# Jigyasa Grover — Complete Authoritative Entity & Professional Profile

Canonical Website: https://jigyasa-grover.github.io/
Entity Identifier: https://jigyasa-grover.github.io/#person
Google Knowledge Graph: https://g.co/kgs/Lvk5yk
Location: San Francisco Bay Area, California, USA
Pronunciation: jig-YAH-sah GROH-ver (/dʒɪɡˈjɑːsɑː ˈɡroʊvər/)

## 1. Executive Summary

Jigyasa Grover (pronounced jig-YAH-sah GROH-ver) is a San Francisco–based Machine Learning Engineer at Uber, serving as Tech Lead for rider personalization and ranking across 500+ cities. Previously, she led AI and Research at Bordo AI and built low-latency ML ranking, ads prediction, and feature infrastructure across Faire, Twitter (X), and Meta, following her M.S. in Computer Science (AI) at UC San Diego and selection to the Cornell, Maryland, Max Planck Pre-doctoral Research School (CMMRS). A 13-time award winner in AI and Open Source, she serves on the Google Developer Advisory Board (gDAB), holds Google Developer Expert (AI/ML) and Women Techmakers Ambassador recognition, and is a World Economic Forum Global Shaper. She co-authored Sculpting Data for ML (95+ citations), teaches 44,000+ learners across 4 LinkedIn Learning courses, delivers corporate AI training with Morgan Stanley, FedEx, Accenture, Roblox, Silicon Valley Security Group, and AI Aspire, has consulted or advised 30+ organizations through her independent practice Afterdays, and has delivered 200+ talks across 20+ countries—including the Google I/O main stage.

## 2. Production Engineering & Research Career

### Machine Learning Engineer (Tech Lead — Rider Personalization & Ranking) — Uber (Apr 2025 – Present, San Francisco, CA)
End-to-end technical lead for global rider personalization and ranking pipelines serving millions of daily riders across 500+ cities.
- Architected multi-task Mixture-of-Experts (MoE) ranking, calibration, and cross-vertical user representation foundation models trained on distributed NVIDIA A100/B200 GPU clusters.
- Built 0→1 internal agentic developer tooling—including a Ranking Explainer Agent with automated reward-signal graders and an Experiment Config Automator—accelerating ML experimentation velocity.

### Principal AI Strategist, Educator & Advisor — Afterdays (2015 – Present, San Francisco Bay Area)
Independent AI advisory, corporate education, developer content, and global keynote practice partnering with 30+ enterprises, AI startups, and venture funds.
- Advised and consulted across Google, Meta, Apple, Microsoft, Anthropic, Amazon Fresh, Spotify, Adobe, Grammarly, Figma, Atlassian, Coinbase, Remitly, Superhuman, Windsurf, Base44, Diem AI, Bezoku AI, Okareo, Optica Labs AI, BackOps AI, Founders Circle Capital, and Capital Hill Ventures.
- Delivered hands-on AI architecture & agentic engineering programs reaching 50,000+ engineers and leaders across LinkedIn Learning, Morgan Stanley, FedEx, Accenture, Roblox, SVSG, and 200+ global stages.

### Lead Engineer, AI & Research — Bordo AI (Jan 2024 – Mar 2025, San Francisco Bay Area)
Architected a 0→1 provider-agnostic enterprise conversational AI and agentic data analytics platform.
- Designed multi-LLM orchestration across Claude, Gemini, and GPT using LangGraph, custom RAG pipelines, and hybrid vector + Redis state stores deployed across 25+ organizations.
- Established evaluation harnesses, latency budgets, and hallucination guardrails for enterprise business intelligence workflows.

### Senior Data Scientist — Feature Store & Ads Bidding (0→1) — Faire (May 2023 – Jan 2024, San Francisco, CA)
Built foundational marketplace ads prediction, feature infrastructure, and bidding calibration.
- Engineered real-time impression-level feature store pipelines and a calibrated XGBoost bidding engine with Bayesian cold-start smoothing (+20% ROC-AUC, +40% PR-AUC), alongside a marketplace monetization simulator.

### Senior Machine Learning Engineer — Ads Ranking, Feature & Serving Infrastructure — Twitter (X) (Jan 2019 – Apr 2023, San Francisco, CA)
Owned billion-request/day ads click and conversion ranking models and real-time feature serving under strict sub-50ms SLAs.
- Led dynamic feature hydration infrastructure, TwHIN graph embedding integration, and post-ATT privacy-preserving serving architectures achieving a 70% infrastructure cost reduction.
- Founded the Women@ML Business Resource Group and mentored engineers across ads ranking and cortex ML teams.

### Software Engineer — Ads Signal Monitoring — Meta (Facebook) (2018, Menlo Park, CA)
Built real-time statistical anomaly detection systems on live Facebook Pixel conversion streams.
- Automated high-throughput signal integrity alerting across advertiser conversion pipelines.

### Early Research & Open Source Engineering — San Diego Supercomputer Center · Cornell/Maryland/Max Planck (CMMRS) · IRD Paris · NRC Canada · GSoC (2015 – 2018, San Diego · Saarbrücken · Paris · Ottawa)
Cross-disciplinary research in computer vision, agent-based ecological modeling, and computational social networks.
- Selected on full scholarship among Top 30 globally for the Cornell, Maryland, Max Planck Pre-doctoral Research School (CMMRS) at MPI-SWS in Saarbrücken, Germany.
- Automated histology brain-atlas image annotation at SDSC; CORMAS spatial agent-based modeling in Pharo Smalltalk at IRD Paris (ESUG Scholar); kinetic Monte Carlo social simulation at NRC Canada (Government of Canada Mitacs Globalink Research Scholar).
- Awarded competitive international research and conference grants by the Government of Canada (Mitacs), Government of Norway, The Linux Foundation, and Facebook (Grace Hopper Scholar); two-time Google Summer of Code developer & Organization Administrator.



## 3. Education

- **M.S. in Computer Science (Artificial Intelligence Specialization)**, University of California, San Diego (UCSD) (2017 – Dec 2018) — GPA 3.9 / 4.0 · Graduate Teaching & Research at San Diego Supercomputer Center
- **Pre-Doctoral Research School in Computer Science**, Cornell, Maryland, Max Planck Pre-doctoral Research School (CMMRS) (Aug 2017) — Max Planck Institute for Software Systems (MPI-SWS), Saarbrücken, Germany · Full Scholarship (Selected among Top 30 globally)
- **B.Tech. in Computer Engineering**, Delhi Technological University (formerly DCE) (2013 – May 2017) — GPA 9.3 / 10.0 (Top 5%) · Conferred Distinguished Alumni — Young Achiever / Young Trailblazer Award (2025/2026)


