Projects

Selected Projects

Enhancing Long‑term memory in Agents by Learning during Inference

• Supervisor: Prof. Daniel Fried and  Prof. Graham Neubig, CMU, LTI, Sep 2024 - Ongoing

•  Improving Web‑Agents during inference with auto‑eval(feedback) to deduce granular sub‑trajectories and induce memory.

Evaluating Robust use of information in Long Context

• Supervisor:  Prof. Graham Neubig, CMU, LTI, Jan 2024 - June 2024

• Analyzing robustness of diverse language models to use relevant information in long‑context irrespective of its position by analysing effect of positional encodings, attention, architecture.

• Empirically demonstrated that most long‑context adaptation methods(e.g: RoPE, attention based retrieval) are still susceptible to the lost‑in‑the‑middle problem

• Also studied this in the context of imminent tasks ‑ RepoEval (repository Level code generation) showing significant drop in function calling performance as context length increases

Mitigating Hallucinations in Language Models

• Supervisor: Prof. Maarten Sap, CMU, LTI, Sep 2023 - June 2024

• Improving factual correctness in summarization models to mitigate intrinsic hallucination with automatic feedback.

• Developing fine‑grained reward models to capture factual contradictions and unsupported spans in the generated summary

Offensive Tweet Detection 

• Supervisor: Prof. Swati Agarwal , BITS Pilani, K K Birla Goa Campus, Jun 2021 - Dec 2021.

• This project aims to detect hate speech, trolling, and offensive language in tweets using NLP classification models. Built a Federated learning setup to jointly learn from diverse code-switched, code-mixed Hindi-English tweet datasets.

Stock Price Volatility Prediction post Earnings calls release

• Code, Report , Topics: Multimodal Deep Learning, Time series

• Predicted stock price volatility following earnings calls release on the text-audio paired MAEC dataset. Used SentenceBERT to encode text document and audio features such as pitch & intensity. Built text and audio encoder using BiLSTMs. Tested CNN and LSTM layer over fused representation of audio and text encoding.

Analysing and Predicting the outcome of a Volleyball Match

• Report, Topics: Machine Learning, Data scraping

• Built a web scraper to extract NCAA Volleyball match data. Generated features using rolling window averages for past matches. Designed additional team-wise and player-wise features after analysing game-play and literature. Compared the performance of varying models using player-wise and team-wise aggregates.

Modelling The Cognitive Functions of Consumer Behaviour

• Code, Report, Supervisor: Prof. Bharat Deshpande, BITS Pilani, K K Birla Goa Campus, Jan 2020 - June 2020.  Topics: Brain wave technology, Machine Learning

• Aim: To predict consumer behaviour using brain wave measurement technology. Processed the collected brain data-EEG signals using MATLAB EEGlab toolkit. Developed ML classification models on this data to predict consumer behaviour.