Bio

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My specific research objectives involve bridging the gap between Operational 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 Operational 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.

Academic background

• University of Toronto (2015-2017).
  • MASc in Industrial Engineering with a Focus in Operational Research.
  • Advisor: Prof. Viliam Makis
  • Thesis: Comparative Study between Statistical Fraud Detection Methods on eCommerce Networks
• University of Toronto (2010–2015).
• BASc with Honours in Mechanical Engineering
• Minor in Robotics & Mechatronics

Employment

• 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

• Advanced Micro Devices (2013-2014).
• Reliability Engineering Intern

Awards

• 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)