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    "authors": ["Rishabh Misra", "Jigyasa Grover"],
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      "title": "Keeping it Low-Key: Modern-Day Approaches to Privacy-Preserving Machine Learning",
      "venue": "Springer Nature — Data Protection in a Post-Pandemic Society",
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      "title": "Sarcasm Detection using News Headlines Dataset",
      "venue": "AI Open (Vol. 4)",
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      "doi": "10.13140/RG.2.2.29923.22566"
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      "url": "https://www.kaggle.com/rmisra/imdb-spoiler-dataset",
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      "title": "Advanced LLM Inference Optimization Techniques",
      "date": "2026-05-02",
      "url": "https://rishabhmisra.github.io/advanced-llm-inference-optimization/",
      "summary": "An overview of advanced LLM inference optimization techniques, detailing how model parallelism, Grouped-Query Attention, and specialized serving engines overcome the Memory Wall."
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      "title": "From AI Enthusiast to Inference Architect: Mastering GPU Profiling",
      "date": "2026-04-15",
      "url": "https://rishabhmisra.github.io/gpu-profiling-inference-architect/",
      "summary": "Stop obsessing over prompts and start profiling GPUs. Master Arithmetic Intensity, KV Caching, and Scheduling to transition from an AI Enthusiast to an Inference Architect."
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    {
      "title": "Speculative Decoding: The Mathematics of Acceptance Rate",
      "date": "2026-02-10",
      "url": "https://rishabhmisra.github.io/speculative-decoding-acceptance-rate/",
      "summary": "Speculative Decoding can make LLMs 3x faster—or slow them down. Master the mathematics of the Acceptance Rate to determine when and how to deploy this powerful optimization technique."
    },
    {
      "title": "Quantization Tradeoffs in LLMs: The Unpacking Tax",
      "date": "2026-01-15",
      "url": "https://rishabhmisra.github.io/quantization-tradeoffs-unpacking-tax/",
      "summary": "Quantizing an LLM to 4-bit doesn't guarantee faster latency. Understand the hidden 'Unpacking Tax' and learn how batch sizes dictate whether quantization helps or hurts your performance."
    },
    {
      "title": "You Don't Need More H100s: Understanding PagedAttention",
      "date": "2025-11-20",
      "url": "https://rishabhmisra.github.io/pagedattention-vllm/",
      "summary": "Stop buying more GPUs. Learn how PagedAttention and vLLM decouple physical from logical memory to eliminate fragmentation and unlock continuous batching for massive throughput gains."
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    {
      "title": "KV Cache: The Silent Killer of Your Inference Budget",
      "date": "2025-10-05",
      "url": "https://rishabhmisra.github.io/kv-cache-silent-killer/",
      "summary": "The KV Cache is the hidden memory tax causing your LLM applications to crash. Discover the math behind the bottleneck and how fragmentation eats your inference budget."
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    {
      "title": "Why Your Team Built the Wrong Recommender",
      "date": "2025-08-10",
      "url": "https://rishabhmisra.github.io/wrong-recommender-encode-generate/",
      "summary": "Traditional Retrieve-and-Rank models treat user history like a grocery list. Learn why the industry is shifting toward 'Encode and Generate' paradigms to capture temporal user intent."
    },
    {
      "title": "The Visual Cold-Start Trap in Recommender Systems",
      "date": "2025-07-20",
      "url": "https://rishabhmisra.github.io/visual-cold-start-trap/",
      "summary": "Uncover the 'Visual Cold-Start Trap' in recommendation systems and learn how to use Contrastive Learning and Cross-Attention to ensure new products get the visibility they deserve."
    },
    {
      "title": "The LLM-ERS Pattern: Where LLMs Actually Belong in RecSys",
      "date": "2025-05-15",
      "url": "https://rishabhmisra.github.io/llm-ers-pattern/",
      "summary": "Why using massive LLMs for real-time ranking fails in production, and how the LLM-ERS pattern offers a scalable blueprint by keeping retrieval fast and utilizing LLMs for offline augmentation."
