking, Multitasking, Research & Development, Continuous Learning

🌱 I’m currently learning

Advanced techniques in Computer Vision and Natural Language Processing
Cloud Computing and MLOps practices for scalable AI solutions

👩‍💻 Projects

ISS Image Geolocation
Conversational Stock Investment Advisor
A Comparative Study of Style Transfer Models
3D Text2Live
Biased Prosecution Project
... and many more!

📄 Publications

"NLP-Based AI-Powered Sanskrit Voice Bot." - Artificial Intelligence Applications and Reconfigurable Architectures
"Virtual Voice Assistant for Smart Devices." - ECS Transactions
"Drone Detection using YOLO and SSD: A Comparative Study." - International Conference on Signal and Information Processing

💬 Languages

English: Fluent
Hindi: Native
Marathi: Native
Spanish: Beginner
German: Beginner

🎨 Hobbies

Drawing and painting, Swimming, Loving dogs

📫 How to reach me

Email: [email protected]
Phone: (+1) (857)-260-8766

Connect with me:

VedikaSrivastava's Projects

cs506-spring2023

Code and Slides for Boston University CS 506

cs_501_hw2_a

Code for a simple calculator

ds-cpcs-biased-prosecution

CS 506 CPCS Biased Prosecution Project

machinelearning

The repository showcases the easiest way to use some basic ML algorithms. While some algorithms have been implemented from scratch, library functions are used in others.

ml-549-course

Course website for CS/DS 549 Spark! Machine Learning X-Lab Practicum

ml-terc-image-geolocation

DS 549 Machine Learning Practicum TERC Windows on Earth Image Geolocation Project

optimizationtechniques

The purpose of optimization is to achieve the “best” design relative to a set of prioritized criteria or constraints. These include maximizing factors such as productivity, strength, reliability, longevity, efficiency, and utilization. This decision-making process is known as optimization. This repository discusses some of the matchematical techniques used to find optimal solution to optimizing constraints.

pca

PCA is the process of computing the principal components, which can be achieved by eigen vectors and eigen values, and using them to perform a change of basis on the data, sometimes using only the first few principal components and ignoring the rest. PCA, is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set. PCA is a most widely used tool in exploratory data analysis and in machine learning for predictive models.

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