We teach best what we most need to learn. Richard Bach, Author, Illusions: Adventures of a Reluctant Messiah Every time Dr. Maren has to learn something, she teaches a course on the topic.

Dr. Maren teaches artificial intelligence and text analytics through Northwestern University’s Master of Science in Data Sciences (MSDS) program (named Master of Science in Predictive Analytics up through Winter, 2018) – see her NU Courses; all courses are taught completely online.

 

 

Books

Dr. Maren is currently developing Statistical Mechanics, Neural Networks, and Machine Learning. It is specifically designed to help people who are learning artificial intelligence, particularly energy-based models in machine learning. This requires understanding some statistical mechanics. This book is the easiest ramp into the requisite statistical mechanics that you can possibly find.

A course based on this text will be suitable for both upper-level undergraduate and graduate studies. Shortened versions can be presented as an intensive for a corporate or government group.

 

 

Reviewer for Journals

 

• Entropy, see Publons for listing of reviews,
• Frontiers in Systems Neuroscience
• Kozma & Freeman, Cinematic Operation of the Cerebral Cortex Interpreted via Critical Transitions in Self-Organized Dynamic Systems
• N. Newton, Understanding and Self-Organization

 

 

Mentoring and Coaching

As a mentor, coach and entrepreneurship facilitator, Dr. Maren has helped several students achieve their post-graduate degrees, and has counseled and nurtured others towards goals ranging from book publication to developing business plans and/or strategic vision statements.

 

 

Courses Taught

• Northwestern University, School of Professional Studies, Master of Science in Data Sciences (currently Predictive Analytics) (2015 – current):
  • AI & Deep Learning,
  • Text Analytics,
  • Time Series & Forecasting, and
  • Thesis.
• Harper College, Dept. of Chemistry (2015):
  • Introductory Chemistry.
• George Mason University, Dept. of Applied IT (2009 – 2010):
  • Cloud Computing, and
  • Capstone Program – both Regular and Entrepreneurial Sections.
• Marymount University, IT Department (2009 – 2010):
  • Cloud Computing, and
  • Business Processes.

• Georgetown University (2001 – 2002):
  • Knowledge Discovery (jointly listed with Computer Science and the School of Foreign Service, Science, Technology, and International Affairs Program), and
  • Computer Programming (for non-majors) (Computer Science).

• Radford University (1990 – 1991):
  • Computer Science (Computer Science Dept.)
• Northwestern University, University of Tennessee / University of Tennessee Space Institute (1987 – 1990):
  • Digital Pattern Recognition, and
  • Neural Networks (as short courses offered by UTSI).
• University of Wisconsin at River Falls (1979 – 1983):
  • Undergraduate mathematics.

 

 

Teaching Philosphy

Occasionally, when Dr. Maren has completed a major work, she organizes events, writes grant proposals, and does research to build on what she has just learned and taught. This was the case in 1990 – 1991, when she secured a one-year position at Radford University.

After completing the Handbook of Neural Computing Applications in 1990, Dr. Maren sought to work with the renowned neurophysiologist, Dr. Karl Pribram, who had retired from Stanford University to found the Brain Research Laboratory at Radford University. At Radford, Dr. Maren taught computer science, did innovative work in neural networks (resulting in the CORTECON network employing statistical mechanics as well as artificial neural connections), obtained research funding from the Jeffries Foundation as well as NSF support for a workshop on Adaptive Human-Computer User Interfaces, brought in colloquia speakers for the BRAINS symposia, and spent as much time as possible with Dr. Pribram and his associates at the BRAINS Research Laboratory.

In the course of teaching herself neural network technology in the late 1980’s and early 1990’s, Dr. Maren taught numerous courses on neural networks, both as conference-associated workshops and as loosely-disguised graduate-level courses in digital signal processing, which she taught at both the University of Tennessee Space Institute and the University of Tennessee, Knoxville. (Appointment at the latter through the Dept. of Nuclear Engineering.) She also wrote and edited the first comprehensive discussion of both neural network methods and their uses in various disciplines, as senior author of the Handbook of Neural Computing Applications (Academic, 1990).

 

 

Authorship

Dr. Maren is currently developing Statistical Mechanics, Neural Networks, and Machine Learning. It is specifically designed to help people who are learning artificial intelligence, particularly energy-based models in machine learning. A thorough and comprehensive package of teaching and supporting reference materials will make it possible for faculty to readily adopt and include this course, appropriate for computer science and multi-disciplinary majors.

Recently, following an interest in the psychology of archetypes, she authored Unveiling: The Inner Journey, published under the pen name Alay’nya by Mourning Dove Press in 2011. Unveiling has been adopted worldwide as a women’s body/mind/psyche/energy integration pathway guide. It currently has more than twenty Amazon reviews, with a 90% five-star rating.

As an early innovator in the field of “deep learning” using neural networks, she was the senior author of the first comprehensive neural networks handbook, the Handbook of Neural Computing Applications (Academic, 1990).

 

 

Pedagogic Education

• Northwestern University, School of Professional Studies:
  • Course Improvement (2016): Worked with distance learning instructional designer to totally redesign the Time Series & Forecasting course
• Harper College, Dept. of Chemistry:
  • STOMP (Successful Teaching Online Mentoring Program) (2015): Learned pedagogic approaches for effective online teaching. STOMP ePortfolio.

Educational Background

• Arizona State University, Ph.D., Chemistry (1981):
  • Dissertation Topic: Theoretical Models for Solid-State Phase Transitions
  • Dissertation Advisor: Professor Sheng H. Lin
• University of North Dakota, B.S. (1976):
  • Major: Mathematics
  • Minor: Chemistry, with additional concentrations in physics and geology
  • Languages studied: German (through second year), Russian, French, Akkadian
• University of Wisconsin at River Falls, Additional Coursework (1982 – 1983):
  • Computer Science, first two years of courses compressed into one year, while teaching a full-time load in mathematics
• University of Minnesota (1983):
  • Computer Science, LISP programming language