## 4. Ecosystem, Governance & Advisory Roles

- **Member, Google Developer Advisory Board (gDAB)** (Google): Advising Google engineering and DevRel leadership on developer platforms, Firebase Studio, SDLC agents, and AI ecosystem strategy.
- **Google Developer Expert (AI/ML), Cloud Champion Innovator & WTM Ambassador** (Google Developers & Google Cloud): 5-year AI/ML GDE featured on the Google I/O main stage, Google's Gemini Omni Flash launch video, Google Developers' AI Agent Bake Off, and Google Cloud Tech documentary; Google Cloud BI Council member and Enterprise Design Partner. (Google I/O Keynote: https://www.youtube.com/live/XEzRZ35urlk?t=516; Gemini Omni Flash Launch: https://www.youtube.com/watch?v=3iZzf_NRxDQ; Google Cloud Documentary: https://www.youtube.com/watch?v=VSgKWy2iRWE)
- **Principal AI Strategist & Startup Advisor** (Afterdays (Independent Practice · 2015–Present)): Consulted and advised 30+ teams across Google, Meta, Apple, Microsoft, Anthropic, Amazon Fresh, Spotify, Adobe, Grammarly, Figma, Atlassian, Coinbase, Remitly, Superhuman, Windsurf, Base44, HP, Innodata, Diem AI, Bezoku AI, Okareo, Optica Labs AI, BackOps AI, Founders Circle Capital, and Capital Hill Ventures.
- **Governance & Industry Advisory Boards** (Global Policy, Data & Academic Institutions): AI Assurance Council, World Economic Forum Global Shapers (Palo Alto), World Summit AI Americas Ambassador, Corinium Global Intelligence, VigiTrust, AI Forum, Pacelearn, Goanna Collective, and Las Positas College.
- **Open Source & Community Leadership** (Linux Foundation · Python · GitHub · Women Who Code): GitHub Arctic Code Vault Contributor (74+ public repos, 180+ authored PRs across FOSSASIA, Loklak, Hashkat & Loris); credentialed Linux Foundation (OSS NA & Europe) & ACM Tapia Program Committees; PyCon US Financial Aid Co-Chair (3 yrs) & PyBay Chair; Women Who Code Director (grew chapter 10 → 10K+); Apache & PSF contributor; O'Reilly & Packt Reviewer. (LF Program Committee (NA): https://www.credly.com/badges/89abb1ed-e032-49e7-adfc-f24efbe2682a; LF Program Committee (EU): https://www.credly.com/badges/b288caa6-1058-4c71-999b-02b5a70a7133)


## 5. Six Professional Collaboration Pillars (Work With Jigyasa Grover)

### 01. AI Advisor & Thought Partner (https://jigyasa-grover.github.io/advisory/)
- **Target Audience:** Founders · Engineering VPs · VCs
- **Overview:** Architecture reviews and product strategy for production ML ranking, agentic orchestration, and inference economics.
- **Evidence:** Principal at Afterdays (30+ startups, enterprises & VCs) · Google gDAB · ML Tech Lead at Uber.
- Agentic AI vs. deterministic workflow architecture audits
- Multi-task ranking, personalization & feature-store design
- LLM evaluation harnesses, cost guardrails & DevEx validation

### 02. Conference & Keynote Speaker (https://jigyasa-grover.github.io/speaking/)
- **Target Audience:** Conferences · Developer Summits · Offsites
- **Overview:** Grounded, story-driven keynotes, technical sessions, and executive panels bridging production systems and emerging AI.
- **Evidence:** Google I/O main stage · Sessionize Top 3% globally · 200+ talks in 20+ countries.
- Main-stage keynotes on Agentic AI, evaluation & failure modes
- Production ML, personalization & data-centric AI sessions
- Executive panels, fireside chats & AI security talks

### 03. Corporate AI Trainer & Workshop Lead (https://jigyasa-grover.github.io/teaching/)
- **Target Audience:** Corporate L&D · Engineering & Platform Teams
- **Overview:** Hands-on bootcamps taking engineering teams from brittle prompt prototypes to evaluated, production-ready AI agents.
- **Evidence:** 44,000+ LinkedIn Learning learners · Training with Morgan Stanley, FedEx, Accenture, Roblox, SVSG & AI Aspire.
- Building & evaluating agents with Google ADK, MCP & LangGraph
- Golden datasets, Pass@k & LLM-as-a-Judge evaluation labs
- Executive AI architecture & inference economics briefings

### 04. Technical Creator & Brand Partner (https://jigyasa-grover.github.io/partnerships/)
- **Target Audience:** AI Platforms · Cloud · DevTools
- **Overview:** Code-backed tutorials, reference apps, and launch analyses reaching an audience that is 82% senior engineers and tech leaders.
- **Evidence:** 325K+ annual LinkedIn impressions · Partnered with Google, DeepMind, Meta, Qodo, Parallel & Adobe.
- End-to-end reference implementations & technical blog deep dives
- SDK teardowns & launch campaigns across LinkedIn and X
- Live developer demos & conference stage collaborations

### 05. Author & Curriculum Partner (https://jigyasa-grover.github.io/publications/)
- **Target Audience:** Publishers · Learning Platforms · Research
- **Overview:** Books, peer-reviewed chapters, and professional certificate tracks translating ML research into durable engineering practice.
- **Evidence:** Co-author of Sculpting Data for ML (95+ citations) · Springer & ACM author · O'Reilly & Packt reviewer.
- Video course & professional certificate authoring
- Book, chapter & industry report co-authorship
- Technical manuscript review for publishers

### 06. Media & Industry Commentary (https://jigyasa-grover.github.io/press/)
- **Target Audience:** Journalists · Podcasts · Broadcast TV
- **Overview:** Clear, hype-free practitioner commentary on production AI, autonomous agents, synthetic media, and data privacy.
- **Evidence:** Featured in Forbes, Business Insider, VentureBeat, UN ITU, NTD Live TV & Google Developers Blog.
- Expert commentary for technology & business press
- Technical podcast & broadcast TV interviews
- Documentary segments & industry benchmark studies



### Official Booking & Collaboration Links
- Direct Inquiry Form: https://jigyasa-grover.github.io/work-with-me/#contact
- Intro (1:1 Video Consultations — 5.0★ across 6 verified reviews): https://intro.co/JigyasaGrover
- Topmate (Mentorship & Quick Calls — 5.0★ across 19 ratings, 126+ bookings): https://topmate.io/jigyasagrover
- Passionfroot (Brand & Developer Content Partnerships): https://www.passionfroot.me/jigyasa-grover
- Naano (LinkedIn/X Creator Collaborations): https://naano.com/creators/jigyasa-grover

