I am a computer science PhD student in Boğaziçi University, with a research focus on robust generalization in deep learning and probabilistic latent variable models. I have conducted research in a wide range of topics including compressibility in neural networks, probabilistic tensor factorization methods, human-AI complementarity (just published on Nature Medicine!), causal discovery, and robust drug-target affinity predictions. In Summer 2022, I was a research scientist intern at DeepMind London, working on learning to defer to experts based on ML model predictions, as well as learning from multiple experts with noisy annotations.

I have industry experience on research and deployment of machine learning systems, both as a team lead and freelance consultant, involving work on multivariate time series analysis, AutoML system development, and predictive maintenance among others. At the height of my involvement in the industry, I was in charge of the team that oversaw the forecasting of withdrawals from 8000 ATMs daily.

I have a BA and MA in psychology from my alma mater, where I mainly focused on psycholinguistics research, as well as experiment design and statistical inference. My thesis was on embodied language processing, investigating how understanding long texts utilized perceptual representations. In my research visit to UC Berkeley’s Concepts and Cognition Lab, I investigated how different wording choices a language affords can affect a person’s causal explanations.

To learn more, feel free to see my CV, visit my GitHub, Google Scholar, or LinkedIn pages, or e-mail me through melih.barsbey at gmail.com.