Kalanand Mishra
🔬 AI Scientist • Leader • Author

Kalanand Mishra

Welcome! I am an applied scientist and AI leader based in the San Francisco Bay Area with over two decades of experience bridging foundational scientific discovery and modern artificial intelligence.

In fundamental physics, I contributed to discoveries recognized in the 2013 and 2008 Nobel Prizes in Physics (including the Higgs boson discovery at CERN and CP violation at SLAC). In industry, I direct Applied AI teams building multi-task recommendation architectures, causal transformer models, and customer behavior foundation models.

📚 Books Catalog 💡 Patents 🎙️ Conference Talks 🏆 Recognition 🎬 Media 🤝 Outreach 🎓 Google Scholar 💼 LinkedIn
Featured Publications

Published Books

Comprehensive, mathematically grounded, and production-tested guides for engineers, scientists, and researchers.

Heard In Data Science Interviews 2nd Edition Cover
✨ 2nd Edition (2026) 1,009 Questions Python 3 & PyTorch

Heard In Data Science Interviews

Over 650 Most Commonly Asked Interview Questions & Answers
Now with 1000+ Questions • Updated for the Generative AI & Agentic Era

The definitive technical interview manual. Rebuilt from the ground up for modern AI technical loops—featuring end-to-end Python 3 and PyTorch implementations, mathematical derivations, transformer architectures (MHA/GQA, RoPE), Generative AI & Agentic workflows (RAG, LoRA, Tool Calling), and distributed computing.

📖 Explore Dedicated Book Page → 🛒 Buy on Amazon 📝 Official Errata
View All Books & Academic Volumes on the Books Catalog Page →
Inventions & IP

Patents

Patented innovations spanning transformer architectures, contextual bandit experimentation, dynamic message generation, and conversational personalization.

US Patent 12,099,858-B2 • Granted

System & Method for UX Optimization

Personalization engines enabling 1,000+ annual contextual bandit and A/B tests to automate real-time user experience decisioning and reward maximization.

📄 View Patent on USPTO →
US Patent 11,704,503-B2 • Granted

Profile-Based Dynamic Message & Communication Generation

Algorithmic frameworks for dynamically synthesizing context-aware user profile representations and personalized message generation pipelines.

📄 View Patent on USPTO →
US Patent 11,314,945-B1 • Granted

Profile-Based User Personalization

Machine learning methods for user personalization in a multi-turn conversation.

📄 View Patent on USPTO →
Patent Application P15821-US-PROV-01 • Filed

Dynamic Latent-Patching (DP-Rec) for Long-Sequence Transformers

Dynamic sequence patching architectures improving long-context transformer efficiency-accuracy trade-offs by 25%+ over SOTA at fixed inference FLOP budgets.

⚡ Research presented at RecSys 2026
Presentations & Keynotes

Conference Talks & Papers

Invited keynotes, conference talks, and technical papers on recommender systems, transformers, healthcare AI, and open-source models.

RecSys 2026

DP-Rec: Dynamic Latent-Patching for Efficient Long-Sequence Recommendation

Techniques to scale transformer sequence models for billion-scale real-time recommendation workloads with sub-millisecond latency.

EcoTag 2026 • Open Source AI

Mobile Vision-Language Models for Garment Lifecycle CO₂ Estimation

Open-source AI initiative applying on-device vision models to estimate environmental impact directly from garment labels.

📊 View Talk (PDF) • GitHub Repo →
RecSys 2025 • Paper

FinTRec: Multi-Channel Real-Time Recommender Systems

Production architecture delivering 50%+ performance improvements across enterprise multi-channel ecosystems.

📄 Read Paper on arXiv →
NVIDIA GTC 2024 • Talk S61614

Scaling a Transformer-Powered Recommender System

Integrating causal transformers, multi-task learning, and GPU latency-throughput profiling to deliver $500M+ in business value.

📊 View Talk Slides (PDF) →
NVIDIA GTC 2022 • Case Study A41348

Transformer-Powered Personalized Recommendation at Scale

Building Capital One's first enterprise recommender system serving 150M customers with contextual personalization.

📊 View Presentation (PDF) →
TechConnect 2018 • AI Demo

Automatic Detection of Health Conditions from Electronic Medical Records

Clinical entity resolution and diagnostic language models trained on 30M PubMed papers (~100B tokens).

