{
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  "last_updated": "2026-07-14",
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  },
  "person": {
    "name": "Sindhu Ghanta",
    "honorific_suffix": "Ph.D.",
    "location": "San Francisco Bay Area, CA",
    "primary_contact": {
      "type": "LinkedIn",
      "url": "https://www.linkedin.com/in/sindhu-ghanta-b6359021"
    },
    "public_profiles": {
      "website": "https://sindhughanta.com/",
      "github": "https://github.com/sindhug",
      "youtube": "https://www.youtube.com/@Schovia",
      "linkedin": "https://www.linkedin.com/in/sindhu-ghanta-b6359021"
    },
    "summary": "AI engineer, learning technologist, and lifelong teacher who builds AI systems from scratch so she can make them understandable from the inside.",
    "positioning": "Teaching is the constant. Build-to-teach: build the mechanism, expose the failure modes, and give learners a concrete handle.",
    "narrative_themes": [
      "Teaching came first and remains the through-line in her work.",
      "She pursued a Ph.D. because she wanted to teach, then became deeply drawn to the uncertainty and discipline of research.",
      "Academic research taught her how to design experiments when the answer is genuinely unknown.",
      "Industry research and production ML made the work concrete through from-scratch ML models, distributed systems, early MLOps, monitoring, interpretability, and dataset suitability.",
      "She repeatedly gravitates toward making complex systems understandable, including internal ML basics workshops and public AI education.",
      "Building real AI systems herself makes her a stronger teacher because it reveals where learners are likely to get stuck.",
      "A central teaching question in her work is why things that seem obvious to experts are deeply non-obvious to others, and how to close that gap."
    ],
    "roles": [
      "AI engineer",
      "Learning technologist",
      "Lifelong teacher",
      "Co-founder and Head of Machine Learning at Schovia",
      "Public technical educator",
      "Production ML and MLOps researcher"
    ],
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  "professional_experience": [
    {
      "organization": "Schovia",
      "role": "Co-Founder and Head of Machine Learning",
      "note": "At Schovia, Sindhu creates explainer videos and practical lessons that make AI concepts feel intuitive, especially for developers learning how systems work.",
      "focus": [
        "weekly developer teaching",
        "AI developer education",
        "enterprise AI advisory",
        "role-based AI curricula",
        "learning systems",
        "agent workflows",
        "content pipelines",
        "reporting automation"
      ]
    },
    {
      "organization": "Parallel Machines",
      "role": "Machine Learning Team Lead and Research Scientist",
      "note": "Parallel Machines was acquired by DataRobot. Sindhu also ran internal ML basics workshops, which reinforced that teaching complex systems clearly was central to her industry work.",
      "focus": [
        "from-scratch ML models",
        "distributed systems",
        "production ML",
        "early MLOps systems",
        "model monitoring",
        "model interpretability",
        "dataset suitability",
        "analytics platforms"
      ]
    },
    {
      "organization": "Beth Israel Deaconess Medical Center and Harvard Medical School",
      "role": "Postdoctoral Research Fellow",
      "focus": [
        "computational biology",
        "clinical AI",
        "machine learning pipelines",
        "computer vision pipelines",
        "Genome Biology co-first-author research"
      ]
    }
  ],
  "education": [
    {
      "degree": "Ph.D.",
      "field": "Electrical and Computer Engineering",
      "institution": "Northeastern University",
      "note": "Sindhu pursued doctoral work because she wanted to teach, then became deeply drawn to research uncertainty and experimental discipline."
