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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Dr. Phani Siginamsetty — Data Scientist & AI Researcher</title>
<link rel="stylesheet" href="styles.css">
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.5.0/css/all.min.css">
<script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js"></script>
</head>
<body>
<!-- ═══ SIDEBAR ═══════════════════════════════════════════ -->
<aside class="sidebar" id="sidebar">
<div class="sidebar-profile">
<div class="avatar-wrap">
<div class="avatar-ring"></div>
<div class="avatar"><span class="initials">PS</span></div>
</div>
<div class="sidebar-name">Dr. Phani Siginamsetty</div>
<div class="sidebar-role">Data Scientist · PhD</div>
<div class="sidebar-location"><i class="fa-solid fa-location-dot"></i> Chennai, India</div>
<div class="social-links">
<a href="mailto:siginamsettyphani@gmail.com" title="Email"><i class="fa-solid fa-envelope"></i></a>
<a href="tel:+918125636250" title="Phone"><i class="fa-solid fa-phone"></i></a>
<a href="https://linkedin.com/in/phani-kumar-630613101" target="_blank" title="LinkedIn"><i class="fa-brands fa-linkedin-in"></i></a>
<a href="https://github.com/phanikumar96" target="_blank" title="GitHub"><i class="fa-brands fa-github"></i></a>
<a href="https://scholar.google.com/citations?user=mvFV_Edq61oC&hl=en" target="_blank" title="Google Scholar"><i class="fa-solid fa-graduation-cap"></i></a>
</div>
</div>
<nav class="sidebar-nav">
<a href="#hero" class="nav-item active">
<span class="nav-icon"><i class="fa-solid fa-house"></i></span>
<span class="nav-label">Home</span>
</a>
<a href="#skills" class="nav-item">
<span class="nav-icon"><i class="fa-solid fa-microchip"></i></span>
<span class="nav-label">Skills</span>
</a>
<a href="#experience" class="nav-item">
<span class="nav-icon"><i class="fa-solid fa-briefcase"></i></span>
<span class="nav-label">Experience</span>
</a>
<a href="#projects" class="nav-item">
<span class="nav-icon"><i class="fa-solid fa-code-branch"></i></span>
<span class="nav-label">Projects</span>
</a>
<a href="#research" class="nav-item">
<span class="nav-icon"><i class="fa-solid fa-microscope"></i></span>
<span class="nav-label">Research & IP</span>
</a>
<a href="#education" class="nav-item">
<span class="nav-icon"><i class="fa-solid fa-user-graduate"></i></span>
<span class="nav-label">Education</span>
</a>
<a href="#resume" class="nav-item">
<span class="nav-icon"><i class="fa-solid fa-file-pdf"></i></span>
<span class="nav-label">Resume</span>
</a>
</nav>
<div class="sidebar-footer">siginamsettyphani@gmail.com</div>
</aside>
<!-- Mobile header -->
<header class="mobile-header">
<span class="mobile-brand">Dr. Phani Siginamsetty</span>
<button class="mobile-menu-toggle" id="menuToggle"><i class="fa-solid fa-bars"></i></button>
</header>
<!-- ═══ MAIN ═══════════════════════════════════════════════ -->
<main class="main">
<!-- ── HERO ─────────────────────────────────────────────── -->
<section id="hero" class="hero reveal">
<canvas id="hero-canvas"></canvas>
<div class="hero-layout">
<!-- LEFT -->
<div class="hero-left">
<div class="hero-eyebrow">Associate Data Scientist · Hexaware Technologies · Chennai, India</div>
<h1 class="hero-name">
Dr. Phani<br>
<span class="grad">Siginamsetty</span>
</h1>
<div class="hero-roles">
<span class="role-static">I build</span>
<span class="role-typed" id="typedRole">Autonomous AI Agents</span>
</div>
<p class="hero-subtitle">
PhD-qualified Data Scientist and AI Engineer with 5+ years bridging cutting-edge academic research with enterprise-grade production systems.
Expert in designing autonomous Multi-Agent Systems — where LLM-powered agents plan, reason, use tools, and collaborate to solve complex real-world workflows end-to-end.
Deep hands-on experience across the full AI lifecycle: from RAG pipeline architecture and LLM fine-tuning (PEFT/QLoRA) to quantized edge deployment, MLOps, and scalable cloud infrastructure on AWS.
Proven ability to translate research breakthroughs in multilingual NLP, multimodal AI, and quantum-motivated algorithms into production-ready systems — backed by 11 patents and 7 peer-reviewed publications in Elsevier, IEEE, and Springer.
Passionate about building intelligent systems that are not just accurate, but autonomous, explainable, and deployable at scale.
