Pranish Kafle

B.E. in Computer Engineering

Kathmandu University · 2021–2025

Muzzle-Based Livestock Identification via Siamese Deep Metric Learning

2024 – present

• Contributed to a Plantsat initiative in collaboration with the United Nations Development Programme (UNDP) in Nepal, aimed at scalable livestock identification for farmers and agricultural programs.
• Designed and compared Siamese Network architectures with VGG16, ResNet50, ResNet152, ViT-Base, and ViT-Large backbones; twin branches share weights to generate high-dimensional image embeddings for identity verification.
• Conducted multi-environment field testing across multiple farms in Nepal, discovering that single-condition muzzle datasets are insufficient — the model required images from diverse lighting, angles, and environmental contexts to generalize well.
• Collaborated with a slaughterhouse to collect muzzle images at controlled post-mortem intervals (12 hr before, 2 hr before, immediately before, immediately after, and 2 hr after slaughter), capturing how muzzle shape compresses and changes over time, enabling the model to adapt to deformation conditions.
• Built and deployed a dedicated mobile data-collection application to capture muzzle images across all farm sites and time intervals, enabling structured large-scale annotation.
• Optimized embedding representations in a 128-dimensional latent space via Contrastive Loss (margin = 2.0), minimizing distance for same-identity pairs and pushing different-identity pairs beyond threshold.
• Integrated Gradient-weighted Class Activation Mapping (Grad-CAM) to visually confirm that network attention focuses on anatomical muzzle groove patterns rather than background noise — a critical step for model trustworthiness and academic validity.
• Evaluated binary classification performance using accuracy, precision, recall, F1-score, and confusion matrices by tuning Euclidean distance decision boundaries.

An Empirical Comparison of Embedding Models for Nepali Legal Document Retrieval

Abhiyan Dhakal, Kausik Paudel, Pranish Kafle, Sugat Sujakhu, Prakash Poudyal, Anita Jadhari

ICAIL 2026 · 2026 · Short paper – presented 9 June 2026

Compares retrieval behavior rather than treating embedding choice as an implementation detail, with attention to the constraints of Nepali legal text.

Official programme

Detecting Image Forgeries and Deepfakes: A Comparative Study of CNN and Transformer Models with a Custom-Curated Dataset

Pranish Kafle

NCCI 2025, Dhulikhel, Nepal · 2025 · Published

Created a custom-curated dataset and evaluated CNN versus Transformer architecture performance on image forgery and deepfake detection.

Feasibility of Artificial Intelligence Driven Analysis in the Context of Nepalese Legal System

Abhiyan Dhakal, Sugat Sujakhu, Pranish Kafle, Kausik Paudel, Prakash Poudyal

Proceedings of ICAIL 2025, pp. 498–499 · 2025 · Published

Processes legal documents for retrieval, expands user queries, and evaluates rule-recall, rhetorical-understanding, and interpretation-oriented questions.

DOI

Software Engineer

Plantsat · Aug 2024 – Present

• Developed core mobile features primarily using Flutter while integrating platform-specific functionality with native Android (Kotlin).
• Designed and implemented a CI/CD pipeline for a Flutter monorepo architecture and implemented unit test cases in the app.
• Configured multiple build flavours (development, QA, production) for testing and release workflows.
• Implemented state management using BLoC and structured modules following Domain Driven Design and Clean Architecture principles.
• Built offline-first functionality with local persistence and background synchronization to support unstable network conditions.
• Integrated REST APIs using Dio and implemented structured networking layers with error handling and interceptors.
• Added Firebase services including FCM notifications, Crashlytics, and analytics for production monitoring.
• Implemented camera-based workflows and real-time frame processing for image analysis features.
• Integrated an on-device AI/ML model to perform real-time inference directly on mobile camera frames.
• Contributed to the Plantsat–UNDP Nepal livestock identification project: designed and evaluated Siamese neural network architectures (VGG16, ResNet50, ResNet152, ViT-Base, ViT-Large) using Contrastive Loss for non-invasive muzzle biometric identification, and built the mobile data-collection application used across multiple field environments.

Junior Mobile App Developer

Emberce Technology · Feb 2023 – Aug 2024

• Developed mobile application features using Kotlin and Flutter across multiple outsourced projects.
• Designed and implemented responsive UI supporting a wide range of device sizes, including multiple mobile screen sizes, tablets, and large-screen devices to ensure consistent user experience across platforms.
• Implemented Android architecture using MVVM and integrated REST APIs using Retrofit.
• Worked on healthcare applications including modules for skin analysis, vitamin tracking, and blood pressure monitoring.
• Collaborated with backend teams to integrate APIs and manage application data flows.

Medease — Healthcare Management & Booking System

An integrated healthcare management application featuring real-time appointment booking, consultation chat, video consulting, and a built-in medical products e-commerce system.

Stack: Flutter · Kotlin · WebRTC · Firebase · Node.js

Lunar Calibration for Light Pollution Measurement

A collaborative Android application developed with students from MIT and NYU that detects and measures light pollution from lunar photographs. The app handles camera-based image capture, analysis, and all computation entirely on-device.

Stack: Android · Kotlin · OpenCV · On-Device ML

Mobile App Development Community Coordinator

Kathmandu University Computer Club · 2024–2025

Coordinated mobile app development initiatives and workshops at Kathmandu University.

Mentor

KU Hackfest · 2024–2025

Mentored participants at the biggest in-person hackathon in Nepal.

Winner

Hack For Business Hackathon · 2024

Won the Hack For Business Hackathon.