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“ahmd-mohsin”
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[minor] made minor changes
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‎src/components/ExperienceSection.tsx‎

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import { Section } from "@/components/Section";
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import { motion } from "framer-motion";
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const interests = [
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const experiences = [
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{
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title: "LLM Inference and Test-Time Scaling",
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desc: "Working on test-time training methods for scientific discovery under uncertainty, with a focus on adaptive compute allocation, agentic planning, and stratified scaling search for test-time reasoning in large language models and diffusion language models.",
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venues: "CoLM'26, NeurIPS'26, Ongoing",
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org: "Stanford Artificial Intelligence Laboratory (SAIL)",
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dateRange: "December 2025 – Present",
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advisor: "Prof. Emily Fox",
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projects: [
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{
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name: "Internet of Evolving Agents",
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bullets: [
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"Co-developed a modular multi-agent ecosystem where autonomous agents evolve their capabilities, reputation, and social connections over time through Bayesian reputation updates, dynamic team formation, and social graph evolution. The framework enables emergent specialization and self-organizing collaboration for complex task execution (NeurIPS 2026, in progress).",
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],
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},
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{
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name: "Test-Time Compute and Reasoning in Large Language Models",
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bullets: [
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"Currently working on adaptive test-time compute strategies for improving reasoning accuracy in LLMs, focusing on dynamic control of inference depth, tool usage, and verification under strict compute constraints. The work studies principled trade-offs between accuracy, latency, and reliability via adaptive compute allocation.",
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],
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},
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{
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name: "Bayesian Preference Alignment for Mathematical Reasoning",
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bullets: [
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"Developed active learning frameworks for Bayesian General Preference Models and Continuous-Utility Direct Preference Optimization (CU-DPO) to align small language models for mathematical reasoning tasks, enabling sample-efficient preference learning with calibrated uncertainty (ICML 2026 and CoLM 2026).",
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],
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},
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],
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},
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{
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title: "Evolving Agentic Systems",
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desc: "Developing Internet of Evolving Agents frameworks for self-evolving multi-agent systems with dynamic reputation modeling and social graph-based coordination mechanisms.",
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venues: "NeurIPS'26, Ongoing",
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org: "Intel Corporation, Ph.D. Researcher",
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dateRange: "September 2024 – December 2024",
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advisor: "Dr. John M. Cioffi",
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projects: [
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{
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name: "Neural Gaussian Radio Fields for Environment Perception",
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bullets: [
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"Worked on 3D computer vision-based channel estimation for next-generation wireless networks.",
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"Implemented a CUDA-based differentiable real-time pipeline with 1 ms inference time, leading to KDD 2026 submission.",
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],
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},
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],
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},
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{
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title: "Reinforcement Learning for LLMs",
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desc: "Research on preference optimization, active learning, and alignment methods for large language model reasoning systems. Current work also explores reinforcement learning approaches for reward decomposition to mitigate sycophancy and improve alignment.",
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venues: "ICML'26, NeurIPS'26, Ongoing",
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org: "Samsung Semiconductors, Ph.D. Researcher",
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dateRange: "June 2024 – September 2025",
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advisor: "Dr. John M. Cioffi",
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projects: [
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{
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name: "Deep Reinforcement Learning Accelerated Optimization: Graph Neural Networks for Accelerating Low-Rank SDP Solvers (expected NeurIPS 2026)",
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bullets: [
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"Developed a constraint-graph representation of SDPs and a GNN encoder (Graph Attention) with sequence prediction to learn rank trajectories directly from problem structure.",
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"Integrated learned rank schedules into low-rank solvers to remove hand-tuned rank heuristics and reduce trial-and-error, yielding up to 3× speedups on large-scale benchmarks.",
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],
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},
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],
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},
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];
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export function ResearchInterestsSection() {
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export function ExperienceSection() {
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return (
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<Section id="research-interests" title="Research Interests">
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<Section id="experience" title="Experience">
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<motion.div
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initial={{ opacity: 0, y: 20 }}
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whileInView={{ opacity: 1, y: 0 }}
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viewport={{ once: true }}
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transition={{ duration: 0.5 }}
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transition={{ duration: 0.4 }}
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>
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<fieldset className="mb-10 rounded-2xl border border-yellow-500/40 px-6 pb-6 pt-3">
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<legend className="px-3 text-xs font-mono uppercase tracking-[0.25em] text-yellow-400/80">
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Main Area
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</legend>
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<p className="text-sm leading-relaxed text-muted-foreground">
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My primary research spans LLM inference and test-time scaling,
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including test-time training for scientific discovery under
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uncertainty, adaptive compute allocation, agentic planning, and
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stratified scaling search for reasoning in large language models and
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diffusion language models. I also work on evolving agentic systems
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through Internet of Evolving Agents frameworks with dynamic
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reputation modeling and social graph-based coordination. In
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addition, I focus on reinforcement learning for LLMs, including
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preference optimization, active learning, alignment, and reward
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decomposition methods to reduce sycophancy and improve reasoning
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reliability.
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</p>
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</fieldset>
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<div className="overflow-hidden rounded-2xl border border-yellow-500/40">
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{interests.map((interest, i) => (
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<div className="border border-white/20">
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{experiences.map((exp, i) => (
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<div
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key={i}
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className="border-b border-yellow-500/25 px-6 py-5 last:border-b-0"
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data-testid={`row-interest-${i}`}
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className="px-6 py-5 border-b border-white/20 last:border-b-0"
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data-testid={`row-experience-${i}`}
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>
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<p className="mb-2 font-display text-base font-bold text-yellow-400 md:text-lg">
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{interest.title}
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</p>
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<p className="mb-3 text-sm leading-relaxed text-muted-foreground">
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{interest.desc}
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<p className="font-display font-bold text-md md:text-lg text-white mb-1">
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{exp.org}
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</p>
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<span className="text-xs font-mono text-yellow-500/70">
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{interest.venues}
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<span className="text-xs font-mono text-white/40 block mb-3">
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{exp.dateRange} · Advisor: {exp.advisor}
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</span>
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{exp.projects.map((project, j) => (
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<div key={j} className="mb-4 last:mb-0">
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<p className="font-semibold text-sm text-white/90 mb-2">
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Project: {project.name}
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</p>
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<ul className="list-disc list-inside text-sm text-muted-foreground leading-relaxed space-y-1">
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{project.bullets.map((bullet, k) => (
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<li key={k}>{bullet}</li>
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))}
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</ul>
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</div>
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))}
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</div>
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))}
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</div>
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</motion.div>
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</Section>
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);
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}
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}

