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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>Adnan Syed | Distributed Systems & AI/ML Engineer</title>
<meta name="description" content="Saleeq Adnan Syed — Software Engineer, MSCS Boston University Jan 2026. Distributed systems, AI/ML, security engineering. 50,000+ events/min streaming pipelines, IEEE published. Post-OPT EAD, no sponsorship required currently." />
<!-- CHANGED: canonical + full Open Graph + Twitter Card for link-preview quality -->
<link rel="canonical" href="https://adnansyed.me/" />
<meta property="og:type" content="profile" />
<meta property="og:url" content="https://adnansyed.me/" />
<meta property="og:title" content="Adnan Syed | Distributed Systems & AI/ML Engineer" />
<meta property="og:description" content="MSCS Boston University Jan 2026. Streaming pipelines at 50,000+ events/min, Rust benchmark harnesses, MITRE ATT&CK detection tooling. IEEE published." />
<meta property="og:image" content="https://adnansyed.me/avatar.jpg" />
<meta property="profile:first_name" content="Saleeq Adnan" />
<meta property="profile:last_name" content="Syed" />
<meta name="twitter:card" content="summary" />
<meta name="twitter:title" content="Adnan Syed | Distributed Systems & AI/ML Engineer" />
<meta name="twitter:description" content="MSCS Boston University Jan 2026. Streaming pipelines at 50,000+ events/min, Rust benchmark harnesses, MITRE ATT&CK detection tooling. IEEE published." />
<meta name="twitter:image" content="https://adnansyed.me/avatar.jpg" />
<!-- CHANGED: font stack per spec — Bricolage Grotesque (display) / IBM Plex Sans (body) / IBM Plex Mono (data). Non-render-blocking via print-swap trick + display=swap, latin subset. -->
<link rel="preconnect" href="https://fonts.googleapis.com" />
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
<link rel="preload" as="style"
href="https://fonts.googleapis.com/css2?family=Bricolage+Grotesque:opsz,wght@12..96,600;12..96,800&family=IBM+Plex+Mono:wght@400;600&family=IBM+Plex+Sans:wght@400;500&display=swap&subset=latin" />
<link rel="stylesheet"
href="https://fonts.googleapis.com/css2?family=Bricolage+Grotesque:opsz,wght@12..96,600;12..96,800&family=IBM+Plex+Mono:wght@400;600&family=IBM+Plex+Sans:wght@400;500&display=swap&subset=latin"
media="print" onload="this.media='all'" />
<noscript>
<link rel="stylesheet"
href="https://fonts.googleapis.com/css2?family=Bricolage+Grotesque:opsz,wght@12..96,600;12..96,800&family=IBM+Plex+Mono:wght@400;600&family=IBM+Plex+Sans:wght@400;500&display=swap&subset=latin" />
</noscript>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css" />
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"email": "mailto:saleeq1269@gmail.com",
"jobTitle": "Software Engineer",
"description": "Distributed systems, AI/ML, and security engineering. MSCS, Boston University (Jan 2026).",
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"sameAs": [
"https://github.com/CyberCoder-IITM",
"https://www.linkedin.com/in/syedsq/",
"https://ieeexplore.ieee.org/document/10838018"
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<style>
/* ================================================================
CHANGED: full token system rebuilt per spec.
Type: Bricolage / Plex Sans / Plex Mono. Numbers always mono + tabular.
Color: one ink, one surface, one semantic red; primary changes
TEMPERATURE per mode (cyan / amber). Third accents, glows, glass — deleted.
Spacing: 8px scale.
