My specific research objectives involve bridging the gap between Operations Research
and Machine Learning via computational methods and analyis. I have previously completed projects
in time series forecasting and stochastic modelling, notably involving Hidden Markov Models. I also
hold some conference publications, specifically in the field of Operational Research.
I am currently working on research related to deep learning, specifically bridging the gap
between Deep Learning and Operations research. Also, I'm intereted in Reinforcement Learning and Stochastic Processes.
Furthermore, I have experience in building production grade machine learning pipelines at scale
for eCommerce and Ad-Tech industries.
- Technical University of Munich (2021-Present).
- PhD in Operations Research
- Chair of Logistics and Supply Chain Management
- Advisor: Prof. Dr. Stefan Minner
- Working Thesis: Application of Reinforcement Learning and Monte Carlo Methods for Markov Decision Processes in the Logistics Domain
- University of Toronto (2015-2017).
- University of Toronto (2010–2015).
- BASc with Honours in Mechanical Engineering
- Minor in Robotics & Mechatronics
- Zalando SE (2020-Present).
- Applied Scientist - AB Testing for Markets & Demand Planning
- Loblaw Digital (2018-2020).
- Data Scientist - Demand Forecasting & Customer Fulfilment
- StackAdapt (2016-2018).
- Data Scientist - Real Time Bidding Optimization & Fraud Detection
- Paytm Labs (2015-2016).
- Visiting Scientist - eCommerce Recommendation & Fraud Detection
- Mitacs Accelerate Industry Government Joint Research Grant (2015)
- Wallace G Chalmers Engineering Design Award (2013)
- Faculty of Applied Science Engineering Research Fellowship (2012)
- Cancer Care Ontario IDEA Challenge Development Grant (2012)
- Magna Family Scholarship (2010)