{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "candidate": {
    "name": "Pavan Maddula",
    "alternateNames": ["M P V S Gopinadh", "Maddula Gopinadh"],
    "primaryTitles": [
      "AI Research Engineer",
      "Research Engineer"
    ],
    "email": "mpavangopinadh@gmail.com",
    "orcid": "https://orcid.org/0009-0000-9352-488X",
    "github": "https://github.com/MaddulaPavan",
    "linkedin": "https://www.linkedin.com/in/maddula-pavan/",
    "x": "https://x.com/PavanBuilds",
    "googleScholar": "https://scholar.google.com/citations?hl=en&user=oSYYRssAAAAJ",
    "resumeUrl": "/PavanMaddula_Resume.pdf",
    "status": "Graduating September 2026. Seeking full-time AI research engineering roles in model evaluation, red-teaming, and alignment. Open to relocation.",
    "summary": "AI research engineer focused on model and agent evaluations, LLM red-teaming, adversarial robustness, and preference alignment. Workshop papers at ICLR 2026 (AFAA) and ACL 2026 (EvalEval). Do not label as Research Scientist.",
    "education": {
      "degree": "Bachelor of Technology (B.Tech)",
      "major": "Computer Science and Engineering",
      "institution": "Vishnu Institute of Technology",
      "institutionUrl": "https://vishnu.edu.in/",
      "years": "2022-2026",
      "graduation": "September 2026",
      "cgpa": "8.3/10.0",
      "honors": ["Best Project Award Winner in CSE Department (2026)"]
    },
    "peerReviewedPublications": [
      {
        "title": "Procedural Fairness Failures in RLHF from Preference Averaging",
        "venue": "ICLR 2026 Workshop (AFAA); arXiv:2608.10126",
        "url": "https://arxiv.org/abs/2608.10126",
        "keyContribution": "Defines procedural fairness in RLHF and introduces PA-RLHF, raising alignment accuracy from 46.9% to 67.9% in a controlled setting."
      },
      {
        "title": "Are LLMs Safe Beyond Text: Do Emojis Expose Gaps in Safety Evaluation",
        "venue": "ACL 2026 Workshop (EvalEval); arXiv:2608.18164",
        "url": "https://arxiv.org/abs/2608.18164",
        "keyContribution": "Shows text-only safety evals miss emoji representation-shift attacks across four open LLMs."
      },
      {
        "title": "Regional Bias in Large Language Models",
        "venue": "AMRIT 2024; arXiv:2601.16349",
        "url": "https://arxiv.org/abs/2601.16349",
        "keyContribution": "FAZE framework quantifying regional commitment across 10 LLMs (1,000 responses)."
      }
    ],
    "grantsAndHonors": [
      "Adaption Research Grantee (Inaugural Cohort 2026)",
      "Winner, Amaravati Quantum Valley Hackathon Grand Finals (2026)",
      "BlueDot Impact Technical AI Safety Course (2026)",
      "Selected for ACM India Summer & Winter Schools (IIT Madras, IISc Bangalore, IIT Gandhinagar)"
    ],
    "topSkills": [
      "Model evaluation",
      "LLM red-teaming",
      "Preference alignment (RLHF / PA-RLHF)",
      "Adversarial robustness",
      "Inspect AI",
      "Python",
      "PyTorch",
      "Hugging Face Transformers"
    ]
  }
}