================================================================ */
*, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }
:root {
/* spacing scale (8px base; 4px micro allowed inside components only) */
--s1: 8px; --s2: 16px; --s3: 24px; --s4: 32px; --s5: 48px; --s6: 64px; --s7: 96px; --s8: 128px;
/* shared color */
--ink: #0B0E12;
--surface: #12161C;
--border: #1E242D;
--text: #E6EAEF;
--muted: #98A2B0;
--semantic: #F2495C;
/* CYBER mode default — cold / precision */
--primary: #58C7E0;
--primary-dim: rgba(88, 199, 224, 0.10);
/* type */
--f-display: 'Bricolage Grotesque', sans-serif;
--f-body: 'IBM Plex Sans', sans-serif;
--f-mono: 'IBM Plex Mono', monospace;
--ease: cubic-bezier(0.2, 0.8, 0.2, 1);
}
body.sde-mode {
/* SDE mode — warm / systems */
--primary: #E8A33D;
--primary-dim: rgba(232, 163, 61, 0.10);
}
html { scroll-behavior: smooth; }
body {
background: var(--ink);
color: var(--text);
font-family: var(--f-body);
font-size: 0.9375rem; /* 15px */
line-height: 1.65;
overflow-x: hidden;
-webkit-font-smoothing: antialiased;
}
/* the "world re-tempers" — only color properties transition, never transform */
body, .stat, .metric, .eyebrow, .cs-label, .primary-ink,
.cta-primary, .cta-ghost, .tag, .pill, .card, .cs, .oss,
.social-link, .cert-card, .toggle, .toggle-thumb {
transition: background-color .4s var(--ease), border-color .4s var(--ease), color .4s var(--ease);
}
/* every number is mono + tabular */
.metric, .stat strong, .oss-stat, .cs-result strong { font-variant-numeric: tabular-nums; }
a { color: inherit; }
/* focus visibility (a11y) */
a:focus-visible, button:focus-visible {
outline: 2px solid var(--primary);
outline-offset: 3px;
border-radius: 4px;
}
/* ---------- MODE TOGGLE: sliding thumb ---------- */
.toggle {
position: fixed;
top: var(--s3); right: var(--s3);
z-index: 1000;
display: flex;
background: var(--surface);
border: 1px solid var(--border);
border-radius: 999px;
padding: 4px;
}
.toggle-thumb {
position: absolute;
top: 4px; left: 4px;
width: calc(50% - 4px);
height: calc(100% - 8px);
background: var(--primary-dim);
border: 1px solid var(--primary);
border-radius: 999px;
transition: transform .2s ease-out, background-color .4s var(--ease), border-color .4s var(--ease);
}
body.sde-mode .toggle-thumb { transform: translateX(100%); }
.toggle-btn {
position: relative; z-index: 1;
flex: 1;
padding: 7px 16px;
border: none; background: transparent; cursor: pointer;
font-family: var(--f-mono); font-size: 0.72rem; font-weight: 600;
letter-spacing: 0.08em; text-transform: uppercase;
color: var(--muted);
transition: color .25s var(--ease);
white-space: nowrap;
}
.toggle-btn[aria-pressed="true"] { color: var(--text); }
main { position: relative; z-index: 10; }
/* ---------- eyebrow / section furniture ---------- */
.eyebrow {
font-family: var(--f-mono); font-size: 0.75rem; font-weight: 400;
letter-spacing: 0.08em; text-transform: uppercase;
color: var(--primary);
margin-bottom: var(--s1);
}
section {
max-width: 1040px; margin: 0 auto;
padding: var(--s7) var(--s3);
}
.section-title {
font-family: var(--f-display); font-weight: 600;
font-size: clamp(1.625rem, 3.5vw, 2.5rem);
line-height: 1.1;
margin-bottom: var(--s5);
}
/* ---------- HERO ---------- */
.hero {
min-height: 92vh;
display: flex; flex-direction: column; justify-content: center;
max-width: 1040px; margin: 0 auto;
padding: var(--s7) var(--s3) var(--s6);
}
.hero-photo {
width: 68px; height: 68px; border-radius: 50%;
object-fit: cover; margin-bottom: var(--s4);
border: 1px solid var(--border);
}
.hero-caption {
font-family: var(--f-mono); font-size: 0.75rem;
letter-spacing: 0.04em; color: var(--muted);
margin-bottom: var(--s2);
}
.hero-name {
font-family: var(--f-display); font-weight: 800;
font-size: clamp(2.75rem, 7vw, 5.5rem);
line-height: 0.95; letter-spacing: -0.02em;
margin-bottom: var(--s4);