‎src/components/ResearchInterestsSection.tsx‎

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viewport={{ once: true }}
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transition={{ duration: 0.5 }}
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>
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<fieldset className="mb-10 rounded-2xl border border-yellow-500/30 bg-yellow-500/5 px-6 pb-6 pt-3 shadow-[0_0_30px_rgba(234,179,8,0.08)]">
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<fieldset className="mb-10 rounded-2xl border border-yellow-500/40 px-6 pb-6 pt-3">
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<legend className="px-3 text-xs font-mono uppercase tracking-[0.25em] text-yellow-400/80">
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Main Area
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</legend>
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<p className="text-sm leading-relaxed text-yellow-50/80">
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<p className="text-sm leading-relaxed text-muted-foreground">
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My primary research spans LLM inference and test-time scaling,
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including test-time training for scientific discovery under
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uncertainty, adaptive compute allocation, agentic planning, and
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</p>
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</fieldset>
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<div className="overflow-hidden rounded-2xl border border-yellow-500/25 bg-black/40 shadow-[0_0_30px_rgba(234,179,8,0.06)]">
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<div className="overflow-hidden rounded-2xl border border-yellow-500/40">
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{interests.map((interest, i) => (
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<div
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key={i}
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className="px-6 py-5 border-b border-yellow-500/15 last:border-b-0 transition-colors duration-300 hover:bg-yellow-500/[0.04]"
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className="border-b border-yellow-500/25 px-6 py-5 last:border-b-0"
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data-testid={`row-interest-${i}`}
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>
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<p className="mb-2 font-display text-md font-bold text-yellow-300 md:text-lg">
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<p className="mb-2 font-display text-base font-bold text-yellow-400 md:text-lg">
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{interest.title}
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</p>
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<p className="mb-3 text-sm leading-relaxed text-yellow-50/75">
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<p className="mb-3 text-sm leading-relaxed text-muted-foreground">
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{interest.desc}
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</p>
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<span className="text-xs font-mono text-yellow-400/50">
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<span className="text-xs font-mono text-yellow-500/70">
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{interest.venues}
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</span>
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</div>

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