She received her Ph.D. in mathematics from M.I.T. in 1968 (the same year Princeton first allowed women to enter their graduate program). She then taught at UC Berkeley, founded the Mills College Department of Mathematics and Computer Science (the first CS department at a women's college), was senior researcher at the International Computer Science Institute and Deputy Director of the Mathematical Sciences Research Institute in Berkeley. Straddling the historic handover of Hong Kong from Britain to China on July 1, 1997, Lenore spent two years, 1996-1998, at the City University of Hong Kong as Visiting Professor of Mathematics and Computer Science. Here she completed her book, Complexity and Real Computation, with colleagues and co-authors Felipe Cucker, Mike Shub and Steve Smale. Her research, from her early work in model theory and differential fields (logic and algebra) to her more recent work in developing a theory of computation and complexity over the real numbers (mathematics and computer science), has focused on merging seemingly unrelated areas.

Lenore is also well known for her work in increasing the participation of girls and women in mathematics and scientific fields. In 2005, in recognition of this work, she received the US Presidential Award for Excellence in Science, Mathematics and Engineering Mentoring. She has served the professional community in numerous capacities including as President of the Association for Women in Mathematics, vice-President of the American Mathematical Society and is currently a member of the M.I.T. Mathematics Visiting Committee. Her most recent creation, and passion, is Project Olympus, a high tech innovation center at Carnegie Mellon.

Lenore first visited China in 1979 as universities were re-opening after years of turmoil. She is excited to see the amazing changes that have taken place in less than 20 years and to meet current students with promise to become the innovative thinkers for the 21st Century.

Presentation Title: Machine Understanding / Thinking Out of the Box

Abstract:
Machine Understanding: Machine Learning is a well-researched area of computer science. It has many applications from helping stem the current flood of email spam to recognizing objects in visual images.

Manuel will present a new area -- currently under construction -- which we call Machine Understanding. The goal of Machine Understanding is to make Machine Learning more robust by extending it to high-level (abstract) relationships between learned concepts. In our approach, a machine understands material by searching for and detecting significant properties of what it is studying, attaching names (mnemonics) to those properties, preparing templates (models) for future use, then continuing recursively to build on this foundation.

To give an idea how this works, we will look at Freudenthal's LINCOS (LINgua COSmica), a language designed for communication with extraterrestrial beings. These beings are presumed to be intelligent aliens who have no other knowledge of our world or language except what is conveyed by LINCOS itself. We discuss the design of a program/computer that can learn on its own to understand any language such as LINCOS that is designed to be inferable by an intelligent motivated but otherwise unknowledgeable entity.

This is joint work with Ryan Williams.

Thinking Out of the Box: What does it mean to think out of the box? How is it done? What are some examples? Yun Ying, a semi-retired physics professor in Nanjing has been promoting out-of-the-box thinking in her physics classes, see:
http://www.sciencemag.org/cgi/content/full/317/5834/74b

Lenore will discuss our own experience with promoting out-of-the-box thinking, both in education (with examples from CS4ALL, a program for high school (and K-8) teachers of computer science) and in the research arena (with examples from the ALADDIN Center which promotes synergy between algorithm theory and practice).