IBM Research-Zurich

From Switzerland to Princeton via IBM Research

Share this post:

This is the story of one of our recent Science Week visitors at our lab

Each year, as part of our Science Week offering, the THINK Lab at IBM Research – Zurich hosts students who are considering a career in science, engineering, or other technical fields from many high schools in Switzerland. Our young visitors get to meet with our researchers, who describe their own careers and talk about what they do every day. We also show them our laboratories.

We’ve been offering the Science Week for over a decade, and the waiting list of schools that want to send us their students is very long. One school that is always on the list is our Lab’s neighbor: The Zurich International School (ZIS).

The hero of our story is Millian Gehrer, who studied at ZIS, and who actually visited us twice, having decided after his first visit that he too wanted to become a technologist.

To keep it short, he founded a software company as a hobby, got himself accepted to Princeton University, and worked this past summer at the Zurich lab as an intern.

While he was here we interviewed him, and we think you will enjoy watching him tell us his views on what he experienced.

More IBM Research-Zurich stories

Who. What. Why. New IBM algorithm models how the order of prior actions impacts events

To address the problem of ordinal impacts, our team at IBM T. J. Watson Research Center has developed OGEMs – or Ordinal Graphical Event Models – new dynamic, probabilistic graphical models for events. These models are part of the broader family of statistical and causal models called graphical event models (GEMs) that represent temporal relations where the dynamics are governed by a multivariate point process.

Continue reading

IBM’s Squawk Bot AI helps make sense of financial data flood

In our recent work, we detail an AI and machine learning mechanism able to assist in correlating a large body of text with numerical data series used to describe financial performance as it evolves over time. Our deep learning-based system pulls out from large amounts of textual data potentially relevant and useful textual descriptions that explain the performance of a financial metric of interest – without the need of human experts or labelled data.

Continue reading

COVID-19 a year later: What have we learned?

We’ve learned a lot during the past year about how to address global crises, but in my mind, one lesson cannot be ignored: The need for more strategic collaborations across institutions and sectors.

Continue reading