Lipika Parida
Bachelor's in Applied AI & Data Science Student at IIT Jodhpur | AI Researcher | NeuroAdapt
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Lipika Parida is an AI researcher, entrepreneur, and undergraduate student pursuing a Bachelors in AI and Data Science at IIT Jodhpur. A science enthusiast and biology lover since childhood, she won a block-level science exhibition in Class 8 and has since explored the intersection of artificial intelligence, neuroscience, and adaptive learning. Through her research internship and projects involving AI agents, large language models (LLMs), and retrieval-augmented generation (RAG), she builds and experiments with intelligent systems. Her current research project, NeuroAdapt, investigates how neural networks can learn new tasks while retaining previously acquired knowledge. Beyond technology, Lipika is a Taekwondo district gold medalist (2025), an Olympiad medalist, and an aspiring public speaker who has participated in Model United Nations (MUNs) and numerous essay competitions. She also enjoys painting and drawing, bringing together scientific curiosity, creativity, and entrepreneurial ambition in her pursuit of meaningful innovation.
Area of Expertise
Can We Trust AI-Generated Study Materials? Building an Open-Source Quality Checker
AI can generate study notes and practice questions in seconds, but how can students know whether the answers are supported by their course material or whether important syllabus topics have been missed?
In this session, I will explore the design of SyllabusCheck, an open-source tool for auditing AI-generated study materials against a syllabus and source documents. We will examine evidence-linked question checks, syllabus coverage analysis, duplicate detection, and human review of flagged issues.
The session will demonstrate how to evaluate such a tool using a labelled set of valid and intentionally flawed questions, including the limitations of automated verification. Attendees will leave with a practical approach to making AI-assisted learning resources more transparent, testable, and trustworthy.
Beyond Static AI: Teaching Models to Adapt Without Forgetting
What happens when an AI model needs to keep learning instead of being trained once and deployed forever?
In this session, I explore the problem of continual learning through my journey of building and experimenting with NeuroAdapt, an adaptive AI research prototype. A model may perform well when learning one task, but when it is trained on new tasks, its performance on previously learned knowledge can deteriorate — a phenomenon known as catastrophic forgetting.
I’ll break down why this happens, introduce the stability-plasticity dilemma, and explore approaches that can help AI systems learn new information while preserving what they already know. Rather than presenting continual learning as just another ML concept, I’ll share the experiments, challenges, failures, and lessons from actually trying to build and evaluate an adaptive system.
The session will also explore how ideas from neuroscience can inspire the design of AI systems that are more capable of adapting over time, and what developers should consider when building models for environments where the data, tasks, and requirements continuously change.
Lipika Parida
Bachelor's in Applied AI & Data Science Student at IIT Jodhpur | AI Researcher | NeuroAdapt
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