Jhosimar Figueroa
Private Tutor
Peru
Summary
👤
Senior🎓
Top SchoolJhosimar Figueroa is a seasoned ML/DL researcher and educator with 11 years of experience in computer vision and natural language processing. He has led AI projects and educational initiatives, published and presented at venues such as SIBGRAPI, ICML, and NeurIPS workshops, and founded the ML/DL Meetup AQP to promote the Peruvian AI community. As a freelance ML developer, he distills large models like BERT into lightweight architectures with minimal loss (94% performance retained, 97% size reduction) and has built 2D/3D CNNs for medical imaging, Siamese networks for re-identification, and fine-tuned detectors achieving mAP over 60% in rural Peru. He has tutored students at UPC and globally, earning top-rated reviews on platforms like Superprof and Tusclasesparticulares, and has taught DL/algorithms courses at the university level. Based in Peru, he holds an MSc in Computer Science from UNICAMP and a BSc in Systems Engineering from UNSA, and is actively seeking deep learning and research opportunities, open to relocation.
11 years of coding experience
6 years of employment as a software developer
Bachelor of Science (B.Sc.), Systems Engineering, Bachelor of Science (B.Sc.), Systems Engineering at Universidad Nacional de San AgustínMaster of Science (M.Sc.), Computer Science, Master of Science (M.Sc.), Computer Science at Universidade Estadual de Campinas
Spanish, English, Portuguese, French
Github Skills (10)
tensorflow210
machine-learning10
deep-learning10
support-vector-machines10
tensorflow10
neural-networks10
python10
image-classification10
dropout9
batch-normalization9
Programming languages (3)
Github contributions (5)
jariasf/CS231n
May 2018 - Sep 2020
My assignment solutions for CS231n - Convolutional Neural Networks for Visual Recognition
Role in this project:
ML Engineer
ML EngineerContributions:45 commits, 42 pushes, 1 branch in 2 years 4 months
Contributions summary:Jhosimar's contributions center around implementing various machine learning models, including k-Nearest Neighbor (kNN), Support Vector Machines (SVM), Softmax, and two-layer and fully-connected neural networks, demonstrating a focus on core machine learning concepts. Their work includes both naive and vectorized implementations of loss functions, stochastic gradient descent, and the integration of dropout and batch normalization techniques for regularization and improved model performance. The user appears to have successfully completed multiple assignments, indicating a strong grasp of fundamental deep learning principles and their application in image classification.
deep-learningrecognitionvisual-recognitionconvolutionalneural-networks
jariasf/GMVAE
May 2019 - Oct 2020
Contributions:10 commits, 9 pushes, 2 branches in 1 year 5 months
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