}
.hero-claim {
max-width: 46ch;
font-size: 1.0625rem; line-height: 1.55;
color: var(--text);
margin-bottom: var(--s5);
}
.cta-row { display: flex; gap: var(--s2); flex-wrap: wrap; margin-bottom: var(--s6); }
.cta {
display: inline-flex; align-items: center; gap: var(--s1);
padding: 12px 22px; border-radius: 8px;
font-family: var(--f-mono); font-size: 0.8rem; font-weight: 600;
letter-spacing: 0.02em; text-decoration: none;
border: 1px solid var(--border);
transition: border-color .25s var(--ease), background-color .25s var(--ease), color .25s var(--ease), transform .15s var(--ease);
}
.cta-primary { background: var(--primary); color: var(--ink); border-color: var(--primary); }
.cta-primary:hover { transform: translateY(-2px); }
.cta-ghost:hover { border-color: var(--primary); color: var(--primary); transform: translateY(-2px); }
/* static 4-stat strip (no ticker) */
.stat-strip {
display: flex; flex-wrap: wrap; gap: var(--s5);
padding-top: var(--s4);
border-top: 1px solid var(--border);
}
.stat { display: flex; flex-direction: column; gap: 4px; }
.stat strong {
font-family: var(--f-mono); font-weight: 600; font-size: 1.5rem;
color: var(--primary); line-height: 1;
}
.stat span {
font-family: var(--f-mono); font-size: 0.72rem;
letter-spacing: 0.04em; color: var(--muted); text-transform: uppercase;
}
/* ---------- mode-swap containers ---------- */
.swap > .cyber-only { display: block; }
.swap > .sde-only { display: none; }
body.sde-mode .swap > .cyber-only { display: none; }
body.sde-mode .swap > .sde-only { display: block; }
/* crossfade applied by JS on switch */
.swapping-out { opacity: 0; transform: translateY(-4px); transition: opacity .15s var(--ease), transform .15s var(--ease); }
.swapping-in { animation: swapIn .2s var(--ease) 0.1s both; }
@keyframes swapIn { from { opacity: 0; transform: translateY(4px); } to { opacity: 1; transform: translateY(0); } }
/* ---------- CASE STUDIES ---------- */
.cs {
border: 1px solid var(--border);
border-radius: 14px;
background: var(--surface);
padding: var(--s5);
margin-bottom: var(--s3);
}
.cs-head {
display: flex; align-items: baseline; justify-content: space-between;
gap: var(--s2); flex-wrap: wrap;
margin-bottom: var(--s4);
}
.cs-title { font-family: var(--f-display); font-weight: 600; font-size: 1.375rem; }
.cs-stack { font-family: var(--f-mono); font-size: 0.72rem; color: var(--muted); letter-spacing: 0.02em; }
.cs-row { margin-bottom: var(--s3); }
.cs-row:last-of-type { margin-bottom: 0; }
.cs-label {
font-family: var(--f-mono); font-size: 0.7rem; font-weight: 600;
letter-spacing: 0.08em; text-transform: uppercase;
color: var(--primary); display: block; margin-bottom: 4px;
}
.cs-row p { color: var(--text); max-width: 74ch; }
.cs-result { border-left: 2px solid var(--primary); padding-left: var(--s2); }
.cs-result strong { color: var(--primary); }
/* ---------- OPEN SOURCE credibility block ---------- */
.oss {
border: 1px solid var(--border);
border-radius: 14px;
background: var(--surface);
padding: var(--s5);
display: flex; flex-direction: column; gap: var(--s2);
}
.oss-status {
display: inline-flex; align-items: center; gap: var(--s1);
font-family: var(--f-mono); font-size: 0.72rem; font-weight: 600;
letter-spacing: 0.04em; text-transform: uppercase;
color: var(--semantic);
}
.oss-dot { width: 8px; height: 8px; border-radius: 50%; background: var(--semantic); }
.oss-title { font-family: var(--f-display); font-weight: 600; font-size: 1.25rem; }
.oss p { color: var(--text); max-width: 74ch; }
.oss-meta { font-family: var(--f-mono); font-size: 0.75rem; color: var(--muted); }
.oss-link {
align-self: flex-start;
display: inline-flex; align-items: center; gap: var(--s1);
font-family: var(--f-mono); font-size: 0.75rem; font-weight: 600;
letter-spacing: 0.02em; text-decoration: none;
