{ "$schema": "https://schema.org/Person", "meta": { "documentType": "ai-crawler-dossier", "version": "1.0.0", "lastUpdated": "2026-10-12", "canonicalUrl": "https://vyomuchat.pages.dev", "endpoints": { "profileJson": "https://vyomuchat.pages.dev/profile.json", "wellKnownAiProfile": "https://vyomuchat.pages.dev/.well-known/ai-profile.json", "llmsTxt": "https://vyomuchat.pages.dev/llms.txt", "llmsFullTxt": "https://vyomuchat.pages.dev/llms-full.txt", "sitemapXml": "https://vyomuchat.pages.dev/sitemap.xml" } }, "identity": { "name": "Vyom Uchat", "fullName": "Vyom Parag Uchat", "handles": ["CptPrice743", "vyomuchat"], "role": "Engineer & Researcher", "tagline": "I figure out why systems break under load, then build the architecture that holds.", "subtext": "Serverless pipelines, vector retrieval, edge scheduling. The stack follows the problem.", "status": "Open to Engineering & Research Roles", "location": { "city": "Gandhinagar", "state": "Gujarat", "country": "India" }, "contact": { "email": "vyom.uchat@gmail.com", "portfolio": "https://vyomuchat.pages.dev", "aboutPage": "https://vyomuchat.pages.dev/about", "playgroundPage": "https://vyomuchat.pages.dev/playground", "github": "https://github.com/CptPrice743", "linkedin": "https://www.linkedin.com/in/vyom-uchat-7a7527312" }, "biography": [ "Computer science graduate from Pandit Deendayal Energy University (PDEU, 2022 - 2026). Works on problems where the wrong architecture costs you: scheduling 30,000+ tasks across edge-cloud networks, extracting structured knowledge from video and carousel images, and auditing railway signaling systems that cannot afford downtime.", "Each project starts with a concrete constraint: a 512MB memory cap, a sub-second latency budget, or IEC 62443 compliance. Specializes in serverless event pipelines, in-memory vector search, metaheuristic scheduling, and Purdue Model network segmentation.", "Advocates for software that is predictable, well-structured, and quiet, with fewer moving parts, explicit failure modes, and deep respect for user attention." ] }, "education": { "degree": "B.Tech in Computer Science & Engineering", "institution": "Pandit Deendayal Energy University (PDEU)", "period": "2022 - 2026", "location": "Gandhinagar, Gujarat, India", "focus": "Distributed systems, operating system internals, computer networks, and metaheuristic optimization.", "courseworkEmphasis": [ "Distributed Operating Systems", "Cloud Infrastructure", "Network Protocol Security", "Algorithm Design & Optimization" ] }, "experience": [ { "role": "Cybersecurity Analyst Intern", "organization": "ALSTOM (Regional APAC Cybersecurity Dept)", "period": "May 2025 - July 2025", "location": "Bangalore, India (Hybrid)", "summary": "Ran the Initial Risk Assessment for Alstom's R1 railway signaling system under IEC 62443. Mapped a 4-zone trust architecture across Depot and Main Station topologies, then authored a 40-page technical specification covering 23 industrial cybersecurity risks.", "metrics": [ "23 Critical Risks Mitigated", "4-Zone Purdue Model Mapped (25+ sub-zones, 5 formal conduits CDT-01 to CDT-05)", "40-Page Technical Specification Authored", "100% IEC 62443 Alignment (reduced 6 Very High and 8 High severity risks to 0 post-mitigation)" ], "highlights": [ "Ran the Initial Risk Assessment (IRA) across Alstom's R1 railway signaling topology, identifying trust boundaries and conduit vulnerabilities across CBI, ATC, and ATS subsystems.", "Mapped 4 trust zones (A through D) with 25+ sub-zones, establishing zone-specific Security Level Targets and 5 formal conduits (CDT-01 to CDT-05) with MACsec, IPsec, and 802.1X NAC controls.", "Authored a 40-page technical specification defining defense-in-depth mitigations for 23 industrial risks, reducing 6 Very High and 8 High severity ratings to 0 post-mitigation." ], "technologies": [ "IEC 62443", "Purdue Model", "ICS/OT Security", "Threat Modeling", "Network Segmentation", "Risk Assessment", "MACsec", "IPsec", "802.1X NAC" ] }, { "role": "Undergraduate Researcher", "organization": "Pandit Deendayal Energy University (PDEU) - Under Dr. Punit Gupta", "period": "2025 - 2026", "location": "Gandhinagar, Gujarat, India", "summary": "Conducted applied research in Cloud-Edge-Mist metaheuristic task scheduling (PureEdgeSim) and lightweight deep learning computer