Kunal Biswas

Product Review Analysis (Trimester 1 Project)

• Developed multiple applications, including Sentiment Analysis on customer reviews.
• Implemented a suggestion system to improve products by distinguishing constructive criticism from non-constructive feedback.
• Created an inventory stacking system based on sales data and customer feedback, tailored to region and season.

Fake News Campaign Detection (Trimester 2 Project) for Social Network Analysis

• Utilized Graph Neural Networks (GNN) to detect the spread of fake news and assess the impact of specific networks in disseminating misinformation.
• Implemented Bidirectional LSTM for keyword detection to identify fake news, contributing to a research paper.
• Graduation with Distinction,

3D indoor map generator using Advance Computers Vision

• Tech Stack: Node.js, OpenCV, WebSockets, Express.js, Three.js, QR Code Scanning, REST API
• Developed a 3D indoor navigation system leveraging advanced computer vision and Node.js to generate dynamic route maps for large complexes such as hospitals, malls, and universities.
• Implemented real-time indoor mapping using OpenCV and Three.js to visualize paths in 3D.
• Enabled users to scan a QR code at entry points to connect to the server and retrieve their current location.
• Designed a location-based routing algorithm to compute and display the optimal path to the selected destination.
• Built a backend with Express.js and WebSockets for live updates and smooth navigation assistance.
• Ensured scalability and cross-platform support for integration in multi-building infrastructures.

Custom Annotation Tool & Automated Model Trainer for YOLO/ONNX Models For Advance Computer Vision

• Tech Stack: Python, OpenCV, Flask, HTML/CSS, JavaScript, LabelImg, YOLOv5/YOLOv8, ONNX, Reinforcement Learning, NumPy, Pandas
• Developed a custom annotation and model training pipeline to streamline dataset preparation and training for advanced computer vision models.
• Built a lightweight annotation tool inspired by Roboflow and LabelImg with a user-friendly HTML interface to upload videos, extract frames (FPS-based), and define dataset split ratios (train/val/test).
• Integrated automated frame extraction using OpenCV and handled dataset organization dynamically through the backend.
• Implemented reinforcement learning-based annotation assistance to reduce manual effort and improve label accuracy.
• Enabled seamless YOLOv5/YOLOv8 and ONNX model training post-annotation, with automated hyperparameter tuning and output of optimized `best.pt` or `best.onnx` model files.
• Designed the entire workflow to support rapid prototyping and deployment of custom object detection models for various use cases.

Stock market fluctuations analysis based on Stock news for Financial Analysis

• Tech Stack: Python, BeautifulSoup, Requests, Pandas, NLTK/VADER, Matplotlib, Yahoo Finance API, Groww Web Scraping, Jupyter Notebook
• Conducted financial analysis by correlating stock price fluctuations with sentiment analysis of stock-related news.
• Built a data pipeline to scrape stock price data from Yahoo Finance and related news from the Groww website, based on stock ticker and date range.
• Combined 7-day historical stock metrics (open, close, volume, etc.) with corresponding news headlines to create a unified dataset.
• Performed sentiment analysis using NLTK/VADER to quantify the emotional tone of news articles and assess their impact on stock performance.
• Automated data collection for a one-month period to identify patterns and correlations between news sentiment and market movement.
• Visualized findings through insightful plots and trends to support financial reporting and decision-making.

Audio translation with voice cloning for Speech Understanding

• Tech Stack: Python, FFmpeg, OpenAI Whisper, Coqui TTS, PyDub, IndicTrans2, Hugging Face Transformers
• Built a complete speech-to-speech pipeline to enable multilingual accessibility of classroom lectures with realistic voice cloning, especially for Indian languages.
• Extracted high-quality audio from a 2-hour classroom video using FFmpeg.
• Transcribed and detected source language using OpenAI Whisper for accurate speech-to-text conversion.
• Translated transcribed text into Indian languages (e.g., Hindi, Bengali, Tamil) using IndicTrans2 and Hugging Face Transformer models.
• Synthesized translated speech using Coqui TTS with speaker voice cloning for a natural and consistent audio output.
• Processed and merged audio segments using PyDub for clean and continuous playback.
• Designed for long-form content, with use cases in education, accessibility, and e-learning platforms.

Speech and Noise Separation with Classification for Forensic Audio Analysis for speech understanding

• Tech Stack: Python, Demucs, Spleeter, Noisereduce, Librosa, OpenSMILE, scikit-learn, PyTorch
• Built an advanced audio processing pipeline for forensic analysis by separating speech from multiple background noises and classifying environmental sounds.
• Leveraged Demucs and Spleeter for high-fidelity speech and noise separation in overlapping audio scenarios.
• Integrated Noisereduce and Librosa for noise suppression, feature extraction, and signal enhancement.
• Developed a noise classification module using OpenSMILE for feature extraction and scikit-learn for sound category prediction (e.g., crowd, vehicle, music).
• Implemented a demasking algorithm to reconstruct masked or distorted speech segments, improving clarity and speaker intelligibility.
• Designed for use in forensic environments, enabling investigators to extract usable speech and noise insights from complex audio evidence.