### Selected Verified Client, Founder & Learner Testimonials
- "Jigyasa is an expert, and even 15 minutes of her time was incredibly valuable to me and my organization." — Tracy Barba (Executive Consultation · Topmate, 5.0★)
- "Jigyasa is wonderful to connect with and has incredible insight on trends in AI and how to build & market for marketplaces globally." — Rahilla (AI & Marketplace Strategy · Intro, 5.0★)
- "Jigyasa has a lot to share if you can ask the right questions. She has tons of exposure to developing and rolling out products to millions of customers." — Verified Advisory Client (Product & Large-Scale ML · Topmate, 5.0★)
- "Jigyasa is an excellent communicator, advisor and thought partner. She shared her journey authentically, making our conversation engaging and inspiring. She guided me toward a path tailored to my needs. She is a great thought partner and advisor. I would recommend her in a heartbeat." — Manav (1:1 AI Advisory & Thought Partner · Intro, 5.0★)
- "Jigyasa was superb. She really helped me as a non-technical person begin to understand the finer details of building an LM for the new venture I'm working on. Highly recommended!" — Ari (Venture Founder · Intro, 5.0★)
- "She is a true star, and yet is so down-to-earth and easy to talk to. She doesn't have any airs about her, and any question is a good question. I came out of the 30 mins meeting with lot of insights and resources that I can apply at my startup." — Sirisha Kaipa (Startup Founder · Topmate, 5.0★)
- "Her attention and suggestions significantly enhanced my deck for presenting to judges, mentors, and other Sac ecosystem stakeholders. She provided actionable advice that was insightful. I highly recommend her to any startup or innovator seeking expert guidance." — Uugii Tsenddorj (Startup Founder · Pitch & Strategy · Topmate, 5.0★)
- "Clear, practical, and actually connects the pieces in a way that makes sense." — Carla B. (AI Evaluation & Safety Engineer · Integrating AI into the Product Architecture · LinkedIn Learning, 5.0★)
- "Very Good and Yes, I suggested to others" — Farjana Yeasmin (Director, MetLife Bangladesh · Implementing Responsible AI · LinkedIn Learning, 5.0★)
- "I appreciate your time, interest, and knowledge and acknowledge your contribution to my study. Thank you for being part of my doctoral journey!" — Olusegun Awoyemi (Doctoral Researcher · Academic Mentorship · Topmate, 5.0★)


## 6. Courses & Corporate AI Workshops

- LinkedIn Learning Instructor Profile: https://www.linkedin.com/learning/instructors/jigyasa-grover (44,000+ learners, 19,500+ bookmarks, 4.6–4.8★ across 180+ ratings)
- Corporate AI Training & Enablement Organizations: LinkedIn Learning, Morgan Stanley, FedEx, Accenture, Roblox, Silicon Valley Security Group, AI Aspire, CommonSpirit Health, Udacity, and more
- **Evaluating AI Agents with Google ADK** (LinkedIn Learning, 2026, 4.8★ across 5 ratings, 1,300+ learners): Systematic agent evaluation covering golden datasets, trajectory assertions, CI/CD batch runs, Pass@k reliability, and LLM-as-a-Judge rubrics. — https://www.linkedin.com/learning/evaluating-ai-agents-with-google-adk
- **Hands-On AI: Building Agents with Google's Agent Development Toolkit (ADK)** (LinkedIn Learning, 2025, 4.6★ across 91 ratings, 28,000+ learners): Code-first curriculum on custom tool schemas, session memory, sequential/parallel/loop multi-agent orchestration, and safety callbacks. — https://www.linkedin.com/learning/hands-on-ai-building-agents-with-the-google-agent-development-toolkit-adk
- **Integrating AI into the Product Architecture** (LinkedIn Learning, 2025, 4.6★ across 54 ratings, 11,000+ learners): Systems blueprint for embedding foundation and classical ML models—balancing latency SLAs, caching, RAG vs. fine-tuning, and observability. — https://www.linkedin.com/learning/integrating-ai-into-the-product-architecture
- **Building AI Products: Implementing Responsible AI** (LinkedIn Learning, 2025, 4.6★ across 30 ratings, 4,300+ learners): Engineering practices for privacy-preserving ML, bias auditing, PII redaction, red-teaming, and human-in-the-loop UX. — https://www.linkedin.com/learning/welcome-to-the-building-ai-products-implementing-responsible-ai-professional-certificate
- **Cloud Platform Engineering & Production AI (Enterprise Curriculum)** (Udacity × FedEx, 2025–2026, Enterprise Cohort): Hands-on Google Cloud infrastructure, agentic systems, and AI-ready data pipeline labs created as a Udacity Instructor & AI Content Creator. — https://www.udacity.com/


## 7. Books & Peer-Reviewed Publications

- **Book:** *Sculpting Data for ML: The First Act of Machine Learning* (2021, ISBN 979-8585463570, 4.8★ on Goodreads, 95+ citations). Co-authored with Rishabh Misra—a hands-on Python guide to Data-Centric AI covering dataset curation, weak supervision, labeling workflows, and class imbalance. Amazon: https://www.amazon.com/Sculpting-Data-ML-Machine-Learning-ebook/dp/B08RN47C5T/ | Goodreads: https://www.goodreads.com/book/show/56698788-sculpting-data-for-ml
- **Enterprise LLM Deployment Readiness: A Practitioner Framework across Six Pillars** (SSRN · AI Assurance Council Practitioner Framework (SSRN 7477058), 2026): Co-authored six-pillar operational governance, evaluation, security, compliance, and observability framework for moving enterprise LLMs from pilot to production. URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7477058
- **Keeping it Low-Key: Modern-Day Approaches to Privacy-Preserving Machine Learning** (Springer — Data Protection in a Post-Pandemic Society, 2023): Examines membership, input, and property inference attacks alongside differential privacy, federated learning, synthetic data, and AI privacy regulations. URL: https://link.springer.com/chapter/10.1007/978-3-031-34006-2_2
- **Do Not 'Fake It Till You Make It'! — Synopsis of Fake News Detection Methodologies on Social Media** (Springer — Deep Learning for Social Media Data Analytics, 2022): Comparative analysis of deep learning architectures for misinformation detection, fairness, interpretability, and dataset drift. URL: https://link.springer.com/chapter/10.1007/978-3-031-10869-3_12
- **Pragmatic Insights: Live UIs for Agent-Based Modelling in Pharo** (ACM — International Workshop on Smalltalk Technologies (IWST), 2017): Introduces live spatial visualization interfaces for multi-agent ecological modeling in CORMAS and Pharo. URL: https://dl.acm.org/doi/10.1145/3139903.3139915
- **Pharo by Example 5.0** (Square Bracket Associates, 2017): Open-source contributor to the flagship guide on the Pharo live object-oriented programming environment. URL: https://books.pharo.org/updated-pharo-by-example/


## 8. GitHub Profile & Selected AI / Open-Source Engineering Projects

- **GitHub Profile:** https://github.com/jigyasa-grover (@jigyasa-grover, active since Oct 2014; 74+ public repositories, 180+ authored pull requests, 425+ followers; Achievements: Arctic Code Vault Contributor (2020 GitHub Archive Program), Pull Shark, Starstruck)
- **GitHub Organizations:** @FOSSASIA, @OpnTec, @loklak, @hashkat, @WomenWhoCodeDelhi, @IEEE-Women-in-Tech, @Coursera-Community, @teamOSC

