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<!DOCTYPE html>
<html lang="en" class="light">
<head>
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<title>Alex Khoo Shien How | Quant Finance & AI/ML</title>
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<body>
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<!-- ============ HERO ============ -->
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<div class="hero-badge">Quant Finance • AI/ML • Portfolio Systems</div>
<img src="assets/profile.jpg" alt="Alex Khoo Shien How" class="hero-photo">
<h1 class="hero-name">Alex Khoo<br><span class="hero-name-light">Shien How</span></h1>
<p class="hero-tagline">Building regime-aware, risk-controlled, explainable portfolio & trading systems.</p>
<div class="hero-ctas">
<a href="#projects" class="btn btn-primary">View Projects</a>
</div>
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<a href="https://www.linkedin.com/in/alex-khoo-shien-how/" target="_blank" rel="noopener noreferrer" aria-label="LinkedIn">
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</a>
<a href="mailto:AL0001OW@e.ntu.edu.sg" aria-label="Email">
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</div>
<div class="hero-now">
<span class="now-dot"></span>
Currently working on <strong>Self-Evolving Fuzzy Ensemble (eFE) for Algorithmic Trading</strong> (FYP, 2026).
</div>
</div>
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</section>
<!-- ============ ABOUT ============ -->
<section class="section reveal-section" id="about">
<div class="container">
<h2 class="section-title">About</h2>
<div class="about-content">
<p>
I am a final-year student at <strong>Nanyang Technological University (NTU)</strong> pursuing a double degree in
<strong>Artificial Intelligence</strong> and <strong>Business Analytics</strong> -
Recognized by the <strong>ASEAN Undergraduate Scholarship</strong>, <strong> Canada ASEAN-SEED Scholarship </strong> and <strong> Tunku Abdul Rahman University College Merit Scholarship </strong>.
</p>
<p>
My work sits at the intersection of quantitative research and applied AI — building data-driven systems
for portfolio construction, algorithmic trading, risk management, and enterprise ML pipelines. I bring a
product-engineering mindset to research: every model should be deployable, explainable, and robust under
regime change.
</p>
</div>
</div>
</section>
<!-- ============ EXPERIENCE ============ -->
<section class="section reveal-section" id="experience">
<div class="container">
<h2 class="section-title">Experience</h2>
<div class="timeline">
<div class="timeline-item">
<div class="timeline-marker"></div>
<div class="timeline-content">
<div class="timeline-header">
<h3>Management Consultant Intern</h3>
<span class="timeline-company">Phillip General Insurance (Cambodia) / Phillip Capital</span>
<span class="timeline-date">Dec 2025</span>
</div>
<ul class="timeline-bullets">
<li>Spearheaded a 5-person consulting team as Team Lead, orchestrating cross-functional collaboration with C-suite stakeholders to align operational feasibility, IT infrastructure capabilities, and strategic partnership priorities on direct-digital sales strategy formulation.</li>
<li>Developed probabilistic lead-scoring and churn models using XGBoost with sigmoid and isotonic calibration to optimize customer acquisition & retention strategies, projected to generate $40M in incremental profit and 15% cost efficiency gains.</li>
<li>Engineered end-to-end machine learning pipeline including feature construction, cross-validation, and calibrated probability outputs for decision threshold optimization.</li>
<li>Applied F1-optimized decision threshold selection via precision-recall curve analysis on calibrated probability outputs to maximize the precision-recall trade-off, enabling quantitative segmentation of leads and at-risk policyholders into actionable risk-ranked intervention tiers.</li>
<li>Designed a Next.js + FastAPI full-stack real-time analytics dashboard with model retraining triggers.</li>
</ul>
<a href="https://drive.google.com/drive/folders/1qdQiajiDRyFaB_UDhujnxLMQxwaTMo8V?usp=share_link" target="_blank" rel="noopener noreferrer" class="btn btn-outline" style="margin-top:1rem;display:inline-flex;align-items:center;">
<svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor" style="margin-right:8px"><path d="M6.28 3L2 10.5l4.28 7.5h11.44L22 10.5 17.72 3H6.28zm1.04 1.5h9.36l3.64 6.38-3.64 6.12H7.32L3.68 10.88 7.32 4.5zM8.5 9.75L6 14h12l-2.5-4.25H8.5zm1.5 1.5h4l1.5 2.5H8.5l1.5-2.5z"/></svg>
View Selected Presentation Slides
</a>
</div>
</div>
<div class="timeline-item">
<div class="timeline-marker"></div>
<div class="timeline-content">
<div class="timeline-header">
<h3>Business Analyst Intern</h3>
<span class="timeline-company">Biobot Surgical</span>
<span class="timeline-date">May – Jul 2024</span>
</div>
<ul class="timeline-bullets">
<li>Conducted regional revenue segmentation for APAC and EMEA using 63 customer profiles, supporting pricing strategy review through analysis on client purchasing patterns.</li>
<li>Redesigned product evaluation manual in collaboration with leadership improving consistency in adoption assessment across client sites</li>
</ul>
</div>
</div>
</div>
</div>
</section>
<!-- ============ OVERSEAS EXCHANGE ============ -->
<section class="section reveal-section" id="exchange">
<div class="container">
<h2 class="section-title">Overseas Exchange</h2>
<div class="timeline">
<div class="timeline-item">
<div class="timeline-marker"></div>
<div class="timeline-content">
<div class="timeline-header">
<h3>Singapore Management University — AY25/26 SUSEP</h3>
<span class="timeline-company">Local Exchange</span>
<span class="timeline-date">Aug – Dec 2025</span>
</div>
<ul class="timeline-bullets">
<li>Nominated as one of 25 students university-wide to participate in an immersive local exchange programme.</li>
<li>Courses: Entrepreneurial Finance (Venture Capital), Management Communication, IT Solution Architecture.</li>
</ul>
</div>
</div>
<div class="timeline-item">
<div class="timeline-marker"></div>
<div class="timeline-content">
<div class="timeline-header">
<h3>Fudan University, Shanghai, China — AY24/25 Summer Exchange</h3>
<span class="timeline-company">Summer Exchange</span>
<span class="timeline-date">Jun – Jul 2025</span>
</div>
<ul class="timeline-bullets">
<li>Courses: Chinese Financial Markets (A-), International Investment Law (A), Artificial Intelligence in Fintech (A).</li>
</ul>
</div>
</div>
<div class="timeline-item">
<div class="timeline-marker"></div>
<div class="timeline-content">
<div class="timeline-header">
<h3>Schulich Business School (York University), Canada — AY24/25 Semester 2 Exchange</h3>
<span class="timeline-company">Semester Exchange</span>
<span class="timeline-date">Jan – Mar 2025</span>
</div>
<ul class="timeline-bullets">
<li>Awarded Canada ASEAN-SEED Scholarship by Global Affairs Canada, Government of Canada.</li>
<li>GEM Ambassador for NTU-Schulich Business School.</li>
<li>Courses: AI in Business, Python for Finance, Strategic Management, Managerial Accounting, Prescriptive Analytics.</li>
</ul>
</div>
</div>
</div>
</div>
</section>
<!-- ============ PROJECTS ============ -->
<section class="section reveal-section" id="projects">
<div class="container">
<h2 class="section-title">Selected Projects</h2>
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<button class="filter-btn active" data-filter="all" role="tab" aria-selected="true">All</button>
<button class="filter-btn" data-filter="quant" role="tab" aria-selected="false">Quant</button>
<button class="filter-btn" data-filter="ml" role="tab" aria-selected="false">ML</button>
<button class="filter-btn" data-filter="llm" role="tab" aria-selected="false">LLM</button>
<button class="filter-btn" data-filter="systems" role="tab" aria-selected="false">Databases</button>
</div>
<div class="projects-grid">
<article class="project-card" data-category="ml">
<div class="project-card-inner">
<div class="project-tags">
<span class="tag tag-ml">Deep Learning</span>
<span class="tag tag-ml">LLM</span>
<span class="tag tag-quant">Quant</span>
</div>
<h3 class="project-title">Quant Learning OS – Responsible AI in Education Hackathon</h3>
<p class="project-desc">
Multi-signal adaptive learning platform for quantitative finance preparation, fusing quiz performance,
live paper trading behavioural analytics via Alpaca (fat-finger risk, revenge-trade detection,
stop-loss discipline), AI mock interviews, and resume evidence into a single composite readiness score.
