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I am a master student in Biomedical Computing at Department of Informatics , Technical University of Munich (TUM), where i learn to combines computer science with aspects of biology and medicine. I received my Engineering Degree in Biomedical Engineering in 2021 at School of Electronics and Telecommunication, Hanoi Univerity of Science and Technology (SET-HUST). I also received my second degree in Computer Science at School of Information and Communication Technology, Hanoi Univerity of Science and Technology (SOICT-HUST). I spent one year as an intern and 6 months as a computer vision engineer at Viettel High Technology Industries Corporation (VHT) where I develop Computer Vision Algorithms for smart camera systems, including People detection and Heatmap Visualization using Fisheye Camera for retail stores, face recognition algorithm for Edge devices and high speed traffic AI Camera. In January 2022 I started my new position as Research Assistant at VinUni-Illinois Smart Health Center (VishC). VishC is a joint center between VinUni and the College of Engineering, University of Illinois at Urbana-Champaign to conduct research on novel sensing and informatics to provide widely accessible health monitoring, screening, and diagnostics for people all over the world. My research focus at VishC is federated learning for healthcare. In October 2022, I started working towards my master degree in Biomedical Computing at TUM. Currently, I am a working student in Computer Vision and Algorithmics at Luma Vision GmbH and a Research student at Lab for AI in Medicine at TU Munich , working on the differential privacy on the fairness of AI algorithms. Email / CV / Google Scholar / Github |
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I am passionate about the intersection of artificial intelligence (AI) and healthcare, and my research interests encompass a range of topics in this field, including machine learning for medical applications, inverse problems in medical imaging, federated and privacy-preserving AI, bias and fairness in AI systems, and deep learning on edge devices. Representative papers are highlighted. |
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Quan Nguyen Minh, Hieu H. Pham, Kok-Seng Wong, Phi Le Nguyen, Truong Thao Nguyen, Minh N. Do IEEE TNSM (Q1 - IF 5.3), 2023 , Accepted arxiv We propose FedDCT (Federated Divide and Collaborative Training), a novel FL architecture that allows for DCML with considerably lower client memory requirements over traditional FL while introducing no additional training cost on the central server. FedDCT enables a cluster of several clients to cooperatively train a large deep learning model, in contrast to standard FL, which demands that each client train the full-size neural network independently during each training round. |
 
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Quan Nguyen Minh, Bang Le Van, Can Nguyen Ngoc, Viet Dung Nguyen, ICISN, 2022 , (Oral Presentation) Springer / Google Scholar / bibtex Inspired by the human visual system, which only focuses on the non-occluded facial regions, we propose a novel Switching Re-placement Attention Network (SRAN) for robust face recognition based on attention mechanism. |
 
