Nazmul Karim

I am actively looking for full-time opportunities in a machine learning researcher/engineering role! Have 5 years of experience in Machine Learning and Signal Processing. I am quick at learning new things and have worked on diverse research projects.

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About Me

Hi, I am Nazmul Karim. I completed my Ph.D.in Electrical Engineering and M.Sc. in Computer Engineering from the University of Central Florida (UCF). I was jointly advised by Nazanin Rahnavard (director of LCWNLAB), and Mubarak Shah (director of CRCV). Before that, I got my B.Sc. in Electrical and Electronics Engineering (EEE) from Bangladesh University of Engineering and Technology (BUET) in 2016. I work primarily in computer vision although I have some notable experience in Natural Language Processing (NLP) too. My research interest includes but is not limited to Large Vision-language Models Robustness, Noisy Labels, Domain Adaptation, Continual Learning, Generative AI, Multimodal AI, 3D Scene Generation, etc. I have published at top-tier journals and conferences like IEEE TIFS CVPR MLSP. I also served as a reviewer in CVPR, MLSP, and Optics Journal.

Experience

Graduate Research Assistant

at University of Central Florida (UCF), Orlando, FL, USA

Timeline
August 2018 - Present
Responsibilities
  • Developed multiple advanced frameworks for the detection and mitigation of Trojans within deep neural networks. These frameworks are built upon concepts such as the geometry of decision boundary and loss landscape, Fisher Information Matrix (FIM), Neural Masking, etc. Achieved SOTA performance across various settings, e.g. Image Classification, Object Detection, Video Action Recognition, 3D Point Cloud, Natural Language Generation, etc.
  • Formulated an innovative training framework for noisy labels at the forefront of the field, resulting in a noteworthy 11% improvement in accuracy. This framework demonstrated comparable efficacy across both synthetic and real-world noisy data scenarios. I have also explored the application of noisy labels in source-free domain adaptation.
  • Engineered multiple diffusion-based frameworks with the capacity to facilitate text-to-image, text-to-video, and text-to-3D scene generation. These frameworks can also support zero-shot high-fidelity content generation.
  • Devised a framework founded on Generative Adversarial Network (GAN), tailored for the compression of images and videos. Accomplished an impressive compression rate of approximately 100-fold, while impeccably preserving the inherent quality of the visual content.

Applied Scientist Intern

at Amazon Web Services, Seattle, WA

Timeline
June 2023 - Sep 2023
Responsibilities
  • Formulating algorithms for Image Matching and Pose Estimation, with a specific emphasis on both indoor and outdoor pose estimation. With the help of foundation models such as CLIP and DinoV2, my goal was to achieve a level of generalization applicable across diverse real-world scenarios

Research Intern

at SRI International, Princeton, NJ

Timeline
May 2022 - Aug 2022
Responsibilities
  • Developed a novel source-free domain adaptation technique and achieved on average 2% performance improvement on 7 different tasks covering both image classification and semantic segmentation.

Machine Learning Researcher

at Semion Inc., Dhaka, Bangladesh

Timeline
Aug 2016 - April 2017
Responsibilities
  • Development of a tumor detection framework through the utilization of convolutional neural networks. Implementation of text summarization and sentiment analysis methodologies employing Long-Short-Term Memory (LSTM) networks.

Lecturer

at Bangladesh University of Business and Technology (BUBT), Dhaka, Bangladesh

Timeline
Aug 2016 - Jul 2018
Responsibilities
  • Served as a mentor for engineering students and an advisor of the University Robotics Club.

Education

Ph.D in Electrical Engineering

from University of Central Florida (UCF)

Completion Year
November 2023
Focus
Computer Vision, Safe and Robust AI, Representation Learning, Compressive Sensing, and Signal Processing.
Advisor
Nazanin Rahnavard, Professor, Department of ECE, UCF
Co-Advisor
Mubarak Shah, Director, Center for Research in Computer Vision (CRCV), UCF

M.Sc. in Computer Engineering

from University of Central Florida (UCF)

Completion Year
May 2020

B.Sc. in Electrical Engineering

from Bangladesh University of Engineering and Technology (BUET)

Completion Year
March 2016

Publications