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Sheikh Ali Ahmed
認證
데이터/AI · 주니어
저는 핀테크, 암호화폐, 그리고 응용 AI 연구 분야에서 시스템 트레이딩 전략, 백엔드 시스템, 머신러닝 모델을 구축해 온 퀀트 연구자이자 소프트웨어 엔지니어입니다. 데이터, 수학, 엔지니어링을 활용해 복잡한 문제를 해결하는 것을 즐기며, 연구와 구현이 유기적으로 결합되어 빠르게 움직이는 환경에서 가장 잘 역량을 발휘합니다.
經歷
Quantitative Researcher
Kronon Labs · 정규직
2024년 10월 - 2025년 11월 · 1년 2개월
Researched and developed various quantitative strategies for the cryptocurrency market, including but not limited to: - Momentum (Cross-sectional + Single Asset) - Statistical Arbitrage - Delta-Neutral Strategies
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backtesting
Quantitative Research
Python
Developed and deployed a django-based web application as a B2B2C platform. Worked on the 'Pixell App Renewal Project' by re-designing the database using PostgreSQL, implementing views using django API, and testing the deployments on EC2 RDS. Backend system implemented using OAuth2.0, with a separate NoSQL app for the chat feature. Built a dashboard for viewing user analytics from scratch. Company Website: https://www.pixell.ai
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Research on Geometric deep learning and symmetric data using concepts of equivariance and Graph Neural Networks. Lab Website: https://vllab.kaist.ac.kr/
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As a backend developer, I employed Django REST framework in order to represent database models, serialize data, and handle API views. I participated in designing the database, programming web scrapers and crawlers in Python for data collection; I employed MySQL and Django Admin for testing. I also learned technologies like Amazon EC2 (PM2) and RDS for server deployment.
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As a developer, I designed and implemented a parser and a binary lifter for the Hitachi SH-4 computer architecture. It's part of the B2R2 project on github: https://github.com/B2R2-org/B2R2. I coded in a functional-first language called F# using .NET tools and framework. Testing was done through Linux subsystems and cross-compilation toolchains.
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學歷
活動
프로젝트
Breast Cancer Histology Using Image Classification
2022년 9월 - 2022년 12월 · 4개월
This is an unofficial implementation of Breast cancer histology in order to contribute to existing healthcare research. Our implementation is based on a dual-CNN architecture to extract patch-wise and global image features respectively. Original paper: https://arxiv.org/abs/1803.04054
프로젝트
Image Captioning Using Bottom-Up and Top-Down Attention
2022년 9월 - 2022년 12월 · 4개월
Developed a Deep Learning model for image captioning using Top-Down and Bottom-Up visual attention mechanisms. Our implementation in based on the original research paper at https://arxiv.org/pdf/1707.07998.pdf. We also implemented an Auto-Reconstructor Network, an Adaptive Attention mechanism.
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R Food - A Food Supply Sustainability Project based on Object Detection & Classification
2022년 9월 - 2022년 12월 · 4개월
As part of the KAIST-SAP ‘Enterprise Business Solutions with AI’ course, we chose the ‘sustainability’ topic and developed a sustainable AI-based solution which can improve food life and food distribution throughout the world. Our implementation is a low-code/no-code solution based on SAP services.
프로젝트
Deep Hough Voting for 3D Object Detection in Point Clouds (Implementation)
2022년 3월 - 2022년 6월 · 4개월
Developed an algorithm for object detection in 3D Point Cloud data, using Hough Voting and Deep Learning on PointCloud++. Bounding boxes are generated directly from the 3D data and Hough Voting is employed in recognizing objects. The implementation is based on TensorFlow 1.2 and PyTorch.
프로젝트
B2R2
Softsec Lab at KAIST · 2022년 1월 - 2022년 3월 · 3개월
Developed Parser and Lifter for SH-4 Computer Architecture.