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Quan Nguyen Minh, Bang Le Van, Can Nguyen, Anh Le and Viet Dung Nguyen AVSS, 2021 (Oral Presentation) arxiv / IEEE / Google Scholar / bibtex In this work, we propose ARPD, a single-stage anchor-free fully convolutional network to detect arbitrarily rotated people in fish-eye images. Our network uses keypoint estimation to find the center point of each object and regress the object's other properties directly. To capture the various orientation of people in fish-eye cameras, in addition to the center and size, ARPD also predicts the angle of each bounding box. |
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Viet Dung Nguyen, Phuc Ngoc Pham, Xuan Bach Nguyen, Thi Men Tran, Quan Nguyen Minh. Intelligent Systems and Networks, 2021. Springer / Google Scholar / bibtex The aim of this work is to incorporate the de-warping of fisheye image using polar to Cartesian transformation to generate a panoramic view to aid in fall detection. |
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● [Nov 2023] 8.5 IELTS ( 9.0 R, 9.0 L, 7.5 W, 8.0 S)
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● [Oct. 2023] I will start my research student position at Lab for AI Medicine at TU Munich ● [Sep. 2023] I will participate in SUMMER SCHOOL ON âMODERN MACHINE LEARNING: FOUNDATIONS AND APPLICATIONSâ in Ninh Binh, Vietnam. ● [Aug. 2023] One paper (first author) is accepted to IEEE Transactions on Network and Service management ( Q1 - IF 5.3). ● [Mar. 2023] I started my working student position in Algorithms and Computer Vision at Luma Vision GmbH, where I will be working on medical ultrasound imaging. ● [Nov. 2022] Our research FedDCT: A Novel Federated Learning Approach for Training Large Convolutional Neural Networks on Resource-constrained Devices is submitted to IEEE Transactions on Network and Service Management . Arxiv link available. ● [Aug. 2022] I participated in 9th Vietnam Summer School of Science at Quy Nhon, Vietnam. The scholarship is to inspire and support young Vietnamese generations who are potential and keen as well as full of passions on pursuing research as a career. ● [July. 2022] I received admission offers for Masters Programs at Department of Informatics, Technical University of Munich and Friedrich-Alexander-Universität . I also received admission offer for PhD Program in Computer Science at VinUniversity with full-ride scholarship. ● [July. 2022] I serve as the organizer for the First VinUni-Illinois Smart Health Center Workshop and VinUni-Illinois Smart Health Center | Pre-PhD Summer School 2022 . ● [Feb. 2022] I started working at Teaching Assistant for course COMP1020: OOP & Data Structures at Vin University, taught by Prof. Hieu Pham and Prof. Kok Seng. ● [Jan. 2022] I joined VinUni-Illinois Smart Health Center as Research Assistant under supervison of Prof. Hieu Pham and Prof. Kok Seng Wong. My research focus is on federated learning for healthcare. My work is a part of VAIPE project: AI-assisted IoT-enabled smart, optimal, and Protective Healthcare monitoring and supporting system for Vietnamese. ● [December. 2021] One paper "Occlusion Robust Face Recognition Based on Mask Learning With Attention Mechanism" got accepted to International Conference on Intelligent Systems & Networks (ICISN 2022). ● [October. 2021] One paper ARPD: Anchor-free Rotation-aware People Detection using Topview Fisheye Camera got accepted to 17th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS 2021). ● [September. 2021] Our submission viettelhightech_000 for the FRVT 1:1 Vendor Test achieved the good results among all Vietnamese clients ● [August. 2021] I started my first full-time Job as an AI engineer at Viettel High Technology Industries Corporation (VHT). My research focus is on efficient deep learning algorithm design for edge devices. ● [June. 2021] Our group at Biosignal and Medical Image Analysis Lab achieved third prize in Hanoi University of Science and Technology Student's Academic Research Contest (University level) ● [2020] I achieved HUST Academic Incentive Scholarship for students with excellent academic results in semester 2020-2. ● [June. 2020] I started my Internship position in Computer Vision/AI at Viettel High Technology Industries Corporation (VHT). ● [Nov. 2019] I became a member of cohort 20 of FPT Young Talents (FYT). FYT is a special educational organization founded in 1999 by the then-CEO of FPT Corporation : Truong Gia Binh aiming to nurture the young talents of Vietnam. ● [Sep. 2018] I was one of 25 HUST's students chosen to participate in a 3-weeks leadership exchange program TFI Scale at Temasek Polytechnic, Singapore. Fully sponsored by the Temasek Foundation. |
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This repository contains the code and experiments for the paper FedDCT:Federated Divide and Collaborative Training, accepted to IEEE Transactions on Network and Service Management
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I worked in a team with other engineers from VHT to design face recogntion algorithms to participate in NIST FRVT 1:1 Verification Test. NIST provides independent evaluations of commercially available and prototype face recognition technologies. Our submission achieved respectable results. |
 
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I reimplemented the famous SORT tracking algorithm from scratch in Java using only OpenCV. Was a fun project to code and i applied this for tracking Vehicle on edge devices.
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I redesigned the classic Mobilenet-SSD network to reduce FLOPS and Params while keeping good accuracy. I pruned the backbone manually, modify the feature map size and anchor configs. The detector achieves 30 FPS on Intel CPU while keeping respectable accuracy.
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My participation for the BKAI-IGH-Neopolyp Segmentation Kaggle competition. Since no public implementations are available, i kept the code as modular and simple as possible so that other participants in the future can use it as a baseline.
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A repository i created to teach Biosignal and Medical Image Analysis Lab's students at HUST the basics of deep learning.
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