Kabir

Research profile of Kabir Murjani — Final-Year Electrical Engineering student at Nirma University, working on Deep RL solvers for NP-hard combinatorial problems at IIM Bangalore.

Install

pip install kabir

Usage

kabir

Or from Python:

import kabir
kabir.whoami()

Output

Kabir Murjani
  Final-Year Electrical Engineering, Nirma University | Deep RL Solvers, IIM Bangalore

ABOUT
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  I am a final year Electrical Engineering student at Nirma University. I
  architect neural nets and exact solvers that run fast.

  I primarily work on reinforcement learning on combinatorial spaces and
  language models. I'm currently working on Deep RL solvers for NP-hard
  combinatorial problems at Indian Institute of Management Bangalore (IIMB), and
  previously engineered and optimized sequential architectures with SIT-NVIDIA
  AI Centre (SNAIC).

  I work on game theory, adversarial economics, and sequential decision
  processes. My hobbies are algorithmic music composition and playing chess.
  Whether you're building in adjacent spaces, exploring topologies, or just up
  for a game, feel free to reach out.

  INTERESTS: Model Quantization, Efficient Inference, Reinforcement Learning,
             Neural Combinatorial Optimization

CORE COMPETENCIES
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  Reinforcement Learning & Game Theory
   • Multi-Agent Simulation & Modeling
   • Attention Economy Dynamics

  Machine Learning & Reasoning Systems
   • Neuro-Symbolic Reasoning
   • CV to NLP XAI Pipelines

  Power Electronics & VLSI
   • Control Systems & Routing Optimization

PUBLICATIONS & ARCHITECTURES
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  LEAN MEMORY, DEEP MASTERY (AAAI 2026)
  Affiliations: Nvidia Corp, Singapore Inst. of Technology, Nirma University
    Proposed a resource-efficient continual alignment framework for frozen LLMs.
    Enables cognitively grounded dialogue tutoring via GRPO-based RLHF.

  SMART ADAPTIVE NETWORK (IEEE ICC 2026)
  Conference: IEEE ICC, Glasgow | Affiliations: Nirma Univ, Toronto Metro Univ
    Proposed an edge AI and hybrid communication framework for critical mHealth
    alert routing. Featured INT8 quantized 1D CNNs and RAG-based context.

  ALPHA FROM ATTENTION (System Model)
    A quantitative framework analyzing the 'invisible current' of human
    attention that moves markets. Uses game theory and semantic modeling to
    capture alpha in the lag between signal appearance and algorithmic action.


CORRESPONDENCE
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  EMAIL: kabirmurjani@gmail.com
  WEB: kabir.codes
  X/TWITTER: @ktbir

Correspondence

• Email: kabirmurjani@gmail.com
• Web: kabir.codes
• X / Twitter: @ktbir

License

MIT