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Generative AI is transforming various industries by enabling machines to create content like images, music, and text. In this blog post, we'll explore how beginners can easily start using generative AI tools without the need for complex setups or powerful hardware.

If you’re feeling stuck because you don’t have a GPU, fear not! There are several effective alternatives that can help you run your projects smoothly. In this blog post, we'll explore two excellent options.

There are specifically two types of Data Poison Attacks - Untargeted & Targeted. However, detecting untergetted attacks is fairly simple as the overall accuracy of the model decreases. But it becomes a problem when the attack is targeted. Until and unless, the query for that specific label/feature is not triggered, you will never understand that you are hacked! There are methods to identify Targeted Attacks but lets try to figure out if these attacks can be identified / corrected using embeddings’ representation, and thus utilising the Neural Collapse to detect poisoned labels/features.

Creating large language models like GPT-3 and other recent developments in AI have made prompt engineering a popular topic. These models, which contain an incredible amount of parameters and are capable of producing text that is cohesive and contextually appropriate, are immensely flexible tools for a wide range of applications.

The creative expressions of humanity have always been reflected in art, in all of its forms. Art has changed, pushed limits, and confounded expectations throughout the ages. The world of art is undergoing yet another seismic transition in the digital era, and the groundbreaking idea of Stable Diffusion lies at the centre of this change.

In today’s world, artificial intelligence (AI) has rapidly penetrated various aspects of our lives, making it an essential technology in areas such as finance, insurance, education, retail, and manufacturing. Concerns about privacy, accountability, transparency, and the possibility of bias all figured prominently in this discourse. The key question remains: Should AI be regulated, and if so, how do we carefully strike the delicate balance between regulation and innovation?

Graduate Research Assistant Fellow and Teaching Assistant | Jan 2025 - ongoing

• Conducting research on Fairness in Machine Learning Algorithms.
• Working as a Teaching Assistant of CMPUT 200 - Ethics of Data Science and Artificial Intelligence (Winter 2025).

Graduate Teaching Assistant - CMPUT 200 | Sept 2024 - Dec 2024

• Worked as a Teaching Assistant of CMPUT 200 - Ethics of Data Science and Artificial Intelligence (Fall 2024).
• Conducted office hours, labs, grading assignments and exams, solving student queries, etc.

Research Internship | Calgary, Canada | May 2023 - August 2023

• Project: Return on Investment (ROI) of Data Analytics
• Used NLP, Active Learning and requirements dependency extraction to construct a full framework to estimate ROI
• Hosted the application on AWS EC2 and implemented CodePipeline to automate the deployment procedure
• Business Impact: Highlighted possible ROI under several circumstances, potentially helping the business to manage resources and prioritise data-driven plans.
• Tech Stack: Deep Learning, Machine Learning, Data Science, NLP, Cloud Computing (AWS), React JS, Flask

Amazon | Apprenticeship | July 2022

• Competed with the engineering students in India in programming skills, logical reasoning, mathematics and machine learning assessments and was selected among the top few students for this training session
• Hands-on: Deep Neural Networks, Sequential Models, Unsupervised Learning, Causal Inference & Reinforcement Learnin

Machine Learning Internship | India | June 2022 - July 2022

• Employed Curriculum Learning to progressively present training samples for effective learning.
• Enhanced comprehension of textual customer complaints by implementing NER and POS tagging methods
• Impact: Improved text analytics and Intent Recognition Algorithms
• Tech stack: Machine Learning, Deep learning, NLP, Spacy, Textacy

Data Science Internship | India | Dec 2021 - Jan 2022

• Formulated predictive models for IT ticket volumes using Deep Neural Networks and worked on Resource Allocation.
• Leveraged NLP techniques to enhance the functionality of ChatBots built with BOT Framework Composer integrating knowledge base with Microsoft Azure.
• Impact: Automation strategies established the groundwork for customer service operations innovation.
• Tech stack: Machine Learning, Deep Learning, Power BI, Microsoft Azure, Bot Composer Framework

Tool for project management

• Developed a platform to manage all the ongoing projects on a University Campus.
• Designed REST APIs for interactive user experience.

Neural Network Pruning Algorithm

• Neural Collapse-Inspired Pruning: Preserves class separability during pruning,in imbalanced datasets.
• Bias Mitigation & Robustness: Reduces bias towards majority classes and tests model robustness under noisy conditions.
• Experimental Results: Demonstrates effective accuracy retention and fairness, especially for minority classes in imbalanced data.

LLM-powered Q&A system using Langchain + Google Palm

• Developed an LLM-powered Q&A system (Langchain + Google Palm) within Streamlit UI to reduce workload for e-learning company.
• Spearheaded the implementation of FAISS vector database for fast retrieval of relevant answers, optimizing system performance and user satisfaction.
• This innovative approach enables real-time response and seamless access to knowledge within the e-learning platform.

A Transfer Learning framework for detecting vehicles in aerial images

• Utilised the potential of transfer learning with VGG16 network architecture.
• Attained quicker convergence on vehicle detection dataset.
• Conducted trials on grey and coloured images and obtained model accuracy of 60%.