</p>
<div class="hero-tags">
<span class="hero-tag"><i class="fa-solid fa-robot"></i> Agentic AI</span>
<span class="hero-tag"><i class="fa-solid fa-network-wired"></i> Multi-Agent Systems</span>
<span class="hero-tag"><i class="fa-solid fa-brain"></i> Generative AI</span>
<span class="hero-tag"><i class="fa-solid fa-magnifying-glass-chart"></i> RAG Pipelines</span>
<span class="hero-tag"><i class="fa-solid fa-sliders"></i> LLM Fine-Tuning</span>
<span class="hero-tag"><i class="fa-solid fa-chart-line"></i> Data Science</span>
<span class="hero-tag"><i class="fa-solid fa-eye"></i> Computer Vision</span>
<span class="hero-tag"><i class="fa-solid fa-language"></i> NLP Research</span>
<span class="hero-tag"><i class="fa-solid fa-cloud"></i> MLOps · AWS</span>
<span class="hero-tag"><i class="fa-solid fa-microchip"></i> Edge AI</span>
<span class="hero-tag"><i class="fa-solid fa-code-branch"></i> LangGraph · CrewAI</span>
<span class="hero-tag"><i class="fa-solid fa-database"></i> Vector Databases</span>
<span class="hero-tag"><i class="fa-solid fa-flask"></i> AI Research</span>
<span class="hero-tag"><i class="fa-solid fa-certificate"></i> 11 Patents</span>
<span class="hero-tag"><i class="fa-solid fa-book-open"></i> 7 Publications</span>
</div>
<div class="hero-cta">
<a href="#projects" class="btn-primary"><i class="fa-solid fa-code-branch"></i> View Projects</a>
<a href="#resume" class="btn-secondary"><i class="fa-solid fa-file-pdf"></i> Resume</a>
<a href="mailto:siginamsettyphani@gmail.com" class="btn-secondary"><i class="fa-solid fa-envelope"></i> Contact</a>
</div>
</div>
<!-- RIGHT: visual panel -->
<div class="hero-right">
<!-- Donut chart -->
<div class="hero-chart-card">
<div class="hero-chart-title">Expertise Distribution</div>
<div class="hero-chart-wrap">
<canvas id="expertiseDonut"></canvas>
<div class="donut-center-label">
<span class="dcl-num">62+</span>
<span class="dcl-sub">Skills</span>
</div>
</div>
<div class="donut-legend" id="donutLegend"></div>
</div>
<!-- Live stat cards -->
<div class="hero-stat-grid">
<div class="hsg-card">
<i class="fa-solid fa-certificate hsg-icon blue"></i>
<div class="hsg-num" data-count="11">0</div>
<div class="hsg-lbl">Patents Filed</div>
</div>
<div class="hsg-card">
<i class="fa-solid fa-check-double hsg-icon green"></i>
<div class="hsg-num" data-count="3">0</div>
<div class="hsg-lbl">Granted</div>
</div>
<div class="hsg-card">
<i class="fa-solid fa-book-open hsg-icon purple"></i>
<div class="hsg-num" data-count="7">0</div>
<div class="hsg-lbl">Publications</div>
</div>
<div class="hsg-card">
<i class="fa-solid fa-star hsg-icon amber"></i>
<div class="hsg-num">9.25</div>
<div class="hsg-lbl">PhD CGPA</div>
</div>
<div class="hsg-card">
<i class="fa-solid fa-briefcase hsg-icon blue"></i>
<div class="hsg-num" data-count="5">0</div>
<div class="hsg-lbl">Yrs Experience</div>
</div>
<div class="hsg-card">
<i class="fa-solid fa-code hsg-icon green"></i>
<div class="hsg-num" data-count="4">0</div>
<div class="hsg-lbl">Key Projects</div>
</div>
</div>
</div>
</div>
</section>
<!-- ── SKILLS ─────────────────────────────────────────────── -->
<section id="skills" class="section reveal">
<div class="section-label"><span>Technical Expertise</span></div>
<h2 class="section-title">Skills & Stack</h2>
<p class="section-desc">Full-spectrum ML/AI stack — hover bars, click categories to explore.</p>
<div class="skills-master">
<!-- Left: bar chart proficiency -->
<div class="skills-bars-panel">
<div class="sbp-title">Core Proficiency</div>
<div class="skill-bars" id="skillBars">
<div class="sbar-item" data-pct="95">
<div class="sbar-label"><span>Generative AI & LLMs</span><span class="sbar-pct">95%</span></div>
<div class="sbar-track"><div class="sbar-fill c-blue"></div></div>
</div>
<div class="sbar-item" data-pct="92">
<div class="sbar-label"><span>Multi-Agent Systems</span><span class="sbar-pct">92%</span></div>
<div class="sbar-track"><div class="sbar-fill c-purple"></div></div>
</div>
<div class="sbar-item" data-pct="90">
<div class="sbar-label"><span>RAG & Vector Search</span><span class="sbar-pct">90%</span></div>
<div class="sbar-track"><div class="sbar-fill c-cyan"></div></div>
</div>
<div class="sbar-item" data-pct="88">
<div class="sbar-label"><span>Python & FastAPI</span><span class="sbar-pct">88%</span></div>
<div class="sbar-track"><div class="sbar-fill c-green"></div></div>
</div>
<div class="sbar-item" data-pct="85">
<div class="sbar-label"><span>Data Science & ML</span><span class="sbar-pct">85%</span></div>
<div class="sbar-track"><div class="sbar-fill c-blue"></div></div>
</div>
<div class="sbar-item" data-pct="83">
<div class="sbar-label"><span>AWS Cloud & MLOps</span><span class="sbar-pct">83%</span></div>
<div class="sbar-track"><div class="sbar-fill c-amber"></div></div>
</div>
<div class="sbar-item" data-pct="80">
<div class="sbar-label"><span>Computer Vision</span><span class="sbar-pct">80%</span></div>
<div class="sbar-track"><div class="sbar-fill c-pink"></div></div>
</div>
<div class="sbar-item" data-pct="78">
<div class="sbar-label"><span>NLP Research</span><span class="sbar-pct">78%</span></div>
<div class="sbar-track"><div class="sbar-fill c-purple"></div></div>
