Vasilis Syrgkanis joins MS&E as Assistant Professor
MS&E welcomes Vasilis Syrgkanis to our faculty!
Prof. Syrgkanis received his PhD in Computer Science from Cornell University, under the supervision of Prof. Eva Tardos, and then spent two years at Microsoft Research, New York as a postdoctoral researcher. Most recently, he was a Principal Researcher at Microsoft Research, New England, where he co-led the project on Automated Learning and Intelligence for Causation and Economics (ALICE).
We caught up with Prof. Syrgkanis to ask about his current work and why he chose MS&E. Read the brief Q&A below.
Can you describe your recent work and the impact you hope it makes, both in your field and in society?
My most recent work lies in the relatively nascent field of causal machine learning. My work tries to re-direct the power of machine learning techniques in uncovering complex patterns from high-dimensional data, to answer causal questions; questions related to what would happen if we intervene in a system and change one of the variables. Understanding the outcomes of such interventions requires understanding the causal relationship of the intervening variable with the outcome of interest. The field of machine learning has traditionally focused on predictive modelling; predicting an outcome of interest in the absence of any intervention, but assuming that the test data come from the same distribution as the training data. Causal modelling is inherently harder than predictive modelling. My work blends techniques from the classical field of causal inference with techniques from machine learning to develop novel methodologies that enable causal modelling from high-dimensional, observational, and experimental data sets and with minimal modelling assumptions.
These techniques are applicable in many domains, such as operations management, healthcare and experimentation in the tech industry. For instance, such what-if questions from complex datasets frequently arose in interactions with product groups during my time as a principal researcher at Microsoft Research, and many of the techniques that I have developed in my research have been applied to problems such as pricing, return-on-investment, and personalization.
My hope with the research group that I will be building with the amazing students at Stanford is to automate the causal machine learning pipeline to the extent possible and make causal machine learning techniques accessible to every decision maker around the world. Currently, despite the many research efforts in causal machine learning and causal AI, applying causal inference correctly still requires expertise and training in the field, and thus it is only accessible to decision makers that can afford hiring such highly-skilled groups. I hope that by building automated causal machine learning systems with decision makers in the loop, we will be able to make causal inference more accessible and enable more robust data-driven decisions.
What does being a part of MS&E mean, or what makes it the right place for you?
I always thought that MS&E was the perfect department for my research interests and to be my academic home. I value the interdisciplinarity of the faculty that spans a broad spectrum of areas from both qualitative and quantitative angles. Moreover, I value immensely the stellar quality of students in the department, and I hope that I'll be able to advise the future leaders of the area and broaden the impact of the field to society.