Adithya Ramachandran

Adithya Ramachandran, M. Sc.

Researcher

Department of Computer Science
Chair of Computer Science 5 (Pattern Recognition)

Room 10.138
Martensstr. 3
91058 Erlangen

Phone number: +49 9131 85-27882
Fax number: +49 9131 85-27270
Email: adithya.ramachandran@fau.de
Website: https://lme.tf.fau.de/person/ramachandran/

Office hours

Each week Fr, 09:00 - 17:00, Room 10.138,

My research focuses on modeling a digital twin and developing methods for water and district heating utility networks to enable ML, and DL solutions that aid in decarbonization. We take a synergistic approach to model consumer, and network behavior through the underlying time-series data and Geographic Information System (GIS) data. With an end-to-end data pipeline and state-of-the-art ML and DL techniques this research intends to place itself as a framework that facilitates efficient water and district heating networks. Key aspects of the utility pipe includes

Utility data processing
- GIS data
- Smart meter data
- SCADA data
- Cadastre maps
Machine Learning and Deep Learning
- Forecasting
- Anomaly detection
- Event detection
- Network optimization
- Demand clustering
- Document digitization
- Establishing graph database
- LLM agents

Type	Title	Status
MA thesis	Link Prediction on Utility Networks Using Graph Neural Networks	running
MA thesis	Deep Learning for Geo-Referencing Historical Utility Documents With Geographical Features	running
MA thesis	Real-time Path Loss Prediction Using Deep Learning for Smart Meter Communication System	finished
Project	TSI Challenge Summer 2024: Heat & Water Demand Forecasting	finished
MA thesis	Explainable Predictive Maintenance: Forecasting and Anomaly Detection of Diagnostic Trouble Codes for Truck Fleet Management	finished
MA thesis	Leakage Detection and Modeling in Water Distribution Systems	finished
MA thesis	Adaptive Training for Heat Demand Prediction of District Heating Network	finished
MA thesis	Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble	finished
MA thesis	A hybrid approach forLeakage Localization in the Water Distribution Network	finished
MA thesis	Adaptive Training of Heat Demand Prediction using Continual Learning	finished
Project	Represent senor data of district heating network using contrastive learning	finished
MA thesis	Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble	finished