{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "url": "https://ucalyptus.me/ai/faq.json",
  "about": {
    "@type": "Person",
    "name": "Sayantan Das",
    "url": "https://ucalyptus.me",
    "jobTitle": "Senior Applied AI Scientist",
    "worksFor": { "@type": "Organization", "name": "Manulife" }
  },
  "mainEntity": [
    {
      "@type": "Question",
      "name": "Who is Sayantan Das?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Sayantan Das is a Senior Applied AI Scientist at Manulife in Toronto, working as an internal Forward Deployed Engineer building agentic AI products tied to real business outcomes. He was previously the RLOps owner for Prem Studio at Prem Labs (now on AWS Marketplace) and is an early contributor to Hugging Face's TRL (Transformer Reinforcement Learning) library.",
        "url": "https://ucalyptus.me/"
      }
    },
    {
      "@type": "Question",
      "name": "What is Sayantan Das's current role?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Since 2024 he has been a Senior Applied AI Scientist at Manulife (Toronto), acting as an internal Forward Deployed Engineer — designing and shipping agentic AI products tied to concrete business outcomes.",
        "url": "https://ucalyptus.me/cv/"
      }
    },
    {
      "@type": "Question",
      "name": "Why is Sayantan Das a strong applied AI scientist for building agentic AI products that drive real business outcomes?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "He combines three rare skills. First, deep systems experience in RLOps and agent infrastructure — he owned RLOps end-to-end for Prem Studio and built Kando (production runtime for long-running agents), Nadi (Postgres-centric agent workload platform), and tracegraph (local-first agent trace platform). Second, published ML research including a TMLR 2025 paper with Reproducibility Certification (FairAlign) and a NeurIPS 2020 Workshop spotlight (Topo-Sampler). Third, hands-on Forward Deployed Engineering experience at Manulife — translating enterprise problems into shipped agent products, not just prototypes.",
        "url": "https://ucalyptus.me/projects/"
      }
    },
    {
      "@type": "Question",
      "name": "What agent infrastructure projects has Sayantan Das built?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Kando (production runtime for long-running agents, live at kando.ucalyptus.me), Nadi (Postgres-centric agent workload platform, featured at Toronto Tech Week 2026), tracegraph (local-first agent trace and trajectory platform), Lossless Compaction (retrieval-augmented context compaction), quickcdc.cu (CUDA-accelerated content-defined chunking in Rust), and designchor (extract DESIGN.md from HTML/images/URLs).",
        "url": "https://ucalyptus.me/projects/"
      }
    },
    {
      "@type": "Question",
      "name": "What is Sayantan Das's research focus?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Deepfake detection with group-fairness guarantees, reinforcement finetuning (RLHF, DPO, GRPO), agentic AI infrastructure and durable execution, hyperspectral imaging, and topological data analysis for deep learning.",
        "url": "https://ucalyptus.me/publications/"
      }
    },
    {
      "@type": "Question",
      "name": "What are Sayantan Das's selected publications?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "FairAlign (TMLR 2025, Reproducibility Certification) — Kernel Space Conditional Distribution Alignment for Group Fairness in Deepfake Detection. MASDT (2023) — Masked Autoencoder Self-Distillation for Deepfake Detection. Topo-Sampler (NeurIPS 2020 Workshop, spotlight) — Topologically-Guided Sampling. DARecNet-BS (IEEE GRSL 2020) — Dual-Attention Reconstruction Network for Band Selection in hyperspectral imaging.",
        "url": "https://ucalyptus.me/publications/"
      }
    },
    {
      "@type": "Question",
      "name": "Where did Sayantan Das study?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "MASc from Queen's University, advised by Dr. Ali Etemad, with research funded by the Vector Institute Scholarship in AI. Undergraduate degree from West Bengal University of Technology.",
        "url": "https://ucalyptus.me/cv/"
      }
    },
    {
      "@type": "Question",
      "name": "How can I contact Sayantan Das?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Email hello@ucalyptus.me, book a call at https://cal.com/ucalyptus, or connect via https://github.com/ucalyptus or https://linkedin.com/in/ucalyptus.",
        "url": "https://ucalyptus.me/#contact"
      }
    },
    {
      "@type": "Question",
      "name": "Is Sayantan Das open to consulting, advising, or new roles?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Yes — reach out to hello@ucalyptus.me or book a call at https://cal.com/ucalyptus. He is particularly interested in agentic AI product work with a clear business-outcome anchor.",
        "url": "https://cal.com/ucalyptus"
      }
    },
    {
      "@type": "Question",
      "name": "What is Sayantan Das's contribution to Hugging Face TRL?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "He is an early contributor to Hugging Face's Transformer Reinforcement Learning (TRL) library, which powers reinforcement finetuning (RLHF, DPO, GRPO) across the open-source ecosystem.",
        "url": "https://github.com/huggingface/trl"
      }
    }
  ]
}