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.

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Articles from Sampath

AI Microrobots: Self-Sensing Innovations for In-Vivo Monitoring

Researchers have introduced a groundbreaking wireless AI microrobot capable of real-time in-situ monitoring and diagnostics. The microrobot integrates a self-sensing mechanism that responds to changes in its microenvironment without the need for internal power. Through the manipulation of electromagnetic fields, the microrobot navigates the body, detects anomalies, and offers potential applications in early disease diagnosis and targeted treatments.

11 Aug 2023

AI-Driven Water Quality Prediction in Arid Landscapes

Researchers employ a combination of artificial intelligence (AI) methods, logical-mathematical models, and physicochemical parameters to predict water quality (WQ) in the challenging context of the Loa River basin in the Atacama Desert. By integrating AI-driven techniques, such as Random Forest (RF), with expert insights, the study introduces a novel method for generating WQ labels and classifications.

10 Aug 2023

MAiVAR-T: Fusing Audio and Video for Enhanced Action Recognition

Researchers introduce MAiVAR-T, a groundbreaking model that fuses audio and image representations with video to enhance multimodal human action recognition (MHAR). By leveraging the power of transformers, this innovative approach outperforms existing methods, presenting a promising avenue for accurate and nuanced understanding of human actions in various domains.

10 Aug 2023

AI-Enhanced Early Detection of Parkinson's Disease Through Vocal Analysis

Researchers introduce an innovative AI model that outperforms existing methods in Parkinson's disease (PD) detection. Leveraging a transformer-based architecture and neural network, this model utilizes vocal features to achieve superior accuracy, providing potential for early intervention in PD cases.

9 Aug 2023

MM-Vet: Benchmarking Multimodal AI with Comprehensive Visual-Language Abilities

Researchers unveil MM-Vet, a pioneering benchmark to rigorously assess complex tasks for Large Multimodal Models (LMMs). By combining diverse capabilities like recognition, OCR, knowledge, language generation, spatial awareness, and math, MM-Vet sheds light on the performance of LMMs in addressing intricate vision-language tasks, revealing the potential for further advancements.

9 Aug 2023

Revolutionizing Plant Disease Detection with Optimized Deep Learning: GJ-GSO-based DbneAlexNet

Amid the imperative to enhance crop production, researchers are combating the threat of plant diseases with an innovative deep learning model, GJ-GSO-based DbneAlexNet. Presented in the Journal of Biotechnology, this approach meticulously detects and classifies tomato leaf diseases. Traditional methods of disease identification are fraught with limitations, driving the need for accurate, automated techniques.

8 Aug 2023

ILNet: Revolutionizing High-Quality Single-Pixel Imaging Using Deep Learning

Researchers introduce ILNet, an image-loop neural network (ILNet) that marries deep learning with single-pixel imaging (SPI), leading to high-quality image reconstruction at remarkably low sampling rates. By incorporating a part-based model and iterative optimization, ILNet outperforms traditional methods in both free-space and underwater scenarios, offering a breakthrough solution for imaging in challenging environments.

8 Aug 2023

Invisible Targets Revealed: The Power of Adaptive Drone Swarms in Occluded Environments

Researchers propose an innovative real-time particle swarm optimization (PSO) technique implemented by drone swarms with synthetic aperture (SA) to improve target detection and tracking in dense forests. The adaptive swarm sampling approach using PSO outperforms blind sampling methods, providing faster and more reliable detection of occluded targets, making it highly beneficial for search and rescue missions, wildlife observation, and security patrols in challenging environments.

6 Aug 2023

Exploring ChatGPT’s Performance as an AI-Generated Text Detector

Researchers have explored ChatGPT's ability to distinguish between human-written and AI-generated text. The study revealed that while ChatGPT performs well in identifying human-written text, it struggles to detect AI-generated text accurately. On the other hand, GPT-4 exhibited overconfidence in labeling text as AI-generated, leading to potential misclassifications.

6 Aug 2023

Advancing Luminescent Biosensors and Nanostructure Synthesis with Machine Learning

This study explores the practical applications of machine learning in luminescent biosensors and nanostructure synthesis. Machine learning techniques are shown to optimize nanomaterial synthesis, improve luminescence sensing accuracy, and enhance sensor arrays for various analyte detection, revolutionizing analytical chemistry and biosensing applications.

5 Aug 2023