Shujaatali Badami

Shujaatali Badami

Applied AI and Optimization Research Scientist

Professional Memberships

// Biography

I am Shujaatali (Ali) Badami, an independent applied AI and optimization research scientist based in Chicago. My research focuses on AI-driven optimization, distributed and edge intelligence, reinforcement learning under uncertainty, and intelligent networked systems for 5G and 6G, IoT, logistics, hospitality and industrial environments.

I have authored multiple IEEE indexed papers, review for IEEE Internet of Things Journal, IEEE Transactions on Quantum Engineering, IEEE Open J. Comms. Soc. and Wiley IJCS, and serve on technical program committees for several IEEE and Springer conferences. I also judge AI startup showcases and hackathons, helping teams stress test their architectures.

// Research Publications

Published Research (IEEE)

IEEE PublishedSole Author

Efficient OneM2M Standard Implementation for Lightweight IoT

IEEE DSIT 2024

A lightweight stack implementation of oneM2M optimized for resource-constrained devices, reducing protocol overhead by 15% in edge environments through efficient resource serialization.

High-Impact Citations

Cited by Prof. Elias Dritsas (h-index 25) in Elsevier Internet of Things (Vol 34). [Google Scholar] [Uni Profile]

Cited by Dr. Saeed Sharif (Univ. of East London) in Adaptive QoS Management. [Google Scholar] [Uni Profile]

Acceptance Rate: ~28% DOI: 10.1109/DSIT61374.2024.10882084

IEEE Xplore

IEEE PublishedSole Author

Quantum Bootstrap in Microcanonical Ensembles

IEEE ICPCT 2025

Introduces a microcanonical resampling method to reduce computational overhead in quantum many-body simulations, optimizing energy usage by 22% compared to standard bootstrap methods.

Acceptance Rate: ~22% DOI: 10.1109/ICPCT64145.2025.10939270

IEEE Xplore

IEEE PublishedSole Author

Optimizing LSM Tree Operations with Deferred Updates

IEEE DSIT 2024

Comparative study on deferred compaction strategies in LSM trees to minimize write amplification in high-throughput systems, improving write speeds by 18% in read-heavy workloads.

Acceptance Rate: ~28% DOI: 10.1109/DSIT61374.2024.10881309

IEEE Xplore

IEEE PublishedSole Author

Optimizing RL in Partially Observable Environments

IEEE ICEACE 2024

Novel compressed suffix memory (CSM) algorithm to handle state uncertainty in robotic reinforcement learning agents, outperforming LSTM baselines in memory-constrained tasks.

Acceptance Rate: ~30% DOI: 10.1109/ICEACE63551.2024.10899025

IEEE Xplore

Pending Publication (Accepted)

Accepted / In-Press (2025)1st Author

Quantum-Resilient IoT Energy Metering on Blockchain

Accepted at NGCCOM 2025 · Quantum Security

Springer Book Series: "Lecture Notes in Networks and Systems"
Accepted, presented and registered papers will be published in this series.

A secure framework for real-time monitoring utilizing post-quantum cryptography (Kyber/Dilithium) to secure IoT data streams on blockchain against quantum threats.

Acceptance Rate: Not Publicly Available

Accepted / In-Press (2025)2nd Author

AI-Driven Green Cognitive Radio Networks for Sustainable 6G

Accepted at CYBPRO 2025 · 6G Networks

Bentham Science Conference Proceedings (Scopus Indexed)
All accepted papers will be published in Bentham Science Conference Proceedings and indexed in Scopus.

Framework integrating deep reinforcement learning and energy harvesting to optimize spectrum efficiency in 6G cognitive radio networks.

Acceptance Rate: Not Publicly Available

Preprints & Working Papers

Accepted at ICBATS 2025Sole Author

Mitigating Tails Switching in Multibranch PoS Systems

Quantum-Inspired Consensus

Proposes a quantum-inspired selection mechanism to prevent fork switching attacks in PoS blockchains, enhancing consensus stability in multibranch environments.

Awaiting IEEE Xplore

DOI: 10.36227/techrxiv.176342174.44007067/v1

View Preprint

Accepted at ICBATS 2025Sole Author

Multi-Formalism Verification Framework for Autonomous Vehicles

Safety Verification

A hybrid verification framework combining model checking and theorem proving for safety-critical autonomous systems to ensure compliance with ISO 26262 standards.

Awaiting IEEE Xplore

DOI: 10.36227/techrxiv.176342174.49042133/v1

View Preprint

// Reviewing & Judging

// Academic Credentials

Credentials verified by World Education Services (U.S. Equivalency)

Reference # 5407216 · Status: Verified

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MSc in Data Science

Liverpool John Moores University (UK) Verify Degree

Master of Business Administration (MBA)

Deakin University (Australia) Verify MyEquals

Executive PG in Data Science

IIIT Bangalore (India) Verify Credential

PG Diploma in Management

IMT Ghaziabad (India) Verify Degree

Shujaatali Badami

Featured In

// Biography