On March 4^th I had a pleasure to attend the third annual conference for Women in Data Science in Cambridge, MA. After missing it last year (ironically, because my daughter decided to arrive a week before the conference!) and hearing so many great things about it from my colleagues, I was determined to attend it this year and excited by an impressive list of distinguished women invited to present their latest research. The one-hour delay of the start due to a mild New England snow storm only amplified my (and everyone else’s) anticipation.

The conference began with opening remarks by Cathy Chute, the Executive Director of the Institute for Applied Computational Science at Harvard. She reminded us that WiDS started at Stanford in 2015 and is now officially a global movement with events happening all around the globe. The one in Cambridge was made possible by a fruitful partnership between Harvard, MIT, and Microsoft Research New England.

Liz Langdon-Gray followed with updates about the Harvard Data Science Initiative (HDSI), which was about to celebrate a two-year anniversary. She also informed us that a highly anticipated Harvard Data Science Review, a brainchild of my former statistics professor and advisor Xiao-Li Meng, is going to launch later this spring. This inaugural publication of HDSI will be featuring “foundational thinking, research milestones, educational innovations, and major applications” in the field of data science. One of its aims is to innovate in content and presentation style and, knowing Xiao-Li’s unparalleled talent to cleverly combine deep rigor with endless entertainment, I simply cannot wait to check out the first volume of the Review when it comes out!

The first invited speaker of the conference was Cynthia Rudin, an Associate Professor of Computer Science and Electrical and Computer Engineering at Duke. Prof. Rudin started with a discussion of the concept of variable “importance” and how most methods that test for it are, usually, model-specific. However, a variable can be important for one model, but not for another. Therefore, a more interesting question to answer is whether a variable is important for any good model, or for a so-called “Rashomon set” of models.

Prof. Rudin then switched to an example that motivated her inquiry – an article on Machine Bias in ProPublica, which claimed that the proprietary “black-box” algorithm COMPAS that predicts recidivism and is used for sentencing convicts in a number of states, is racially biased. After digging deeper into the details of the ProPublica analysis and trying to fit various models to the data herself, Prof. Rudin came to a conclusion that age and criminal history were by far the most important variables in the COMPAS algorithm, not the race! Even though it is still possible to find model classes that mimic COMPAS and utilize race, this variable’s importance is probably much smaller than what was claimed in by ProPublica. Nevertheless, Prof. Rudin concluded that the “black-box” machine learning (ML) algorithm that decides person’s fate was not an ideal solution as it cannot be independently validated and might be sensitive to data errors. Instead, she advocated for the development of interpretable modeling alternatives.

We then heard from Stefanie Jegelka, an Associate Professor at MIT, who talked about tradeoffs between neural networks (NN) that are wide vs. deep. Even though theory states that an infinitely wide NN with 1-2 layer may represent any reasonable function, deep networks have shown higher accuracy results in recent classification competitions (e.g., ILSVRC). Therefore, she concluded, it was important to understand what relationships NNs could actually represent. Then Prof. Esther Duflo, a prominent economist from MIT, discussed a Double Machine Learning approach that used the power of ML apparatus to answer questions of causal nature, akin to those that, usually, require a randomized clinical trial.

Anne Jackson, a Director of Data Science and Machine Learning at Optum, was the only industry speaker at the conference. She talked about building large-scale applications in the industry settings: from data cleaning, understanding the context, to incorporating the developed model into the business process. “What we really need”, she jokingly said, “is a ‘unicorn’ – a PhD in Math, with MS in Computer Science, and a VP-level understanding of business – to get it right!”. She also cautioned against blindly relying on algorithms and, instead, always translating models into the real world. For example, comparing stakes for false-positive vs. false-negatives, considering model drift, etc. Finally, Anne touched upon the futility of efforts for building and supporting custom software. Moving away from this approach, more and more businesses start to utilize “middleware”, which is a “layer of software that connects client and back-end systems and ‘glues’ programs together”.

Finally, the last, but most certainly not least, invited speaker was Prof. Yael Grushka-Cockayne, a Visiting Professor at HBS, whose research interest revolved around behavioral decision making (among many other things). In her fun and engaging talk, she emphasized the importance of going beyond just a simple point estimation when it comes to prediction. She also reminded us of the effectiveness of crowdsourcing when it comes to forecasting, with such notable examples as The Good Judgement Project, where everyone can provide their opinion on an outcome of certain world event and get rewarded by getting it right, and the Survey or Professional Forecasters, which obtains macroeconomic predictions from a group of private-sector economists and produces quarterly reports with aggregated results. The last part of the talk was devoted to the results of Prof. Grushka-Cockayne’s successful collaboration with Heathrow Airport in applying Big Data/ML approach to improve upon passenger transfer experience, which did not sound like an easy feat! Ironically, the data which proved to be most reliable and was ultimately used in the model came from baggage transition records.

In addition to a strong lineup of featured speakers, the conference offered an excellent poster session, where students and Post Docs demonstrated their ML applications in a wide range of diverse fields, including drug development, earthquake prediction, corruption detection, and many others. All in all, this long awaited Cambridge WiDS conference most certainly exceeded my expectations and I am eagerly looking forward to the next year’s event.