Alexandru Aslău #75
Description
Select one:
- I am nominating myself for the PyTorch Ambassador Program.
- I am nominating someone else to become a PyTorch Ambassador.
Please confirm that the nominee meets the following requirements:
- The nominee is 18 years of age or older.
- The nominee agrees to abide by the PyTorch Code of Conduct.
- The nominee agrees to comply with the Linux Foundation Antitrust Policy.
- The nominee meets at least one of the qualifications listed in the PyTorch Ambassador Program Requirements.
Nominee Name
Alexandru Aslău
Nominee Email
Nominee's GitHub or GitLab Handle
https://github.com/AslauAlexandru
(Optional) Organization / Affiliation
https://github.com/AslauAlexandru
City, State/Province, Country
Arad County, Romania
Your Name
Alexandru Aslău
Your Email (Optional)
How has the nominee contributed to PyTorch?
- An active contributor to PyTorch repositories (e.g., commits, PRs, discussions).
- A speaker at PyTorch events or workshops.
- A PyTorch user group organizer or meetup host.
- A researcher or educator using PyTorch in academic work or training.
- An active leader in the PyTorch community with at least one year of experience in:
- Organizing events (virtual/in-person).
- Speaking at AI/ML conferences.
- Mentoring others in PyTorch.
- Creating technical content (e.g., blogs, videos, tutorials).
🏆 How Would the Nominee Contribute as an Ambassador?
I want to contribute to pytorch to enrich my skills in pytorch. I have some projects in pytorch and pytorch lightning.Any additional details you'd like to share?
Lists of some links for projects (for more projects please check the github https://github.com/AslauAlexandru): https://github.com/AslauAlexandru/Week-3-Headstarter-Accelerator-Project-3-Brain-Tumor-Classification-with-Neural-Networks https://github.com/AslauAlexandru/Week-1-Headstarter-Accelerator-Project-1-Customer-Churn-Prediction-with-Machine-Learning https://github.com/AslauAlexandru/Week-2-Headstarter-Accelerator-Bonus-Project-Credit-Card-Fraud-Detection-with-ML https://github.com/AslauAlexandru/Week-5-Headstarter-Accelerator-Project-5-Web-app-for-Codebase-RAG I have unpublished paper in medical image segmantation I developed a new architecture in Deep Learning the name is MADoubleResUnet++, this architecture is combination of Unet, DoubleU-Net, ResUnet and attention mechanism (such as SqeezNet and so on). I use this architecture for segmanting polyps. I have experience in python and I use tensorflow and keras, some pytorch, some pytorch lightning, sklearn, matplotlib, seaborn and so on. Programming Languages: Python, TypeScript/JavaScript, Java, SQL, PHP, C/C++, C# and Matlab (First I use almost all programming languages in Bachelor's degree and in Master's degree and second I use Python (Scikit-learn , TensorFlow, Langchain and many more) and TypeScript/JavaScript in personal projects, Headstarter Accelerator, non-research internships and so on. Software: Scikit-learn , TensorFlow, Langchain, JupyterNotebook, Google Colab, Kaggle Notebook, Pandas, Seaborn, Matplotlib, Numpy, Pytorch Lightning (some), Pytorch (some), Groq, Transformers, Ngrok, google-generativeai, OpenAI (with Groq Api Key), Streamlit and Gradio.
Brain Tumor Classification| Open-Source( ~ inhours)-GithubNov 2024-Nov2024• Used neural networks in Python toclassify 1000 MRI scans into 3 types of possible brain diseases with custommodel•Generated multimodal MRI reports for neurosurgeonsin under 200MS after image classification, construction &trainingCredit CardFraud Detection with ML|Open-source(~ inhours)-GithubOct 2024–Oct 2024• UsedML algorithms (e.g.XGBoost,Random Forest,K-Nearest Neighbors,SVM and so on) in Python to classify fraudulentor not, usedCredit Card Transactions Fraud Detection Dataset, Llama 3.1/3.2, Groqto evaluate accuracy of predicting•In this project, the task is to build an ML modelto determine whether or not a credit card transaction is fraudulent or not.US-Bank Churn Prediction|Open-source (~ inhours)-GithubSep 2025-Oct2025•Used 30k+ data set, Llama 3.1b, Groq and/or Vercel to evaluate accuracy of predicting when banking customer quits•Created an end-to-end solution complete with sending automated personalized email to banking customer based on featureengineering, normalization, model training, evaluating and hyperparameter tuning across 5 LLM models