Manu Lahariya

Manu Lahariya

मनु लहरिया
AI Scientist, Amsterdam
PhD, Artificial Intelligence, AI4E, IDLab

DLL, Netherlands

Universiteit Gent, Belgium

Biography

I did my PhD at the Artificial Intelligence for Energy group (AI4E), IDLab, Ghent university, Belgium, where I worked with this amazing team. My research was focused reinforcement learning, physics-based machine learning, and statistical modeling for designing efficient control for different demand response applications. I explored soft robotic applications of physics-based machine leaning models as differenciable simulators during my research visit at Robust autonomy and decisions lab, University of Edinburgh. I also explored machine learning applications in developing hydrological earth twins as a machine learning researcher for Frontier Development Labs. Currently, I am working as a AI Consultant at dll, where I am developing credit risk assessment machine learning models.

I spend my evenings listening to good music, or practicing a musical instrument. Reading a good book, or writing one myself. I have also invested time of my life on travelling and reading graphic novels.

Download my resumé.

Interests
  • Reinforcement learning
  • Physics Based Machine Learning
  • Deep learning
  • Smart Grids, Demand Response
  • Soft Robotics
Education

  • PhD in Artificial Intelligence, 2023

    Ghent University, Belgium

  • MTech in Aerospace Engineering, 2017

    Indian Institute of Technology, Kharagpur

  • BTech in Aerospace Engineering, 2016

    Indian Institute of Technology, Kharagpur

Master Students

  • Seyedomid Sobhani

Awards & Fellowships

Languages & Skills

  • English                                                   Fluent
  • Hindi                                                        Native
  • Python, R                                               ★★★★☆
  • Excel, SQL, Matlab, Abaqus            ★★★☆☆

Research Projects

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All EU Funded Independent Physics Based Machine Learning Reinforcement learning
Jul 1, 2021
Physics informed simulation & control - Soft Robotics
Physics informed simulation & control - Soft Robotics

Research Visit - Robust Autonomy and Decisions Group, University of Edinburgh.
Feb 23, 2021
Physics RL - Demand Response
Physics RL - Demand Response

Project - Building Information aGGregation, harmonization and analytics platform (BIGG).
Aug 14, 2020
Statistical Modelling - EV Fleet
Statistical Modelling - EV Fleet

Synthetic Data Generator for Electric Vehicle Charging Sessions
Mar 1, 2020
Physics LSTM - Process Modelling
Physics LSTM - Process Modelling

Project - InduFlexControl, Design for flexibility for carbon-free energy-intensive industry
May 1, 2019
Reinforcement Learning - Demand Response
Reinforcement Learning - Demand Response

Definition and evaluation of computationally efficient Markov decision process for coordination of electric vehicle fleet using reinforcement learning
Dec 1, 2016
Data Science | Competitions
Data Science | Competitions

Accuracy Champion | National data science competition - EXL Excellence quotient
Jul 1, 2016
Master Thesis

Experimental screening of boron based solid rocket fuels for hybrid rocket applications
May 1, 2016
Ramjet Engine Ignition
Ramjet Engine Ignition

Research Visit - Defence Research and Development Laboratory, DRDO, India.
Jan 1, 2016
Data Science | Kaggle
Data Science | Kaggle

Multiple data science and machine leaning projects on kaaggle
Jul 1, 2015
Bachelors Thesis

Calculation of Theoretical Performance of Boron-Based Composite Solid Propellant for the Future Applications

Industry Experience

 
 
 
 
 
DLL
Data Scientist/AI Consultant
DLL
Nov 2023 – Present Amsterdam, Netherlands
 
 
 
 
 
3SC Analytics
Data Scientist
3SC Analytics
Mar 2023 – Oct 2023 Amsterdam, Netherlands
 
 
 
 
 
ZS Associates
Decision Analytics Associate
ZS Associates
Jul 2017 – Aug 2018 Pune, India
 
 
 
 
 
Ethnus Consulting Services
Data Science Intern
Ethnus Consulting Services
May 2016 – Jul 2016 Bangalore, India

Publications

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G. Gokhale, N. Tiben, M.S. Verwee, M. Lahariya, B. Claessens, C. Develder (2023). Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households. BuildSys ‘23.

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C. Manna, M. Lahariya, F. Karami, C. Develder (2023). A data-driven optimization framework for industrial demand-side flexibility. Energy, 2023 (Impact Factor: 9).

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M. Lahariya (2023). Physics-informed machine learning based identification and reinforcement learning based control of flexibility in smart grid applications. Ghent University. Faculty of Engineering and Architecture.

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M. Lahariya, C. Innes, C. Develder, S. Ramamoorthy (2022). Learning physics-informed simulation models for soft robotic manipulation: A case study with dielectric elastomer actuators. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022).

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M. Lahariya, K. Farzaneh, C. Develder, G. Crevecoeur (2022). Physics Informed LSTM Network for Flexibility Identification in Evaporative Cooling Systems. IEEE transactions on industrial informatics, 2022 (Impact Factor: 10.215).

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K. Farzaneh, M. Lahariya, G. Crevecoeur (2022). A 𝑸-learning based demand response algorithm for industrial processes with flexibility. Chapter in Handbook of Smart Energy Systems.

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M. Lahariya, N. Sadeghianpourhamami, C. Develder (2022). Computationally efficient joint coordination of multiple electric vehicle charging points using reinforcement learning. Sustainable Energy, Grids and Networks (Under Review).

PDF Abstract arXiv

M. Lahariya, K. Farzaneh, C. Develder, G. Crevecoeur (2021). Physics-informed Recurrent Neural Networks for The Identification of a Generic Energy Buffer System. In DDCLS 2021, Suzhou, China.

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M. Lahariya, D. Benoit, C. Develder (2020). Synthetic Data Generator for Electric Vehicle Charging Sessions: Modeling and Evaluation Using Real-World Data. Energies, MDPI 2020 (Impact Factor 3.004).

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M. Lahariya, D. Benoit, C. Develder (2020). Defining a synthetic data generator for realistic electric vehicle charging sessions. In e-Energy ‘20, Virtual.

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M. Lahariya, N. Sadeghianpourhamami, C. Develder (2019). Reduced state space and cost function in reinforcement learning for demand response control of multiple EV charging stations.. In BuildSys ‘19, NYC, USA.

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S. A. Hashim, M. Lahariya, S. Karmakar, A. Roy (2016). Calculation of Theoretical Performance of Boron-Based Composite Solid Propellant for the Future Applications.. In IDAD ‘16.

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Deep Learning Demand response Independent Physics Based Machine Learning Reinforcement Learning