</div>
</div>
<!-- Mini donut: domain split -->
<div class="sbp-title" style="margin-top:28px">Domain Focus</div>
<div class="domain-donut-wrap">
<canvas id="domainDonut"></canvas>
<div class="domain-legend" id="domainLegend"></div>
</div>
</div>
<!-- Right: accordion tech stack -->
<div class="skills-accordion-panel">
<div class="sbp-title">Technology Stack</div>
<div class="skills-layout">
<div class="skill-row open" data-cat="genai">
<div class="skill-row-header">
<div class="skill-row-icon"><i class="fa-solid fa-robot"></i></div>
<div class="skill-row-info">
<div class="skill-row-title">GenAI & Agentic Frameworks</div>
<div class="skill-row-count">11 technologies</div>
</div>
<div class="skill-row-accent"></div>
<i class="fa-solid fa-chevron-down skill-row-chevron"></i>
</div>
<div class="skill-row-body">
<div class="skill-pills">
<span class="skill-pill">CrewAI</span><span class="skill-pill">AutoGen</span><span class="skill-pill">LangGraph</span><span class="skill-pill">Agno</span><span class="skill-pill">PydanticAI</span><span class="skill-pill">Haystack</span><span class="skill-pill">Tool-Calling</span><span class="skill-pill">Semantic Routing</span><span class="skill-pill">HITL</span><span class="skill-pill">Ragas</span><span class="skill-pill">TruLens</span>
</div>
</div>
</div>
<div class="skill-row" data-cat="llm">
<div class="skill-row-header">
<div class="skill-row-icon"><i class="fa-solid fa-brain"></i></div>
<div class="skill-row-info">
<div class="skill-row-title">LLMs & Model Engineering</div>
<div class="skill-row-count">12 technologies</div>
</div>
<div class="skill-row-accent"></div>
<i class="fa-solid fa-chevron-down skill-row-chevron"></i>
</div>
<div class="skill-row-body">
<div class="skill-pills">
<span class="skill-pill">AWS Bedrock</span><span class="skill-pill">Llama 3.2</span><span class="skill-pill">GPT-4o</span><span class="skill-pill">PEFT</span><span class="skill-pill">QLoRA</span><span class="skill-pill">Unsloth</span><span class="skill-pill">DPO/RLHF</span><span class="skill-pill">DeepSpeed</span><span class="skill-pill">FSDP</span><span class="skill-pill">GGUF</span><span class="skill-pill">AWQ</span><span class="skill-pill">GPTQ</span>
</div>
</div>
</div>
<div class="skill-row" data-cat="ml">
<div class="skill-row-header">
<div class="skill-row-icon"><i class="fa-solid fa-chart-line"></i></div>
<div class="skill-row-info">
<div class="skill-row-title">Data Science & Advanced ML</div>
<div class="skill-row-count">9 technologies</div>
</div>
<div class="skill-row-accent"></div>
<i class="fa-solid fa-chevron-down skill-row-chevron"></i>
</div>
<div class="skill-row-body">
<div class="skill-pills">
<span class="skill-pill">PyTorch</span><span class="skill-pill">Scikit-Learn</span><span class="skill-pill">RL (PPO, DQN)</span><span class="skill-pill">Time-Series</span><span class="skill-pill">XGBoost</span><span class="skill-pill">Random Forest</span><span class="skill-pill">Siamese Networks</span><span class="skill-pill">AutoML</span><span class="skill-pill">Quantum-Motivated Algorithms</span>
</div>
</div>
</div>
<div class="skill-row" data-cat="vision">
<div class="skill-row-header">
<div class="skill-row-icon"><i class="fa-solid fa-eye"></i></div>
<div class="skill-row-info">
<div class="skill-row-title">Vision & Multimodal AI</div>
<div class="skill-row-count">8 technologies</div>
</div>
<div class="skill-row-accent"></div>
<i class="fa-solid fa-chevron-down skill-row-chevron"></i>
</div>
<div class="skill-row-body">
<div class="skill-pills">
<span class="skill-pill">Multimodal RAG</span><span class="skill-pill">OpenCV</span><span class="skill-pill">AWS Textract</span><span class="skill-pill">Document Intelligence</span><span class="skill-pill">Whisper (ASR)</span><span class="skill-pill">ElevenLabs (TTS)</span><span class="skill-pill">CLIP</span><span class="skill-pill">Image & Audio Processing</span>
</div>
</div>
</div>
<div class="skill-row" data-cat="mlops">
<div class="skill-row-header">
<div class="skill-row-icon"><i class="fa-brands fa-aws"></i></div>
<div class="skill-row-info">
<div class="skill-row-title">MLOps, Cloud & Backend</div>
<div class="skill-row-count">11 technologies</div>
</div>
<div class="skill-row-accent"></div>
<i class="fa-solid fa-chevron-down skill-row-chevron"></i>
</div>
<div class="skill-row-body">
<div class="skill-pills">
<span class="skill-pill">Python</span><span class="skill-pill">AWS SageMaker</span><span class="skill-pill">AWS Lambda</span><span class="skill-pill">AWS EC2</span><span class="skill-pill">Docker</span><span class="skill-pill">Kubernetes</span><span class="skill-pill">CI/CD</span><span class="skill-pill">FastAPI</span><span class="skill-pill">Flask</span><span class="skill-pill">JWT/RBAC</span><span class="skill-pill">Optuna</span>
</div>
</div>
</div>
<div class="skill-row" data-cat="data">
<div class="skill-row-header">
<div class="skill-row-icon"><i class="fa-solid fa-database"></i></div>
<div class="skill-row-info">
<div class="skill-row-title">Data Infrastructure & Vector DBs</div>
<div class="skill-row-count">11 technologies</div>
</div>
<div class="skill-row-accent"></div>
<i class="fa-solid fa-chevron-down skill-row-chevron"></i>