Engineered an OpenAI function-calling agentic study planner with a 5-tool structured workflow and
append-only NDJSON audit logging, a fine-tuned DistilBERT query router (3-class, 0.70 confidence
threshold), a full RAG pipeline (FAISS + HuggingFace all-MiniLM-L6-v2) with LLM-as-a-Judge claim
evaluation, and firm-specific interview scoring across 6 quant firms (Jane Street, Citadel, HRT,
Two Sigma, D.E. Shaw, SIG) with distinct 4-dimension weighted rubrics per firm.
</p>
<div class="project-outcomes">
<span class="outcome">Multi-signal Readiness Score</span>
<span class="outcome">Agentic Study Planner</span>
<span class="outcome">DistilBERT Query Router</span>
<span class="outcome">RAG + LLM-as-a-Judge</span>
<span class="outcome">6 Quant Firm Rubrics</span>
</div>
</div>
</article>
<article class="project-card" data-category="quant">
<div class="project-card-inner">
<div class="project-tags">
<span class="tag tag-quant">Quant</span>
<span class="tag tag-ml">ML</span>
<span class="tag tag-featured">FYP 2026</span>
</div>
<h3 class="project-title">Self-Evolving Fuzzy Ensemble (eFE) for Algorithmic Trading</h3>
<p class="project-desc">
A novel framework combining regime detection, deep temporal models (GRU/TCN + attention), concept drift detection,
and RL-based fuzzy rule optimization. Backtested across multiple asset classes against benchmark indices.
</p>
<div class="project-outcomes">
<span class="outcome">Sharpe Ratio Optimization</span>
<span class="outcome">Max Drawdown Control</span>
<span class="outcome">Drift-Adaptive</span>
</div>
</div>
</article>
<article class="project-card" data-category="quant">
<div class="project-card-inner">
<div class="project-tags">
<span class="tag tag-quant">Quant</span>
</div>
<h3 class="project-title">Chinese Financial Markets</h3>
<p class="project-desc">
Analyzed Turn-of-the-Month return anomalies and Amihud illiquidity across global indices and HK-listed equities; investigated IPO spillover effects on peer-company abnormal returns and volume across China A-share, Hong Kong, and US markets using OLS market-model event studies.
</p>
<div class="project-outcomes">
<span class="outcome">Event Study</span>
<span class="outcome">Market Microstructure</span>
<span class="outcome">Cross-Market Analysis</span>
</div>
<a href="https://github.com/alexksh2/Chinese_Financial_Markets_ECON170039" target="_blank" rel="noopener noreferrer" class="btn btn-outline project-btn">
<svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor" style="margin-right:8px"><path d="M12 0C5.37 0 0 5.37 0 12c0 5.3 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61-.546-1.385-1.335-1.755-1.335-1.755-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 21.795 24 17.295 24 12c0-6.63-5.37-12-12-12z"/></svg>
View Repository
</a>
</div>
</article>
<article class="project-card" data-category="quant">
<div class="project-card-inner">
<div class="project-tags">
<span class="tag tag-quant">Quant</span>
<span class="tag tag-featured">Schulich FINE3300</span>
</div>
<h3 class="project-title">Asian Option Pricing via Monte Carlo Simulation</h3>
<p class="project-desc">
Built a Monte Carlo pricing engine using GBM to price Asian options across fixed/floating strikes, arithmetic/geometric averaging, and call/put types for 5 contracts; conducted convergence analysis to determine optimal simulation count (N=8,500) balancing accuracy and cost; analyzed delta hedging via finite differences and sensitivity to moneyness and volatility.
</p>
<div class="project-outcomes">
<span class="outcome">Monte Carlo Simulation</span>
<span class="outcome">Option Pricing</span>
<span class="outcome">Delta Hedging</span>
</div>
<a href="https://github.com/alexksh2/Option_Pricing_FINE3300" target="_blank" rel="noopener noreferrer" class="btn btn-outline project-btn">
<svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor" style="margin-right:8px"><path d="M12 0C5.37 0 0 5.37 0 12c0 5.3 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61-.546-1.385-1.335-1.755-1.335-1.755-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 21.795 24 17.295 24 12c0-6.63-5.37-12-12-12z"/></svg>
View Repository
</a>
</div>
</article>
<article class="project-card" data-category="quant">
<div class="project-card-inner">
<div class="project-tags">
<span class="tag tag-quant">Quant</span>
</div>
<h3 class="project-title">Smart Beta Factor Diagnostic Framework</h3>
<p class="project-desc">
Engineered a 9-factor smart beta diagnostic framework in Python, running full-sample Newey-West HAC regressions and rolling 36-month stability checks across 11 ETFs to decompose factor exposures (MKT, SMB, HML, MOM, BAB, QMJ, RMW, Carry, Illiquidity) and surface alpha, IR, and PASS/WARN/FAIL verdicts for portfolio due diligence. Constructed live alternative factor series from scratch — G10 FX carry (rank-weighted long-short via FRED rates + yfinance FX) and Amihud (2002) IML (30-stock panel, prior-month sort to eliminate look-ahead bias).
