Essa Jan
1st Year Masters @ Brown University
I am a first year MS CS student at Brown University, advised by Stephen Bach. In my previous, truly amazing life, I completed my undergrad in CS at the LUMS, where I was fortunate to be advised by Professor Fareed Zaffar, Professor Yasir Zaki, and Faizan Ahmed.
The next generation of AI systems will not be defined only by their pretrained knowledge, but by their ability to learn, reason, and improve throughout their lifecycle. My research interests lie in post training approaches that enable models to improve their capabilities, adapt over time, and remain safe. I am particularly interested in RL, self improvement, and developing mechanisms to evaluate and enhance model capabilities while ensuring alignment.
News
Research
MultitaskBench: Unveiling and Mitigating Safety Gaps in LLMs Fine-tuning
*Essa Jan, *Nouar Aldahoul, Moiz Ali, Faizan Ahmad, Fareed Zaffar, Yasir Zaki
Investigated how fine-tuning on downstream tasks affects the safety guardrails of large language models. We develop a comprehensive benchmark to evaluate safety degradation and the robustness of different safety solution across multiple task domains.
Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs
*Essa Jan, *Moiz Ali, Fareed Zaffar, Yasir Zaki
We evaluate how LLMs internalize and transfer knowledge injected during fine-tuning through surface-level and deep comprehension tasks. Our study explores whether model size and task demands impact the depth and generalizability of learned knowledge.
YovaCLIP: Multimodal Framework for Forest Fire Detection
Essa Jan, Zubair Anwar, Murtaza Taj
A multimodal pipeline combining vision language models for real-time early detection of smoke and fire in forest environments. Currently deployed across 600+ square kilometer of forest, processed 10,000+ hours of live footage.
Projects
TradeSnap.ai
A LLM based stock trading platform that reduces the entire trading process to a single multilingual conversational interface, significantly lowering barriers to entry for retail traders especially in the developing world.
Urban Power Consumption and Poverty Mapping
Scrapped data and developed ML models to analyze electricity consumption of 3 million consumers, predicting feeder overloads enhanced grid management and identifying poverty hot spots.
SafeNet: AI-Powered Content Moderation
An LLM powered content moderation system to identify and flag harmful/toxic social media content. Built using Kaggle Jigsaw challenge dataset.