</div>
<div class="skill-row-body">
<div class="skill-pills">
<span class="skill-pill">Pinecone</span><span class="skill-pill">Milvus</span><span class="skill-pill">Weaviate</span><span class="skill-pill">Chroma</span><span class="skill-pill">FAISS</span><span class="skill-pill">PostgreSQL</span><span class="skill-pill">MongoDB</span><span class="skill-pill">Spark</span><span class="skill-pill">Pandas</span><span class="skill-pill">NumPy</span><span class="skill-pill">Parquet</span>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- ── EXPERIENCE ─────────────────────────────────────────── -->
<section id="experience" class="section reveal">
<div class="section-label"><span>Career</span></div>
<h2 class="section-title">Work Experience</h2>
<p class="section-desc">5+ years across industry research, enterprise AI, and academia.</p>
<!-- Journey stats bar -->
<div class="journey-stats">
<div class="js-item"><span class="js-num">5+</span><span class="js-lbl">Years</span></div>
<div class="js-divider"></div>
<div class="js-item"><span class="js-num">5</span><span class="js-lbl">Roles</span></div>
<div class="js-divider"></div>
<div class="js-item"><span class="js-num">4</span><span class="js-lbl">Companies</span></div>
<div class="js-divider"></div>
<div class="js-item"><span class="js-num">2020</span><span class="js-lbl">Started</span></div>
<div class="js-divider"></div>
<div class="js-item"><span class="js-num" style="color:var(--green)">Now</span><span class="js-lbl">Present</span></div>
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<div class="cp-card-left">
<div class="cp-role">Associate Data Scientist</div>
<div class="cp-company"><span style="color:var(--blue)">Hexaware Technologies</span> <span class="cp-loc">· Chennai, India</span></div>
<div class="cp-tags">
<span>Agno</span><span>Multi-Agent</span><span>RAG</span><span>LangGraph</span><span>HITL</span>
</div>
</div>
<div class="cp-card-right">
<span class="cp-date-badge" style="--dc:var(--blue);--dca:rgba(79,158,255,0.15)">Mar 2025 – Present</span>
<span class="cp-current-badge">● Current</span>
<i class="fa-solid fa-chevron-down cp-chevron open"></i>
</div>
</div>
<div class="cp-card-body open">
<ul class="cp-bullets">
<li><strong>Autonomous Fraud Detection:</strong> Spearheading real-time fraud detection using stateful multi-agent systems via the Agno framework with advanced tool-calling for complex transaction analysis.</li>
<li><strong>Advanced RAG Pipelines:</strong> Engineering a multi-agent RAG pipeline for "Smart Tutor" using vector databases and semantic routing to deliver personalized content while minimizing hallucinations.</li>
<li><strong>Enterprise Automation:</strong> Designing agentic workflows with LangChain and LangGraph to automate reporting with Human-in-the-Loop (HITL) mechanisms, reducing operational overhead.</li>
</ul>
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</div>
</div>
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<div class="cp-role">Research Assistant</div>
<div class="cp-company"><span style="color:var(--purple)">Volvo Group</span> <span class="cp-loc">· Bangalore, India</span></div>
<div class="cp-tags">
<span>Edge AI</span><span>GGUF/AWQ</span><span>Computer Vision</span><span>CNN</span>
</div>
</div>
<div class="cp-card-right">
<span class="cp-date-badge" style="--dc:var(--purple);--dca:rgba(167,139,250,0.15)">Jun 2024 – Mar 2025</span>
<i class="fa-solid fa-chevron-down cp-chevron"></i>
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</div>
<div class="cp-card-body">
<ul class="cp-bullets">
<li><strong>Edge GenAI & Quantization:</strong> Researched lightweight LLMs for on-device inference using GGUF/AWQ quantization to reduce memory footprint and latency on vehicular hardware.</li>
<li><strong>Computer Vision Diagnostics:</strong> Deployed an optimized CNN pipeline for real-time component recognition within the Vehicle Configuration Manager (VCM) to automate visual inspections.</li>
</ul>
</div>
</div>
</div>
<!-- Node 3 -->
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<div class="cp-role">Data Science Researcher (PhD Scholar)</div>
<div class="cp-company"><span style="color:var(--cyan)">SRM University AP</span> <span class="cp-loc">· Amaravati, India</span></div>
<div class="cp-tags">
<span>RAG</span><span>FastAPI</span><span>mT5</span><span>NLP</span><span>Quantum AI</span><span>Patents</span>
</div>
</div>
<div class="cp-card-right">
<span class="cp-date-badge" style="--dc:var(--cyan);--dca:rgba(34,211,238,0.15)">Sep 2021 – Jul 2024</span>
<i class="fa-solid fa-chevron-down cp-chevron"></i>
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<div class="cp-card-body">
<ul class="cp-bullets">
<li><strong>Healthcare AI:</strong> Architected a secure RAG chatbot for SRM Global Hospital to retrieve medical protocols while ensuring strict data privacy and proprietary data embedding.</li>
<li><strong>Audio Intelligence:</strong> Engineered a MoM automation API using FastAPI, STT, and speaker diarization to autonomously extract abstractive summaries and action items from recordings.</li>
<li><strong>Multilingual NLP:</strong> Developed MATSFT and MMSFT frameworks by fine-tuning mT5 for low-resource Indian languages, resulting in multiple high-impact journal publications.</li>
