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‎index.html‎

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<!doctype html>
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<html lang="en">
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<head>
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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1" />
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<title>Vikram Oberai — Statistics & Machine Learning</title>
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<meta
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name="description"
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content="Vikram Oberai — CMU Freshman studying Statistics + Machine Learning. Projects: ML systems, data analysis, and quantitative tools."
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/>
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<link rel="preconnect" href="https://fonts.googleapis.com" />
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<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
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<link
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href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600;700;800&display=swap"
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rel="stylesheet"
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/>
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<link rel="stylesheet" href="styles.css" />
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</head>
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<body>
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<header class="site-header">
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<div class="container header-inner">
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<a class="brand" href="#hero">Vikram Oberai</a>
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<nav class="nav" id="nav">
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<ul>
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<li><a href="#about">About</a></li>
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<li><a href="#education">Education</a></li>
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<li><a href="#projects">Projects</a></li>
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<li><a href="#contact">Contact</a></li>
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</ul>
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</nav>
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<button id="nav-toggle" aria-label="Toggle navigation">☰</button>
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</div>
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</header>
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<main>
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<section id="hero" class="hero">
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<div class="container hero-grid">
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<div class="hero-copy">
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<h1>Vikram Oberai</h1>
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<p class="lead">
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Freshman at Carnegie Mellon — Statistics + Machine Learning.
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Building production-minded ML and data tools that turn ambiguity
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into measurable impact.
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</p>
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<div class="hero-cta">
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<a class="btn primary" href="#projects">Selected Projects</a>
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<a class="btn ghost" href="#contact">Contact</a>
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</div>
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</div>
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<div class="hero-aside" aria-hidden="true">
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<div class="profile-card">
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<div class="metrics">
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<div>
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<span class="num">3+</span
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><span class="label">Relevant Projects</span>
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</div>
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<div>
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<span class="num">CMU</span
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><span class="label">Statistics & ML</span>
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</div>
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</div>
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</div>
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</div>
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</div>
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</section>
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<section id="about" class="section">
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<div class="container">
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<h2>About</h2>
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<p>
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I'm a problem-solver focused on rigorous data methods and practical
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ML engineering. I prioritize clear assumptions, reproducible
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experiments, and shipping interpretable models. Looking for
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internships where I can apply statistical intuition to real-world
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systems.
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</p>
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</div>
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</section>
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<section id="education" class="section muted">
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<div class="container">
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<h2>Education</h2>
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<div class="education">
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<div>
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<h3>Carnegie Mellon University</h3>
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<p>B.S. Statistics & Machine Learning — Expected 2028</p>
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<p class="edu-notes">
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Selected coursework: Probability, Linear Algebra, Intro ML, Data
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Structures. Activities: CS/ML study groups, research assist.
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</p>
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</div>
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</div>
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</div>
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</section>
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<section id="projects" class="section">
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<div class="container">
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<h2>Selected Projects</h2>
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<div class="projects-grid" id="projects-grid">
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<article class="project-card" data-id="1">
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<div class="card-head">
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<h3>AAPL Earnings NLP Sentiment</h3>
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<p class="muted-sm">
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NLP · Sentiment Analysis · Time-series signal
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</p>
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</div>
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<p class="card-blurb">
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Built an NLP pipeline to extract sentiment from earnings calls
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and evaluate market reaction — combined text embeddings with
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event-aligned returns for causal signal detection.
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</p>
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<div class="card-actions">
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<button class="expand">Details</button>
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<a
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class="link"
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href="https://github.com/CodingGenius14/AAPL_NLP_Earning_Call_Sentiment"
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target="_blank"
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rel="noopener"
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>Repo</a
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>
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</div>
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<div class="card-details">
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<ul>
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<li>
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Preprocessing + speaker segmentation, transformer embeddings
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(fine-tuned)
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</li>
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<li>
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Event study aligning sentiment windows with intraday returns
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</li>
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<li>Backtested hypotheses with careful leakage controls</li>
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</ul>
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</div>
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</article>
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<article class="project-card" data-id="2">
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<div class="card-head">
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<h3>Linear Regression Project — ML Practice</h3>
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<p class="muted-sm">
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Statistics · Model Diagnostics · Explainability
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</p>
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</div>
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<p class="card-blurb">
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Rigorous approach to model selection, residual analysis, and
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feature engineering with reproducible notebooks and automated
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evaluation.
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</p>
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<div class="card-actions">
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<button class="expand">Details</button>
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<a
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class="link"
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href="https://github.com/CodingGenius14/Machine-Learning-Practice/tree/main/linear_regression/project"
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target="_blank"
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rel="noopener"
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>Repo</a
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>
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</div>
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<div class="card-details">
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<ul>
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<li>
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Cross-validation, information criteria, and
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heteroskedasticity-aware inference
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</li>
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<li>
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Automated pipeline for feature transforms and ablation
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studies
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</li>
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</ul>
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</div>
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</article>
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<article class="project-card" data-id="3">
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<div class="card-head">
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<h3>Quant Toolkit (prototype)</h3>
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<p class="muted-sm">Data Engineering · Automation</p>
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</div>
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<p class="card-blurb">
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Small, production-minded utilities for reproducible backtests
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and clean data ingestion used in prototyping ML trading signals.
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</p>
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<div class="card-actions">
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<button class="expand">Details</button>
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<a class="link" href="#" target="_blank" rel="noopener">More</a>
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</div>
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<div class="card-details">
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<ul>
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<li>
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Robust CSV ingestion, schema validation, and lightweight
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caching
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</li>
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<li>
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Focus on clear interfaces for downstream model training
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</li>
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</ul>
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</div>
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</article>
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</div>
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</div>
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</section>
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<section id="contact" class="section muted">
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<div class="container contact-grid">
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<div>
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<h2>Contact</h2>
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<p>
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For internship inquiries or collaborative research:
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<a href="mailto:vikram@example.com">vikram@example.com</a>
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</p>
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<p class="muted-sm">
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LinkedIn · GitHub links available in resume and project pages.
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</p>
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</div>
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<div class="mini-cta">
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<a class="btn primary" href="Updated_Resume.pdf" target="_blank"
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>Download Resume</a
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>
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</div>
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</div>
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</section>
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</main>
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<footer class="site-footer">
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<div class="container">
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<small
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>© Vikram Oberai — Carnegie Mellon University • Built for
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recruiters</small
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>
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</div>
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</footer>
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<script src="scripts.js" defer></script>
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</body>
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</html>