border: 1px solid var(--border); padding: 9px 16px; border-radius: 8px;
transition: border-color .25s var(--ease), color .25s var(--ease);
}
.oss-link:hover { border-color: var(--primary); color: var(--primary); }
/* ---------- compact project grid (hover reveals Tradeoff) ---------- */
.grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(280px, 1fr)); gap: var(--s3); }
.card {
border: 1px solid var(--border); border-radius: 12px;
background: var(--surface); padding: var(--s3);
display: flex; flex-direction: column;
transition: border-color .25s var(--ease), transform .2s var(--ease);
}
.card:hover { border-color: var(--primary); transform: translateY(-2px); }
.card-title { font-family: var(--f-display); font-weight: 600; font-size: 1.125rem; margin-bottom: var(--s1); }
.card-desc { font-size: 0.875rem; color: var(--muted); flex-grow: 1; margin-bottom: var(--s2); }
.card-tradeoff {
max-height: 0; overflow: hidden; opacity: 0;
transition: max-height .3s var(--ease), opacity .3s var(--ease), margin .3s var(--ease);
font-size: 0.8125rem; color: var(--text);
}
.card:hover .card-tradeoff, .card:focus-within .card-tradeoff {
max-height: 120px; opacity: 1; margin-bottom: var(--s2);
}
.card-tradeoff b { color: var(--primary); font-weight: 600; }
.tags { display: flex; flex-wrap: wrap; gap: 6px; margin-bottom: var(--s2); }
.tag {
font-family: var(--f-mono); font-size: 0.7rem;
padding: 3px 9px; border-radius: 4px;
background: var(--primary-dim); color: var(--primary);
border: 1px solid var(--border);
}
.card-link {
align-self: flex-start;
font-family: var(--f-mono); font-size: 0.72rem; font-weight: 600;
letter-spacing: 0.02em; text-decoration: none; color: var(--muted);
transition: color .2s var(--ease), transform .15s var(--ease);
}
.card-link:hover { color: var(--primary); transform: translateX(3px); }
/* ---------- stack ---------- */
.stack-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(240px, 1fr)); gap: var(--s3); }
.stack-block { border: 1px solid var(--border); border-radius: 12px; background: var(--surface); padding: var(--s3); }
.stack-block h3 { font-family: var(--f-display); font-weight: 600; font-size: 1rem; margin-bottom: var(--s2); }
.pills { display: flex; flex-wrap: wrap; gap: 6px; }
.pill {
font-family: var(--f-mono); font-size: 0.72rem;
padding: 4px 10px; border-radius: 999px;
background: var(--primary-dim); color: var(--primary); border: 1px solid var(--border);
}
/* ---------- credentials ---------- */
.certs { display: grid; grid-template-columns: repeat(auto-fit, minmax(240px, 1fr)); gap: var(--s2); }
.cert-card {
border: 1px solid var(--border); border-radius: 10px; background: var(--surface);
padding: var(--s3); display: flex; gap: var(--s2); align-items: flex-start;
transition: border-color .2s var(--ease);
}
.cert-card:hover { border-color: var(--primary); }
.cert-card.featured { border-color: var(--primary); background: var(--primary-dim); }
.cert-ico { color: var(--primary); font-size: 1rem; margin-top: 2px; }
.cert-issuer { font-family: var(--f-mono); font-size: 0.68rem; letter-spacing: 0.06em; text-transform: uppercase; color: var(--muted); margin-bottom: 3px; }
.cert-name { font-size: 0.875rem; font-weight: 500; line-height: 1.4; }
.cert-tag { font-family: var(--f-mono); font-size: 0.62rem; letter-spacing: 0.04em; text-transform: uppercase; color: var(--semantic); display: inline-block; margin-top: 6px; }
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<img class="hero-photo" src="avatar.jpg" alt="Portrait of Saleeq Adnan Syed"
onerror="this.style.display='none'" />
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<p class="hero-caption cyber-only">Security engineering · detection pipelines · BU MSCS Jan 2026</p>
<p class="hero-caption sde-only">Saleeq Adnan Syed — MSCS, Boston University · distributed systems & AI/ML</p>