<li><strong>Quantum AI & IP:</strong> Architected quantum-motivated summarization processors for data compression, leading to multiple Indian Patents including 3 Granted Patents.</li>
</ul>
</div>
</div>
</div>
<!-- Node 4 -->
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<div class="cp-card-header" onclick="toggleCpCard(this)">
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<div class="cp-role">Trainee Engineer (ML Research)</div>
<div class="cp-company"><span style="color:var(--amber)">Tychee Innovations</span> <span class="cp-loc">· Andhra Pradesh, India</span></div>
<div class="cp-tags">
<span>Object Detection</span><span>ML</span><span>Healthcare AI</span><span>Predictive Analytics</span>
</div>
</div>
<div class="cp-card-right">
<span class="cp-date-badge" style="--dc:var(--amber);--dca:rgba(251,191,36,0.15)">Aug 2020 – Jul 2021</span>
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<div class="cp-card-body">
<ul class="cp-bullets">
<li><strong>Industrial Safety Vision:</strong> Deployed real-time object detection to monitor hazardous machinery, triggering emergency stops via spatial tracking of hand proximity to danger zones.</li>
<li><strong>Predictive Analytics:</strong> Developed ML models to forecast patient outcomes and translate clinical data into actionable insights for data-driven healthcare decisions.</li>
</ul>
</div>
</div>
</div>
<!-- Node 5 -->
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<div class="cp-card-header" onclick="toggleCpCard(this)">
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<div class="cp-role">Assistant Professor</div>
<div class="cp-company"><span style="color:var(--green)">Dhanekula Engineering College</span> <span class="cp-loc">· Andhra Pradesh, India</span></div>
<div class="cp-tags">
<span>Python</span><span>DSA</span><span>Mentorship</span><span>Teaching</span>
</div>
</div>
<div class="cp-card-right">
<span class="cp-date-badge" style="--dc:var(--green);--dca:rgba(52,211,153,0.15)">Oct 2020 – Aug 2021</span>
<i class="fa-solid fa-chevron-down cp-chevron"></i>
</div>
</div>
<div class="cp-card-body">
<ul class="cp-bullets">
<li><strong>Software Mentorship:</strong> Instructed Data Structures, Algorithms, and Python, mentoring students in software engineering best practices and technical problem-solving.</li>
</ul>
</div>
</div>
</div>
</div>
</section>
<!-- ── PROJECTS ────────────────────────────────────────────── -->
<section id="projects" class="section reveal">
<div class="section-label"><span>Portfolio</span></div>
<h2 class="section-title">Key Projects</h2>
<p class="section-desc">Enterprise-grade AI systems built end-to-end — spanning GenAI, fraud detection, computer vision, and healthcare.</p>
<!-- Projects analytics row -->
<div class="proj-analytics">
<div class="proj-chart-card">
<div class="proj-chart-title">Tech Stack Distribution</div>
<div class="proj-donut-wrap">
<canvas id="projTechDonut"></canvas>
<div class="proj-donut-center"><span class="pdc-num">4</span><span class="pdc-sub">Projects</span></div>
</div>
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</div>
<div class="proj-chart-card">
<div class="proj-chart-title">Domain Coverage</div>
<canvas id="projDomainBar" style="max-height:160px"></canvas>
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<div class="proj-chart-title">AI Techniques Used</div>
<canvas id="projTechBar" style="max-height:160px"></canvas>
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<div class="projects-grid">
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<div class="proj-icon"><i class="fa-solid fa-graduation-cap"></i></div>
<div class="proj-meta">
<div class="proj-num">01 · SmartTutor</div>
<div class="proj-title">AI-Powered Knowledge Base & Interactive Learning Platform</div>
</div>
</div>
<div class="proj-chips">
<span class="proj-chip">FastAPI</span><span class="proj-chip">AWS Bedrock</span><span class="proj-chip">Amazon Nova Pro</span><span class="proj-chip">Claude Sonnet 4</span><span class="proj-chip">Pinecone</span><span class="proj-chip">AWS Polly</span><span class="proj-chip">PostgreSQL</span><span class="proj-chip">AWS S3</span><span class="proj-chip">JWT/RBAC</span>
</div>
<div class="proj-divider"></div>
<div class="proj-features-grid">
<div class="feat-block">
<div class="feat-block-title"><i class="fa-solid fa-file-lines"></i> Document Processing</div>
<ul>
<li>Multi-format: PDF, DOCX, PPTX, PNG, JPG</li>
<li>Auto-conversion of Word & PowerPoint to PDF</li>
<li>Intelligent image extraction & spatial analysis</li>
<li>Custom prompt instructions per document</li>
<li>Module regeneration with new instructions</li>
</ul>
</div>
<div class="feat-block">
<div class="feat-block-title"><i class="fa-solid fa-robot"></i> AI-Powered Features</div>
<ul>
<li>Structured module generation via PHASE 1–3 analysis</li>
<li>Interactive AI tutor with AWS Polly TTS (4 voices)</li>
<li>Rich image explanations with analogies</li>
<li>Auto question generation & answer validation</li>
<li>Internet search integration for extended context</li>
</ul>
</div>
<div class="feat-block">