‎scripts.js‎

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// Smooth scroll for internal links, mobile nav toggle, project card interactions
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document.addEventListener("DOMContentLoaded", function () {
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// Smooth scroll for same-page anchors
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document.querySelectorAll('a[href^="#"]').forEach((a) => {
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a.addEventListener("click", function (e) {
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const href = this.getAttribute("href");
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if (href.length > 1) {
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const el = document.querySelector(href);
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if (el) {
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e.preventDefault();
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el.scrollIntoView({ behavior: "smooth", block: "start" });
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history.replaceState(null, "", href);
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}
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}
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});
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});
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// Mobile nav toggle
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const navToggle = document.getElementById("nav-toggle");
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const nav = document.getElementById("nav");
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navToggle &&
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navToggle.addEventListener("click", () => {
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nav.classList.toggle("open");
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});
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// Project card expand/collapse
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document.querySelectorAll(".project-card").forEach((card) => {
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const btn = card.querySelector(".expand");
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const details = card.querySelector(".card-details");
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btn &&
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btn.addEventListener("click", () => {
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const open = details.classList.toggle("open");
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btn.textContent = open ? "Hide" : "Details";
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});
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});
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// Gentle reveal on scroll for sections
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const observer = new IntersectionObserver(
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(entries) => {
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entries.forEach((entry) => {
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if (entry.isIntersecting) {
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entry.target.classList.add("reveal");
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observer.unobserve(entry.target);
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}
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});
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},
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{ threshold: 0.12 },
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);
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document
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.querySelectorAll(".section, .project-card, .hero-copy")
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.forEach((el) => observer.observe(el));
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});

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