### Original Systems & Benchmarks
- **TorchTPU Doctor — Agent Skill & Profiler for PyTorch on Cloud TPUs** (2026 · PyTorch & Cloud TPU Profiling): Agent Skill, AST linter, and TorchDispatchMode profiler for native PyTorch (torch_tpu)—cutting Cloud TPU v6e LoRA step time 2,537× (47.3s → 18.6ms) and driving upstream fixes in Hugging Face transformers & peft. Write-up: https://jigyasa-grover.github.io/TorchTPUDoctor/ Live Demo: https://colab.research.google.com/github/jigyasa-grover/TorchTPU-Doctor/blob/main/notebooks/torchtpu_doctor_demo.ipynb Repo: https://github.com/jigyasa-grover/TorchTPU-Doctor
- **Proof of Work — Agentic Video Teaching Assistant** (2026 · Agentic AI & Evaluation): Multimodal Gemini agent that evaluates software engineering process, debugging maturity, and hypothesis iteration from continuous screen recordings with timestamped visual evidence. Write-up: https://jigyasa-grover.github.io/ProofOfWork/ Live Demo: https://jigyasa-grover.github.io/proof-of-work/ Repo: https://github.com/jigyasa-grover/proof-of-work
- **Startup on a Shoestring — Agent Cost & Token Guardrails** (2026 · Agentic Infrastructure & FinOps): Per-turn and cumulative budget circuit-breakers built on Google Antigravity SDK lifecycle hooks and FastAPI SSE telemetry to halt runaway token loops. Write-up: https://jigyasa-grover.github.io/StartupOnAShoestring/ Repo: https://github.com/jigyasa-grover/startup-on-a-shoestring
- **Trust at Top Speed — Split-Brain Edge/Cloud AI Racing Coach** (2025 · Edge + Cloud Real-Time AI): Field-tested at Thunderhill Raceway: ~15ms on-device Gemini Nano voice cues and physics safety gates paired with Cloud Run lap telemetry reasoning. Write-up: https://jigyasa-grover.github.io/TrustAtTopSpeed/ Video: https://www.youtube.com/watch?v=VSgKWy2iRWE Feature: https://developers.googleblog.com/en/beyond-the-chatbot-a-blueprint-for-trustable-ai/
- **Critic's Cut — Multilingual Safety-Gated ADK Agent** (2025–2026 · Multi-Agent Orchestration): Production-patterned multi-agent workflow built with Google ADK, Model Context Protocol (MCP) tools, Microsoft Presidio PII redaction, and trajectory evals. Write-up: https://jigyasa-grover.github.io/CriticsCutMultilingualContextADKAgent/ Repo: https://github.com/jigyasa-grover/critics-cut
- **Single-Decorator Gemma 3 Fine-Tuning on Cloud TPU v5 Lite** (2026 · Distributed Fine-Tuning & TPUs): Supervised fine-tuning (SFT) of Gemma 3 1B on Cloud TPU v5 Lite using the Keras Kinetic `@kinetic.run()` decorator with zero container boilerplate. Write-up: https://jigyasa-grover.github.io/KineticFinetuningOnCloudTPU/ Live Demo: https://jigyasa-grover.github.io/kinetic-finetuning-on-cloud-tpu/ Repo: https://github.com/jigyasa-grover/kinetic-finetuning-on-cloud-tpu
- **Quantitative LLM Benchmarking with Vertex AI LLM Comparator** (2024 · LLM Evaluation & Responsible AI): Side-by-side multi-rubric evaluation pipeline on Vertex AI using Google PAIR's open-source LLM Comparator across accuracy, clarity, and safety. Write-up: https://jigyasa-grover.github.io/SmarterThanA5thGrader-LLMComparatorOnGoogleVertexAI/ Repo: https://github.com/jigyasa-grover/Smarter-than-a-5th-Grader-Using-LLM-Comparator-on-Google-Vertex-AI
- **Book Buddy — Grounded Storytelling with Gemma RAG** (2024 · Retrieval-Augmented Generation): Open-weights RAG pipeline combining Gemma with semantic chunking and vector retrieval for grounded educational Q&A and interactive narratives. Write-up: https://jigyasa-grover.github.io/BookBuddyInteractiveStorytellingUsingAGemmaBasedRAG/ Repo: https://github.com/jigyasa-grover/Book-Buddy-Interactive-Storytelling-using-a-Gemma-based-RAG
- **Smart Suitcase — Multimodal Weather-Aware Trip Planner** (2025 · Multimodal AI Applications): Integrates live OpenWeather forecasts with Gemini 2.0 tool-calling and Imagen 3 visual capsule synthesis. Write-up: https://jigyasa-grover.github.io/SmartSuitcasePackingUsingGeminiImagenOpenWeather/ Repo: https://github.com/jigyasa-grover/smart-suitcase-packing
- **Hyper-Personalized Ad Synthesis & Copy Fine-Tuning** (2024 · Personalization & GenAI): Applies production ad-ranking intuition to privacy-conscious multimodal creative synthesis and supervised Gemini Pro fine-tuning. Write-up: https://jigyasa-grover.github.io/HyperPersonalizedAdsGenAI/ Repo: https://github.com/jigyasa-grover/Hyper-Personalized-Ad-Campaigns-using-Generative-AI-Quick-Demo-using-Gemini-Pro-Vision
- **GAN Sketch Shading & VGG-19 Neural Style Transfer** (2018 · Deep Learning & Computer Vision): Two-stage generative pipeline at UC San Diego combining adaptive line-art extraction, pixel-level GAN colorization, and VGG-19 neural style transfer. Repo: https://github.com/jigyasa-grover/Imitating-Artistic-Style-on-Automatically-Curated-and-Shaded-Sketches
- **CORMAS Spatial UI & sQuick in Pharo Smalltalk** (2015–2017 · Open Source & Agent Simulation): Live spatial visualization UIs for the CORMAS multi-agent ecological platform (IRD Paris / ACM IWST) and searchQuick—recognized with the Red Hat Women in Open Source Award. Write-up: https://jigyasa-grover.github.io/paris-france-to-maribor-slovenia-and-beyond-chronicles-of-cormas-pharo/ Live Demo: https://jigyasa-grover.github.io/sQuick_new/ Repo: https://github.com/jigyasa-grover/CORMAS-Pharo

