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

Empowering Muscle-Controlled Robots: Harnessing sEMG Technology

Researchers delve into the realm of surface electromyography (sEMG), an emerging technology with promising applications in muscle-controlled robots through human-machine interfaces (HMIs). This study, featured in the journal Applied Sciences, delves into the intricacies of sEMG-based robot control, from signal processing and classification to innovative control strategies.

25 Aug 2023

Analog In-Memory Computing: A Breakthrough for Efficient AI Processing

Researchers have unveiled an innovative solution to the energy efficiency challenges posed by high-parameter AI models. Through analog in-memory computing (analog-AI), they developed a chip boasting 35 million memory devices, showcasing exceptional performance of up to 12.4 tera-operations per second per watt (TOPS/W). This breakthrough combines parallel matrix computations with memory arrays, presenting a transformative approach for efficient AI processing with promising implications for diverse applications.

25 Aug 2023

Decoding Emotional Intelligence with AI: Enhancing Accuracy through Eye-Tracking

Researchers explore the integration of AI and psychometric testing to measure emotional intelligence (EI) using eye-tracking technology. By employing machine learning models, the study assesses the accuracy of EI measurements and uncovers predictive eye-tracking features. The findings reveal the potential of AI to achieve high accuracy with minimal eye-tracking data, paving the way for improved measurement quality and practical applications in fields like management and education.

24 Aug 2023

Enhancing Context Length Extrapolation in Large Language Models

Researchers have delved into addressing the limitations of context length in large language models (LLMs) by investigating novel strategies for context length extrapolation. The study introduces various techniques, including Rotary Position Embedding (RoPE), attention with linear biases (ALiBi), xPos, and randomized positional encodings, among others.

24 Aug 2023

Boosting Marine Ranching with AI: Reinforcement Learning for Risk Management

Researchers introduce a pioneering approach using deep reinforcement learning (RL) to enhance marine ranching's efficiency and resilience against disasters. This method, showcased in Energies, employs AI algorithms to optimize decision-making, create environmental models, and simulate disaster scenarios in marine ranching, contributing to sustainable fisheries management and disaster preparedness.

23 Aug 2023

Mouse Pose Analysis: 3D Posture Inference and Behavioral Insights

In a recent Scientific Reports paper, researchers unveil an innovative technique for deducing 3D mouse postures from monocular videos. The Mouse Pose Analysis Dataset, equipped with labeled poses and behaviors, accompanies this method, offering a groundbreaking resource for animal physiology and behavior research, with potential applications in health prediction and gait analysis.

23 Aug 2023

Advancing Wheat Variety Identification: CSKNN Leveraging Hyperspectral Imaging

Researchers present the innovative Cost-sensitive K-Nearest Neighbor using Hyperspectral Imaging (CSKNN) method for accurately identifying diverse wheat seed varieties. By addressing challenges such as noise and limited spatial utilization, CSKNN harnesses the power of hyperspectral imaging, noise reduction, feature extraction, and cost sensitivity, outperforming traditional and deep learning methods.

22 Aug 2023

Exploring Human-Robot Interaction: Trust, Ethics, and the Path Ahead

Human-Robot Interaction (HRI) delves into the intricate relationship between humans and robots, spanning trust, ethics, applications, challenges, and a visionary future. HRI unites various disciplines to create robots that align with human goals, raising questions about trust-building, ethical considerations, and the evolution of robots in diverse roles.

21 Aug 2023

The Convergence of AI and Robotics: Unleashing Intelligent Machines

Intelligent robots, powered by AI, are reshaping industries and society. These robots, equipped with perception, learning, and decision-making capabilities, excel in tasks ranging from manufacturing to healthcare. As AI advances, robots are becoming cooperative collaborators, challenging ethical considerations and reshaping economies while holding the promise of transformative benefits across sectors.

21 Aug 2023

Exploring Graph Neural Networks for Temporal Edge Regression in Food and Agriculture Trade

Researchers investigate the application of Graph Neural Networks (GNNs) for forecasting trade values in food and agriculture between nations. The study introduces robust baselines and evaluates static and dynamic GNN models using the United Nations Trade dataset. The findings highlight the strength of baselines and the potential of GNNs, particularly the Temporal Graph Networks (TGN).

19 Aug 2023