CV

MS in Computer Science

• Awards - Outstanding Master’s Thesis Award 2025, Most Improved Master's Student 2024
• Courses - Design and Analysis of Algorithm, Design and Analysis of Efficient Algorithm (Advanced), Introduction to Deep Learning, Advanced Software Engineering, Computer Architecture, Hardware-Software Acceleration for Machine Learning

BE in Computer Engineering

• Award - Merit-based scholarship for undergraduate studies
• Courses - Computer Programming in C, Object Oriented Programming with C++, Theory of Computation, Data Structure and Algorithm, Discrete Mathematics, Calculus-I, Calculus-II, Probability and Statistics, Object Oriented Analysis and Design, Artificial Intelligence, Database Management System, Distributed System, Computer Networks and Security, Digital Signal Analysis and Processing, Simulation and Modeling, Internet and Intranet, Information System

Machine Learning Engineer

• Brainstorming, developing, and implementing novel machine learning algorithms optimized for hardware applications, focusing on energy efficiency and computational performance.
• Managing and analyzing datasets, preparing data for experiments, and ensuring data integrity for research studies.
• Maintaining and troubleshooting hardware setups and software environments for machine learning projects.
• Assisting in writing research proposals, grant requests, manuscript preparation, and presentations.

Graduate Research Assistant

• Led pioneering research on compressing deep learning models with integrated active learning, achieving a notable improvement in accuracy of 3.62% while reducing up to 40% labeled training samples
• Achieved a 5X reduction in computation and inference latency with the proposed compression technique, ensuring the delivery of robust and scalable solutions for edge devices.
• Published and presented research at peer-reviewed journals and conferences including ICMLA 2023 and TETC 2024.

Machine Learning Engineer - Computer Vision

• Orchestrated the implementation of contemporary Active Learning pipelines, integrating cutting-edge algorithms such as Coreset, Learning Loss, and Vision transformer, resulting in a remarkable reduction of up to 30% in annotation costs for computer vision tasks.
• Spearheaded the development of a comprehensive deep learning system covering all stages from data collection, annotation, processing, training to evaluation, facilitating smart city initiatives by enabling robust detection of persons and vehicles, smoke and fire across diverse environmental conditions, including varying geography, occlusion, lighting, and weather scenarios.

Computer Vision Research Engineer

• Secured first place in the EndoVis FetReg challenge at MICCAI 2021, with a novel self-supervised medical image segmentation framework, improving the performance of UNet and U2Net by 2.5%.
• Successfully collaborated with multiple researchers on pioneering research in advanced deep federated learning techniques for cross-domain surgical image segmentation.
• Published research in the Medical Image Analysis Journal and CVPR 2022 Conference.

AWS AI/ML Interestship

• Gained proficiency in utilizing diverse AWS services including EC2, S3, Lambda, Sagemaker, Lex, Rekognition, and Polly.
• Successfully executed machine learning assignments and projects utilizing AWS services.

Software Developer Intern

• Engineered and deployed a robust user management platform integrated with the PostgreSQL database for a full-stack web application for an innovative image annotation tool.

FDAL - Leveraging Feature Distillation for Efficient and Task-Aware Active Learning

• Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)

CARN - Complexity-Aware Routing Network for Efficient and Adaptive Inference

• Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)

APRIS - Approximate Processing ReRAM In-Sensor Architecture Enabling Artificial-Intelligence-Powered Edge

• IEEE Transactions on Emerging Topics in Computing (TETC)

DECO - Dynamic Energy-aware Compression and Optimization for In-Memory Neural Networks

• 2024 IEEE 67th International Midwest Symposium on Circuits and Systems (MWSCAS)

Resource-Efficient Adaptive-Network Inference Framework with Knowledge Distillation-Based Unified Learning

• 2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)

EnCoDe - Enhancing Compressed Deep Learning Models Through Feature Distillation and Informative Sample Selection

• 2023 International Conference on Machine Learning and Applications (ICMLA)

Histogram of oriented gradients meet deep learning - A novel multi-task deep network for 2D surgical image semantic segmentation

• Medical Image Analysis Journal (MedIA)

Why is the winner the best?

• IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Placental vessel segmentation and registration in fetoscopy - literature review and MICCAI FetReg2021 challenge findings

• Medical Image Analysis Journal (MedIA)

A client-server deep federated learning for cross-domain surgical image segmentation

• MICCAI Workshop on Data Engineering in Medical Imaging (DEMI MICCAI)

SenTer - A reconfigurable processing-in-sensor architecture enabling efficient ternary MLP

• Proceedings of the Great Lakes Symposium on VLSI 2023 (GLSVLSI)

GAN-Based Two-Step Pipeline for Real-World Image Super-Resolution

• ICT with Intelligent Applications - Proceedings of ICTIS 2021

EnCoDe

• EnCoDe merges active learning, model compression, and knowledge distillation to optimize deep learning models for edge devices. This technique addresses issues such as generalization loss, resource intensity, and data redundancy that often affect compressed models.

Medical Image Segmentation

• Winner of the FetReg 2021 challenge at MICCAI 2021 that uses a novel deep multi-task learning method for medical image segmentation leveraging Histogram of Oriented Gradients (HOGs) to generate pseudo-labels.

Real-ImageSR

• A GAN-based two step pipeline for enhancing the resolution of real-word low resolution image by the scale factor of 4. First, the R2B generator transforms real-world LR images to bicubic alike images. Then the nESRGAN+ network super-scales the output of the R2B generator.

Image Super-Resolution

• A full-stack web app to serve the GAN-based real-image super-resolution model using ReactJS, Flask and Tensorflow Serving with Docker

Graduate Program Management

• A Django-based web application to automate the document creation and management of MSC thesis programs in DOECE, IOE, Pulchowk Campus with a centralized PostgreSQL database fro managing records of the program, students, and faculties.

• Recipient of the "Outstanding Master’s Thesis Award" by the College of Engineering, University of Nebraska–Lincoln.

• Presented peer-reviewed research at CVPR 2025, ICCV 2025, MWSCAS 2024, and ICMLA 2023.

• Recipient of the "Most Improved Master's Student Award" by the School of Computing, University of Nebraska–Lincoln.

• Full financial support for MS in Computer Science at the University of Nebraska-Lincoln.

• Secured first place in the EndoVis Fetreg challenge at MICCAI 2021 among 35 global teams.

• Selected scholar for the prestigious PAISS 2021 (PRAIRIE / MIAI AI Summer School).

• Ranked 14th/12,000 in Nepal’s IOE nationwide entrance exam; awarded full merit-based undergraduate scholarship.