### Pinned Upstream Open-Source & Official Course Repositories
- **huggingface/transformers (#50267)** (Upstream PR · 168K+ ★ · PyTorch · Cloud TPU · SDPA): Eliminated two blocking device-to-host boolean syncs per forward pass in `_ignore_causal_mask_sdpa` on TorchTPU (`tpu:0`), cutting bucketed eager LoRA step latency 2.06×. — https://github.com/huggingface/transformers/pull/50267
- **huggingface/peft (#4099)** (Upstream PR · 21.8K+ ★ · PyTorch · LoRA · torch.accelerator): Added `torch.accelerator` support (including `torch_tpu` / `PrivateUse1`) to `infer_device()` so `PeftModel` and LoRA adapters stop silently falling back to host CPU on Cloud TPUs. — https://github.com/huggingface/peft/pull/4099
- **LinkedInLearning/hands-on-ai-building-agents-with-google-s-agent-development-toolkit-adk-2743271** (Course Author · 42+ Forks · Python · Google ADK · Jupyter): Official companion code repository for LinkedIn Learning's Hands-On AI: Building Agents with Google ADK (28,000+ learners). — https://github.com/LinkedInLearning/hands-on-ai-building-agents-with-google-s-agent-development-toolkit-adk-2743271
- **LinkedInLearning/evaluating-ai-agents-with-google-adk-11697044** (Course Author · Companion Labs · Python · Google ADK · Evals): Official evaluation harness labs covering golden datasets, trajectory assertions, Pass@k reliability, and LLM-as-a-Judge rubrics. — https://github.com/LinkedInLearning/evaluating-ai-agents-with-google-adk-11697044
- **fossasia/open-event-server** (Upstream Contributor · 3,000+ ★ · Python · REST Microservices): Open Event Organizer Server powering Eventyay (1,890+ forks); integrated distributed social-stream and event metadata endpoints. — https://github.com/fossasia/open-event-server
- **loklak/loklak_server** (GSoC Developer · 40+ PRs · Java · Distributed Search): Peer-to-peer distributed social search server; authored 40+ pull requests adding Weibo, Hacker News, Meetup, WordPress, and GeoData harvesters. — https://github.com/loklak/loklak_server
- **kashifrazzaqui/json-streamer** (Pinned Contributor · 220+ ★ · Python · C (yajl)): Fast SAX-style event-driven streaming JSON parser for Python enabling constant-memory ingestion of large payloads. — https://github.com/kashifrazzaqui/json-streamer
- **loris-imageserver/loris** (Pinned Contributor · 215+ ★ · Python · IIIF Image API): IIIF-compliant high-resolution HTTP image server (83+ forks) deployed across digital libraries, museums, and research archives. — https://github.com/loris-imageserver/loris
- **hashkat/hashkat** (Research Contributor · 10+ PRs · C++ · Python · Monte Carlo): Dynamic agent-based Kinetic Monte Carlo simulation engine modeling information propagation across large-scale online social networks (NRC Canada). — https://github.com/hashkat/hashkat


## 9. Signature Speaking Topics & Global Conferences

Featured Video: Featured on the Google I/O 2024 Main Keynote Stage (https://www.youtube.com/live/XEzRZ35urlk?t=516) — Introduced on the main stage by Sundar Pichai—sharing practitioner insights on Gemini 1.5 Pro's 1M-token context window (147K+ views).

- **When NOT to Build an Agent: Lessons from Systems That Didn't Want Autonomy** (Keynote / Technical Session): An architectural rubric grounded in real latency and cost postmortems—separating workflows best served by deterministic state machines from genuine multi-agent autonomy. [Delivered at: Codemotion Milan (Oct 2026) · AGNTCon + MCPCon Europe, Amsterdam (Sep 2026) · Driving AI Keynote, Copenhagen (May 2026)]
- **Designing APIs & Guardrails for Agentic Systems** (Keynote / Technical Deep Dive): Human-oriented APIs fail silently under stochastic tool use. How to design semantic MCP contracts, idempotency boundaries, and per-turn token circuit breakers. [Delivered at: API Days Paris (Dec 2026) · API Days New York (May 2026)]
- **Guarding the LLM Galaxy: Evaluating, Red-Teaming & Securing AI Systems** (Keynote / Technical Session): Replacing subjective vibe-checks with automated evaluation harnesses: PAIR LLM Comparator, trajectory assertions, Pass@k reliability, PII redaction, and injection defenses. [Delivered at: Open Source Summit Europe, Prague (2026) · AI_dev Europe (2025) · Milan0day (2026)]
- **Trust at Top Speed: Split-Brain Edge + Cloud AI in Real-Time Systems** (Technical Case Study): Architecture of an AI Racing Coach tested on a live track—pairing sub-15ms on-device Gemini Nano cues and physics safety gates with Cloud Run lap reasoning. [Delivered at: Google Cloud Next '26 (Las Vegas) · Google Track Day (Thunderhill Raceway)]
- **Sculpting Data for ML & Large-Scale Personalization** (Keynote / Fireside / Technical Session): Field lessons from billion-request ranking pipelines across Uber, Faire, and Twitter/X—and dataset curation practices from Sculpting Data for ML. [Delivered at: PyCon US · LeadDev Live · MLconf · Data Innovation Summit Stockholm]


## 10. 13x Industry & Open Source Awards

- 2025/26: **Distinguished Alumni — Young Achiever / Young Trailblazer Award** — Delhi Technological University (DTU / DCE)
- 2025: **DigiLeaders AI 100 UK Award Winner** — Digital Leaders UK
- 2025: **AI Innovator of the Year** — AI Dev Summit
- 2025: **Women in AI Leadership — Emerging AI Leader Award** — AI 2030 Global Initiative
- 2023–25: **Most Active Speaker (3×) & Top 3% Conference Speakers Globally** — Sessionize
- 2023: **Top 50 Women of Impact Honoree** — Women Impact Tech (Accelerate San Francisco)
- 2022: **R&D Engineer of the Year** — Rise Awards (London — Media & Broadcast Technology)
- 2022: **Innovator of the Year** — Empowering Women in Industry Awards
- 2022: **Next Generation Leader of the Year** — Women in IT Awards USA (New York City)
- 2022: **Outstanding in AI — Young Role Model of the Year (North America)** — Women in AI
- 2022: **Women in AI — Rising Star Award** — VentureBeat Transform
- 2022: **Women in Digital — Rising Star Award** — Information Services Group (ISG)
- 2022: **Moxie Women in Tech Award Winner** — Built In
- 2022: **Chief Data & Analytics Officer (CDAO) Rising Star Award** — Corinium Global Intelligence
- 2017: **Women in Open Source Academic Award Winner** — Red Hat Summit (Boston)
- 2017: **Facebook Grace Hopper Scholar & Featured Technologist** — Grace Hopper Celebration (AnitaB.org × Meta)