vision for meteorological satellite imagery (INSAT-3D/3DR INCYDE dataset).", "highlights": [ "Developed a Python-based Queuing Search Algorithm (QSA) task scheduler for 3-tier Cloud-Edge-Mist networks in PureEdgeSim, achieving a 55% execution delay reduction over Genetic Algorithms (0.218s vs 0.4874s) and 99.9% task completion across 300 edge nodes and 30,450 tasks.", "Authored a comprehensive peer-reviewed survey in OPSEARCH (Springer) benchmarking 10 scheduling algorithms across heuristic, metaheuristic, game-theoretic, and reinforcement learning paradigms.", "Benchmarked YOLOv8n and YOLOv11n on INSAT-3D satellite imagery for tropical cyclone detection (0.921 mAP@.50-.95, 0.994 recall), published in Springer Nature (SIST Vol 426)." ] } ], "projects": [ { "id": "recall", "title": "Recall", "subtitle": "Serverless Multimodal Knowledge Ingestion Pipeline", "category": "AWS Serverless & Multimodal AI", "summary": "A serverless pipeline that captures social media links and articles from mobile share sheets, extracts text and audio via Gemini and Whisper, and auto-syncs structured notes directly to Google Keep.", "repositoryUrl": "https://github.com/CptPrice743/Recall", "metrics": [ "Sub-3s carousel OCR batching (up to 10 images per Gemini Vision call)", "Zero idle compute expenditure", "100% serverless scale-to-zero" ], "techStack": [ "AWS Lambda (OCI Container)", "Amazon API Gateway", "Amazon ECR", "Docker", "Python 3.11", "Google Gemini 3.5 Flash Lite Vision", "Groq Whisper (whisper-large-v3)", "Google Keep API (gkeepapi)", "Static ffmpeg" ], "architectureHighlights": [ "Lambda container packaging with static ffmpeg avoiding standard deployment package size limits.", "Ephemeral /tmp/ directory writes on read-only root container protecting session isolation.", "Multimodal batch processing running up to 10 carousel images in a single Gemini Vision call.", "Headless note publication via gkeepapi with auto-assigned 'Clipped' label taxonomy." ] }, { "id": "pixel-pusher", "title": "Pixel Pusher", "subtitle": "Asynchronous Serverless Image Processing Pipeline", "category": "AWS Event-Driven Serverless", "summary": "A serverless image processing pipeline that uses pre-signed S3 URLs for direct client uploads and triggers asynchronous Lambda transformation with a Docker-compiled Pillow layer.", "repositoryUrl": "https://github.com/CptPrice743/serverless-image-resizer", "metrics": [ "Zero backend upload bandwidth load", "Asynchronous S3 event execution", "Scale-to-zero serverless architecture" ], "techStack": [ "AWS Lambda", "Amazon S3 (Dual-Bucket Topology)", "Amazon API Gateway", "Python", "Pillow (Docker-compiled Lambda Layer)", "Docker", "React", "Vite" ], "architectureHighlights": [ "Dual-bucket S3 topology isolating raw input and resized output to prevent recursive invocation loops.", "Pre-signed S3 PUT URL generation offloading heavy file uploads directly to AWS object storage.", "Custom Docker-compiled AWS Lambda layer packaging Pillow for native Python performance.", "Client polling architecture using S3 HEAD requests for instant display upon job completion." ] }, { "id": "research-paper-rag", "title": "PaperPilot", "subtitle": "Low-Latency Document QA Engine with Section Attribution", "category": "Retrieval-Augmented Generation (RAG)", "summary": "A grounded academic document QA system parsing two-column scientific papers with PyMuPDF, indexing in-memory FAISS vector representations, and streaming answers via Groq Llama 3.3 with chunk attribution.", "repositoryUrl": "https://github.com/CptPrice743/research-paper-rag", "metrics": [ "< 512MB backend memory footprint", "Sub-second Llama 3.3 token stream", "100% citation coverage on answers" ], "techStack": [ "FastAPI", "Python 3.11", "FAISS (IndexFlatIP)", "Groq SDK (llama-3.3-70b-versatile)", "HuggingFace Inference API (all-MiniLM-L6-v2)", "PyMuPDF", "Redis", "React", "Vite" ], "architectureHighlights": [ "PyMuPDF vertical-then-horizontal block extraction preventing text mixing across two-column layouts.", "Offloaded embedding generation via HuggingFace API keeping RAM beneath Render 512MB free tier.", "In-memory inner product FAISS index (IndexFlatIP) delivering sub-5ms cosine similarity lookup.", "Redis-backed rate limiting enforcing 100 daily queries and 10 queries/min per IP