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<h1 class="hero-name">Adnan Syed</h1>
<div class="swap">
<p class="hero-claim cyber-only">My SOC platform runs statistics first and intelligence second: Z-score detection, per-IP baselines, threat-feed correlation, then a Gemini agent for ATT&CK attribution — the analyst always gets the final call.</p>
<p class="hero-claim sde-only">Facing a shipping deadline, I put a rolling Z-score inside the stream instead of an ML detector — 50,000+ events/min, 96% anomaly accuracy, zero model drift to manage.</p>
</div>
<div class="cta-row">
<a href="#work" class="cta cta-primary"><i class="fa-solid fa-terminal" aria-hidden="true"></i> View work</a>
<a href="resume.pdf" target="_blank" rel="noopener" class="cta cta-ghost"><i class="fa-solid fa-file-arrow-down" aria-hidden="true"></i> Resume</a>
<a href="mailto:saleeq1269@gmail.com" class="cta cta-ghost"><i class="fa-solid fa-envelope" aria-hidden="true"></i> Contact</a>
</div>
<div class="stat-strip" aria-label="Key metrics">
<div class="stat"><strong>50K+</strong><span>Events / min</span></div>
<div class="stat"><strong>99.9%</strong><span>Pipeline uptime</span></div>
<div class="stat"><strong>30%</strong><span>p99 variance cut</span></div>
<div class="stat"><strong>IEEE</strong><span>Published 2024</span></div>
</div>
</header>
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<p class="eyebrow reveal">Case studies</p>
<h2 class="section-title reveal" style="transition-delay:.09s">Two decisions worth explaining</h2>
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<span class="cs-title">Epidemic Intelligence Platform</span>
<span class="cs-stack">Python · Kafka · Spark · OpenShift · PostgreSQL</span>
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<div class="cs-row"><span class="cs-label">Problem</span><p>Outbreak signals had to surface from a raw health-event stream in real time, not in a nightly batch.</p></div>
<div class="cs-row"><span class="cs-label">Constraint</span><p>A hard delivery deadline: no time to build, train, and validate an ML detection layer, and no appetite for model-drift maintenance after ship.</p></div>
<div class="cs-row"><span class="cs-label">Key decision</span><p>Run a rolling Z-score inside the Spark stream instead of a trained model: O(1) per event, deterministic, and every anomaly carries an explainable numerical reason (deviation from the rolling mean).</p></div>
<div class="cs-row"><span class="cs-label">Tradeoff</span><p>Sacrificed multi-dimensional context and adaptive thresholds; a slow-building signal hovering near the mean can evade a static threshold, and false negatives run higher than a trained classifier would allow.</p></div>
<div class="cs-row cs-result"><span class="cs-label">Verified result</span><p><strong>50,000+ events/min</strong> at <strong>99.9%</strong> uptime on OpenShift Kubernetes, <strong>96%</strong> anomaly accuracy, guarded by an 87-test PyTest suite in CI.</p></div>
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<span class="cs-title">AegisView-SOC</span>
<span class="cs-stack">TypeScript · React · Kafka · MITRE ATT&CK · Gemini 2.5 Flash</span>
</div>
<div class="cs-row"><span class="cs-label">Problem</span><p>SOC analysts need every network event triaged and mapped to a MITRE ATT&CK technique before they look at it.</p></div>
<div class="cs-row"><span class="cs-label">Constraint</span><p>The system supports analyst-in-the-loop investigation, not autonomous blocking — so the design favors high sensitivity and explainability over ML-grade precision.</p></div>
<div class="cs-row"><span class="cs-label">Key decision</span><p>Z-score statistical detection as the first pass, then recover recall in layers: per-IP behavioral baselines make thresholds adaptive per device, external threat feeds (Emerging Threats, Feodo, URLhaus) supply the context the statistics can't see, and a Gemini 2.5 Flash SOC agent re-analyzes each anomaly for a narrative and ATT&CK attribution.</p></div>