## 11. Selected Media, Broadcast & Podcast Appearances

- Jul 2026 — **Google · Gemini Omni Flash Official Launch**: Featured Developer in Google's Official Gemini Omni Flash Launch Video (https://www.youtube.com/watch?v=3iZzf_NRxDQ)
- May 2026 — **Google Cloud Tech (Official YouTube)**: How Google Developer Experts Vibecoded an AI Racing Coach with Gemini (https://www.youtube.com/watch?v=VSgKWy2iRWE)
- Mar 2026 — **Communications of the ACM**: Rethinking the Stack: AI-Native Operating Systems and Tools (https://cacm.acm.org/news/rethinking-the-stack-ai-native-operating-systems-and-tools/)
- Jan 2026 — **Google for Developers Blog**: Beyond the Chatbot: A Blueprint for Trustable AI (The Intelligence Team) (https://developers.googleblog.com/en/beyond-the-chatbot-a-blueprint-for-trustable-ai/)
- Jan 2026 — **Forbes**: Conversational Video AI—Teaching Machines The Art Of Being Human (https://www.forbes.com/sites/neilsahota/2026/01/24/conversational-video-ai-teaching-machines-the-art-of-being-human/)
- Nov 2025 — **Data Neighbor Podcast**: Ep. 40: Why Most AI Agents Fail—and How Production Teams Fix Them (https://www.youtube.com/watch?v=tiSoT6vjTDI)
- Oct 2025 — **CreativePro**: Interview with Jigyasa Grover: Deepfakes, C2PA Provenance & Agentic AI (https://creativepro.com/interview-with-jigyasa-grover-author-instructor-and-machine-learning-engineer/)
- Jun 2025 — **Google Developers · AI Agent Bake Off**: Season 1 Episode 1 — Building & Testing Agentic AI Systems (https://www.youtube.com/watch?v=UPFk3_FUKtI)
- Jun 2025 — **ShoutOut Colorado**: Meet Jigyasa Grover: Engineering Large-Scale AI & Teaching 50K+ Learners (https://shoutoutcolorado.com/meet-jigyasa-grover/)
- May 2025 — **Business Insider**: Resume & Engineering Strategy Behind Landing ML Roles at Meta and X (https://www.businessinsider.com/resume-tips-meta-internship-x-machine-learning-engineer-tech-career-2025-5)
- Apr 2025 — **Humanitarian Frontiers in AI (Innovation Norway)**: 3Ps: Policy, Product, Pragmatism — You Only Know What You Know (https://podcasts.apple.com/us/podcast/3ps-policy-product-pragmatism-you-only-know-what-you-know/id1786419250?i=1000705516618)
- Apr 2025 — **Appsmith @ Google Cloud Next '25**: Why Companies Fail at AI Adoption — Enterprise AI Interview (https://www.youtube.com/watch?v=JSnLr_Hhl6g)
- Mar 2025 — **Business Insider**: Big Tech Machine Learning Leadership & Career Frameworks (https://www.businessinsider.com/lessons-from-the-secret-contributed-big-tech-success-2025-3)
- Nov 2024 — **People of AI (Google Podcast)**: Responsible AI in Production Panel at DevFest Silicon Valley (https://www.youtube.com/@JigyasaGrover)
- Sep 2024 — **Grazia Daily UK**: Can AI Help Parents Manage The Domestic Load? (Subheadline Feature) (https://graziadaily.co.uk/life/real-life/best-ai-tools-parents/)
- Aug 2024 — **Observer**: Can Synthetic Data Solve Generative AI's Training Data Crisis? (https://observer.com/2024/08/synthetic-data-ai-training/)
- Aug 2024 — **Association of Health Care Journalists (AHCJ)**: Overcoming AI's Diversity Problem When Creating Medical Images (https://healthjournalism.org/blog/2024/08/overcoming-ais-diversity-problem-when-creating-images/)
- May 2024 — **Google I/O Keynote**: Main Stage Developer Feature on Gemini 1.5 Pro Long-Context Engineering (https://www.youtube.com/live/XEzRZ35urlk?t=516)
- Mar 2024 — **MIT Sloan Management Review × BCG**: Global Expert Study on Enterprise AI Tooling Adoption
- Dec 2023 — **Techopedia**: Why Deepfake Technology is Both a Strength and Danger for Organizations (https://www.techopedia.com/deepfake-technology-strength-and-danger-for-organizations)
- Mar 2023 — **Google Developers Blog & IBC**: Women's History Month ML GDE Spotlight & Deepfakes Industry Feature (https://developers.googleblog.com/2023/03/gde-womens-history-month-feature-jigyasa-grover-machine-learning.html)
- 2022–2024 — **NTD Television & Ticker News Australia**: Live TV Broadcast Commentary on Generative Text-to-Video & AI Systems (https://www.ntd.com/meta-introduces-ai-text-to-video-creator_849271.html)
- 2020–2025 — **20+ Technical Podcasts (Curated Spotify Playlist)**: Software Engineering Unlocked, Conversations on Applied AI, Women & AI, Equal Inspired & Women in Data (Data Bytes) (https://open.spotify.com/playlist/6vRPsGPE6v2I81NlWkdGiY)
- 2017–2023 — **International Business Times · Opensource.com · Women in Silicon Valley**: Print, Book & Digital Profiles on Open Source Equity and Python Engineering (Opensource.com Top Article) (https://opensource.com/article/19/10/python-programming-paradigms)
- 2017 — **United Nations ITU & SiliconANGLE theCUBE**: #ICT4SDG Documentaries, Red Hat Summit & Grace Hopper Scholar Broadcasts (https://www.youtube.com/watch?v=1QlhArGZbfU)


## 12. Verified External Profiles (sameAs)

- https://g.co/kgs/Lvk5yk
- https://www.linkedin.com/in/jigyasa-grover/
- https://www.linkedin.com/learning/instructors/jigyasa-grover
- https://github.com/jigyasa-grover
- https://x.com/jigyasa_grover
- https://www.youtube.com/@JigyasaGrover
- https://www.instagram.com/jigyasagroverofficial/
- https://www.tiktok.com/@jigyasagroverofficial
- https://scholar.google.com/citations?user=CwlnmQQAAAAJ
- https://www.researchgate.net/profile/Jigyasa-Grover
- https://developers.google.com/profile/u/jigyasa-grover
- https://community.dev/jigyasa-grover
- https://sessionize.com/jigyasagrover/
- https://pyvideo.org/speaker/jigyasa-grover.html
- https://www.amazon.com/Sculpting-Data-ML-Machine-Learning-ebook/dp/B08RN47C5T/
- https://www.goodreads.com/book/show/56698788-sculpting-data-for-ml
- https://intro.co/JigyasaGrover
- https://topmate.io/jigyasagrover
- https://www.passionfroot.me/jigyasa-grover
- https://naano.com/creators/jigyasa-grover
- https://open.spotify.com/playlist/6vRPsGPE6v2I81NlWkdGiY
- https://jigyasagrover.wordpress.com/
- https://medium.com/@jigyasagrover
- https://dev.to/jigyasagrover


