Ph.D. candidate researching revenue management and pricing optimization. Passionate about improving business decisions using data science and optimization. Careful, persistent, patient, respectful, and competent. A genuine team player committed to group success and growth. Sincere and honest with a high level of personal and professional integrity.

Previous corporate experience allows me to look into the industry from multiple perspectives. I have spent several years in Yandex (Russian Google), starting with a recommender system prototype and then improving speech recognition at Yandex SpeechKit. Later, I also participated in two Scotiabank internships, firstly doing the data science around deposit time series clustering and secondly looking into recency-frequency-monetary value marketing for day-to-day acquisition campaigns.

My current academic research focuses primarily on resort revenue management and sea cargo modeling. Previously, I studied discrete optimization and approximation algorithms for scheduling on uniform processors. Now, I am looking for opportunities to improve my knowledge and experience in discrete optimization related to revenue management, modern stochastic subgradient methods, and decomposition for reinforcement learning.

Professionally, I am searching for a post-doctoral opportunity related to operations research, computer science, machine learning, and artificial intelligence, preferably somewhere in between. My current industrial and theoretical research stack is most relevant to decision-making and data analytics in revenue management. Decisions or machine learning in healthcare and manufacturing are highly actual among neighboring disciplines. I am also open to investigating discrete approximation algorithms again or starting research related to theoretical stochastic optimization and deep reinforcement learning.