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@AISL-at-Imperial-College-London

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buseskorkmaz/README.md

👋 Hi there! I'm Buse

Welcome to my GitHub profile! I'm a PhD student at Imperial College London, with a focus on Large Language Models. My research aims to improve LLMs by identifying their limitations and enhancing their understanding capabilities.

🔍 Research Interests

  1. Scientific Language Models

    • Developing models tailored for scientific literature and discourse.
    • Enhancing the accuracy and efficiency of LLMs in understanding and generating scientific content.
  2. Factually Correct Debiased Generations

    • Ensuring LLMs generate factually accurate and unbiased outputs.
    • Researching methodologies to mitigate biases and enhance fairness in model predictions.

🧑‍💻 Professional Experience

  • Applied Scientist Intern @ Amazon [October 2024 - Ongoing]

    • Working on innovating machine translation methods.
  • AI Research Intern @ IBM Research [Summer 2024]

    • Explored the knowledge and language quality gaps in debiased language models. Investigated faithful and fair language generation methods.
  • AI Research Intern @ IBM Research [Summer 2023]

    • Developed a computational feedback based fine-tuning method for harnessing generative capabilities of large language models on sensitive downstream tasks where human evaluations are ambigious and expensive to obtain.
  • Machine Learning Engineer II @ Comcast NBCUniversal

    • Implemented machine learning solutions to improve the forecasting models for click-rate, video-completion rate and as such metrics.

🚀 Current Projects

📊 GitHub Activity

Buse's GitHub Activity Summary Top Langs


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  1. FMs-at-work Public

    This repository hosts the source code of paper "Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring" (AAAI 2025, AI Governance Workshop).

    Jupyter Notebook 1 1

  2. Integrating-Table-Representations-into-LLMs Public

    This repository hosts the source code and data of paper "Integrating Table Representations into LLMs for Improved Scholarly Document Comprehension" (ACL 2024, Scholarly Document Processing Workshop).

    Python

  3. Contrastive-Learning-for-Alignment-Tax Public

    Code and datasets for a contrastive learning framework to mitigate alignment tax while preserving model capabilities.

    Python

  4. Sentiment-Analysis-with-Deep-Learning Public

    Sentiment analysis of financial news by state-of-the-art NLP models and various machine learning models

    Jupyter Notebook 2

  5. Hotel-Recommendation-with-Recommender-Systems Public

    ACM Competition Theme: Providing hotel recommendations through session-based click data.

    Python 1

  6. Return-Detection-with-Machine-Learning Public

    Data Mining Cup Competition: Detect e-commercial products with a high returning probability based on past data with machine learning models

    Python 1

547 contributions in the last year

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Loading A graph representing buseskorkmaz's contributions from March 03, 2024 to March 03, 2025. The contributions are 100% commits, 0% pull requests, 0% issues, 0% code review.   Code review   Issues   Pull requests 100% Commits

Contribution activity

March 2025

buseskorkmaz has no activity yet for this period.
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