J. Senthilnath (0000-0002-1737-7985)

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J. Senthilnath is a Scientist in the Institute for Infocomm Research (I2R) at Agency for Science, Technology and Research (A*STAR), Singapore. He received the PhD degree in Aerospace Engineering from the Indian Institute of Science (IISc), India. His current research interests include artificial intelligence, multiagent systems, generative models, online learning, reinforcement learning and optimization. He has published over 100 papers in top journals/conferences (CIKM, CVPR, JACS, J.RSE, Matter, npj computational materials, Royal Society Open Science, T-ASE, T-CYB, T-GRS, T-KDE, T-NNLS) and won five best paper awards. He is a Senior Member of IEEE and a member of Artificial Intelligence, Analytics And Informatics (AI3), A*STAR. He has been serving as Guest Editor/organizing chair/co-chair/PC members in leading AI and data analytics journals and conferences.

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Agency for Science, Technology and Research: Singapore, SG

2018-07-02 to present | Scientist II (Institute for Infocomm Research)

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J. Senthilnath

https://orcid.org/0000-0002-1737-7985

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Employment (4)

Agency for Science, Technology and Research: Singapore, SG

Nanyang Technological University: Singapore, SG

SDSU and the USGS Earth Resources Observation and Science (EROS) center: Brookings, South Dakota, US

Indian Institute of Science: Bangalore, Karnataka, IN

Education and qualifications (1)

Indian Institute of Science: Bangalore, Karnataka, IN

Professional activities (1)

IEEE : Singapore, SG

Works (50 of 114)

A compact meta-learned neuro-fuzzy technique for noise-robust nonlinear control

A dynamic area approximation-based stochastic multi-UAV target search with noisy measurements

An Efficient Approach With Dynamic Multiswarm of UAVs for Forest Firefighting

Cross-Problem Learning for Solving Vehicle Routing Problems

Explainable machine learning to enable high-throughput electrical conductivity optimization and discovery of doped conjugated polymers

INTERPRETABLE POLICY EXTRACTION WITH NEURO-SYMBOLIC REINFORCEMENT LEARNING

Metacognitive Decision-Making Framework for Multi-UAV Target Search Without Communication

Tackling data scarcity with transfer learning: a case study of thickness characterization from optical spectra of perovskite thin films (vol 2, pg 1334, 2023)

A decentralized learning strategy to restore connectivity during multi-agent formation control

Attention Over Self-Attention: Intention-Aware Re-Ranking With Dynamic Transformer Encoders for Recommendation

DATA GENERATION WITH STRUCTURE ENFORCING ADVERSARIAL LEARNING

Learning Relation in Crowd Using Gated Graph Convolutional Networks for DRL-Based Robot Navigation

PASE: An autonomous sequential framework for the state estimation of dynamical systems

Tethered Multicopter Guidance in GPS-Denied Environments Through Reinforcement Learning

A Simulation Approach to Analyze Bridge-Defects in a 6T-SRAM Bit Cell

An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties

An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties (vol 5, pg 336, 2022)

BS-McL: Bilevel Segmentation Framework With Metacognitive Learning for Detection of the Power Lines in UAV Imagery

Capturing functional relations in fluid-structure interaction via machine learning

Data Oversampling with Structure Preserving Variational Learning

Delamination prediction in composite panels using unsupervised-feature methods with wavelet-enhanced wave

DO-GAN: A Double Oracle Framework for Generative Adversarial Networks

DRBM-ClustNet: A Deep Restricted Boltzmann–Kohonen Architecture for Data Clustering

Extracting film thickness and optical constants from spectrophotometric data by evolutionary optimization

GPS-Denied Three Dimensional Leader-Follower Formation Control Using Deep Reinforcement Learning

Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion

LATENT PRESERVING GENERATIVE ADVERSARIAL NETWORK FOR IMBALANCE CLASSIFICATION

Optimal Connectivity during Multi-agent Consensus Dynamics via Model Predictive Control

Planning sequential interventions to tackle depression in large uncertain social networks using deep reinforcement learning

Searching for Heavy-Tailed Probability Distributions for Modeling Real-World Complex Networks

Significance of activation functions in developing an online classifier for semiconductor defect detection

SIMULTANEOUS SMOOTHING AND SHARPENING USING iWGIF

Integrated UAV-Based Multi-Source Data for Predicting Maize Grain Yield Using Machine Learning Approaches

Machine Learning-Based Approaches for Predicting SPAD Values of Maize Using Multi-Spectral Images

Combined two-level damage identification strategy using ultrasonic guided waves and physical knowledge assisted machine learning

Comparing data driven and physics inspired models for hopping transport in organic field effect transistors

FPA clust: evaluation of the flower pollination algorithm for data clustering

Integrated phenology and climate in rice yields prediction using machine learning methods

Integrating spectral and textural information for identifying the tasseling date of summer maize using UAV based RGB images

Predicting Antimicrobial Activity of Conjugated Oligoelectrolyte Molecules via Machine Learning

SGDOL: Self-evolving Generative and Discriminative Online Learning for Data Stream Classification

Temperature compensation for guided waves using convolutional denoising autoencoders

Transfer Learning-Based Artificial Intelligence-Integrated Physical Modeling to Enable Failure Analysis for 3 Nanometer and Smaller Silicon-Based CMOS Transistors

Two-step machine learning enables optimized nanoparticle synthesis

Integrating Spectral and Textural Information for Monitoring the Growth of Pear Trees Using Optical Images from the UAV Platform

Autonomous Vehicle for Obstacle Detection and Avoidance Using Reinforcement Learning

Mission Aware Motion Planning (MAP) Framework With Physical and Geographical Constraints for a Swarm of Mobile Stations

Multitemporal time series analysis using machine learning models for ground deformation in the Erhai region, China

Multitemporal time series analysis using machine learning models for ground deformation in the Erhai Region, China (vol 192, 464, 2020)

Rapid and Accurate Thin Film Thickness Extraction via UV-Vis and Machine Learning

Peer review (130 reviews for 29 publications/grants)