Alejandro Jaimes

Biography

Alex is Chief Scientist & SVP of AI at Dataminr. Alex is a leader in AI and as an Engineering executive and scientist has built and led AI teams at large companies such as Yahoo and at several startups, where he has led efforts to build AI products used by millions of people across multiple B2C and B2B industries (real-time event detection/emergency response, healthcare, self-driving cars, media, telecomm, etc.). He has 15+ years of intl. experience in research (Columbia U., KAIST) and product impact at scale (Yahoo, Telefónica, IBM, Fuji Xerox, Siemens, AT&T Bell Labs, DigitalOcean, and IDIAP-EPFL) in the USA, Japan, Chile, Switzerland, Spain, and South Korea. He has been a professor (KAIST, South Korea), and has 100+ patents and publications (h-index 40) in top tier conferences and journals in diverse topics in AI. His work has received 6K+ citations and he has been featured widely in the press (MIT Tech review, CNBC, Vice, TechCrunch, Yahoo! Finance, etc.). He has given 100+ invited talks at the top academic and industry conferences (UN AI for Good Global Summit, ICML & NeurIPs workshops, KDD, O’Reilly AI, Strata, Velocity, the Deep Learning Summit (Re-Work), Tech Open Air, the Future of Technology Summit, CogX, Stanford, Cornell, & Columbia Universities, etc.). He is a mentor at Endeavor (which leads the high-impact entrepreneurship movement around the world) and Techstars; he is a member of the advisory board of Digital Divide Data (a non-for profit that creates sustainable tech opportunities for underserved youth, their families, and their communities in Asia and Africa), and was an early voice in Human-Centered AI (Computing). He is one of ten experts in the Colombian Government’s Artificial Intelligence Expert Mission, which will evaluate and produce concrete recommendations in the short, medium and long term to implement an AI Policy. Colombia’s AI Expert Mission is one of the first of its kind in the region, and one of the first to focus on developing measures for the development of education and employment policies for the fourth industrial revolution. Alex is an active member of the research community (publishing and being in the program committee of several top-tier conferences). He holds a Ph.D. and an M.S. from Columbia University.

Talks and Events

2022 Talk: AI & Public Data For Humanitarian And Emergency Response

When an emergency event, or an incident relevant for peacekeeping or humanitarian needs first occurs, getting the right information as quickly as possible is critical in saving lives. When an event is ongoing, information on what is happening can be critical in making decisions to keep people safe and take control of the particular situation unfolding. In both cases, first responders, peacekeepers, and others have to quickly make decisions that include what resources to deploy and where. Fortunately, in most emergencies, people use social media to publicly share information. At the same time, sensor data is increasingly becoming available. But a platform to detect emergency situations and deliver the right information has to deal with ingesting thousands of noisy data points per second: sifting through and identifying relevant information, from different sources, in different formats, with varying levels of detail, in real time, so that relevant individuals and teams can be alerted at the right level and at the right time. In this talk we will describe the technical challenges in processing vast amounts of heterogeneous, noisy data in real time from the web and other sources, with a particular emphasis on how Knowledge Graph technology enables us to make sense of this wealth of information. We will highlight the importance of a human-centered approach to entity and relationship collection and curation, and the benefits of complementing that approach with selected information from publicly available Knowledge Graphs. Finally, we will touch on the use of Knowledge Graph entities and relationships and ML/DL to achieve world-class event detection from public multimodal data.