<div class="cs-row"><span class="cs-label">Tradeoff</span><p>Pure statistical detection misses low-amplitude, distributed attacks that stay near the mean — accepted, because the analyst plus AI enrichment adjudicates true vs. false positives; for autonomous blocking or a tighter false-positive SLA, this design would be insufficient and would need ML.</p></div>
<div class="cs-row cs-result"><span class="cs-label">Verified result</span><p>Full TypeScript/React pipeline from raw telemetry to a configurable, ATT&CK-attributed alert feed, with multi-signal confidence fusion (Z-score + compliance rules + protocol breakdown + forensic chain integrity).</p></div>
</article>
<article class="cs sde-only reveal" style="transition-delay:.18s">
<div class="cs-head">
<span class="cs-title">HaloSpec</span>
<span class="cs-stack">Rust · Adaptive control · HTTP API</span>
</div>
<div class="cs-row"><span class="cs-label">Problem</span><p>Speculative-decoding throughput has to be measured against latency stability, but p99 latency swings hard under real runtime contention.</p></div>
<div class="cs-row"><span class="cs-label">Constraint</span><p>The harness ran as a single local process against one inference endpoint, with contention simulated by a single CPU busy-loop thread and every other knob pinned (max_tokens, step counts, draft-length bounds). No cluster and no hardware headroom to absorb latency spikes — the controller had to stay stable on its own.</p></div>
<div class="cs-row"><span class="cs-label">Key decision</span><p>Drive draft-length convergence with an EMA-smoothed adaptive controller instead of a fixed threshold, deriving dynamic thresholds from smoothed latency percentiles under non-stationary runtime conditions.</p></div>
<div class="cs-row"><span class="cs-label">Tradeoff</span><p>EMA smoothing lags sudden load spikes by design — accepted, because that damping is exactly what cut p99 variance under contention.</p></div>
<div class="cs-row cs-result"><span class="cs-label">Verified result</span><p><strong>30%</strong> p99 latency variance reduction under CPU load injection, with an SLO-aware scoring model quantifying throughput-vs-stability tradeoffs.</p></div>
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</section>
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<span class="oss-status"><span class="oss-dot" aria-hidden="true"></span> Open — in review</span>
<span class="oss-title">Backstage · community-plugins PR #9703</span>
<p>Opened a pull request to Backstage (CNCF community-plugins) adding README auto-detection for the azure-devops plugin. Currently in review — addressed multiple rounds of maintainer feedback.</p>
<span class="oss-meta">32/32 tests passing · CI green</span>
<a class="oss-link" href="https://github.com/backstage/community-plugins/pull/9703" target="_blank" rel="noopener"><i class="fa-brands fa-github" aria-hidden="true"></i> View pull request</a>
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</section>
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<p class="eyebrow reveal">Also built</p>
<h2 class="section-title reveal" style="transition-delay:.09s">More projects</h2>
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<div class="card-title">HaloSpec</div>
<p class="card-desc">Rust harness for LLM speculative decoding. EMA-adaptive controller cutting p99 latency variance 30% under CPU contention; SLO-aware throughput-vs-stability scoring.</p>
<p class="card-tradeoff"><b>Tradeoff:</b> EMA smoothing lags sudden load spikes by design — accepted, because damping is exactly what cut p99 variance 30% under CPU contention.</p>
