Mradul Lakhotiya Mradul-Lakhotiya

Mradul Lakhotiya

Full-Stack Developer | Machine Learning Enthusiast | Competitive Programmer

Computer Science Engineering student at UPES with expertise in building scalable systems, ML/AI applications, and competitive programming. Passionate about solving complex problems and shipping production-ready code.

Key Stats

Languages: Java, Python, TypeScript, C++, Dart, JavaScript, C
Specializations: Full-Stack Development • System Design • Machine Learning • DSA & Competitive Programming
Experience: Database Systems • Mobile Development (Flutter) • Web Development • Neural Networks
Recent Focus: Production Systems, Performance Optimization, AI/ML Applications

Featured Projects

AI/ML & Advanced Systems

SNNStudio | GitHub

Train Spiking Neural Networks with custom datasets and hyperparameters

Tech Stack: TypeScript (52.5%), Python (20.5%), CSS, JavaScript
Impact: Interactive ML framework for advanced neural network research
Highlights: Custom hyperparameter tuning, dataset management, visualization pipeline

LightweightDB - B+ Tree Indexing Engine | GitHub

Dynamic B+ Tree implementation for efficient database operations

Tech Stack: Python (Jupyter Notebook), Performance visualization
Capabilities: O(log n) insert, delete, search, and range queries
Features: Benchmarking suite, complexity analysis, optimization techniques
Real-world Application: Database indexing, query optimization

Fake Review Detection System | GitHub

Flask API using BERT for product review classification

Tech Stack: Python, Flask, BERT (NLP), Jupyter Notebook
Model Performance: Advanced NLP for binary classification
Deployment: REST API with production-ready error handling

Book Recommendation System | GitHub

Collaborative filtering & content-based recommendation engine

Tech Stack: Python, Jupyter Notebook, scikit-learn
Algorithms: Matrix factorization, similarity metrics
Real-world Use Case: E-commerce recommendation pipeline

Kepler Objects of Interest Classification | GitHub

Random Forest model for astronomical data classification

Tech Stack: Python, scikit-learn, data analysis
Metrics: Feature importance analysis, model evaluation

Mobile & Cross-Platform Development

ExpenseApp | GitHub

Full-featured expense tracking application with dark mode

Tech Stack: Dart/Flutter (67%), C++ (16.6%), native bindings
Features: Real-time sync, dark/light theme, offline support
Platform: iOS & Android with native performance optimization
Architecture: Clean code principles, state management

Sidetake - Screen Time & Focus Blocking App | GitHub

Productivity application with advanced focus session management

Tech Stack: Dart/Flutter (77.5%), Python (13.1%), PostgreSQL, TypeScript
Features:
- Profile management & focus sessions
- App limits & scheduling
- Distraction blocking
- Streak tracking & activity analytics
Backend: Supabase (PostgreSQL), real-time database
Notable: Full-stack production app with multi-platform support

Web Development & Full-Stack

Tapestria - Library Management System | GitHub

Beautifully designed digital library management platform

Tech Stack: TypeScript (primary), modern web stack
Description: Seamlessly organize, preserve, and access books & records
Architecture: Full-stack system design

Frontend Library Management System | GitHub

Tech Stack: TypeScript
Focus: Modern frontend architecture, state management

Blind Drop - Privacy-Focused File Sharing | GitHub (Collaborator)

Secure, login-free file sharing with zero digital traces

Tech Stack: JavaScript (94.3%), CSS, Docker
Features: Privacy-first architecture, no user tracking
Deployment: Containerized with Docker
Notable: Security-first design approach

NGO Site - Aasra-Nishaktjan Welfare Society | GitHub

Tech Stack: HTML/CSS
Social Impact: Community outreach & information platform

Data Structures & Algorithms (DSA)