</p>
<div class="project-outcomes">
<span class="outcome">Newey-West HAC</span>
<span class="outcome">Rolling 36M Stability</span>
<span class="outcome">9 Factors • 11 ETFs</span>
<span class="outcome">PASS/WARN/FAIL Scoring</span>
</div>
<a href="https://github.com/alexksh2/Smart-Beta-Strategies" target="_blank" rel="noopener noreferrer" class="btn btn-outline project-btn">
<svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor" style="margin-right:8px"><path d="M12 0C5.37 0 0 5.37 0 12c0 5.3 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61-.546-1.385-1.335-1.755-1.335-1.755-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 21.795 24 17.295 24 12c0-6.63-5.37-12-12-12z"/></svg>
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</a>
</div>
</article>
<article class="project-card" data-category="ml">
<div class="project-card-inner">
<div class="project-tags">
<span class="tag tag-ml">ML</span>
<span class="tag tag-systems">Computer Vision</span>
</div>
<h3 class="project-title">Kaggle Cassava Leaf Disease Classification</h3>
<p class="project-desc">
Hybrid ensemble approach combining ResNeXt, ViT, EfficientNet, and CropNet architectures with stacking
and voting strategies for robust plant disease detection.
</p>
<div class="project-outcomes">
<span class="outcome">90.1% Accuracy</span>
<span class="outcome">Ensemble Stacking</span>
</div>
<a href="https://github.com/alexksh2/Cassava_Leaf_Disease_Classification_SC4000_Machine_Learning" target="_blank" rel="noopener noreferrer" class="btn btn-outline project-btn">
<svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor" style="margin-right:8px"><path d="M12 0C5.37 0 0 5.37 0 12c0 5.3 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61-.546-1.385-1.335-1.755-1.335-1.755-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 21.795 24 17.295 24 12c0-6.63-5.37-12-12-12z"/></svg>
View Repository
</a>
</div>
</article>
<article class="project-card" data-category="ml">
<div class="project-card-inner">
<div class="project-tags">
<span class="tag tag-ml">NLP</span>
<span class="tag tag-ml">ML</span>
</div>
<h3 class="project-title">NLP Sentiment Classification</h3>
<p class="project-desc">
Developed and fine-tuned a BiLSTM model for sentiment classification using Optuna hyperparameter optimisation, incorporating multi-head self-attention and min-max pooling frameworks to achieve 79% accuracy.
</p>
<div class="project-outcomes">
<span class="outcome">79% Accuracy</span>
<span class="outcome">BiLSTM + Optuna</span>
<span class="outcome">Multi-head Self-Attention</span>
<span class="outcome">Min-Max Pooling</span>
</div>
</div>
</article>
<article class="project-card" data-category="ml">
<div class="project-card-inner">
<div class="project-tags">
<span class="tag tag-ml">ML</span>
<span class="tag tag-ml">Computer Vision</span>
<span class="tag tag-systems">Robotics</span>
</div>
<h3 class="project-title">Autonomous Maze Robot – YOLOv8 Image Recognition</h3>
<p class="project-desc">
Developed and fine-tuned a YOLOv8 object detection model in Python as part of a robotic system capable of autonomously traversing a maze while detecting images displayed within the arena. Automated image annotation and augmentation pipelines to generate and label 35k+ training images, significantly improving model training workflow efficiency.