🎥 Watch Demo Video (MP4) →
Honors & Awards

Recognition

Honors across artificial intelligence leadership, high-energy physics discoveries, and scientific research.

2025 • Enterprise AI Innovation

Capital One TechX Award

Awarded for pioneering Capital One's first internal customer behavior foundation model and driving cross-channel personalization.

2014 • Scientific Leadership

Aspen Institute Fellowship & Conference Grant

Awarded grant to organize the prestigious Frontiers in Particle Physics conference at the Aspen Center for Physics.

🌐 View Conference Program →
2013 • Nobel-Recognized Discovery

Fermilab Distinguished Researcher Award

Honored for leading research in the $H \to WW^*$ channel directly enabling the experimental discovery of the Higgs boson.

🏛️ View Fermilab Fellow Profile →
2013 • Research Report

Chair & Editor, Quantum Chromodynamics Panel

Chaired the international panel and edited the comprehensive research report on QCD electroweak corrections for high-energy colliders.

📄 Read Research Report on arXiv →
Press & Spotlights

Popular Media & Featured Appearances

Interviews and press coverage of research milestones.

🎬
Capital One AI Campaign (2025)

Featured in National AI & Engineering Campaign

Discussing the architecture of customer foundation models, real-time AI decisioning, and research leadership.

▶️ Watch on YouTube →
🏆
American Institute of Physics (AIP)

2013 Physics Nobel Prize Feature Articles

Featured in AIP's retrospective articles covering the experimental discovery of the Higgs boson at the CERN Large Hadron Collider.

📰 Read AIP Nobel Article →
Community & Mentorship

Outreach

Mentoring data science practitioners, organizing technical seminars, and advancing educational initiatives.

2024–2026

"Ace the Case" Workshops

Led hands-on technical preparation workshops for Data Science and Machine Learning candidates tackling industry interview loops.

2020–2025

Monthly ML Tech Talk Series

Hosted and organized monthly seminars highlighting cutting-edge results in machine learning, Transformers, and Recommender Systems.

2017–2018

Hearst AI University

Co-founded corporate learning initiative; designed and led interactive AI adoption and applied NLP workshops across engineering teams.

2010–2014

CERN-Fermilab Summer School

Co-organized international graduate summer school; taught statistical inference, hypothesis testing, and advanced data modeling.

2009–2012

CERN LHC Working Group

Co-led the CMS Collaboration electroweak working group on precision W and Z boson physics at the Large Hadron Collider.

2005–2006

SLAC Detector Operations

Led Cherenkov detector operations and particle identification systems at the Stanford Linear Accelerator Center BaBar experiment.

Academic Research

Publications & Scientific Discoveries

Primary author of 22 foundational papers (h-index 21, average citations >250) and co-author of 700+ scientific publications across particle physics and machine learning.

Observation of an Excess of Events in the Search for the Standard Model Higgs Boson
Physics Letters B 716 (2012) 30–61 • CERN CMS Collaboration • 14,000+ Citations
Primary experimental analysis in $H \to W W^* \to 2\ell 2\nu$ channel, directly contributing to the landmark discovery of the Higgs boson recognized in the 2013 Nobel Prize in Physics (Englert & Higgs).
arXiv:1207.7235 →
Evidence for Charge-Parity (CP) Violation in B Meson Decays
Physical Review Letters 99 (2007) 161802 • SLAC BaBar Collaboration
Measurement of direct CP violation in $B^0 \to K^+ \pi^-$ decays, establishing matter-antimatter asymmetry in the quark sector and contributing to the 2008 Nobel Prize in Physics (Kobayashi & Maskawa).
arXiv:hep-ex/0703037 →
The Physics of the B Factories
European Physical Journal C 74 (2014) 3026 • 900+ Pages Comprehensive Monograph
Co-author of the definitive reference work summarizing a decade of experimental physics and particle identification methodologies.
arXiv:1406.6311 →
FinTRec: Multi-Channel Real-Time Recommender Systems
arXiv:2511.14865 (2025) • Machine Learning Systems
Real-time multi-task recommender architecture optimizing latency, throughput, and cross-channel personalization.
arXiv:2511.14865 →
🎓 Full Google Scholar Profile → 📚 Complete InspireHEP Catalog (700+ Papers) →