<div class="feat-block-title"><i class="fa-solid fa-database"></i> Knowledge Management</div>
<ul>
<li>Vector search via Pinecone + Titan Embed (1024-dim)</li>
<li>Document sharing with edit proposals & review</li>
<li>Course publishing for trainee access</li>
<li>Real-time progress tracking during processing</li>
<li>Markdown-supported module curation</li>
</ul>
</div>
<div class="feat-block">
<div class="feat-block-title"><i class="fa-solid fa-users"></i> Multi-User & RBAC</div>
<ul>
<li>Roles: Super Admin, Trainer, Trainee</li>
<li>Process-based org grouping (departments)</li>
<li>Admin approval workflow for new trainers</li>
<li>Configurable rate limits per role (SlowAPI)</li>
<li>JWT auth with bcrypt password hashing</li>
</ul>
</div>
<div class="feat-block">
<div class="feat-block-title"><i class="fa-solid fa-microphone"></i> Interactive Learning</div>
<ul>
<li>TTS lectures with pause-for-questions</li>
<li>Raise Hand feature during live sessions</li>
<li>Voice input via Google Speech Recognition</li>
<li>Quiz system with instant AI feedback</li>
<li>Preview mode for trainers before publishing</li>
</ul>
</div>
<div class="feat-block">
<div class="feat-block-title"><i class="fa-solid fa-shield-halved"></i> Security & Infra</div>
<ul>
<li>AWS RDS PostgreSQL + SQLAlchemy ORM</li>
<li>S3 server-side AES256 encryption</li>
<li>CORS + SQL injection protection</li>
<li>Async operations via asyncio</li>
<li>Rotating file handler logging</li>
</ul>
</div>
</div>
<div class="proj-stack-section">
<div class="proj-stack-label">Full Tech Stack</div>
<div class="proj-stack-chips">
<span class="proj-stack-chip">Python 3.9+</span>
<span class="proj-stack-chip">FastAPI</span>
<span class="proj-stack-chip">PostgreSQL (AWS RDS)</span>
<span class="proj-stack-chip">SQLAlchemy</span>
<span class="proj-stack-chip">Pinecone</span>
<span class="proj-stack-chip">AWS Bedrock</span>
<span class="proj-stack-chip">Amazon Nova Pro v1</span>
<span class="proj-stack-chip">Claude Sonnet 4</span>
<span class="proj-stack-chip">Titan Embed v2 (1024-dim)</span>
<span class="proj-stack-chip">AWS Polly</span>
<span class="proj-stack-chip">AWS S3</span>
<span class="proj-stack-chip">PyMuPDF</span>
<span class="proj-stack-chip">Google Speech API</span>
<span class="proj-stack-chip">JWT</span>
<span class="proj-stack-chip">bcrypt</span>
<span class="proj-stack-chip">SlowAPI</span>
<span class="proj-stack-chip">asyncio</span>
<span class="proj-stack-chip">Showdown.js</span>
</div>
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</div>
</div>
<div class="proj-card accent-purple">
<div class="proj-banner"></div>
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<div class="proj-icon"><i class="fa-solid fa-shield-halved"></i></div>
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<div class="proj-num">02 · Fraud Prevention</div>
<div class="proj-title">Enterprise Fraud Prevention System — Citi Bank</div>
</div>
</div>
<div class="proj-chips">
<span class="proj-chip">Python</span><span class="proj-chip">XGBoost</span><span class="proj-chip">Multi-Agent</span><span class="proj-chip">AWS</span>
</div>
<div class="proj-divider"></div>
<ul class="proj-list">
<li><strong>Hybrid Risk Engine:</strong> Dual-layered system fusing statistical anomaly detection (XGBoost) with GenAI-driven forensics, reducing investigation time and false positives.</li>
<li><strong>Autonomous Rule Discovery:</strong> Multi-agent workflow for live transaction monitoring, detecting zero-day fraud patterns with Human-in-the-Loop oversight.</li>
<li><strong>Argus Agent:</strong> AI Data Analyst using <code>msoffcrypto</code> to securely decrypt and parse sensitive financial datasets locally for evidence-based risk verdicts.</li>
</ul>
</div>
</div>
<div class="proj-card accent-amber">
<div class="proj-banner"></div>
<div class="proj-body">
<div class="proj-top">
<div class="proj-icon"><i class="fa-solid fa-file-invoice-dollar"></i></div>
<div class="proj-meta">
<div class="proj-num">03 · Cheque Verification</div>
<div class="proj-title">Automated Bank Cheque Verification System</div>
</div>
</div>
<div class="proj-chips">
<span class="proj-chip">PyTorch</span><span class="proj-chip">AWS Textract</span><span class="proj-chip">OpenCV</span><span class="proj-chip">Siamese NN</span>
</div>
<div class="proj-divider"></div>
<ul class="proj-list">
<li><strong>Forensic Digitization:</strong> End-to-end vision pipeline using AWS Textract and OpenCV for layout analysis and digitization of MICR codes and payee details with high OCR accuracy.</li>
<li><strong>Signature Verification:</strong> PyTorch-based Siamese Neural Network for one-shot learning, using contrastive loss and feature embeddings to detect forged signatures.</li>
<li><strong>Cross-Modal Logic:</strong> NLP algorithms cross-verifying extracted semantic data (numeric vs. written amounts) to flag discrepancies for manual review.</li>
</ul>
</div>
</div>
<div class="proj-card accent-green">
<div class="proj-banner"></div>
<div class="proj-body">
<div class="proj-top">
<div class="proj-icon"><i class="fa-solid fa-heart-pulse"></i></div>
<div class="proj-meta">
<div class="proj-num">04 · Medical AI</div>
<div class="proj-title">Personalized Medical AI Assistant</div>
