Julian Gold had spent most of his academic research career in the world of pure mathematics. Now, Gold is a data scientist at Princeton University, where he is applying his background in pure mathematics and probability to computational biology and bioinformatics – fields that use computational methods to analyze enormous and complex data sets. Through Princeton's Schmidt DataX Initiative, he is part of a team developing tools for understanding growing tissue.
To power homes and other buildings across large land areas, electrical energy is carried on high-voltage transmission lines as alternating current (AC), in which electrons periodically switch direction. A transformer steps down the high voltage in power lines to allow electricity to be safely used in people’s homes. The resulting power from…
With machine learning making headways into a variety of research fields and industries and garnering media headlines, a five-day Wintersession mini-course offering an introduction to machine learning became a popular draw, with more than 200 people signing up for at least one of the five days.
The mini-course, Introduction to…
In the last quarter of this year, news organizations have been reporting that the United Kingdom is headed toward a recession, with economists saying that Brexit, the economic decoupling of the United Kingdom and the European Union, is a major factor.
The economic downturn has not been a surprise to many economists. In fact, in 2017,…
In recent years, scientists have noticed that conclusions in some published research that heavily use machine learning cannot be reproduced.
To uncover why this is happening, Sayash Kapoor, a computer science doctoral student affiliated with the Center for Information Technology Policy (CITP), and Arvind Narayanan, professor of computer science, a participating faculty member of the Center for Statistics and Machine Learning (CSML) and CITP associated faculty, published the paper, “Leakage and the Reproducibility Crisis in ML-based Science.”
The project was conceived by Princeton professors Minjie Chen, Niraj Jha and Yuxin Chen, who were awarded DataX seed funding for the original proposal.
Magnetic components are typically the largest and least efficient components in power electronics. To address these issues, this project proposes the development of an open-source machine-learning based magnetics design platform in order to transform the modeling and design of power magnetics.
A two-day DataX workshop that covered a wide range of scientific topics, from Bayesian inference techniques to looking at machine learning in the context of the larger world, was held from May 13th to the 14th at Princeton University’s Friends Center. According to its organizers, the event, “Tutorial Workshop on Machine Learning for Experimental Science,” was meant to disseminate current topics and techniques in the field so that scholars may advance their research.
Data scientists Brain Arnold and Jose Garrido Torres, supported by the Schmidt DataX Initiative, are featured in a new series of videos talking about their role and impact in research with Princeton University scholars.
Eight new interdisciplinary research projects have won seed funding from Princeton University’s Schmidt DataX Fund, marking the third round of grants undertaken by the fund. The fund, supported through a major gift from the Schmidt Futures Foundation, provides grants to explore using artificial intelligence and machine learning to accelerate discovery.
The eight funded projects involve 13 faculty across seven departments and programs, from computer science to Near Eastern studies.
On March 4th, DataX sponsored part one of a workshop on cloud computing with a focus on setting up an integrated development environment for local and cloud computing.Twenty people attended, both in person and via Zoom. Part two of the workshop will be on April 1st, which will show attendees on how to build virtual machines in Microsoft Azure and access these using PyCharm. Read more about the March 4th workshop and how to register for the next one.