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    {
      "title": "Building LLM-Powered Recommendation Systems: My LinkedIn Learning Course",
      "date": "2025-04-10",
      "url": "https://rishabhmisra.github.io/linkedin-learning-course/",
      "summary": "An exclusive sneak peek into my upcoming LinkedIn Learning course, which explores the paradigm shift of integrating Large Language Models into massive-scale personalization pipelines."
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    {
      "title": "Unlock Hyper-Personalized Advertising with Generative AI",
      "date": "2025-03-20",
      "url": "https://rishabhmisra.github.io/hyper-personalized-advertising-with-genai/",
      "summary": "Explore how Generative AI is moving digital marketing from broad segmentation to hyper-personalization by dynamically synthesizing visual and textual ad creatives in real-time."
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    {
      "title": "What is Generative AI? Peeling the magic behind the buzz",
      "date": "2025-02-10",
      "url": "https://rishabhmisra.github.io/what-is-generative-ai/",
      "summary": "A technical primer on Generative AI, breaking down the mechanics of Transformers and Diffusion models, and exploring how self-supervised learning is transforming the industry."
    },
    {
      "title": "Common Pitfalls in ML Interviews and How to Avoid Them",
      "date": "2025-01-15",
      "url": "https://rishabhmisra.github.io/common-pitfalls-in-ml-interviews/",
      "summary": "Discover the five critical mistakes candidates make in Machine Learning interviews—from neglecting fundamentals to ignoring production reality—and learn how to overcome them."
    },
    {
      "title": "Machine Learning Glossary",
      "date": "2019-01-01",
      "url": "https://rishabhmisra.github.io/Machine-Learning-Glossary/",
      "summary": "A comprehensive, easy-to-digest glossary designed to quickly clarify popular concepts, evaluation metrics, and algorithms encountered in Machine Learning interviews and literature."
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    {
      "title": "Would this clothing product fit me?",
      "date": "2018-12-01",
      "url": "https://rishabhmisra.github.io/Would-This-Clothing-Product-Fit-Me/",
      "summary": "An explanation of my RecSys paper addressing the challenge of providing accurate, personalized fit guidance in online fashion retail using advanced machine learning models."
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      "date": "2017-12-07",
      "url": "https://rishabhmisra.github.io/Inference-Using-EM-Algorithm/",
      "summary": "A deep dive into the mathematical derivation of the Expectation-Maximization (EM) algorithm, demonstrating how to tackle inference problems involving latent variables."
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      "title": "Maximum Likelihood Estimates - Motivation for EM algorithm",
      "date": "2017-10-18",
      "url": "https://rishabhmisra.github.io/Maximum-Likelihood-Estimates-Motivation-For-EM-Algorithm/",
      "summary": "Understand the core premise of Maximum Likelihood Estimates in Logistic Regression, and how this foundational concept motivates the powerful Expectation-Maximization (EM) algorithm."
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      "title": "Introduction to Support Vector Machines - Soft Margin Formulation and Kernel Trick",
      "date": "2017-07-04",
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      "summary": "The second part of the SVM series, explaining how the Soft Margin formulation and the Kernel Trick allow models to elegantly handle noisy and linearly inseparable datasets."
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      "title": "Introduction to Support Vector Machines - Motivation and Basics",
      "date": "2017-06-28",
      "url": "https://rishabhmisra.github.io/Introduction-to-Support-Vector-Machines-Motivation-and-Basics/",
      "summary": "A foundational introduction to Support Vector Machines (SVM), covering the geometric intuition behind finding the optimal margin classifier for linearly separable data."
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    {
      "title": "Text Prediction - Behind the Scenes",
      "date": "2016-12-30",
      "url": "https://rishabhmisra.github.io/Text-Prediction-Behind-the-Scenes/",
      "summary": "An inside look at how mobile keyboard applications use language models and smoothing techniques to predict your next word with increasing accuracy."
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