</p>
<div class="project-outcomes">
<span class="outcome">85.7% Accuracy</span>
<span class="outcome">35k+ Training Images</span>
<span class="outcome">Real-time Inference</span>
<span class="outcome">Automated Annotation</span>
</div>
</div>
</article>
<article class="project-card" data-category="ml">
<div class="project-card-inner">
<div class="project-tags">
<span class="tag tag-ml">ML</span>
<span class="tag tag-quant">RL</span>
</div>
<h3 class="project-title">Reinforcement Learning Double Q-Learning</h3>
<p class="project-desc">
Developed a Reinforcement Learning agent using Double Q-Learning with Prioritised Experience Replay and Epsilon-Greedy Policy to balance a pole on a cart under constraints, achieving an average reward exceeding 195 within 100 episodes.
</p>
<div class="project-outcomes">
<span class="outcome">Avg Reward >195</span>
<span class="outcome">100 Episodes</span>
<span class="outcome">Prioritised Experience Replay</span>
<span class="outcome">Epsilon-Greedy Policy</span>
</div>
<a href="https://github.com/alexksh2/Reinforcement-Learing-Double-Q" target="_blank" rel="noopener noreferrer" class="btn btn-outline project-btn">
<svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor" style="margin-right:8px"><path d="M12 0C5.37 0 0 5.37 0 12c0 5.3 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61-.546-1.385-1.335-1.755-1.335-1.755-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 21.795 24 17.295 24 12c0-6.63-5.37-12-12-12z"/></svg>
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</a>
</div>
</article>
<article class="project-card" data-category="systems">
<div class="project-card-inner">
<div class="project-tags">
<span class="tag tag-systems">Databases</span>
</div>
<h3 class="project-title">Electric Vehicle Conversion Proposal</h3>
<p class="project-desc">
Conducted an end-to-end feasibility study on transitioning Singapore's entire vehicle population to electric vehicles, benchmarking against the Singapore Green Plan targets. Built Tableau dashboards to visualise adoption trends, infrastructure gaps, and policy alignment. Leveraged SQL and MongoDB to query and analyse EV registration data and charging station geographic distribution across the United States as a comparative case study.
</p>
<div class="project-outcomes">
<span class="outcome">SQL & MongoDB</span>
<span class="outcome">Tableau</span>
<span class="outcome">Feasibility Study</span>
<span class="outcome">Singapore Green Plan</span>
</div>
</div>
</article>
<article class="project-card" data-category="llm">
<div class="project-card-inner">
<div class="project-tags">
<span class="tag tag-systems">Databases</span>
<span class="tag tag-ml">LLM</span>
</div>
<h3 class="project-title">LLM-based SQL Plan Explanation & Cost Comparison</h3>
<p class="project-desc">
Utilised a Llama3-8B-8192 model with text parsing techniques to generate natural language explanations of PostgreSQL query execution plans and perform automated cost comparison between the default SQL Plan and the Alternative Query Plan (AQP).
</p>
<div class="project-outcomes">
<span class="outcome">Llama3-8B-8192</span>
<span class="outcome">PostgreSQL</span>
<span class="outcome">SQL vs AQP Cost Analysis</span>
</div>
<a href="https://github.com/alexksh2/Database-SC3020-What-If-Query-Plan" target="_blank" rel="noopener noreferrer" class="btn btn-outline project-btn">
<svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor" style="margin-right:8px"><path d="M12 0C5.37 0 0 5.37 0 12c0 5.3 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61-.546-1.385-1.335-1.755-1.335-1.755-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 21.795 24 17.295 24 12c0-6.63-5.37-12-12-12z"/></svg>
View Repository
</a>
</div>
</article>
<article class="project-card" data-category="llm">
<div class="project-card-inner">
<div class="project-tags">
<span class="tag tag-ml">LLM</span>
<span class="tag tag-ml">Agentic RAG</span>
</div>
<h3 class="project-title">Agentic RAG Insurance Retention System</h3>
<p class="project-desc">
Built an Agentic RAG-based insurance churn prediction system using LangChain, LangGraph, and Groq (Llama 3.3 70B), integrating FAISS vector search over policy documents and client notes with custom tools for churn scoring, policy guardrail checks, and automated CSV reporting — enabling grounded, auditable retention action plans for at-risk clients.
