Draft:Maria Dimakopoulou

Maria Dimakopoulou
Nationality	Greek
Alma mater	Stanford University
Known for	Reinforcement Learning, Contextual Bandits, Causal Inference
Awards	"Arvanitidis" Stanford Graduate Fellowship, Onassis Foundation Graduate Fellowship
Scientific career
Fields	Machine Learning, Reinforcement Learning, Contextual Bandits, Causal Inference
Institutions	Netflix, Stanford University
Doctoral advisor	Benjamin Van Roy, Susan Athey

Maria Dimakopoulou is a Greek computer scientist and Senior Research Scientist at Spotify, specializing in machine learning, particularly in the areas of reinforcement learning, contextual bandits, and causal inference.

Early Life and Education

After graduating with a Dipl. Ing. from the School of Electrical and Computer Engineering with a perfect 10/10 GPA, National Technical University of Athens, Dimakopoulou completed her PhD in Management Science & Engineering at Stanford University. She was advised by Benjamin Van Roy and Susan Athey. Her PhD research was funded by the "Arvanitidis" Stanford Graduate Fellowship in Memory of William K. Linvill and the Onassis Foundation Graduate Fellowship.

Career and Research

Dimakopoulou's research focuses on the development and application of machine learning algorithms. She has made significant contributions to reinforcement learning and contextual bandits, which are critical for decision-making processes under uncertainty.

Key Contributions

• Reinforcement Learning and Contextual Bandits: Developed algorithms for contextual bandit problems, which are used in recommendation systems and personalized healthcare treatments.
• Causal Inference: Worked on methods to ensure robust predictions and decisions from observational data, addressing challenges in adaptive data collection.
• Innovative Algorithms and Estimators: Co-authored influential papers on off-policy evaluation and risk minimization, improving the reliability of machine learning models.

Selected Publications

• Bibaut, A., Dimakopoulou, M., Kallus, N., Chambaz, A., & van der Laan, M. (2021). Post-Contextual-Bandit Inference. NeurIPS.
• Bibaut, A., Kallus, N., Dimakopoulou, M., Chambaz, A., & van der Laan, M. (2021). Risk Minimization from Adaptively Collected Data. NeurIPS.
• Su, Y., Dimakopoulou, M., Krishnamurthy, A., & Dudik, M. (2020). Doubly Robust Off-Policy Evaluation with Shrinkage. ICML.
• Dimakopoulou, M., Vlassis, N., & Jebara, T. (2019). Marginal Posterior Sampling for Slate Bandits. IJCAI.

Awards and Recognition

• "Arvanitidis" Stanford Graduate Fellowship in Memory of William K. Linvill
• Onassis Foundation Graduate Fellowship