<div class="tags"><span class="tag">Rust</span><span class="tag">Adaptive Control</span><span class="tag">HTTP API</span></div>
<a class="card-link" href="https://github.com/CyberCoder-IITM/HaloSpec" target="_blank" rel="noopener">Repository →</a>
</div>
<div class="card reveal" style="transition-delay:.24s">
<div class="card-title">AetherFlow</div>
<p class="card-desc">3D drone-swarm logistics digital twin: A* multi-agent pathfinding and cyber-physical threat modeling via Three.js/WebGL, FastAPI simulation backend with parameterized scenario replay.</p>
<p class="card-tradeoff"><b>Tradeoff:</b> parameterized replay gives up live improvisation — accepted, because deterministic runs are what make swarm-scenario regression testing possible.</p>
<div class="tags"><span class="tag">TypeScript</span><span class="tag">Three.js</span><span class="tag">WebGL</span><span class="tag">FastAPI</span></div>
<a class="card-link" href="https://github.com/CyberCoder-IITM/AetherFlow-2D-3D" target="_blank" rel="noopener">Repository →</a>
</div>
</div>
</div>
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<div class="card-title">PeerDorm</div>
<p class="card-desc">Event-driven MERN platform at 1,000+ concurrent users with sub-100ms Socket.IO messaging; Redux state management cut UI latency 25%. Oral presentation, IEEE Asian Conference 2024.</p>
<p class="card-tradeoff"><b>Tradeoff:</b> a single Redux store adds boilerplate on every feature — accepted for predictable state flow, which cut UI latency 25% at 1K+ concurrent users.</p>
<div class="tags"><span class="tag">React</span><span class="tag">Node.js</span><span class="tag">MongoDB</span><span class="tag">Socket.IO</span><span class="tag">IEEE 2024</span></div>
<a class="card-link" href="https://github.com/CyberCoder-IITM/Peerdorm-Social" target="_blank" rel="noopener">Repository →</a>
</div>
<div class="card reveal" style="transition-delay:.24s">
<div class="card-title">AetherFlow</div>
<p class="card-desc">Real-time 3D simulation with Three.js/WebGL rendering, A* multi-agent pathfinding, and a TypeScript/FastAPI event-driven architecture with parameterized workflow replay.</p>
<p class="card-tradeoff"><b>Tradeoff:</b> parameterized replay gives up live improvisation — accepted, because deterministic runs are what make swarm-scenario regression testing possible.</p>
<div class="tags"><span class="tag">TypeScript</span><span class="tag">Three.js</span><span class="tag">WebGL</span><span class="tag">FastAPI</span></div>
<a class="card-link" href="https://github.com/CyberCoder-IITM/AetherFlow-2D-3D" target="_blank" rel="noopener">Repository →</a>
</div>
<div class="card reveal" style="transition-delay:.3s">
<div class="card-title">Rental Price Predictor</div>
<p class="card-desc">6-model stacking ensemble (XGBoost, LightGBM, CatBoost, Random Forest) over 29,985 listings; NLP sentiment analysis, PowerTransformer scaling. MSE 0.055 — 66% under the 0.16 baseline.</p>
<p class="card-tradeoff"><b>Tradeoff:</b> a 6-model stacking ensemble sacrifices interpretability and inference speed — accepted for accuracy: MSE 0.055, 66% under baseline.</p>
<div class="tags"><span class="tag">Python</span><span class="tag">XGBoost</span><span class="tag">LightGBM</span><span class="tag">NLP</span></div>
<a class="card-link" href="https://github.com/CyberCoder-IITM/Rental-Price-Predictor" target="_blank" rel="noopener">Repository →</a>
</div>
</div>
</div>
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</section>
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<h2 class="section-title reveal" style="transition-delay:.09s">Technical stack</h2>
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<div class="stack-block reveal" style="transition-delay:.18s">
<h3>Security engineering</h3>