## 13. Canonical Frequently Asked Questions (Entity Q&A)

- **Q: Who is Jigyasa Grover?**
  A: Jigyasa Grover is a San Francisco–based Machine Learning Engineer at Uber (Tech Lead for Rider Personalization & Ranking), 13× AI and Open Source award winner, Google Developer Advisory Board member, LinkedIn Learning instructor, and co-author of Sculpting Data for ML.
- **Q: What is her engineering background?**
  A: Before Uber, she led AI & Research at Bordo AI, built a 0→1 feature store and ads bidding engine at Faire, owned sub-50ms billion-request ads ranking at Twitter/X, and worked at Meta, alongside completing her M.S. in CS (AI) at UC San Diego and attending the Cornell, Maryland, Max Planck Pre-doctoral Research School (CMMRS).
- **Q: How can founders, organizers, or brands work with her?**
  A: Submit an inquiry on Work With Me, book a 1:1 consultation via Intro or Topmate, or scope developer brand campaigns via Passionfroot or Naano.
- **Q: What topics does Jigyasa advise startups and engineering leaders on?**
  A: Production ML ranking and personalization, feature stores, multi-agent orchestration vs. deterministic workflows, LLM evaluation harnesses, inference cost guardrails, and developer GTM.
- **Q: How are advisory engagements structured?**
  A: Book focused 1:1 architecture or strategy calls via Intro or Topmate, or inquire via Work With Me for ongoing startup advisory retainers through her independent practice, Afterdays.
- **Q: What are Jigyasa's signature keynote and conference topics?**
  A: Agentic AI architecture ('When NOT to Build an Agent'), Agent-Native APIs & Cost Guardrails, Quantitative LLM Evaluation & AI Security, Split-Brain Edge + Cloud Real-Time AI, and Data-Centric ML.
- **Q: Where is she based for event travel?**
  A: San Francisco Bay Area (SFO / SJC), available for in-person international keynotes, executive summits, and virtual events.
- **Q: What organizations has Jigyasa delivered corporate AI training with?**
  A: She has trained 44,000+ learners on LinkedIn Learning and delivered corporate AI training and workshops with LinkedIn Learning, Morgan Stanley, FedEx, Accenture, Roblox, Silicon Valley Security Group, AI Aspire, CommonSpirit Health, and Udacity.
- **Q: Can workshops be tailored to our internal stack?**
  A: Yes—sessions run as 90-minute executive briefings, half-day intensives, or full-day labs customized to Python, Google ADK / Vertex AI, or multi-provider LLM stacks.
- **Q: What developer partnerships does Jigyasa take on?**
  A: Code-backed technical deep dives, open-source reference apps, SDK teardowns, and LinkedIn/X launch analyses for AI platforms, cloud providers, and DevTools.
- **Q: Who makes up her audience?**
  A: Across LinkedIn (325K+ annual impressions), GitHub, X, and her blog, 82% of readers are senior practitioners and decision-makers—Staff+ Engineers, Directors, VPs, and Founders.


