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Hi there 👋

I am a computer vision researcher with experience in developing cutting-edge AI-based solutions for autonomous vehicles, leading several teams and concurrently working as an AI researcher. I have experience in designing traditional/deep learning methods for 2D & 3D image processing techniques, utilizing supervised/semi-supervised/unsupervised learning, working with different sensors (RGB pinhole/wideangle/Fisheye cameras, Monochrome cameras, NIR/LWIR cameras, Neuromorphic sensors, LiDAR, IMU, etc.), and deep generative models.

I am currently working as a Group Lead and AI researcher in DeltaX.ai. Prior to that, I was working as a vision researcher in CVIP Lab, Gachon University, Republic of Korea.

Professional Experience

  • Group Lead | Nov 2023 - Present | Automotive Perception Group, AI Lab, DeltaX.ai, Rep. of Korea.

    • Leading several teams working on building ADAS solutions and SCMS-DMS-OMS solutions.

  • Team Lead and AI Researcher | Oct 2022 - Nov 2023 | X-Camera ADAS Team, AI Lab, DeltaX.ai, Rep. of Korea.

    • Led a team of developers working on large-scale, complex, and cutting-edge systems.
    • Prepared industrial proposals for projects and PoCs.
    • Managed the delivery of multiple, complex, simultaneous development projects from design through to release.
    • Led cutting-edge R&D projects which eventually progressed to industrial projects/PoCs.
    • Monitored the performance of the team and reported on performance metrics.
    • Conducted job-related administrative works.

  • Vision Researcher | Mar 2019 - Sep 2022 | Computer Vision and Image Processing (CVIP) Lab, Gachon University, Rep. of Korea.

    • Worked with deep learning-based generative image synthesis tasks such as image inpainting, image colorization etc., and stereo depth estimation using bio-inspired vision systems.
    • Participated in various industrial projects regarding depth estimation, face detection etc.
    • Provided lectures on the concepts of computer vision/ deep learning and hands-on training on Python and PyTorch.
    • Conducted job-related administrative works.

  • Analyst, Business Development | Jul 2018 - Jan 2019 | Apex DMIT Ltd. (former 'Kazi IT Center'), Dhaka, Bangladesh.

    • Participated in analysing and managing overseas assets.
    • Managed several assets and freelance contractors through direct/indirect supervisions.
    • Performed several administrative duties such as participating/supervising asset bidding, compiling reports, attending/organizing seminars etc.

  • Teaching Assistant | Feb 2018 - Jun 2018 | Dept. of EEE, University of Liberal Arts, Bangladesh.

    • Provided lectures on course-specific concepts and hands-on training on circuits and embedded systems.
    • Allocated tasks, projects and performed grading.
    • Conducted job-related administrative works.



Here are some queries about me....

  • 🔭 I’m currently working on color filter arrays and event cameras.
  • 💬 I usually work with PyTorch. I have experiences with TensorFlow (1.x), Keras and MATLAB.
  • 📫 How to reach me: smnadimuddin at gmail dot com

News

  • Nov 20, 2023 - [Job] I have been assigned as a Group Lead at DeltaX.ai. I will be overseeing the Automotive Perception Group and mainly be working with several teams to build ADAS, DMS, OMS and SCMS solutions.
  • Apr 14, 2023 - [Conference] Our team has successfully participated in CVPR’23 2nd Monocular Depth Estimation Challenge.
  • Oct 25, 2022 - [Job] I have joined DeltaX.ai as a Team Lead and AI Researcher.
  • Sep 4, 2022 - [Journal] Our paper “Multi-Scale Attention-Guided Non-Local Network for HDR Image Reconstruction” has been published in the Sensors (Q1, Impact Factor - 3.847) Journal.
  • Aug 1, 2022 - [Conference] Our team has successfully participated in ECCV’22 AIM Challenge on Reversed ISP (Track 1).
  • Jun 26, 2022 - [Journal] Our paper “Unsupervised Deep Event Stereo for Depth Estimation” has been accepted in the IEEE Transactions on Circuits and Systems for Video Technology- (Q1, Impact Factor - 5.859).
  • May 10, 2022 - [Journal] Our paper “SIFNet: Free-form image inpainting using color split-inpaint-fuse approach” has been published in the Computer Vision and Image Understanding Journal (Volume 221, August 2022, 103446) - (Q1, Impact Factor - 4.886).
  • Feb 3, 2021 - [Conference] Our paper “Deep Event Stereo Leveraged by Event to Image Translation” is published in AAAI Conference on Artificial Intelligence (AAAI-21).
  • Jul 21, 2020 - [Conference] Our team has successfully participated in ECCV AIM Challenge on Image Extreme Inpainting.
  • Jun 4, 2020 - [Journal] Our paper “Global and local attention-based free-form image inpainting” has been published in the Sensors (Q1, Impact Factor - 3.847) Journal.

Sayed Nadim's Projects

advancedml icon advancedml

Reading list for the Advanced Machine Learning Course

awesome-physics icon awesome-physics

🌌 A collaborative list of awesome software for exploring Physics concepts

awesome-physics-learning icon awesome-physics-learning

:comet: Collection of the most awesome Physics learning resources in the form of notes, videos and cheatsheets.

computer-science icon computer-science

:mortar_board: Path to a free self-taught education in Computer Science!

deep-learning-in-production icon deep-learning-in-production

In this repository, I will share some useful notes and references about deploying deep learning-based models in production.

deeplearning-500-questions icon deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

event_stereo_iccv2019 icon event_stereo_iccv2019

This is repository for "Learning an event sequence embedding for dense event-based deep stereo" .

examples icon examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

image-adaptive-3dlut icon image-adaptive-3dlut

Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time

image-quality-evaluation-metrics icon image-quality-evaluation-metrics

Implementation of Common Image Evaluation Metrics by Sayed Nadim (sayednadim.github.io). The repo is built based on full reference image quality metrics such as L1, L2, PSNR, SSIM, LPIPS. and feature-level quality metrics such as FID, IS. It can be used for evaluating image denoising, colorization, inpainting, deraining, dehazing etc. where we have access to ground truth.

inpainting-evaluation-metrics icon inpainting-evaluation-metrics

The goal of this repo is to provide a common evaluation script for image inpainting tasks. It contains some commonly used image quality metrics for inpainting (e.g., L1, L2, SSIM, PSNR and LPIPS).

jamnet icon jamnet

The source code for the paper: Joint Appearance and Motion Learning for Efficient Rolling Shutter Correction (CVPR2023)

mediapipe icon mediapipe

Cross-platform, customizable ML solutions for live and streaming media.

ours-project icon ours-project

Step-by-step instructions to build a smartphone that is open-source, upgradeable, repairable, and Big Tech free.

pseudo-lidars-with-stereo-vision icon pseudo-lidars-with-stereo-vision

This project focuses on harnessing the power of Pseudo-LiDARs and 3D computer vision for unmanned aerial vehicles (UAVs). By integrating Pseudo-LiDAR technology with Stereo Global Matching (SGBM) algorithms, we aim to enable UAVs to perceive their surroundings in three dimensions accurately.

pytorch-cifar100 icon pytorch-cifar100

Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)

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