</p>
<div class="project-outcomes">
<span class="outcome">LangChain & LangGraph</span>
<span class="outcome">Llama 3.3 70B</span>
<span class="outcome">FAISS Vector Search</span>
<span class="outcome">Automated CSV Reporting</span>
</div>
</div>
</article>
<article class="project-card" data-category="llm">
<div class="project-card-inner">
<div class="project-tags">
<span class="tag tag-ml">LLM</span>
<span class="tag tag-ml">RAG</span>
</div>
<h3 class="project-title">Transformer-Based Resume Evaluation & RAG Pipeline</h3>
<p class="project-desc">
Implemented robust multi-format document parsing and ingestion (PDF, DOCX, TXT), transforming unstructured resumes into clean, machine-readable text for downstream transformer-based embedding, retrieval, and LLM analysis. Built a transformer-based RAG system combining Sentence-Transformer semantic retrieval (FAISS) with LLM-constrained JSON extraction to enforce evidence-backed outputs and generate interpretable career-match scores.
</p>
<div class="project-outcomes">
<span class="outcome">Sentence-Transformers</span>
<span class="outcome">FAISS Retrieval</span>
<span class="outcome">RAG Pipeline</span>
<span class="outcome">JSON-constrained Extraction</span>
</div>
<a href="https://github.com/alexksh2/Resume-Evaluation" target="_blank" rel="noopener noreferrer" class="btn btn-outline project-btn">
<svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor" style="margin-right:8px"><path d="M12 0C5.37 0 0 5.37 0 12c0 5.3 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61-.546-1.385-1.335-1.755-1.335-1.755-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 21.795 24 17.295 24 12c0-6.63-5.37-12-12-12z"/></svg>
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</a>
</div>
</article>
<article class="project-card" data-category="ml">
<div class="project-card-inner">
<div class="project-tags">
<span class="tag tag-ml">ML</span>
<span class="tag tag-systems">Healthcare</span>
</div>
<h3 class="project-title">Parkinson Disease Prediction</h3>
<p class="project-desc">
Predictive classification model for early Parkinson's disease diagnosis using biomedical voice measurements and machine learning techniques.
</p>
<div class="project-outcomes">
<span class="outcome">Classification</span>
<span class="outcome">Biomedical Data</span>
</div>
<a href="https://github.com/alexksh2/Parkinson-Disease-Prediction" target="_blank" rel="noopener noreferrer" class="btn btn-outline project-btn">
<svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor" style="margin-right:8px"><path d="M12 0C5.37 0 0 5.37 0 12c0 5.3 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61-.546-1.385-1.335-1.755-1.335-1.755-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 21.795 24 17.295 24 12c0-6.63-5.37-12-12-12z"/></svg>
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</a>
</div>
</article>
<article class="project-card" data-category="ml">
<div class="project-card-inner">
<div class="project-tags">
<span class="tag tag-ml">ML</span>
<span class="tag tag-systems">Healthcare</span>
</div>
<h3 class="project-title">Heart Disease Predictive Diagnosis</h3>
<p class="project-desc">
Machine learning pipeline for predictive diagnosis of heart disease using clinical and demographic patient data with multiple classification algorithms.
</p>
<div class="project-outcomes">
<span class="outcome">Classification</span>
<span class="outcome">Clinical Data</span>
</div>
<a href="https://github.com/alexksh2/Heart-Disease-Predictive-Diagnosis" target="_blank" rel="noopener noreferrer" class="btn btn-outline project-btn">
<svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor" style="margin-right:8px"><path d="M12 0C5.37 0 0 5.37 0 12c0 5.3 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61-.546-1.385-1.335-1.755-1.335-1.755-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 21.795 24 17.295 24 12c0-6.63-5.37-12-12-12z"/></svg>
View Repository
</a>
</div>
</article>
</div>
</div>
</section>
<!-- ============ RESEARCH ============ -->
<section class="section reveal-section" id="research">
<div class="container">
<h2 class="section-title">Research Interests</h2>
<div class="research-grid">
<div class="research-card">
<h3>Quantitative Finance</h3>
<ul>
<li>Market regime detection & classification</li>
<li>Portfolio construction & optimization</li>
<li>Risk overlays & drawdown management</li>
<li>Drift monitoring & model explainability</li>
</ul>
</div>
<div class="research-card">
<h3>AI / Machine Learning</h3>
<ul>
<li>Sequence models & temporal architectures</li>
<li>Probabilistic calibration</li>
<li>Ensemble learning & model stacking</li>
<li>MLOps & production ML systems</li>
</ul>
</div>
</div>
</div>
</section>
