Dr. Sampath Lonka

Ph.D, Mathematics

Large Language Models (LLM), Natural Language Processing (NLP), Deep Learning, Data Science, Calculus

Dr. Sampath Lonka is a scientific writer based in Bangalore, India, with a strong academic background in Mathematics and extensive experience in content writing. He has a Ph.D. in Mathematics from the University of Hyderabad and is deeply passionate about teaching, writing, and research.

Sampath enjoys teaching Mathematics, Statistics, and AI to both undergraduate and postgraduate students. What sets him apart is his unique approach to teaching Mathematics through programming, making the subject more engaging and practical for students.

Sampath works in various fascinating research areas, including Natural Language Processing, Deep Learning, Topological Data Analysis, and AI for Mathematics. He has developed several impactful Computer Vision and Language Models that effectively tackle real-world problems. These research interests and work reflect Sampath's commitment to exploring the intersections of mathematics and cutting-edge technologies.

Beyond the professional realm, Sampath has diverse interests and enjoys reading captivating literature, embarking on adventurous travels, and experimenting with culinary delights in the kitchen.

Sampath's unwavering passion for mathematics, expertise in AI, and dedication to nurturing young minds comes from his unique blend of academic knowledge, research accomplishments, and teaching experience.

Articles from Sampath

Navigating Challenges in AI Regulation: Insights from the European AI Act

This publication analyzes challenges in the European AI Act (AIA), offering insights applicable to subsequent versions. The research, focused on the April 2021 draft, categorizes critiques into regulation, compliance, and anticipated impact themes. Notable concerns include the AIA's broad scope, ambiguous wording, unrealistic provider requirements, and potential negative effects on innovation and industry, providing valuable guidance for further AI regulation research.

23 Nov 2023

Navigating the Dynamic Landscape of AI Skills in the Workforce

The AI-driven transformation of the labor market demands a nuanced approach to skill measurement. Researchers, in a recent AISeL publication, illuminate the dynamic nature of AI skills and propose a novel dynamic co-occurrence method, addressing limitations in static approaches. Leveraging empirical analysis, they reveal the evolving landscape of AI skills and demonstrate the superior performance of the dynamic method in capturing emergence and evolution.

22 Nov 2023

Enhancing Continual Learning in AI: A Multi-Learner Approach with Active Forgetting

This research proposes a novel approach to continual learning in artificial neural networks, addressing the challenge of balancing memory stability and learning plasticity. Inspired by the biological active forgetting mechanism observed in the Drosophila mushroom body’s γMB subset, the study introduces a synaptic expansion-renormalization framework, employing multiple learning modules to actively regulate forgetting.

21 Nov 2023

DeepMind's GraphCast: Revolutionizing Global Weather Forecasting

DeepMind's GraphCast model, featured in Nature, emerges as a groundbreaking innovation in weather forecasting. Outperforming traditional and AI-based methods, GraphCast provides highly accurate global weather predictions within minutes, showcasing the potential of machine learning to transform and enhance the efficiency of this critical scientific field.

19 Nov 2023

Quantum Discord and Coherence in DQC1 for Machine Learning

Researchers investigate the application of the deterministic quantum computing with one qubit (DQC1) model in supervised machine learning. By exploring quantum discord and coherence, the study on IBM hardware demonstrates DQC1's efficiency in estimating complex kernel functions, offering potential advancements in quantum machine learning despite challenges related to hardware noise and coherence consumption.

19 Nov 2023

Deep Learning-Based Manatee Crowd Counting Using Anisotropic Gaussian Kernel for Low-Quality Images

This paper presents a novel approach for automatically counting manatees within a region using deep learning, even when provided with low-quality images. Manatees, being slow-moving aquatic mammals often found in aggregations in shallow waters, pose challenges such as water surface reflections, occlusion, and camouflage.

17 Nov 2023

Revolutionizing Electric Scooter Safety: Innovative Modules and AI Models Mitigate Accidents

This paper addresses the safety concerns associated with the increasing use of electric scooters by introducing a comprehensive safety system. The system includes a footrest with a force-sensitive sensor array, a data-collection module, and an accelerometer module to address common causes of accidents, such as overloading and collisions.

16 Nov 2023

Sweet Potato Virus Disease Detection Using Deep Learning-Based Object Detection Framework

This research, featured in Agronomy, introduces SPVDet, a deep learning-based object detection framework for identifying Sweet Potato Virus Disease (SPVD) in ground and aerial high-resolution images. The study showcases the superior performance of SPVDet and its lightweight version, SPVDet-Nano, emphasizing their potential for real-time detection and high-throughput phenotyping in sweet potato plantations.

15 Nov 2023

Automated Synthesis of Martian Oxygen Catalysts: Unleashing a Robotic AI Chemist

Researchers introduce a groundbreaking Robotic AI Chemist designed for autonomous synthesis and optimization of catalysts for the oxygen evolution reaction (OER) using Martian meteorites. The study addresses the critical challenge of oxygen production for sustainable Mars exploration through in situ resource utilization, presenting an all-in-one system that combines robotic capabilities with artificial intelligence, outpacing traditional trial-and-error approaches by five orders of magnitude.