address." ] }, { "id": "deadlines", "title": "Deadlines", "subtitle": "Full-Stack Healthcare Appointment Scheduling Platform", "category": "Full-Stack Web Application", "summary": "A role-based appointment scheduling platform featuring granular weekly doctor availability definitions, calendar-specific exception overrides, and collision-free booking locks.", "repositoryUrl": "https://github.com/CptPrice743/appointment-scheduling", "liveDemoUrl": "https://deadlines.vyom-uchat.workers.dev/", "metrics": [ "Zero overlapping booking collisions", "3-tier role-based access control (Patient, Doctor, Admin)", "Compound-indexed query optimization" ], "techStack": [ "MongoDB Atlas", "Express.js", "React.js", "Node.js", "JWT", "bcrypt", "Mongoose", "Chart.js" ], "architectureHighlights": [ "Three-tier Role-Based Access Control partitioning Patient, Doctor, and Admin operational scopes.", "Dynamic doctor availability model combining weekly recurring templates with date-specific overrides.", "Server-side concurrency checks preventing double bookings on shared appointment slots.", "Encrypted session tokens using JSON Web Tokens (JWT) with bcrypt password hashing." ] }, { "id": "prism-jewellery", "title": "Prism Jewellery", "subtitle": "Dynamic E-Commerce Architecture", "category": "Relational Web Systems", "summary": "A complete e-commerce store engineered with vanilla PHP and MySQL, featuring persistent session-to-database cart synchronization, asynchronous AJAX quantity management, algorithmic coupon validation, and order transaction isolation.", "techStack": [ "PHP", "MySQL", "JavaScript (Fetch / AJAX)", "HTML5", "CSS3" ] } ], "researchPublications": [ { "id": "yolo-cyclone-detection", "title": "A Comparative Analysis of YOLOv8 and YOLOv11 for Tropical Cyclone Detection: The Impact of Data Augmentation on Model Performance", "status": "Published", "venue": "Springer (Smart Innovation, Systems and Technologies, Vol. 426)", "year": "2025", "authors": ["Dr. Punit Gupta", "Liza Chaurasiya", "Vyom Uchat"], "doi": "10.1007/978-981-92-1030-5_19", "url": "https://link.springer.com/chapter/10.1007/978-981-92-1030-5_19", "abstract": "Tropical cyclones are high-impact meteorological events whose timely and accurate detection is critical for public safety. This research provides a rigorous comparative analysis of two lightweight object detection models, YOLOv8n and YOLOv11n, trained on the INCYDE satellite imagery dataset. Benchmarked using mean Average Precision (mAP), YOLOv11n on unaugmented imagery achieved superior performance with mAP@.50-.95 of 0.921 and detection recall of 0.994. Counter-intuitively, standard geometric augmentations degraded performance across both models, demonstrating that data strategies must be domain-aware.", "keyFindings": [ "YOLOv11n achieved 0.921 mAP@.50-.95 and 99.4% detection recall on unaugmented INSAT-3D/3DR satellite imagery.", "Standard geometric image augmentations degraded model accuracy across both YOLOv8n and YOLOv11n, proving meteorological detection requires domain-aware unwarped data.", "Lightweight model profile enables real-time storm warning telemetry directly on edge systems." ], "keywords": [ "Tropical Cyclone Detection", "Object Detection", "YOLOv11", "YOLOv8", "Data Augmentation", "Satellite Imagery", "INCYDE Dataset" ] }, { "id": "metaheuristic-task-scheduling-review", "title": "Metaheuristic Task Scheduling Optimization Algorithm for Edge Computing : A Review", "status": "Published", "venue": "OPSEARCH, Springer", "year": "2026", "authors": ["Vyom Uchat", "Dr. Punit Gupta", "Aaditya Jha", "Tejash Karamata"], "doi": "10.1007/s12597-026-01293-w", "url": "https://link.springer.com/article/10.1007/s12597-026-01293-w", "abstract": "Time-critical IoT applications require intelligent offloading across the Edge-Cloud continuum. Juggling tasks in dynamic environments is NP-hard. We benchmarked 10 scheduling algorithms across heuristic, metaheuristic, game-theoretic, and reinforcement learning paradigms on latency, power draw, and SLA violation rates. Standard off-the-shelf implementations suffer from slow convergence and local optima traps under network congestion. This review maps key trade-offs and architectural requirements for real-time edge schedulers.", "keyFindings": [ "Benchmarked 10 distinct task scheduling algorithms across