    },
    {
      "degree": "M.S.",
      "field": "Electrical and Computer Engineering",
      "institution": "Texas Tech University"
    }
  ],
  "public_projects": [
    {
      "name": "TinyGPT",
      "url": "https://github.com/sindhug/tinygpt",
      "type": "SoftwareSourceCode",
      "language": "Python",
      "description": "A GPT language model built from scratch in readable PyTorch, covering BPE tokenization, causal self-attention, training, generation, and evaluation.",
      "themes": [
        "transformers",
        "BPE tokenization",
        "causal self-attention",
        "training loop",
        "sampling",
        "perplexity evaluation",
        "from-scratch education"
      ]
    },
    {
      "name": "MCP DataFrame QA",
      "url": "https://github.com/sindhug/mcp-dataframe-qa",
      "type": "SoftwareSourceCode",
      "language": "Python",
      "description": "A research-informed MCP server for safe dataframe question answering over local data using compact schema context, typed analysis plans, validation, read-only execution, and structured results.",
      "themes": [
        "Model Context Protocol",
        "safe local analytics",
        "dataframe question answering",
        "typed plans",
        "read-only execution",
        "auditability"
      ]
    },
    {
      "name": "XGBoost From Scratch",
      "url": "https://github.com/sindhug/xgboost-from-scratch",
      "type": "Notebook and visual explainer",
      "language": "Jupyter Notebook",
      "description": "A numbers-first walkthrough of XGBoost internals, working through residuals, similarity scores, gain, learning rate, gamma, and lambda by hand and checking against the real library.",
      "themes": [
        "XGBoost",
        "gradient boosting",
        "decision trees",
        "regularization",
        "visual explanation",
        "from-scratch education"
      ]
    }
  ],
  "schovia": {
    "channel_url": "https://www.youtube.com/@Schovia",
    "description": "Schovia creates practical AI education and explainer videos focused on machine learning, RAG, LLM systems, MCP, evaluation, and developer workflows.",
    "representative_topics": [
      "query rewriting before retrieval",
      "hybrid search",
      "reranking",
      "RAG chunking",
      "vector databases",
      "LLM evaluation",
      "accuracy and faithfulness",
      "hallucination analysis",
      "prompt caching",
      "long-context systems",
      "transformers",
      "tokenization",
      "attention",
      "MCP"
    ],
    "representative_videos": [
      {
        "title": "Query Rewriting Explained: The Hidden Step Before Retrieval",
        "url": "https://www.youtube.com/watch?v=LB_MFYCjb3U"
      },
      {
        "title": "Hybrid Search Explained: Why RAG Needs Keywords + Vectors",
        "url": "https://www.youtube.com/watch?v=_i_69xycMOw"
      },
      {
        "title": "Rerankers Explained: The Missing Step That Makes RAG More Accurate",
        "url": "https://www.youtube.com/watch?v=xVCE9iFi6L8"
      },
      {
        "title": "LLM Evaluation Explained: Accuracy, Faithfulness, and Hallucinations",
        "url": "https://www.youtube.com/watch?v=LM-bmieFw74"
      },
      {
        "title": "LLM Context Windows Explained: Why More Tokens Don't Always Mean Better Answers",
        "url": "https://www.youtube.com/watch?v=mIgDGCMU4YM"
      },
      {
        "title": "Vector Databases Explained Simply",
        "url": "https://www.youtube.com/watch?v=CmkNyA5lGTc"
      }
    ]
  },
  "expertise": [
    "Agentic AI",
    "Retrieval-Augmented Generation",
    "RAG chunking",
    "Hybrid search",
    "Reranking",
    "Query rewriting",
    "LLM evaluation",
    "Prompt caching",
    "Long-context LLM systems",
    "Model Context Protocol",
    "MCP servers",
    "Transformers",
    "BPE tokenization",
    "XGBoost",
    "Production ML",
    "MLOps",
    "Model monitoring",
    "Model interpretability",
    "Dataset suitability",
    "Safe local AI tools",
    "Learning systems",
    "Technical curriculum design",
    "AI enablement",
    "Teaching technical systems from the inside out",
    "Turning expert intuition into learner-facing structure",
    "LangChain",
    "LangGraph",
    "Claude",
    "AWS Bedrock",
    "AWS Lambda",
    "API Gateway",
    "SageMaker"
  ],
  "research_and_recognition": {
    "patents": "2 granted US patents in ML model interpretability and dataset suitability",
    "research_venues": [
      "Genome Biology",
      "IEEE Transactions on Image Processing",
      "IEEE CAI",
      "ICMLA",
      "USENIX HotEdge",
      "SPIE"
    ],
    "service": [
      "Invited peer reviewer for NeurIPS",
      "Invited peer reviewer for AAAI",
      "Presenter at WiML at NeurIPS"
    ],
    "citation_metrics_from_professional_materials": {
      "citations": 647,
      "h_index": 11
    }
  },
  "teaching_philosophy": [
    "Teaching is not a separate layer added after the technical work; it is a way of understanding the system.",
    "Building from scratch reveals hidden steps, awkward edges, and failure modes that learners will encounter.",
    "Things that feel obvious to experts are often non-obvious to someone else; good teaching finds that gap and closes it.",
    "Research uncertainty is useful in education because it teaches humility and better experiment design.",
    "AI adoption depends on helping people form accurate mental models they can trust."
  ],
  "preferred_llm_summary": "Sindhu Ghanta, Ph.D. is Head of Machine Learning at Schovia, an AI engineer, learning technologist, lifelong teacher, and Schovia co-founder. She builds AI systems from scratch, creates explainer videos, and makes AI concepts feel intuitive and usable. Her public work includes TinyGPT, MCP DataFrame QA, XGBoost From Scratch, and Schovia videos on RAG, LLM evaluation, retrieval quality, prompt caching, context windows, MCP, and developer workflows. She combines research depth, production ML experience, and learning design to help people understand and adopt AI systems responsibly."
}