V C Premchand Yadav
AI Researcher
Amaravati,India
I’m V. C. Premchand Yadav, a Computer Science student specializing in AI & Machine Learning at VIT-AP University, and an AI researcher and developer passionate about building intelligent systems that move from research to real-world applications. My work spans Generative AI, LLMs, machine learning, computer vision, graph learning, quantum-inspired computing, and AI systems engineering. I build and experiment with open-source architectures and developer-focused tools, including Q-TensorFormer, a resource-adaptive Transformer architecture, alongside projects in quantitative AI, molecular intelligence, autonomous systems, and edge AI. I’m particularly interested in the intersection of AI research and practical engineering—designing efficient models, exploring new architectures, and turning emerging technologies into usable systems. I actively publish research and open-source implementations through GitHub, Hugging Face, Kaggle, and academic platforms. Beyond individual projects, I enjoy learning from the developer community, collaborating with builders, sharing ideas, and exploring how Google technologies and modern AI can be applied to solve meaningful problems. I’m excited to be part of GDG Hyderabad DevFest to connect with fellow developers, researchers, and technology enthusiasts, exchange ideas, and contribute to the growing developer ecosystem.
Co-Founder
Chandra Creations Palamaner
- As the Co-Founder of Chandra Creations Palamaner, I lead content strategy, digital distribution, and community growth for one of Palamaner's premier local news and cultural media platforms. Across our primary channels, we have built an engaged audience of over 77,000+ followers (58.7K+ on YouTube, 19K+ on Instagram) and generated more than 25+ million cumulative video views across 3,500+ hyper-local stories, cultural festivals, and community broadcasts.
Founder
RICE
- • Led an end-to-end analytics initiative, transforming complex environmental, soil, and market tabular datasets into predictive
- recommendation models and actionable business insights for the agricultural sector.
- • Conducted comprehensive Exploratory Data Analysis (EDA) to uncover optimization opportunities; built, validated, and iteratively
- deployed predictive models using statistical performance metrics.
• Engineered a quantitative research system combining TimesFM/Prophet/Merton forecasting models with regime-conditioned ensembles for statistical prediction under changing market conditions. • Implemented Black-Litterman, HRP, and risk-parity portfolio optimization alongside VaR/CVaR validation, purged crossvalidation, walk-forward backtesting, Monte Carlo simulation, and stress testing, with automated statistical benchmarking.
Resource-Adaptive Quantum-Tensor Transformer Developed a resource-adaptive Transformer architecture combining Tensor-Train compression, uncertainty-aware compute allocation, dynamic early exits, and optional quantum-inspired token routing. Integrated quantization, KV-cache optimization, and automated validation tooling to investigate inference efficiency, memory reduction, and latency–accuracy trade-offs.
Molecular Intelligence & Drug Discovery Platform Developed a computational drug discovery framework integrating GraphCL pretraining, GATv2/GPS graph encoders, protein–ligand cross-attention, and contrastive molecular–protein representation learning. Integrated ESM-2 protein embeddings, active learning, a protein-conditioned variational autoencoder, FAISS retrieval, and molecular property prediction across multiple protein targets.
Space Debris Mitigation & Autonomous Coordination - Designed an autonomous space-debris mitigation framework combining YOLOv8-based edge vision, object tracking, and Unscented Kalman Filters for robust debris state estimation. - Integrated multi-agent mission coordination using the Consensus-Based Bundle Algorithm (CBBA), with MATLAB and NASA GMAT simulations for mission-planning evaluation.
Quantum-Classical Hybrid Intelligence Systems - Developed Q-RouteX, a trust-aware network-routing framework combining graph neural networks, traffic forecasting, intrusion detection, reinforcement learning, and QAOA-inspired routing strategies. - Built QADS, a hybrid autonomous decision planner integrating A*, PPO, and PennyLane-based quantum approaches with entropy-gated switching between planning strategies.
Cognitive Bias Detection & Explainable NLP - Developed a multilingual NLP framework for identifying cognitive biases, logical fallacies, and potentially manipulative reasoning patterns in textual content. - Implemented token-level explainability and text-correction workflows, combining cross-lingual embeddings with adversarial bias detection across 13+ languages.
Campus Sustainability & Predictive Analytics - Designed a data-driven sustainability platform to analyze energy, water, and infrastructure utilization across campus environments. - Applied exploratory data analysis and predictive modeling to identify resource-consumption patterns and evaluate potential efficiency improvements.
Bachelor of Technology
VITAP University · 9.35
Intermediate
Allen Career Institute · 95.9
ICSE
Emmaus Swiss High School · 92.6
Kaggle All Certificates
Kaggle
Jan 2026Oracle GenAI
Oracle
Aug 2025Oracle Data Science
Oracle
Jan 2026SmartBridge GenAI with Google
SmartBridge
Jul 2025AWS Cloud Basics and Architect
AWS
Nov 2025
Attendee · Oct 24, 2026