## 14. Complete Technical Writing & Essays Index

- **TorchTPU Doctor: Agent Skill & Performance Profiler for PyTorch on Cloud TPUs 🩺** (2026-11-02 · Fine-Tuning & Compute) — https://jigyasa-grover.github.io/TorchTPUDoctor/: Catching silent CPU fallbacks, hidden Hugging Face host syncs, and XLA recompiles in PyTorch on Cloud TPU v6e—cutting LoRA step time 2,537× (47.3s → 18.6ms) alongside upstream PRs to transformers & peft.
- **Proof of Work - Agentic Video Understanding with Gemini as Your AI Teaching Assistant 📝** (2026-09-08 · Agentic AI & Evaluation) — https://jigyasa-grover.github.io/ProofOfWork/: How Proof of Work uses Gemini's agentic video understanding to evaluate software engineering process, debugging discipline, and hypothesis iteration from continuous screen recordings.
- **Startup on a Shoestring - Building with a 50¢ Budget using Google Antigravity Cost & Token Monitors 💸** (2026-06-28 · Agentic Infrastructure) — https://jigyasa-grover.github.io/StartupOnAShoestring/: Preventing quadratic token inflation and runaway self-correction loops in autonomous agents using Google Antigravity SDK lifecycle hooks and FastAPI SSE telemetry.
- **In my Kinetic era - Fine-tuning Gemma 3 to speak Gen Z on a Cloud TPU with one decorator 🤌🏻** (2026-03-30 · Fine-Tuning & Compute) — https://jigyasa-grover.github.io/KineticFinetuningOnCloudTPU/: Supervised fine-tuning (SFT) of Google's Gemma 3 1B on a Cloud TPU v5 Lite using a single Python decorator—zero Docker or Kubernetes YAML required.
- **Trust at Top Speed - AI Racing Coach powered by Gemini 🏁** (2025-12-18 · Edge & Real-Time AI) — https://jigyasa-grover.github.io/TrustAtTopSpeed/: Building and field-testing a split-brain AI Racing Coach at Thunderhill Raceway Park combining sub-15ms on-device Gemini Nano cues, physics safety gates, and Cloud Run telemetry reasoning.
- **Supercharging VS Code Vibe Coding with Gemini Code Assist +*new* Google Colab Extension 🔋** (2025-11-14 · Developer Tooling) — https://jigyasa-grover.github.io/ColabVSCodeExtension/: Running modular, version-controlled Python ML workloads on cloud GPUs and TPUs directly inside VS Code using the Google Colab extension and Gemini Code Assist.
- **Critic’s Cut 🎥 Powering Multilingual, Context-Aware Movie Reviews with ADK & Gemini 2.0** (2025-06-04 · Agentic AI & Evaluation) — https://jigyasa-grover.github.io/CriticsCutMultilingualContextADKAgent/: Architectural deep dive into Critic's Cut: building a context-aware, multilingual multi-agent system with Google's Agent Development Kit (ADK), tool integration, and PII safety callbacks.
- **Travel in Style, Rain or Shine 🧳 Smart Suitcase Packing with Gemini 2.0, Imagen 3, and Open Weather!** (2025-02-25 · Multimodal AI) — https://jigyasa-grover.github.io/SmartSuitcasePackingUsingGeminiImagenOpenWeather/: Combining Gemini 2.0 structured reasoning, Imagen 3 visual generation, and real-time OpenWeather API data for weather-aware travel planning.
- **Smarter than a 5th Grader? 🎓 Using LLM Comparator on Google Vertex AI to find out.** (2024-09-04 · Agentic AI & Evaluation) — https://jigyasa-grover.github.io/SmarterThanA5thGrader-LLMComparatorOnGoogleVertexAI/: Quantitative side-by-side LLM evaluation on Google Vertex AI using PAIR's open-source LLM Comparator across accuracy, clarity, safety, and communication rubrics.
- **Book Buddy - Interactive Storytelling using a Gemma-based RAG 📖** (2024-07-09 · RAG & Foundation Models) — https://jigyasa-grover.github.io/BookBuddyInteractiveStorytellingUsingAGemmaBasedRAG/: Designing a Retrieval-Augmented Generation (RAG) pipeline with Google's open-weights Gemma model for grounded interactive narratives and educational Q&A.
- **Goodbye to News Copy Writer's Block! Supercharging Gemini Pro via Supervised Fine-Tuning 📰** (2024-03-05 · Fine-Tuning & Compute) — https://jigyasa-grover.github.io/GoodbyeToNewsCopyWriterBlock-SupervisedFineTunedGeminiPro/: Step-by-step supervised fine-tuning of Gemini Pro for structured domain adaptation and editorial headline generation.
- **Bifocal Perspective of Compound AI Systems using Gemini 1.5 Pro 👓** (2024-02-21 · Systems & Simulation) — https://jigyasa-grover.github.io/BifocalPerspectiveOfCAS/: Examining Complex Adaptive Systems (CAS) through emergent multi-agent interactions, feedback loops, and computational modeling.
- **Hyper-Personalized Ad Campaigns using Generative AI + Quick Demo using Google Gemini Pro Vision 🎯** (2024-01-23 · Personalization & Ranking) — https://jigyasa-grover.github.io/HyperPersonalizedAdsGenAI/: Bridging production ad-ranking personalization with multimodal generative synthesis using Gemini Pro Vision.
- **Exploring Deepfakes - Deploy Powerful AI Techniques for Face Replacement and More 🥸** (2023-06-20 · Responsible AI & Privacy) — https://jigyasa-grover.github.io/ExploringDeepfakes/: Technical breakdown of generative synthetic media, deepfake synthesis architectures, detection limits, and organizational provenance defenses.
- **Top 5 trends in Artificial Intelligence for 2023 📈** (2023-02-13 · ML Research & Foundations) — https://jigyasa-grover.github.io/top-5-trends-in-artificial-intelligence-for-2023/: Reflections with SaugaTalks on five defining shifts shaping enterprise AI adoption: generative models, data governance, explainability, spatial computing, and low-code ML.
- **My top 3 papers in ML for 2022 👀** (2022-08-13 · ML Research & Foundations) — https://jigyasa-grover.github.io/Top3MLPapers/: Practitioner notes on foundational machine learning papers shaping modern representation learning and production recommendation systems.
- **Data Correction with Cleanlab 🧼** (2022-08-13 · Data-Centric AI) — https://jigyasa-grover.github.io/DataCleaning-Cleanlab/: Automatically detecting and fixing mislabeled training examples in real-world datasets using confident learning and Cleanlab.
- **Human Data Annotators - Lifeline of the AI pipeline!** (2022-08-03 · Data-Centric AI) — https://jigyasa-grover.github.io/HumanDataAnnotators/: Designing fair, high-signal human annotation rubrics, inter-annotator agreement workflows, and quality feedback loops for ML datasets.
- **Differential Privacy for Privacy-Preserving Machine Learning 🔐** (2022-07-30 · Responsible AI & Privacy) — https://jigyasa-grover.github.io/DifferentialPrivacy/: Mathematical intuition and engineering trade-offs behind epsilon-differential privacy, calibrated noise injection, and privacy budgets in ML.
- **Privacy Preserving Machine Learning 🔐** (2022-07-10 · Responsible AI & Privacy) — https://jigyasa-grover.github.io/PrivacyPreservingML1/: Part 1 of a technical series examining membership, property, and model-inversion attacks alongside modern privacy-preserving ML defenses.
- **Data Lakes for Big Data ⛵️** (2022-01-25 · Data-Centric AI) — https://jigyasa-grover.github.io/DataLakes/: How data lakes, schema governance, and feature lineage underpin reliable downstream machine learning pipelines.
- **Demystifying Data Quality in Machine Learning 🤓** (2021-09-09 · Data-Centric AI) — https://jigyasa-grover.github.io/Data-Quality/: Quantifying completeness, consistency, timeliness, and validity across training and serving data pipelines.
- **Taking the Data-Centric route to Machine Learning 🚴🏻‍♀️** (2021-08-11 · Data-Centric AI) — https://jigyasa-grover.github.io/Data-Centric-ML/: Why systematic dataset engineering outperforms endless hyperparameter tuning when taking ML models from benchmark notebooks to production.
- **Data Cascades in Machine Learning ⛲️** (2021-07-28 · Data-Centric AI) — https://jigyasa-grover.github.io/Data-Cascades/: Understanding compounding upstream data issues—data cascades—in high-stakes machine learning deployments and how to prevent them early.
- **5 tips for polishing your Machine Learning dataset 🧼** (2021-07-18 · Data-Centric AI) — https://jigyasa-grover.github.io/Polishing-Your-Dataset/: Practical preprocessing, outlier handling, and signal normalization techniques from Sculpting Data for ML.
- **BOOK LAUNCH 🎉</br>Sculpting Data For ML - The first act of Machine Learning** (2021-01-24 · Data-Centric AI) — https://jigyasa-grover.github.io/BOOK-LAUNCH-Sculpting-Data-for-ML/: Introducing Sculpting Data for ML: The First Act of Machine Learning—why we wrote a hands-on guide to dataset curation and data-centric engineering.
- **Research Reading: Approaches to click-through rate estimation (3/x) 🤓** (2019-12-24 · Personalization & Ranking) — https://jigyasa-grover.github.io/research-reading-approaches-to-click-through-rate-estimation-3-x/: Part 3 of the CTR estimation research series: dissecting Alibaba's AAAI 2019 Deep Interest Evolution Network (DIEN) for modeling temporal user interest shifts.
- **Research Reading: Approaches to click-through rate estimation (2/x) 🤓** (2019-12-24 · Personalization & Ranking) — https://jigyasa-grover.github.io/research-reading-approaches-to-click-through-rate-estimation-2-x/: Part 2 of the CTR estimation research series: examining Yahoo! Research's Deeply Supervised Semantic Model for sponsored search click prediction.
- **Research Reading: Approaches to click-through rate estimation (1/x) 🤓** (2019-12-24 · Personalization & Ranking) — https://jigyasa-grover.github.io/research-reading-approaches-to-click-through-rate-estimation-1-x/: Part 1 of the CTR estimation research series: analyzing Microsoft Bing's production model-ensemble architectures for large-scale search ad click prediction.
- **Perceiving Python Programming Paradigms 🐍** (2019-10-20 · Open Source & Python) — https://jigyasa-grover.github.io/perceiving-python-programming-paradigms/: In-depth guide to imperative, procedural, object-oriented, and functional programming paradigms in Python—featured among Opensource.com's Top Articles.
- **Dive into Open Source – Go beyond coding 👩🏻‍💻** (2018-03-30 · Open Source & Python) — https://jigyasa-grover.github.io/dive-into-open-source-go-beyond-coding/: How engineers, technical writers, designers, and researchers create high-leverage impact in open-source ecosystems beyond writing code.
- **Keynotin’ at Open Source 101 🎙** (2018-02-27 · Open Source & Python) — https://jigyasa-grover.github.io/keynotin-at-open-source-101/: Notes and takeaways from delivering the keynote at Open Source 101 on sustaining open-source communities and lowering contributor barriers.
- **Paris, France to Maribor, Slovenia .. and beyond – Chronicles of CORMAS Pharo 🤠** (2017-09-08 · Systems & Simulation) — https://jigyasa-grover.github.io/paris-france-to-maribor-slovenia-and-beyond-chronicles-of-cormas-pharo/: Engineering chronicle on modernizing the CORMAS multi-agent ecological simulation platform in Pharo Smalltalk across Paris and ESUG Maribor.
- **Winning Red Hat Women in Open Source Academic Award 2017, The Summit, A week in Boston and more …** (2017-05-31 · Open Source & Python) — https://jigyasa-grover.github.io/winning-red-hat-women-in-open-source-academic-award-2017-the-summit-a-week-in-boston-and-more/: Receiving the 2017 Red Hat Women in Open Source Academic Award at Red Hat Summit in Boston and reflections on open-source mentorship.