N°01
Mervin
Mandanna
I build intelligent vision systems that transform raw pixels into understanding.
- Focus
- Computer Vision · Applied ML
- Based
- Bangalore, India
- Status
- Open to research collaboration
- Role
Computer Vision Engineer
01 — About
ResearchProfile
I work at the intersection of computer vision and applied machine learning — currently building deep learning pipelines that turn raw line-scan imagery into automated defect detection for railway inspection systems. Earlier research at IIT Roorkee applied the same static/dynamic ML mindset to a different signal: classifying Android malware through hybrid static, dynamic, and deep-learning analysis.
Alongside applied CV work, I build full-stack systems that put models into use — from a reinforcement-learning pipeline for fleet decision-making to an agentic browser-automation platform. I currently serve as Secretary of ByteXync, my college's technical club, where I help run hackathons and technical programming for the CSE department.
Current Focus
Research Interests
Future Direction
Longer-term, I'm drawn to Quantum Machine Learning — exploring how quantum computing could eventually reshape how learning algorithms represent and search high-dimensional problems.
02 — Trajectory
Experience&Education
Intern — Scientist
January 2026 — PresentPatil I-Labs, Patil Rail Infrastructure Pvt. Ltd · Bangalore, India
Building computer vision and deep learning systems for Machine Vision Inspection Systems (MVIS), automating railway wheel inspection and defect detection.
Problem
Manual inspection of railway wheels for defects is slow and inconsistent; line-scan imagery needs to be turned into reliable, automated defect signals.
Impact
Preprocessing and synthetic-data pipelines now underpin downstream defect-detection model development.
- Designed preprocessing pipelines for line-scan wheel imagery — geometric correction, wheel boundary extraction, and image normalization.
- Built synthetic data generation and segmentation workflows to improve model development and evaluation for real-world deployment.
Research Intern — Android Malware Detection
September 2025 — November 2025Indian Institute of Technology (IIT) Roorkee
Researched Android malware detection via static, dynamic, and hybrid ML/DL analysis; co-authored a paper on a hybrid detection framework.
Problem
Static or dynamic analysis alone each miss classes of Android malware; a hybrid ML/DL approach was needed to improve detection robustness.
Impact
Co-authored a paper advancing hybrid ML/DL approaches to Android malware detection.
- Built feature extraction pipelines with Androguard, DroidBox, and Cuckoo Sandbox.
- Trained and compared SVM, Random Forest, and CNN models across static, dynamic, and hybrid feature sets.
Frontend Developer
February 2025 — August 2025Win Research Center · Bangalore, India
Built and maintained frontend features for interactive research dashboards using React, Next.js, and Tailwind CSS.
Problem
Cross-functional research tooling needed faster, more responsive dashboards for day-to-day use.
Impact
Reduced load times by 30% through state-management and rendering optimizations.
- Built and maintained frontend features using React.js, Next.js, and Tailwind CSS.
- Optimized state management and reduced load times to improve day-to-day UX.
Bachelor of Engineering, Computer Science
Expected May 2027Dayananda Sagar College of Engineering · Bangalore, India
CGPA: 8.47. Coursework and independent research spanning machine learning, computer vision, and systems.
03 — Research
FeaturedResearchProjects
Two end-to-end systems that pair a modeling approach — reinforcement learning and adaptive AI automation — with a production-shaped full-stack architecture.
KMRL
A multi-objective optimization system that uses reinforcement learning to decide which trainsets are fit for service.
Problem
A metro operator needs to select which trainsets from a fleet are serviceable and ready for active duty, weighing operational status and certification constraints simultaneously — a combinatorial decision that doesn't reduce to a single metric.
Method
- Modeled trainset selection as a multi-objective optimization problem evaluating 25 trainsets against operational and certification parameters.
- Applied reinforcement learning to learn serviceability policies and automate the selection of 13 active units.
- Exposed the decision pipeline through a full-stack architecture for scalable, repeatable analysis.
Challenges
Balancing multiple, sometimes conflicting objectives (operational readiness vs. certification constraints) inside a single reward signal that produces stable, explainable policies.
Architecture
FastAPI backend hosting the RL environment and optimization logic, with a Next.js frontend for interactive decision analysis and visualization.
Results
- Evaluated 25 trainsets on operational and certification parameters.
- Automated selection of 13 active units for service.
Tools
Mimiker AI
An AI-powered browser automation platform that records human workflows and replays them adaptively when the UI changes.
Problem
Traditional browser automation breaks the moment a target UI changes structure, forcing manual rewrites of brittle scripts for repetitive web workflows.
Method
- Built a record-and-replay engine that captures user workflows directly from browser interactions.
- Used Gemini AI to interpret UI structure changes and adaptively update automation steps instead of failing outright.
- Deployed the resulting workflows as reusable, cloud-hosted automations.
Challenges
Making automation resilient to UI drift — detecting when a recorded selector or flow no longer matches the live page, and adapting the workflow rather than failing silently.
Architecture
Next.js frontend for workflow recording/management, Flask backend orchestrating Playwright-driven browser automation, MongoDB for workflow storage, Gemini AI for adaptive step reinterpretation.
Results
- Improved task efficiency by 70% through reusable, adaptive cloud workflows.
Tools
04 — Engineering
OtherEngineeringWork
Full-stack and frontend work outside the research track — club infrastructure, hackathon builds, and applied web projects.
05 — Publications
Publications
A Hybrid Machine Learning Framework for Android Malware Detection
In PreparationCo-authored with the research team at IIT Roorkee
Manuscript — target venue to be announced
Static and dynamic analysis of Android applications each capture different, incomplete signals of malicious behavior. This work combines feature extraction across static, dynamic, and hybrid analysis pipelines — built with Androguard, DroidBox, and Cuckoo Sandbox — and evaluates SVM, Random Forest, and CNN classifiers to advance ML/DL-based hybrid detection approaches.
06 — Toolkit
TechnicalSkills
Machine Learning
Computer Vision
Security & Malware Analysis
Backend & Data
Programming Languages
Frontend
Tools & Infrastructure
07 — Record
Achievements
Research & Industry Internships
Flagship AI Research Projects
Hackathon & CTF Placements
Co-authored Paper
CodeChef Rating
Commit and Conquer
Runner-upOpen Source Contribution Hackathon.
Infrentia
4th PlaceHackathon conducted by PES University.
BotCraft
3rd PlaceDiscord Bot Development Competition.
Cipher Chase
16th OverallCapture-the-flag competition organized by IIIT Bangalore.
Secretary, ByteXync
Leading ByteXync, the official technical club of Dayananda Sagar College of Engineering's CSE department.
08 — Contact
Let'sTalk
Open to research collaborations, internships, and conversations about computer vision and applied ML.