Tianhui Z.-Fine-tuning Thesis Work - Generative AI for Automating Software Uplifts
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Experience
Fine-tuning Thesis Work - Generative AI for Automating Software Uplifts
Ericsson
- Explored state-of-the-art Generative AI techniques, mining selected GitHub repositories with all versions from the last 3 years.
- Built and maintained a cleaned dataset of 1M+ for LLM fine-tuning to detect breaking changes from third-party library updates.
- Fine-tuned CodeT5+ and Code Llama with the QLoRA method, achieving an F1-score above 95%, highlighting GenAI’s potential in software upgrades.
Reinforcement Learning – Lunar Lander
KTH Royal Institute of Technology
- Implemented Deep Q-Learning with a replay buffer and target networks.
- Optimized hyperparameters (discount factor, buffer size, learning rate), improving average reward to 200+ after ~350 episodes.
- Evaluated training curves, showing DQN outperforming the random agent.
Parallel Sobel Filtering for Fast Image Processing
KTH Royal Institute of Technology
- Implemented image preprocessing and matrix transformation in Python.
- Designed and parallelized the Sobel filter in C with MPI on Dardel (the top supercomputer), employing red-black communication to avoid deadlocks.
- Achieved strong parallel efficiency and near-linear speed-up up to 128 processors on 150k+ pixel images.
Industry experience
See where this freelancer has spent most of their professional time.
Experienced in Telecommunication and Education.
Business area experience
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Experienced in Information Technology and Research and Development.
Summary
Applied & Computational Mathematics graduate with expertise in Python, Machine Learning, and Generative AI. Experienced in fine-tuning LLMs and building large-scale datasets. Skilled in data preprocessing, feature engineering, and evaluation, with hands-on experience in ML pipelines and real-world projects.
Skills
Programming: Python (Pandas, Numpy), Sql (Bigquery, Mysql), R, Matlab
Ml Techniques: Fine-Tuning, Hyperparameter Optimization, Model Evaluation
Tools & Platforms: Git, Jupyter, Google Colab, Fastapi, Databricks, Kubernetes
Visualization: Tableau, Matplotlib
Adaptability
Communication
Persistence
Teamwork
Languages
Education
KTH Royal Institute of Technology
Master of Applied and Computational Mathematics · Applied and Computational Mathematics · Stockholm, Sweden
University of Science and Technology Beijing
Bachelor of Mathematics and Applied Mathematics · Mathematics and Applied Mathematics · Beijing, China
Statistics
Experience
Global experience
Expertise
Qualifications
Profile
Frequently asked questions
Have questions? Find more information here.
Tianhui is based in Stockholm, Sweden.
Tianhui speaks the following languages: English (Advanced), Chinese (Advanced), Swedish (Intermediate).
Tianhui has at least 1 year of experience. During this time, Tianhui has worked in at least 3 different roles and for 2 different companies. The average length of individual experience is 4 months. Note that Tianhui may not have shared all experience and actually has more experience.
Based on recent experience, Tianhui would be well-suited for roles such as: Fine-tuning Thesis Work - Generative AI for Automating Software Uplifts, Reinforcement Learning – Lunar Lander, Parallel Sobel Filtering for Fast Image Processing.
Tianhui's most recent position is Fine-tuning Thesis Work - Generative AI for Automating Software Uplifts at Ericsson.
In recent years, Tianhui has worked for Ericsson and KTH Royal Institute of Technology.
Tianhui is most experienced in industries like Telecommunication and Education.
Tianhui is most experienced in business areas like Information Technology and Research and Development.
Tianhui holds a Master in Applied and Computational Mathematics from KTH Royal Institute of Technology and a Bachelor in Mathematics and Applied Mathematics from University of Science and Technology Beijing.
The availability of Tianhui needs to be confirmed.
Daily rate distribution
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The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 18 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
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