latency, energy consumption, and SLA deadline violation rates.", "Identified convergence bottlenecks and local optima traps during sudden network congestion spikes.", "Defined concrete architectural guidelines and hybrid optimization strategies for real-time edge schedulers." ], "keywords": [ "Task Scheduling", "Edge Computing", "Fog Computing", "Metaheuristics", "Swarm Intelligence", "Genetic Algorithms", "IoT Resource Allocation" ] }, { "id": "queuing-search-algorithm", "title": "Queuing Search Algorithm Metaheuristic Task Scheduling Algorithm for Cloud-Edge Computation", "status": "Under Peer Review", "venue": "OPSEARCH, Springer", "year": "2025", "authors": ["Vyom Uchat", "Dr. Punit Gupta", "Aaditya Jha", "Tejash Karamata"], "abstract": "Task scheduling across the Cloud-Edge-Mist continuum is an NP-hard resource allocation challenge. We propose a task scheduling framework built around a tailored adaptation of the Queuing Search Algorithm (QSA), inspired by human queuing behavior to gravitate solutions toward faster service regions while avoiding local optima. Integrated with the PureEdgeSim simulation toolkit across 300 devices and 30,450 tasks, QSA reduced average task execution delay by 55% (0.218s vs 0.4874s for Genetic Algorithms) and achieved a 99.9% task completion rate across the full three-tier architecture.", "keyFindings": [ "Reduced average task execution delay by 55% (0.218s vs 0.4874s for Genetic Algorithms).", "Achieved a 99.9% task completion rate across 300 simulated edge devices processing 30,450 tasks in PureEdgeSim.", "Outperformed Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Lion Optimization (ALO), and Energy Valley Optimizer (EVO) in load balancing and energy efficiency." ], "keywords": [ "Edge Computing", "Cloud Computing", "Mist Computing", "Task Scheduling", "Metaheuristic Optimization", "Queuing Search Algorithm", "PureEdgeSim" ] } ], "skillsMatrix": { "aiMlAndAutomation": [ "Retrieval-Augmented Generation (RAG)", "FAISS", "Sentence-Transformers", "Llama 3", "Groq API", "Google AI Studio / Gemini Multimodal API", "Groq Whisper", "Qdrant Vector DB", "PyMuPDF", "YOLOv8 / YOLOv11", "Prompt Engineering" ], "programmingLanguages": [ "Python", "Java", "JavaScript (ES6+)", "TypeScript", "C", "C++", "SQL", "PHP", "HTML5/CSS3" ], "frameworksAndBackend": [ "FastAPI", "React.js", "Next.js", "Node.js", "Express.js", "Spring Boot", "Hibernate", "RESTful API Design" ], "databasesAndCaching": [ "MongoDB", "MySQL", "Redis", "Qdrant", "FAISS" ], "cloudDevOpsAndTools": [ "AWS (Lambda, S3, API Gateway, ECR)", "Cloudflare Pages & Workers", "Docker", "Git & GitHub", "Postman", "PowerBI", "Render", "Vercel" ], "cybersecurityAndSystems": [ "IEC 62443", "NIST SP 800-82", "Purdue Model Segmentation", "Industrial Control Systems (ICS/OT)", "Initial Risk Assessment (IRA)", "Threat Modeling" ] }, "certifications": [ { "title": "Google Cybersecurity Professional Certificate", "issuer": "Google / Coursera", "year": "2024", "competencies": "Network packet inspection (Wireshark, tcpdump), SIEM telemetry querying, and automated Python incident response." }, { "title": "Automation Business Analyst Foundation", "issuer": "UiPath Academy", "year": "2024", "competencies": "RPA process workflow mapping, feasibility assessment, gap analysis, and deterministic state machine modeling." } ], "workflowsAndInterests": { "hardwareEcosystem": "MacBook Air M3 (16GB RAM, 256GB SSD) with 1TB Samsung T7 external SSD for local LLM and dataset caching; M3 iPad Air with Apple Pencil Pro for handwritten system architecture diagrams; Samsung Galaxy S24 with custom Bluetooth clipboard sync pipeline to macOS.", "productivityStack": [ "Raycast", "Obsidian", "Goodnotes", "Bitwarden", "NextDNS", "LM Studio (local quantized LLMs on Apple Silicon)" ], "engineeringPhilosophy": "Quiet software over needless decoration (inspired by Dieter Rams and Swiss typographic precision). Predictable systems, explicit failure modes, and minimal moving parts.", "technicalReading": "Cloud outage post-mortems, distributed consensus papers (Raft, Paxos, edge queuing), and real-world system failure analysis.", "gamingInterests": [ "Cyberpunk 2077", "Baldur's Gate 3", "Sleeping Dogs", "Grand Theft Auto: San Andreas", "Dave the Diver", "Dredge", "Katana Zero", "Escape from Duckov", "Dirt 4" ] } }