About Me
Hi, I’m Lav Kush Meena—a passionate tech enthusiast and researcher pursuing my B.Tech and Masters in IT at the Indian Institute of Information Technology, Gwalior (May 2025). My work spans machine learning, deep learning, NLP, and reinforcement learning, with hands-on experience in building scalable ML pipelines and optimizing model performance.
I have earned multiple bronze medals in Kaggle competitions and recently interned at ResoluteAI where I not only fine-tuned deep learning models but also contributed to end-to-end ML solutions. My role involved designing data pipelines, optimizing training workflows, and developing proof-of-concept prototypes for classification, regression, and anomaly detection tasks—reducing training time by 24% and boosting overall model performance by 9%.
In my free time, I love playing chess (Lichess rapid rating: 1800).
Research Interests
- Machine Learning
- Deep Learning
- Natural Language Processing: LLMs, fine-tuning, prompt engineering
- Reinforcement Learning: Policy gradient methods, real-world deployment
Education
Bachelor of Technology (B. Tech) in Information Technology
ABV-Indian Institute of Information Technology and Management Gwalior, Madhya Pradesh, India
2020-2024
Thesis: Enhancing MLFQ Scheduling using Reinforcement Learning
Applied PPO and ACKTR Reinforcement learning algorithms to improve traditional Multilevel Feedback Queue (MLFQ) scheduling. The system adapts dynamically to workload changes, achieving better turnaround time, response time, and throughput.
Master of Business Administration (MBA)
ABV-Indian Institute of Information Technology and Management Gwalior, Madhya Pradesh, India
2024-2025
Thesis: AI-Driven Optimization of Blood Supply Chain, Optimizing Inventory Management of Platelets with Deep Reinforcement Learning and Reward Shaping
Designed DRL-based models (DQN, PPO, Actor-Critic) to optimize blood supply chain.
Designed a DQN- and PPO-based model for platelet inventory optimization in hospital settings, tackling perishability and demand uncertainty. By integrating reward shaping, the model aligned with optimal base-stock policies and reduced platelet wastage by 4.89% while maintaining a 93.29% service level.
Kaggle Competitions & Medals
Check out my Kaggle profile: Loki003
- LEAP - Atmospheric Physics using AI (ClimSim): Bronze Medal (Top 10%) [Link to solution]
- Kaggle - LLM Science Exam: Bronze Medal (Top 9%) [Link to solution]
- Child Mind Institute — Problematic Internet Use: Bronze Medal (Top 7%) [Link to solution]
- LLMs - You Can't Please Them All: Bronze Medal (Top 8%) [Link to details]
- Santa 2024 - The Perplexity Permutation Puzzle: Bronze Medal (Top 9%) [Link to details]
- ISIC 2024 - Skin Cancer Detection with 3D-TBP: Bronze Medal (Top 6%) [Link to details]
Overall: 6 Competition Medals (0 Gold, 0 Silver, 6 Bronze), Kaggle Competition Ranking: 715/204,742.
Publications
- Lav Kush Meena, et al. “AI-Driven Blood Supply Chain Management: A Reinforcement Learning Approach.” In AI and Data Science in Precision Medicine, Predictive Analytics, and Medical Practice, edited by Olfa Boubaker, Elsevier. (Revised Draft – Accepted)
- Lav Kush Meena, et al. “Optimizing Inventory Management of Platelets with Deep Reinforcement Learning and Reward Shaping.” Engineering Applications of Artificial Intelligence, Elsevier. (Under Review)
- Lav Kush Meena, et al. “Enhancing MLFQ Scheduling using Reinforcement Learning.” Journal of Parallel and Distributed Computing, Elsevier. (Under Review)
Contact & Links
Feel free to reach out via email or connect with me on these platforms:
- Email: imluv034@gmail.com, img_2020034@iiitm.ac.in
- GitHub: github.com/luv003
- Kaggle: kaggle.com/loki003
- Resume: Download PDF