Mani Krishna Mudiganti
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Researcher
Unknown Researcher
Research Areas
Biography and Research Information
OverviewAI-generated summary
Mani Krishna Mudiganti's research focuses on the application of machine learning techniques to address complex problems in various domains. His work investigates the detection of silent failure patterns in microservice telemetry using reliability-aware sequence modeling. Additionally, he explores multi-modal tool wear prediction by fusing acoustic spectrograms and vibration signals with deep learning. Mudiganti also studies confidence-aware machine learning for early insider threat detection through uncertainty drift analysis, as well as the adversarial vulnerabilities and robustness of machine learning models in sensor-driven materials experiments. His research further extends to unsupervised machine learning for anomaly detection in energy-material experimental measurements. Mudiganti has a total of five publications.
Metrics
- Publications: 5
Selected Publications
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ML-Based Detection of Silent Failure Patterns in Microservice Telemetry Using Reliability-Aware Sequence Modeling (2026)
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Multi-Modal Tool Wear Prediction via Fusion of Acoustic Spectrograms and Vibration Signals Using Deep Learning (2026)
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Confidence-Aware Machine Learning for Early Insider Threat Detection Using Uncertainty Drift Analysis (2026)
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Adversarial Vulnerabilities and Robustness of Machine Learning Models in Sensor-Driven Materials Experiments (2026)
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Unsupervised Machine Learning for Anomaly Detection in Energy-Material Experimental Measurements (2026)
Collaboration Network
Top Collaborators
- Unsupervised Machine Learning for Anomaly Detection in Energy-Material Experimental Measurements
- Adversarial Vulnerabilities and Robustness of Machine Learning Models in Sensor-Driven Materials Experiments
- Confidence-Aware Machine Learning for Early Insider Threat Detection Using Uncertainty Drift Analysis
- Multi-Modal Tool Wear Prediction via Fusion of Acoustic Spectrograms and Vibration Signals Using Deep Learning
- ML-Based Detection of Silent Failure Patterns in Microservice Telemetry Using Reliability-Aware Sequence Modeling
- Unsupervised Machine Learning for Anomaly Detection in Energy-Material Experimental Measurements
- Adversarial Vulnerabilities and Robustness of Machine Learning Models in Sensor-Driven Materials Experiments
- Confidence-Aware Machine Learning for Early Insider Threat Detection Using Uncertainty Drift Analysis
- Multi-Modal Tool Wear Prediction via Fusion of Acoustic Spectrograms and Vibration Signals Using Deep Learning
- ML-Based Detection of Silent Failure Patterns in Microservice Telemetry Using Reliability-Aware Sequence Modeling
- Unsupervised Machine Learning for Anomaly Detection in Energy-Material Experimental Measurements
- Adversarial Vulnerabilities and Robustness of Machine Learning Models in Sensor-Driven Materials Experiments
- Confidence-Aware Machine Learning for Early Insider Threat Detection Using Uncertainty Drift Analysis
- Multi-Modal Tool Wear Prediction via Fusion of Acoustic Spectrograms and Vibration Signals Using Deep Learning
- ML-Based Detection of Silent Failure Patterns in Microservice Telemetry Using Reliability-Aware Sequence Modeling
- Unsupervised Machine Learning for Anomaly Detection in Energy-Material Experimental Measurements
- Adversarial Vulnerabilities and Robustness of Machine Learning Models in Sensor-Driven Materials Experiments
- Confidence-Aware Machine Learning for Early Insider Threat Detection Using Uncertainty Drift Analysis
- Multi-Modal Tool Wear Prediction via Fusion of Acoustic Spectrograms and Vibration Signals Using Deep Learning
- ML-Based Detection of Silent Failure Patterns in Microservice Telemetry Using Reliability-Aware Sequence Modeling
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