About me
PhD candidate at RIT working on self-supervised learning, continual learning, and vision foundation models.
Hello! I'm a PhD candidate in Imaging Science at Rochester Institute of Technology (RIT), advised by Dr. Christopher Kanan. My research focuses on self-supervised learning, continual learning, and vision foundation models. I'm particularly interested in learning transferable visual representations and developing models that can adapt efficiently to new tasks and changing data distributions.
During my PhD, I've worked on developing efficient continual learning methods, including memory replay and sleep-inspired learning approaches. I've also investigated out-of-distribution generalization and transfer learning in pretrained vision models. My research has led to publications at ICML, NeurIPS, TMLR, and BMVC.
I also have industry research experience in medical imaging AI. At Siemens Healthineers, I worked on self-supervised pretraining of 3D vision models for medical imaging, investigating how learned representations can improve training efficiency for downstream tasks such as lung nodule detection, segmentation, and classification.
I received my BS in Mechatronics Engineering from Universidad Nacional de IngenierĂa in Peru, where my work included deep learning for diabetic retinopathy detection and SLAM for mobile robot exploration.
You can find my papers on the Publications page and my full CV on its own page.
News
- Sep 2026New!Our paper "Input- and Feature-Space Correction for Cross-Hospital IHC Grading" got accepted to the ASCI Workshop at NeurIPS 2026!
- May 2025Our paper "Controlling Neural Collapse Enhances Out-of-Distribution Detection and Transfer Learning" got accepted at ICML 2025!
- Sep 2024Our paper "What Variables Affect Out-Of-Distribution Generalization in Pretrained Models?" got accepted at NeurIPS 2024!
- May 2024Our TMLR paper "SIESTA: Efficient Online Continual Learning with Sleep" got accepted to the journal track of CoLLAs 2024!
- May 2024Our paper "Human Emotion Estimation through Physiological Data with Neural Networks" got accepted at SoSE 2024!
- Apr 2024Our paper "GRASP: A Rehearsal Policy for Efficient Online Continual Learning" got accepted at CoLLAs 2024!
- Oct 2023Our paper "SIESTA: Efficient Online Continual Learning with Sleep" got accepted in Transactions on Machine Learning Research (TMLR)!
- Aug 2023Joined Siemens Healthineers as an Image Analytics Intern working on self-supervised learning for medical imaging.
- Apr 2023Our paper "How Efficient Are Today's Continual Learning Algorithms?" got accepted to the CLVision Workshop at CVPR 2023!
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- Oct 2022Gave an invited talk about "Classifying Images By Combining Self-Supervised and Continual Learning" as part of the Center for Human-aware AI (CHAI) seminars.
- Jan 2022Got accepted into the AWARE-AI NRT program from RIT as a Trainee!
- Oct 2021Our paper "Self-Supervised Training Enhances Online Continual Learning" got accepted for poster presentation at BMVC 2021!
- Apr 2021Gave an invited talk at the Continual AI Reading Group about our paper "Self-Supervised Training Enhances Online Continual Learning".
- Apr 2020Joined the Machine and Neuromorphic Perception Laboratory as a PhD student working on self-supervised learning and lifelong machine learning.
- Apr 2019Got admitted to the Rochester Institute of Technology Imaging Science PhD program!
- May 2018Joined Everis (now NTT DATA Europe & Latam) as a Machine Learning Engineer working on recommendation systems, image classification, object detection, and tracking.
- Apr 2017Joined Siemens Healthineers as a Research Intern working on lung cancer detection.
- Dec 2015Obtained my BS in Mechatronics Engineering from Universidad Nacional de IngenierĂa in Lima, Peru.