Ashutosh Shukla

About Ashutosh Shukla

I’m an Artificial General Intelligence (AGI) researcher and data scientist passionate about building systems that can truly learn, reason, and adapt — bridging the gap between narrow AI and human-like cognition.My journey began with just the grit in mind then grasped a formal foundation in Artificial Intelligence and Machine Learning from the University of Texas, where I built practical AI models across domains like computer vision, natural language processing, and deep learning. However, I soon realized that true innovation lies beyond pre-defined curricula — so I chose to self-learn, experiment, and build independently.Over the years, I’ve explored areas like self-supervised learning, reinforcement learning, LLM architectures, and quantum-enhanced ML to move closer toward systems capable of generalization, abstraction, and adaptive intelligence.My focus today is on developing architectures and learning paradigms that push current boundaries — combining classical machine learning with cognitive modeling, emergent behavior, and quantum computation.I enjoy exploring deep research problems, mentoring others who are curious about AGI, and collaborating on open-source and theoretical projects that question the fundamental nature of intelligence.Core Interests: AGI · Machine Learning · Deep Learning · Quantum Computing · Reinforcement Learning · Self-Supervised Learning · LLMs · Cognitive Modeling · AI Ethics

Experience

1. Founder

   Ai Makes Possible

   Feb 2014 — Present · Bilaspur, IN

   Founder & lead researcher at AI makes Possible. I research and build scalable AI systems aiming toward robust general intelligence, while applying quantum computing research where it can accelerate model training and probabilistic inference.Key responsibilities• Architect and implement deep learning and RL systems (transformers, LLM fine-tuning, actor-critic/RLHF pipelines) from research prototype to production.• Lead research into AGI-aligned model architectures, multi-modal representation learning, and continual learning methods.• Explore quantum-classical hybrid algorithms (variational circuits, QAOA, quantum kernels) for optimization and sampling tasks relevant to ML workflows.• Build data pipelines, experiment tracking, and MLOps automation to reproduce experiments and deploy research safely.• Publish and open-source prototypes, collaborate with external researchers, and mentor junior researchers.Selected achievements• Developed a modular LLM fine-tuning and evaluation pipeline used to iterate on AGI research prototypes. Reduced iteration time by considerable amount.• Designed a hybrid classical-quantum proof-of-concept for hybrid model training pipeline, demonstrating evidence that hybrid approaches can accelerate hyperparameter search by nearly 2 times.• Published technical reports and code (see portfolio / GitHub: https://github.com/AshutoshBuilds/) and presented at internal workshops and external meetups.

Education

• The University of Texas at Austin

  Post Graduate Program in Artificial Intelligence and Machine Learning, Artificial Intelligence · Machine Learning · Deep Learning · Computer Vision · NLP

• Chandigarh Group of Colleges, Landran, Punjab

  Bachelor of Computer Applications in AI & ML, Computer Science

• Saint Francis Senior Secondary School

  10+2