RAG-based Legal Document Intelligence System with Graph & Vector Databases

• Tech Stack: Python, LangChain, ChromaDB, Neo4j (Community Edition), Hugging Face Transformers, LLaMA 3 (8B via Groq), BEIR, SentenceTransformers, PyPDF2, NetworkX, Streamlit
• Designed and deployed a Retrieval-Augmented Generation (RAG) system to streamline legal research by enabling intelligent querying within and across legal documents.
• Deployed LLaMA 3 (8B) on Groq for low-latency, high-performance response generation integrated into the RAG pipeline.
• Trained a SentenceTransformer model using the BEIR benchmark dataset to enhance general-purpose semantic search capabilities.
• Replaced FAISS with ChromaDB, an open-source vector database, for managing dense document embeddings and efficient retrieval.
• Built graph representations of legal documents using Neo4j and NetworkX, enabling inter-document citation tracking and intra-document clause linking.
• Utilized Hugging Face Transformers (e.g., Legal-BERT) for embedding generation and legal language understanding.
• Extracted and structured text from court judgments and legal PDFs using PyPDF2.
• Developed a user-friendly Streamlit interface where legal professionals can query by natural language and receive targeted, explainable summaries and references.
• Significantly reduced time and cognitive effort in searching legal precedents and analyzing multi-case dependencies.

• Developed facial and retinal recognition systems using Haarcascade and OpenCV with device cameras.
• Implemented Optical Character Recognition (OCR) for converting handwriting and PDF documents to Word files using Python and Pytesseract.
• Created Text-to-Speech and Speech-to-Text applications for voice-based Google searches utilizing the gTTS (Google Text-to-Speech) module.
• Executed a machine learning project on environmental noise recognition for potential noise cancellation applications, using Python and the Librosa module for sound data analysis.
• Built basic games using Unity 3D and designed objects with Blender.

• Contributed to Data Beagle, a data privacy product, by developing core features for detecting sensitive information such as SSNs, PII, and PHI, and building dynamic Solr query generators to improve search and compliance automation.
• Designed and implemented a log analysis POC using an Agentic approach, classifying log types, categorizing them, and triggering actions like Jira ticket creation, email alerts, or Slack notifications based on predefined scenarios.
• Developed and integrated a pause and resume mechanism for a distributed large-scale data migration tool, improving reliability and control over long-running jobs.
• Built a school bus safety system using computer vision for monitoring child activity and safety compliance, deployed on NVIDIA Jetson for real-time, on-premise inference in school buses across the USA.

• Demonstrated strong team management and coordination skills, effectively leading cross-functional teams to achieve project milestones.
• Proactively identified and resolved critical issues during Document ingestion phases, ensuring seamless transitions between design iterations.
• Spearheaded R&D initiatives to explore cutting-edge AI/ML techniques, driving continuous innovation
• Developed and implemented document parsers for various formats, including DOCX, Excel, Text, PDF, and Scanned PDF.
• Utilized RAG, Stuff Chain, LangChain LLM, and AutoGen Multi-Agent LLM for advanced document processing.
• Leveraged OCR and computer vision techniques for efficient image processing.

• Healthcare AI/ML Engineer with Expertise in Python and Deep Learning
  Project: Health Insurance Plan Prediction
  
  - Developed a deep learning model (ANN) to predict most suitable health insurance plans based on customer requirements, enhancing brokers' ability to sell plans effectively.
  - Utilized parameters such as budget, medical history, specific diseases, age, and disability to tailor recommendations.
  - Analyzed a variety of Medicare, Medicaid, and general health insurance policies to find optimal match for customers.
  - Leveraged data visualization to gain insights that drive creation of new plans and optimization of existing ones, catering to regional preferences and identifying top-performing brokers.
  
  Project: Pharmacy Business Management (PBM) - Optimal Drug Inventory Stacking [Prototype]
  
  - Designed and implemented a deep learning model (ANN) to predict optimal amount of medicines for inventory, preventing shortages and overstock situations, thereby maximizing profit for PBM industries.
  - Completed an end-to-end project utilizing Flask to create an API for model deployment and developed a web application for user interaction.
  - Deployed solution using Amazon EC2 services for robust and scalable hosting.

• Crafted and executed SQL queries tailored to client specifications, improving data retrieval efficiency by 30%.
• Maintained comprehensive documentation of program development and revisions, ensuring clear project tracking and knowledge transfer.
• Swiftly addressed and resolved critical issues, reducing system downtime by 20%.
• Managed, maintained, and secured data across multiple systems, enabling team to conduct business analyses with 99% data accuracy.
• Evaluated customer requirements and optimized existing databases, enhancing performance and meeting client specifications, resulting in 20% increase in database query speed.