<!-- ============ SKILLS ============ -->
<section class="section reveal-section" id="skills">
<div class="container">
<h2 class="section-title">Skills</h2>
<div class="skills-grid">
<div class="skill-group">
<h3>Languages</h3>
<div class="skill-chips">
<span class="chip">Python</span>
<span class="chip">R</span>
<span class="chip">C/C++</span>
<span class="chip">Java</span>
<span class="chip">SQL</span>
<span class="chip">MongoDB</span>
</div>
</div>
<div class="skill-group">
<h3>ML / DL</h3>
<div class="skill-chips">
<span class="chip">PyTorch</span>
<span class="chip">TensorFlow</span>
<span class="chip">NumPy</span>
<span class="chip">Pandas</span>
<span class="chip">scikit-learn</span>
<span class="chip">XGBoost</span>
</div>
</div>
<div class="skill-group">
<h3>Tools & Platforms</h3>
<div class="skill-chips">
<span class="chip">Bloomberg Terminal</span>
<span class="chip">AWS</span>
<span class="chip">Tableau</span>
<span class="chip">Power BI</span>
<span class="chip">CapitalIQ</span>
<span class="chip">Git</span>
</div>
</div>
<div class="skill-group">
<h3>Soft Skills</h3>
<div class="skill-chips">
<span class="chip">Intercultural Communication</span>
</div>
</div>
</div>
</div>
</section>
<!-- ============ CERTIFICATIONS ============ -->
<section class="section reveal-section" id="certifications">
<div class="container">
<h2 class="section-title">Certifications & Activities</h2>
<div class="certs-grid">
<div class="cert-group">
<h3>Certifications</h3>
<ul class="cert-list">
<li>London Institute of Banking and Finance — Level 4 Diploma of Applied Finance <span class="cert-year">2024</span></li>
<li>Bloomberg Market Concepts (BMC) <span class="cert-year">2024</span></li>
<li>BlackRock Sustainable Investing Across Asset Classes <span class="cert-year">2024</span></li>
</ul>
</div>
<div class="cert-group">
<h3>Activities</h3>
<div class="activity-content">
<div class="activity-header">
<p><strong>NTU Investment Banking Club</strong> — Asset Management Analyst</p>
</div>
<ul class="timeline-bullets" style="margin-top:10px;">
<li>Advocated a buy position for eBay by analyzing its strategic positioning for sustainable growth, aligning with key trends and prudent capital management using different valuation methods (DCF, relative valuation).</li>
<li>Pitched a sell position for Nintendo with theses on cyclical nature of earnings, intensified market competition within the gaming industry, and global supply chain disruptions resulting in hardware procurement difficulties.</li>
<li>Advocated a buy position for Ralph Lauren with theses on expanding operating margins via a strategic pivot towards a global direct-to-consumer model and strong brand positioning.</li>
</ul>
</div>
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</section>
<!-- ============ AWARDS ============ -->
<section class="section reveal-section" id="awards">
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<h2 class="section-title">Awards & Achievements</h2>
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<span class="award-badge badge-global">2nd Global</span>
<div class="award-body">
<p class="award-title">Southeast Asian Mathematical Olympiad X (Champions Division)</p>
<p class="award-year">2022</p>
</div>
</div>
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<span class="award-badge badge-champion">Champion</span>
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<p class="award-title">University Science Malaysia Maths Quiz (Team)</p>
<p class="award-year">2017, 2019</p>
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<span class="award-badge badge-gold">Gold Team</span>
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<p class="award-title">Singapore and Asian Schools Math Olympiad</p>
<p class="award-year">2016</p>
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<span class="award-badge badge-silver">Silver & 2nd National</span>
<div class="award-body">
<p class="award-title">Southeast Asian Mathematical Olympiad</p>
<p class="award-year">2021</p>
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<div class="award-item">
<span class="award-badge badge-silver">Silver</span>
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<p class="award-title">Singapore and Asian Schools Math Olympiad</p>
<p class="award-year">2016 – 2019, 2021</p>
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</div>
<div class="award-item">
<span class="award-badge badge-silver">Silver</span>
<div class="award-body">
<p class="award-title">IMONST International Mathematical Olympiad Malaysia</p>
<p class="award-year">2020</p>
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<p>Alex Khoo Shien How</p>
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