Projects & Experience

Transforms temporary activation interventions into persistent architectural alignment without inference-time compute overhead.

Achieved robust safety behavior comparable to standard runtime steering while preserving general coherence.

Internalizes safety concepts directly into the target architecture's residual stream projections.

Working to improve the 72% accuracy achieved on the Synthetic European dataset by refining KAN architecture.

Tracked and optimized model performance: train loss 2.84, test loss 4.57.

Managed regularization (±2.05) to improve model stability and generalization.

Machine Learning Intern, Glance

Built and optimized catalog retrieval pipelines for the Glance app, improving content discovery across millions of items.

Researched and reimplemented SOTA embedding techniques to develop internal catalog-focused embeddings tailored to Glance's content domain.

Achieved a 2× improvement in retrieval metrics by reimplementing SOTA techniques, significantly enhancing catalog search relevance and ranking quality.

Machine Learning Contributor (Contract), Shipd by Datacurve (YC W24)

Awarded monetary prizes for top-tier performance in high-stakes predictive modeling and algorithmic problem-solving challenges.

Developed and optimized ML models under strict competitive constraints in a fast-paced environment.

Research Fellow, AI Safety Camp (AISC)

Working with Ihor Kendiukhov to evaluate scalability and security guarantees of novel control protocol classes for advanced AI systems.

Extending Greenblatt et al.'s framework by designing hierarchical and parallel control topologies to optimize safety-usefulness trade-offs.

Conducting scaling experiments to verify generalization of control guarantees across diverse model capabilities.

Independent Interpretability Researcher, Mentored by David Africa (UK AI Security Institute)

Conducted independent research on mechanistic interpretability with direct technical guidance from a Research Scientist at the UK AI Safety Institute.

Developed a framework to permanently embed steering vectors into model weights, enabling persistent safety behaviors without inference-time overhead.

Research Intern, International Institute of Information Technology, Hyderabad

Researched Universal Semantic Representations and developed techniques for generating natural language from abstract syntactic-semantic structures.

Built Controlled Image-to-Text Generation systems for scientific images ensuring accurate, context-aware, domain-specific descriptions.

Conducted workshops on prompt engineering for linguistics researchers.

Machine Learning Intern, Co-build.tech

Enhanced query performance by 25% by optimizing document embedding strategies for semantic similarity retrieval.

Automated mapping of legal clause dependencies, improving analysis efficiency by 30%.