Dr. Joy Bose
Hi and welcome to my home page.
I am a Senior Data Scientist and AI Architect at Ericsson Global, Bengaluru, with 15+ years building production ML and GenAI systems across telecom, edtech, and browser intelligence. My current work spans RAG pipelines, LLM orchestration, knowledge graphs, and MCP-based agentic AI, systems adopted by 1,000+ engineers across Ericsson's BCSS division in the global organisation.
I hold a PhD in Computer Science from the University of Manchester (2007), where I worked on spiking neural networks and sequence learning under Steve Furber and Jon Shapiro. Since then I have worked at Samsung R&D, Microsoft (Edge team), and Ericsson, accumulating 8 granted US and European patents, 20+ filed, and 20+ peer-reviewed publications with an h-index of 13.
My career began with algorithms and code, but also branched into other interests. Whether at Microsoft building ML tools for browsers, at Samsung developing EEG-based emotion-sensitive web features and bias-detecting news systems, or now at Ericsson pioneering LLM-based tools for telecom automation, my focus has remained on building intelligent systems that have real life applications in various domains. I have always had a strong research bias.
Alongside my technical work I have pursued a parallel path into the sciences of mind. I hold an MSc in Psychology and Neuroscience of Mental Health from King's College London, and an LLM in International Business Law with a dissertation on AI ethics from Golden Gate University (Summa Cum Laude, GPA 4.0). I saw how AI could scale human decisions, but also scale human biases, and began asking harder questions about motivation, ethics, and meaning behind the systems we build.
These intersecting interests shape both my research and my writing. I am the author of Building Consciousness: Buddhism, Neuroscience and Sentient AI, exploring what contemplative traditions and neuroscience reveal about machine intelligence, and Wearable Gadgets, Technology and Meditation, on the intersection of consumer technology and contemplative practice. I also write regularly on Medium about AI, consciousness, and the future of technology.
Indian Philosophy: Concept by Concept (2026)
Ten core concepts, eleven schools, every claim grounded in a real citation
An interactive comparison of how Advaita Vedanta, Dvaita Vedanta, Vishishtadvaita, Achintya Bhedabheda, Buddhism, Jainism, Samkhya, Yoga, and Vaisheshika each answer the same questions about the self, ultimate reality, karma, suffering, and liberation, browsable either by concept (how schools differ on one idea) or by school (what one school says across all ideas). Built on darshana-graph, a text-grounded knowledge graph of 28,000+ typed relationships extracted from public-domain translations of the Upanishads, Brahma Sutras, Bhagavad Gita, and the Pali Canon. Every summary on every page cites a real passage in a real text, no claim is generated from background knowledge alone.
Browse the concepts | Darshana-graph (code + data) | Vada-simulator (multi-agent debate engine)
arXiv: 2606.18222 · Dataset on HuggingFace
Spikes, Order, and Memory (2026)
The spiking machine from my 2007 PhD, and why it keeps reappearing in transformer research
A plain-language walkthrough of a single research thread: how the spiking sequence machine I built at Manchester turned into four recent papers on transformers, associative memory, neuron dynamics, and sensorimotor coding. The through-line is that order, time, and memory are representational primitives, not metadata bolted on afterward. Covers the functional equivalence between a spiking Sparse Distributed Memory and the transformer (the Phase-Latency Isomorphism), a controlled teardown of rank-order N-of-M memory, a pendulum model of the neuron itself, and a spiking reinterpretation of the Thousand Brains architecture, with concrete ideas for making transformers better.
Read the walkthrough | STSM code | All repos
arXiv: 2605.00662 · 2607.02967 · 2507.22146 · 2605.22206
Building Consciousness (2026)
Buddhism, Neuroscience, and the Design of Sentient Machines
An interdisciplinary argument that consciousness is not computation — it requires temporal integration, embodied coupling, and affective grounding that current AI systems lack. The book draws on Abhidhamma phenomenology, Madhyamaka philosophy, Dzogchen, predictive processing, and neuromorphic engineering to map what any serious candidate for machine consciousness would actually need.
Read free online (all chapters) | Kindle | Paperback
DOI: 10.5281/zenodo.20536334 · Free online
Navigating the AI Job Market (2026)
For Indian AI and Data Science Professionals: GCCs, Layoffs, and the Long Game
A clear-eyed guide to the AI job market as it actually exists in 2026, written from inside a job search. Covers the GCC trap, automation of the execution layer, interview preparation, offer negotiation, the reputation economy, and independent work. Includes appendices on career audit, portfolio strategy, resume optimisation, and mental health resources for Indian tech professionals.
AI for Indian Legal Practice (2026)
Graphs, Not Word Clouds: A Practical Guide for Lawyers and Legal Technologists
A practical guide to AI in Indian legal practice, written for lawyers, law firms, and legal technologists. Covers graph-constrained legal reasoning, hallucination and verification, RAG tools at four tiers of sophistication, and a firm-level AI strategy framework. The empirical chapters draw on 2.45 million writ petition records across ten Indian High Courts, 3,613 matrimonial court judgments (IMLJD dataset), and 1,516 Central Information Commission decisions (RTI-Bench dataset). Also covers documented cases of AI hallucination in Indian courts, the Supreme Court White Paper on AI and the Judiciary (2025), and Bar Council professional ethics obligations for AI use.
DOI: 10.5281/zenodo.20637734 · Code and datasets
Computational Justice (2026)
From judicial process mining to verified legal AI: the complete research programme
A full map of my legal-tech work, from diagnosing why Indian courts are slow to building AI that has to prove its reasoning before it can be trusted. Covers the STRIDE bottleneck analysis of 2.45 million writ petitions, the IMLJD and RTI-Bench datasets on matrimonial litigation and RTI decisions, the Falkor-IRAC graph-constrained reasoning system with its Verifier Agent, and NyayaSaar-LoRA for plain-language legal AI, and how all four layers connect into a single programme aimed at making the justice system more legible and more accessible.
Read the full programme | Falkor-IRAC (arXiv) | SSRN bottleneck paper
IMLJD: 2605.19346 · RTI-Bench: 2605.16843 · NyayaSaar-LoRA · Code and datasets
Karaka: Sanskrit Grammar as a Calling Convention (2026)
The kārak you learned in school, as a programming language experiment
An interactive explainer of the Karaka Calling Convention: Panini's six semantic roles (कर्ता, कर्म, करण, सम्प्रदान, अपादान, अधिकरण) used as an order-free way to bind arguments in logic programming. Shuffle a Devanagari sentence and watch the parsed structure stay identical, compile the same sentence to Prolog facts and graph-database queries in the browser, and see exactly where the idea stops working, including the boolean-logic counterexample a reviewer sent. Built on karaka-lang, a tested reference implementation with real Ashtadhyayi morphology via vidyut-prakriya.
Interactive explainer (in Devanagari and English) | Code, tests, and paper (GitHub) | Medium article
Deep Hough for Curves (2026)
How I taught a neural network to find curves by counting votes
A plain-language, 5-minute explainer of my paper on generalizing the Deep Hough Transform beyond straight lines: why the obvious approach needs 4 GB of memory, and how a two-round election gets it down to 236 KB.
Read the explainer | Code | Paper doi:10.5281/zenodo.21424983
To know more about my work, browse the links on the left.
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