CV
Experience
Data scientist and AI lead with more than ten years in time series, forecasting and decision support for energy and industrial systems.
2025 – now
Staff Engineer – AI · Falkor (formerly Kongsberg Digital)
- Sets technical direction for AI across product teams in simulation, grid, and oil and gas; mentors engineers and defines modelling standards, quality KPIs and reviews.
- Delivered a paid ML and optimisation tool for energy nominations for a major energy company.
- Per-transformer load forecasts feeding load flow and switching optimisation for restoration, capacity and congestion; physics-informed surrogates in digital-twin evaluation loops.
2023 – 2025
Data Scientist, Utilities and Smart Grids · Kongsberg Digital
- Designed, deployed and maintained ML services forecasting transformer load, generation and consumption from real-time sensor data of distribution system operators, used daily in grid operations.
- Owned forecast quality: accuracy KPIs, evaluation gates, fleet-scale monitoring and root-cause analysis of forecast errors.
2022 – 2023
AI Researcher · Smart Innovation Norway
- Forecasting and optimisation-based decision support for energy systems, with partners across organisations.
2020 – 2022
Postdoctoral Fellow, AI and Networks · University of Agder
- Federated and reinforcement learning for resource-constrained IoT and sensor devices: on-device training and inference under memory, energy and latency budgets. Taught PhD and master's courses and co-supervised a PhD student.
Education
2015 – 2020
PhD, Electrical Engineering
Indian Institute of Technology Kanpur. IoT, time series, energy-efficient on-device algorithms, wireless sensor networks.
2013 – 2015
M.Tech, Electronics and Communication
Thapar University, Patiala
2009 – 2012
B.Tech, Electronics and Communication
Kurukshetra University
Honours
2026
Equinor AI Hackathon, best forecasts
Wind power forecast for Hywind Tampen
2025
Wind–AI Competition, 2nd in accuracy
Time-series wind power forecasting challenge
2016 – 2020
TCS Research Fellowship
For PhD research
2014
Junior Research Fellowship
UGC-CSIR NET, rank 97
Teaching and supervision
University of Agder
- Wireless Embedded Systems and Sensor Networks, master's course, autumn 2020 and 2021 (full course)
- Deep Learning, PhD course, autumn 2020 (part)
- Deep Reinforcement Learning, PhD course, spring 2021 (part)
- Co-supervised a PhD student on AI-based prediction and allocation of spectral resources
IIT Kanpur
- Teaching assistant, Statistical Signal Processing; tutor, Basic Electronics
- Co-supervised two master's students and several undergraduate summer projects
Skills
- Forecasting: wind, generation, load and consumption; probabilistic and quantile forecasts; ensembles; weather-driven features; backtesting and root-cause analysis
- ML and modelling: gradient boosting, deep learning, anomaly detection, physics-informed ML, optimisation, reinforcement learning, federated and edge AI
- Tools and MLOps: PyTorch, TensorFlow, scikit-learn, XGBoost, ONNX, MLflow, Azure ML, Azure DevOps, Docker, CI/CD
- Programming: Python, SQL, C++, MATLAB
- Leadership: technical direction, mentoring, KPI and review standards, customer and partner delivery