Competitive Programming Repository | GitHub

Comprehensive DSA problem solutions & implementations

Tech Stack: Java (99.8%), JavaScript
Coverage:
- Advanced sorting & searching
- Graph algorithms (DFS, BFS, Dijkstra, Bellman-Ford)
- Dynamic programming patterns
- Tree data structures & traversals
- Hash tables & advanced hashing
- Greedy algorithms
- Bit manipulation
Practice: Consistent problem-solving for internship/interview prep
Mentorship: ACM Technical Mentorship (DSA focus)

Daily Coding Practice | GitHub

Languages: C, Java, Python, JavaScript, HTML, CSS
Purpose: Consistent skill development across multiple languages

Technical Mentorship Materials | GitHub

Focus: ACM Technical Mentorship program content
Tech: C, interview prep, foundation building

Technical Skills

Languages & Proficiency

Category	Technologies
Backend	Java, Python, C++, C, TypeScript
Frontend	TypeScript, JavaScript, HTML, CSS
Mobile	Dart/Flutter (cross-platform)
ML/AI	Python, BERT, TensorFlow/scikit-learn, Jupyter
Databases	PostgreSQL, Supabase, B+ Tree indexing
Tools & Frameworks	Flask, React ecosystem, Docker, Git

Domain Expertise

System Design: Database indexing, API design, architecture patterns
Machine Learning: NLP (BERT), classification, recommendation systems
Competitive Programming: Advanced DSA, time complexity optimization
Mobile Development: Cross-platform apps, native integration
Web Development: Full-stack applications, modern tech stack
Performance Optimization: Algorithm optimization, database queries
Software Engineering: Clean code, design patterns, testing

Project Statistics

15+ GitHub repositories with active development
5+ Full-stack applications deployed
3+ ML/AI projects with production implementations
Competitive Programming: Consistent DSA practice & mentorship
Multi-platform: iOS, Android, Web, Cross-platform

🎓 Education & Achievements

Degree: Bachelor's in Computer Science Engineering (CSE)
Institution: UPES (University of Petroleum & Energy Studies)
Year: 1st Year, pursuing excellence
Mentorship: ACM Technical Mentorship Program (DSA)
Focus: Competitive Programming, System Design, Modern Tech Stack

Current Objectives

Internship/Job Focus

Seeking Full-Stack Development, Backend Engineering, or ML Engineering internships
Strong foundation in system design and scalable architecture
Proven expertise in production-ready code and performance optimization
Active in competitive programming for algorithm optimization

Market-Aligned Skills

In-Demand Tech: TypeScript, Python, Dart, PostgreSQL
System Design: Database systems, API design, microservices awareness
Full-Stack Capability: From database to frontend, Docker containerization
ML/AI Competency: NLP, classification models, data pipeline design
Competitive Programming: Problem-solving, algorithm optimization, complexity analysis

Learning Path & Continuous Growth

System Design: Database internals (B+ Trees), query optimization
AI/ML: Deep Learning, Transformers (BERT), advanced NLP
Backend Systems: Scalability, caching strategies, distributed systems
DevOps & Deployment: Docker, CI/CD pipelines, cloud infrastructure

Get in Touch

Email: mradullakhotiya18@gmail.com | Mradul.125405@stu.upes.ac.in
LinkedIn: in/mradul-lakhotiya
GitHub: github.com/Mradul-Lakhotiya
Discord: mradul_lakhotiya

Why Hire Me?

Proven Ability:

Built 5+ full-stack projects from scratch to deployment
Implemented advanced algorithms & system designs
Applied ML/AI in real-world scenarios
Strong competitive programming foundation

Market Ready:

Familiar with modern tech stacks (TypeScript, Flutter, Python)
Experience with databases, APIs, and system architecture
Production-quality code with clean practices
Quick learner with self-driven development

Well-Rounded:

Full-stack capabilities (backend, frontend, mobile, ML)
System design thinking for scalable applications
Problem-solving excellence from competitive programming
Mentorship experience & knowledge sharing

Last Updated: May 2026 | Actively seeking internship opportunities in Full-Stack Development, Backend Engineering, and Machine Learning