Computational sustainability is a new interdisciplinary research field with the overarching goal of developing computational models, methods, and tools to help manage the balance between environmental, economic, and societal needs for a sustainable future. I will provide examples of computational sustainability problems, ranging from wildlife conservation and biodiversity, to poverty mitigation, to materials discovery for renewable energy materials. I will also highlight cross-cutting computational themes and challenges for AI at the intersection of constraint reasoning, optimization, machine learning, citizen science, and crowd-sourcing. Finally I will discuss the need for a new approach that views computational sustainability problems as “natural” phenomena, amenable to a scientific methodology, in which principled experimentation, to explore problem parameter spaces and hidden problem structure, plays as prominent a role as formal analysis.
Machine learning (and in particular Deep learning) has made a significant impact in the scientific community. In this talk, I will present how we are using these methods in order to extract knowledge from the exponentially growing technical literature. In particular, I will explain how we are using ML in order to ingest and understand complicated technical documents, extract knowledge out of these documents and eventually put this extracted knowledge into context using knowledge graphs. This approach is very general and allows us to build advanced systems, with which you can query the data encased in your documents in a natural way. We will demo this idea during the talk with a use-case in Material Science.
Short Bio: Dr. Peter Staar joined the IBM Research – Zurich Laboratory in July of 2015 as a post-doctoral research fellow in the Foundations of Cognitive Solutions project. The Belgium-born scientist first came to IBM Research as a summer student in 2006. Prior to joining IBM Research, Peter was a post-doctoral researcher in Theoretical Physics and PASC (Platform for Advanced Scientific Computing) at the Swiss Federal Institute of Technology (ETH) in Zurich, Switzerland. He earned his PhD in Theoretical Physics and his M.Sc. degree in Physics at ETH Zurich in 2013 and 2009, respectively, and his B.S. degree in Physics (cum laude) from the Catholic University Leuven, Belgium. Peter has twice been a finalist for the prestigious ACM Gordon Bell award, first in 2013 for his paper entitled “Taking a Quantum Leap in Time to Solution for Simulations of High-Tc Superconductors” and then in 2015 for his paper entitled “An Extreme-Scale Implicit Solver for Complex PDEs: Highly Heterogeneous Flow in Earth Mantle.”