</div>
</div>
<div class="proj-chips">
<span class="proj-chip">Llama 3</span><span class="proj-chip">Agno</span><span class="proj-chip">LangGraph</span><span class="proj-chip">MongoDB</span>
</div>
<div class="proj-divider"></div>
<ul class="proj-list">
<li><strong>Clinical Guardrails & RAG:</strong> Domain-specific agent using Llama 3 and Vector DBs, with query expansion and re-ranking to ground answers exclusively in verified medical literature.</li>
<li><strong>Stateful Memory:</strong> Persistent context-retention engine using LangGraph and MongoDB to map longitudinal symptoms and medical history for personalized health insights.</li>
</ul>
</div>
</div>
</div>
</section>
<!-- ── RESEARCH & IP ───────────────────────────────────────── -->
<section id="research" class="section reveal">
<div class="section-label"><span>Intellectual Property</span></div>
<h2 class="section-title">Research & Patents</h2>
<p class="section-desc">11 patents filed · 3 granted · 7 peer-reviewed publications in Elsevier, IEEE & Springer.</p>
<!-- ── Top: stat cards + two charts ── -->
<div class="rs-top-row">
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<div><div class="rs-stat-num" data-count="11">0</div><div class="rs-stat-lbl">Patents Filed</div></div>
</div>
<div class="rs-stat s2">
<div class="rs-stat-icon"><i class="fa-solid fa-check-double"></i></div>
<div><div class="rs-stat-num" data-count="3">0</div><div class="rs-stat-lbl">Granted</div></div>
</div>
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<div class="rs-stat-icon"><i class="fa-solid fa-book-open"></i></div>
<div><div class="rs-stat-num" data-count="7">0</div><div class="rs-stat-lbl">Publications</div></div>
</div>
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<div class="rs-stat-icon"><i class="fa-solid fa-calendar"></i></div>
<div><div class="rs-stat-num">2022</div><div class="rs-stat-lbl">First Patent</div></div>
</div>
</div>
<!-- Patent status donut -->
<div class="rs-chart-card">
<div class="rs-chart-title">Patent Status</div>
<div class="rs-chart-wrap">
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<span class="rs-dc-num">11</span>
<span class="rs-dc-sub">Total</span>
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</div>
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</div>
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<div class="rs-chart-card">
<div class="rs-chart-title">Publications by Venue</div>
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<i class="fa-solid fa-certificate"></i> Patents
<span class="tab-count">11</span>
</button>
<button class="tab-btn" data-tab="tab-pubs">
<i class="fa-solid fa-book-open"></i> Publications
<span class="tab-count">7</span>
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<div id="tab-patents" class="tab-pane active">
<div class="patent-grid">
<div class="patent-card" data-domain="Education AI">
<div class="patent-top">
<div class="patent-title">System and a method for automated exam evaluation and personalized learning feedback</div>
</div>
<div class="patent-meta">
<span class="patent-no">202541018210</span>
<span class="patent-year">2025</span>
<span class="patent-domain">Education AI</span>
</div>
</div>
<div class="patent-card" data-domain="LLM">
<div class="patent-top">
<div class="patent-title">A System and a Method for Managing API Calls in A Large Language Model</div>
</div>
<div class="patent-meta">
<span class="patent-no">202441096836</span>
<span class="patent-year">2024</span>
<span class="patent-domain">LLM</span>
</div>
</div>
<div class="patent-card" data-domain="Healthcare">
<div class="patent-top">
<div class="patent-title">A System and a Method for Healthcare Data Processing and Decision Support</div>
</div>
<div class="patent-meta">
<span class="patent-no">202441076761</span>
<span class="patent-year">2024</span>
<span class="patent-domain">Healthcare</span>
</div>
</div>
<div class="patent-card" data-domain="NLP">
<div class="patent-top">
<div class="patent-title">System and method for multilingual fake news detection in multimodal information</div>
</div>
<div class="patent-meta">
<span class="patent-no">202441030030</span>
<span class="patent-year">2024</span>
<span class="patent-domain">NLP</span>
</div>
</div>
<div class="patent-card" data-domain="Healthcare">
<div class="patent-top">
<div class="patent-title">A Healthcare Summarization System and A Method Thereof</div>
</div>
<div class="patent-meta">
<span class="patent-no">202441005845</span>
<span class="patent-year">2024</span>
<span class="patent-domain">Healthcare</span>
</div>
</div>
<div class="patent-card" data-domain="Education AI">
<div class="patent-top">
<div class="patent-title">A System and a Method for Personalized E-Content Generation Based on Student Performance</div>
</div>
<div class="patent-meta">
<span class="patent-no">202441003347</span>
<span class="patent-year">2024</span>
<span class="patent-domain">Education AI</span>
</div>
</div>
<div class="patent-card is-granted" data-domain="NLP">
<div class="patent-top">
<div class="patent-title">System and method for deriving multilingual meeting minutes</div>
<span class="badge-granted"><i class="fa-solid fa-check"></i> Granted</span>
