Draft:Maria Dimakopoulou
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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[1], specializing in machine learning, particularly in the areas of reinforcement learning, contextual bandits, and causal inference.
Early Life and Education
[edit]After graduating with a Dipl. Ing. from the School of Electrical and Computer Engineering[2] with a perfect 10/10 GPA[3], National Technical University of Athens, Dimakopoulou completed her PhD in Management Science & Engineering at Stanford University[4]. She was advised by Benjamin Van Roy and Susan Athey. Her PhD research was funded by the "Arvanitidis" Stanford Graduate Fellowship[5] in Memory of William K. Linvill and the Onassis Foundation Graduate Fellowship.
Career and Research
[edit]Dimakopoulou's research[6] 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
[edit]- 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
[edit]- 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
[edit]- "Arvanitidis" Stanford Graduate Fellowship in Memory of William K. Linvill
- Onassis Foundation Graduate Fellowship
References
[edit]- ^ Maria Dimakopoulou's Homepage, https://mdimakopoulou.github.io
- ^ Dimakopoulou, M., Improving Reliability & Efficiency Of Performance Monitoring In Linux, Diploma Thesis, National Technical University of Athens, http://dx.doi.org/10.26240/heal.ntua.5508
- ^ Huffington Post, Techs and the City: Part 2 Femmes Up, https://www.huffpost.com/entry/techs-and-the-city-part-2_b_9589002
- ^ Dimakopoulou, M., Coordinated exploration in concurrent reinforcement learning, PhD Thesis, Stanford University, https://purl.stanford.edu/hs944xz0420
- ^ Maria Dimakopoulou at Stanford University, https://explorecourses.stanford.edu/instructor/madima)
- ^ M. Dimakopoulou Google Scholar profile, https://scholar.google.com/citations?user=ySLrpsYAAAAJ&hl=en