About Me
I am Rishit Kar, a third-year undergraduate student at DJ Sanghvi College of Engineering, Mumbai University, with a strong passion for research and innovation in Artificial Intelligence. My academic journey is driven by a deep curiosity to explore how AI can address complex, real-world challenges across diverse domains.
Through my research collaborations with prestigious institutions like IIT Patna and IIT Mandi, I have gained hands-on experience in developing sophisticated AI solutions. My work spans from building explainable deep learning models for medical imaging to implementing geometric deep learning architectures for engineering optimization problems.
I have also had some experience in competitive programming, holding Pupil rank on Codeforces and 3-star rating on CodeChef, which has strengthened my problem-solving abilities and algorithmic thinking.
My tech interests and learning focus areas include:
- Explainable AI for Healthcare Applications
- Large Language Models for Natural Language Understanding
- Computer Vision for Medical Imaging
- Data Engineering for ML Pipelines
- Backend Systems for AI Applications
- Open Source Contributions to ML Frameworks
My approach combines rigorous theoretical understanding with practical implementation, always emphasizing the importance of explainability and real-world applicability in AI systems.
Education
Dwarkadas Jivanlal Sanghvi College Of Engineering, Mumbai
BTech, Computer Engineering
2023 - 2027, CGPA- 9.14
Coursework: Artificial Intelligence, Compiler Design and Automata, Advanced Database Management Systems, Database Management Systems, Operating Systems, Data Structures, Analysis of Algorithms, Python Programming, Object-Oriented Programming Systems
Position of Responsibility:
Research Head at DJS ACM - Guiding students about research opportunities, conducting meetings to mentor students in machine learning and AI research, and facilitating research collaboration opportunities within the college community.
Experience
Indian Institute of Technology Patna
Research Collaborator - Deep Learning for Medical Imaging
August 2025 - Present
• Developing deep learning models for comprehensive analysis of NIH chest X-ray datasets
• Focusing on explainability and clinical interpretability for medical AI applications
• Ensuring transparent and interpretable AI-driven medical decision-making processes
Supervised by Dr. Joydeep Chandra
• Contributing to sktime, a unified framework for machine learning with time series data in Python
• Working on pull requests for migration of TensorFlow implementations to PyTorch
• Helping migrate deep learning components to improve framework consistency
• Supporting the open source community with code improvements and framework enhancements
Indian Institute of Technology Mandi (Onsite)
Research Intern - Machine Learning for Propeller Optimization
June 2025 - July 2025
• Collaborated at the Center for Artificial Intelligence and Robotics (CAIR Lab)
• Conducted extensive literature review on geometric deep learning methodologies
• Successfully implemented Dynamic Graph Convolutional Neural Network (DGCNN) architecture
• Performed comprehensive data analysis and feature engineering on complex 3D propeller datasets
Supervised by Dr. Jagadeesh Kadiyam
Projects
Advanced deep learning models for cancer group subtyping using multiomics data
Currently developing sophisticated deep learning architectures for analyzing multiomics datasets to perform cancer group subtyping across different cancer types using unsupervised learning, Variational Autoencoder and explainable AI. Working with integrated genomic data including mRNA expression, copy number variations, and methylation patterns. Implementing novel neural network approaches to identify distinct cancer subtypes and improve diagnostic precision in oncological applications using PyTorch and scikit-learn.
The Ultimate Cross-Platform Clipboard Manager for Developers
Developed a comprehensive clipboard management tool with terminal user interface and published it as a Python package on PyPI. Built using Python with integrated TUI capabilities for enhanced developer productivity. Implemented fuzzy search functionality, real-time monitoring, and cross-platform compatibility. Used pytest for comprehensive testing and achieved proper package distribution through PyPI.
Deep learning ensemble for medical image classification using CNN, ResNet, and Graph Neural Networks
Built a comprehensive deep learning solution for pneumonia classification using chest X-ray images. Implemented custom CNN architecture alongside ResNet and Graph Attention Network (GAT) models for comparative analysis. Developed multimodal learning pipeline integrating spatial and attention-based features. Achieved high test accuracy with strong F1 scores for clinical reliability using TensorFlow and Keras.
High-performance reliable transport protocol with sliding window flow control
Engineered a reliable transport protocol from scratch using Python UDP sockets, incorporating TCP-like features including sliding window flow control and automatic retransmission. Implemented SHA-256 checksums for data integrity and concurrent packet processing. Achieved significant performance improvements over traditional stop-and-wait protocols through optimized throughput testing.
Privacy-focused VS Code extension for cleaning tracking parameters from URLs
Created and published a VS Code extension that automatically removes tracking parameters from URLs on paste, enhancing user privacy and link readability. Implemented offline functionality using regex patterns without external dependencies. Built using JavaScript and VS Code API, with comprehensive documentation and marketplace compatibility.
Skills
Programming Languages
• Python - Advanced proficiency in data science, machine learning, and backend development
• Java - Object-oriented programming and application development
• JavaScript - Frontend development and VS Code extension creation
• C - System programming and algorithm implementation
• SQL (MySQL) - Database design, querying, and optimization
• HTML/CSS - Web development and user interface design
Machine Learning & AI
• Deep Learning Frameworks - TensorFlow, Keras, PyTorch
• ML Libraries - Scikit-Learn, Pandas, NumPy, Matplotlib
• Computer Vision - OpenCV, MediaPipe for image processing and gesture recognition
• Specialized Models - CNN, ResNet, Graph Neural Networks, Variational Autoencoders
• Data Analysis - Feature engineering, model evaluation, and performance optimization
Developer Tools & Platforms
• Version Control - Git, GitHub for collaborative development
• Development Environment - VS Code, Visual Studio, Google Colab, Jupyter Notebooks
• Cloud & Deployment - Vercel, Streamlit for application deployment
• Package Management - PyPI package development and distribution
• Testing - pytest for comprehensive testing frameworks
Technical Knowledge
• Operating Systems - System-level programming and OS concepts
• Database Management - DBMS design, Advanced DBMS, and database optimization
• Network Programming - UDP/TCP protocols, socket programming
• Data Structures & Algorithms - Algorithm analysis and competitive programming
• Software Engineering - Code architecture, design patterns, and best practices