</div>
<div class="patent-meta">
<span class="patent-no">202441001022</span>
<span class="patent-grant-no">Grant: 581292</span>
<span class="patent-year">2024</span>
<span class="patent-domain">NLP</span>
</div>
</div>
<div class="patent-card is-granted" data-domain="Multimodal AI">
<div class="patent-top">
<div class="patent-title">System and method for multimodal multilingual input summarization using quantum motivated processors</div>
<span class="badge-granted"><i class="fa-solid fa-check"></i> Granted</span>
</div>
<div class="patent-meta">
<span class="patent-no">202341005519</span>
<span class="patent-grant-no">Grant: 66614</span>
<span class="patent-year">2023</span>
<span class="patent-domain">Multimodal AI</span>
</div>
</div>
<div class="patent-card" data-domain="FinTech">
<div class="patent-top">
<div class="patent-title">A System and A Method for Generating Trading Coupons</div>
</div>
<div class="patent-meta">
<span class="patent-no">202341007665</span>
<span class="patent-year">2023</span>
<span class="patent-domain">FinTech</span>
</div>
</div>
<div class="patent-card is-granted" data-domain="Engineering">
<div class="patent-top">
<div class="patent-title">A System and A Method for Prediction of The Strength of Concrete</div>
<span class="badge-granted"><i class="fa-solid fa-check"></i> Granted</span>
</div>
<div class="patent-meta">
<span class="patent-no">202341007257</span>
<span class="patent-grant-no">Grant: 582851</span>
<span class="patent-year">2023</span>
<span class="patent-domain">Engineering</span>
</div>
</div>
<div class="patent-card" data-domain="Multimodal AI">
<div class="patent-top">
<div class="patent-title">A System and Method for Performing Multilingual Multimodal Summarization</div>
</div>
<div class="patent-meta">
<span class="patent-no">202241073648</span>
<span class="patent-year">2022</span>
<span class="patent-domain">Multimodal AI</span>
</div>
</div>
</div>
</div>
<div id="tab-pubs" class="tab-pane">
<div class="pub-list">
<div class="pub-card">
<div class="pub-num">[1]</div>
<div class="pub-body">
<div class="pub-title">MATSFT: User query-based multilingual abstractive text summarization for low resource Indian languages by fine-tuning mT5</div>
<div class="pub-venue">
<span class="pub-journal">Alexandria Engineering Journal · Elsevier</span>
<span class="pub-year-badge">2025</span>
<span class="pub-type-badge journal">Journal</span>
</div>
<div class="pub-authors"><strong>Phani, S.</strong>, et al.</div>
<a href="https://doi.org/10.1016/j.aej.2025.04.031" target="_blank" class="doi-link">10.1016/j.aej.2025.04.031 <i class="fa-solid fa-arrow-up-right-from-square"></i></a>
</div>
</div>
<div class="pub-card">
<div class="pub-num">[2]</div>
<div class="pub-body">
<div class="pub-title">Improving Preliminary Clinical Diagnosis Accuracy through Knowledge Filtering Techniques in Consultation Dialogues</div>
<div class="pub-venue">
<span class="pub-journal">Computer Methods and Programs in Biomedicine · Elsevier</span>
<span class="pub-year-badge">2024</span>
<span class="pub-type-badge journal">Journal</span>
</div>
<div class="pub-authors">Abdul, A., <strong>Phani, S.</strong>, et al.</div>
<a href="https://doi.org/10.1016/j.cmpb.2024.108051" target="_blank" class="doi-link">10.1016/j.cmpb.2024.108051 <i class="fa-solid fa-arrow-up-right-from-square"></i></a>
</div>
</div>
<div class="pub-card">
<div class="pub-num">[3]</div>
<div class="pub-body">
<div class="pub-title">MMSFT: Multilingual Multimodal Summarization by Fine-tuning Transformers</div>
<div class="pub-venue">
<span class="pub-journal">IEEE Access</span>
<span class="pub-year-badge">2024</span>
<span class="pub-type-badge ieee">IEEE</span>
</div>
<div class="pub-authors"><strong>Phani, S.</strong>, et al.</div>
<a href="https://doi.org/10.1109/ACCESS.2024.3454382" target="_blank" class="doi-link">10.1109/ACCESS.2024.3454382 <i class="fa-solid fa-arrow-up-right-from-square"></i></a>
</div>
</div>
<div class="pub-card">
<div class="pub-num">[4]</div>
<div class="pub-body">
<div class="pub-title">MMSML: Multilingual Multimodal Summarization for Multimodal Input</div>
<div class="pub-venue">
<span class="pub-journal">Intl. Conference on Data Science and Applications · Springer</span>
<span class="pub-year-badge">2024</span>
<span class="pub-type-badge conf">Conference</span>
</div>
<div class="pub-authors"><strong>Phani, S.</strong>, et al.</div>
<a href="https://doi.org/10.1007/978-981-96-2724-0_5" target="_blank" class="doi-link">10.1007/978-981-96-2724-0_5 <i class="fa-solid fa-arrow-up-right-from-square"></i></a>
</div>
</div>
<div class="pub-card">
<div class="pub-num">[5]</div>
<div class="pub-body">
<div class="pub-title">Recognition for Attendance System Using Reinforcement Learning</div>
<div class="pub-venue">
<span class="pub-journal">FICTA · Springer</span>
<span class="pub-year-badge">2023</span>
<span class="pub-type-badge conf">Conference</span>
</div>
<div class="pub-authors"><strong>Phani, S.</strong>, et al.</div>
<a href="https://doi.org/10.1007/978-981-99-6702-5_15" target="_blank" class="doi-link">10.1007/978-981-99-6702-5_15 <i class="fa-solid fa-arrow-up-right-from-square"></i></a>