<div class="pills"><span class="pill">MITRE ATT&CK</span><span class="pill">Burp Suite</span><span class="pill">Nmap</span><span class="pill">Wireshark</span><span class="pill">VAPT</span><span class="pill">OAuth 2.0 / JWT</span></div>
</div>
<div class="stack-block reveal" style="transition-delay:.24s">
<h3>Systems & backend</h3>
<div class="pills"><span class="pill">Rust</span><span class="pill">Java</span><span class="pill">Python</span><span class="pill">TypeScript</span><span class="pill">Spring Boot</span><span class="pill">FastAPI</span><span class="pill">Node.js</span></div>
</div>
<div class="stack-block reveal" style="transition-delay:.3s">
<h3>Distributed infrastructure</h3>
<div class="pills"><span class="pill">Apache Kafka</span><span class="pill">Apache Spark</span><span class="pill">Kubernetes</span><span class="pill">OpenShift</span><span class="pill">AWS</span><span class="pill">Docker</span><span class="pill">Grafana</span></div>
</div>
<div class="stack-block reveal" style="transition-delay:.36s">
<h3>AI & ML engineering</h3>
<div class="pills"><span class="pill">PyTorch</span><span class="pill">TensorFlow</span><span class="pill">XGBoost</span><span class="pill">LangChain</span><span class="pill">RAG</span><span class="pill">Scikit-learn</span><span class="pill">NLP</span></div>
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</div>
</section>
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<p class="eyebrow reveal">Credentials</p>
<h2 class="section-title reveal" style="transition-delay:.09s">Certifications</h2>
<div class="certs">
<div class="cert-card featured reveal" style="transition-delay:.18s">
<i class="fa-solid fa-graduation-cap cert-ico" aria-hidden="true"></i>
<div>
<div class="cert-issuer">CodePath · May 2026</div>
<div class="cert-name">CYB102 Intermediate Cybersecurity</div>
</div>
</div>
<div class="cert-card reveal" style="transition-delay:.22s">
<i class="fa-solid fa-code-branch cert-ico" aria-hidden="true"></i>
<div>
<div class="cert-issuer">CodePath · Summer 2026</div>
<div class="cert-name">AI301 AI Open Source Capstone</div>
<span class="cert-tag">In progress · Expected Aug 2026</span>
</div>
</div>
<div class="cert-card reveal" style="transition-delay:.26s">
<i class="fa-solid fa-terminal cert-ico" aria-hidden="true"></i>
<div>
<div class="cert-issuer">Red Hat · May 2023</div>
<div class="cert-name">RH134 System Administration II (Certificate of Attendance)</div>
</div>
</div>
<div class="cert-card reveal" style="transition-delay:.3s">
<i class="fa-solid fa-bug cert-ico" aria-hidden="true"></i>
<div>
<div class="cert-issuer">Internshala · Nov 2022</div>
<div class="cert-name">Ethical Hacking & VAPT</div>
</div>
</div>
</div>
<p class="also reveal" style="transition-delay:.34s">Also completed: coursework and job simulations via Cybrary, Coursera, Forage (Deloitte, MasterCard), Snyk CTF, HackerRank, Infosys Springboard, and Microsoft x LinkedIn (Generative AI Essentials).</p>
</section>
<!-- CHANGED: contact copy rewritten to closing-CTA tone (Option C). -->
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<p class="eyebrow reveal">Connect</p>
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<div class="reveal" style="transition-delay:.09s">
<p class="connect-lead">I like problems where the interesting part is the tradeoff.</p>
<p class="connect-copy">An EMA-adaptive controller in Rust cut p99 latency variance 30% under CPU contention. Open to full-time roles in backend, distributed systems, AI/ML, and security engineering. Post-OPT EAD — no sponsorship required currently.</p>
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<span>IEEE Publication<br /><span class="handle">Asian Conference 2024 (Oral)</span></span>
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Adnan Syed /// BU MSCS 2026 /// adnansyed.me <span class="updated">/// Updated Jul 2026</span>
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