Margareta Ackerman
Assistant Professor

408-603-0977, MH 215


I completed my Ph.D. in Computer Science at the University of Waterloo and held Postdoctoral Fellowships at Caltech and UC San Diego. Afterwards, I spent two years as an Assistant Professor at Florida State University. I am currently an Assistant Professor in the Department of Computer Science at San Jose State University.

I specialize in Machine Learning, spanning from foundations to emerging applications in big data and computational creativity.

Twitter: @ackermanmaya

Google scholar page

YouTube channel
for my algorithmic compositions

  • Stanford University, April 13th 2017. Algorithmic Songwriting. CCRMA Classroom, Knoll 217. Open to the public.

  • Google, May 4th 2017 at 1pm. Algorithmic Songwriting.

  • Ericsson, May 17th. Algorithmic Songwriting.

  • LASER series. Stanford University, October 5th 2017.

  • PASEO festival, April 8th 2017. World Premiere of ALYSIA's Compositions. Hammer Theater. Open to the public.

  • IBM, March 16th 2017. Formal Foundations of Clustering.


Palo Alto Weekly: Algorithm and rhyme: Artificial intelligence takes on songwriting

NBC News: How Humans Are Creating Artistic Partnerships with AI

New Scientist: Machine learning lets computer create melodies to fit any lyrics

ABC 27 WTXL: FSU Mobile Lab Shines a Spotlight on Dance.


I specialize in artificial intelligence, with an emphasis on machine learning spanning from formal foundations to emerging applications in big data and computational media.

Computational media and Computational creativity, two related, emerging fields in Artificial Intelligence. These disciplines challenge common perceptions in computer science, the arts, and humanities by redefining the computer as a medium for creative expression. Computational media offers many opportunities to utilize the power of machine learning in new domains, including music, computer games, visual art, and dance while computational creativity challenges us to design systems that act as autonomous creative agents. These exciting new areas of research have also been used to find innovative solutions in scientific discovery and education.

My work in these areas combines my expertise in machine learning and training in the arts in order to transform the performing arts through interactive media. The ultimate goal is to make autonomous creative systems that produce high quality artistic works that are comparable to the works of human artists. My work on computational musicology introduces the first fully data-driven songwriting system, allowing the creation of original songs in any musical style. In another project, we are collaborating with dancers to create a movement-based language that would allow dancers to easily manipulate dynamic sets through native dance moves and rehearsal practices.

My research on machine learning focuses on clustering, which is one of the most popular data mining tools, applied in a wide range of disciplines, from astronomy, bioinformatics, and collaborative filtering, to game design, marketing, and zoology. All these disciplines use clustering to identify groups of similar items in data. Yet, despite its popularity, there is a substantial gap between theory and practice and insufficient interaction between the communities that apply and study clustering. My work focuses on bridging this gap through the integration of clustering theory and practice in the standard batch model, as well as the increasingly popular incremental and streaming settings for clustering big data. To learn more about this research area, have a look at my brief overview on foundations of clustering.

Please visit my research page for information on my current research projects.


Here is a complete list of my publications.
* indicates student co-authors.

  • Progress and Open Problems in Clustering. Conference of the International Federation of Classification Societies (IFCS). Bologna, Italy, 2015.
  • Theoretical Foundations of Clustering. Purdue University. West Lafayette, IN, 2014.
  • Characterization of Linkage-Based Clustering. Carnegie Mellon University. Pittsburgh, PA. 2013.
  • Formal Foundations of Clustering. ID Analytics. San Diego, CA, 2013.
  • Formal Foundations of Clustering. eHarmony. Santa Monica, CA, 2013.
  • Towards Theoretical Foundations of Clustering. Information Theory and Applications Workshop. La Jolla, CA, 2013.
  • Weighted Clustering. University of California, San Diego. La Jolla, CA, 2012.
  • Towards Theoretical Foundations of Clustering. Caltech. Pasadena, CA, 2012.
  • Towards Theoretical Foundations of Clustering. Columbia University. New York, NY, 2012.
  • On Theoretical Foundations of Clustering. University of California, Berkeley. Berkeley, CA, 2011.
  • Characterization of Linkage-Based Algorithms. University of California, San Diego. La Jolla, CA, 2010.
  • Co-PI, with Marie Roch and Simone Baumann-Pickering. Unsupervised Learning (Clustering) of Odontocete Echolocation Clicks. Office of Naval Research, 2015-2017. $400,000.
  • PI. First Year Assistant Professor Award. Foundations of Incremental Clustering. Florida State University, 2015. $20,000.
  • Postdoctoral Fellowship. Natural Sciences and Engineering Research Council of Canada (NSERC), 2012-2014. $80,000.
  • Alexander Bell Canadian Graduate Scholarship. NSERC, 2009-2012. $140,000.
  • President's graduate scholarship. University of Waterloo, 2006- 2011. $50,000.
  • David R. Cheriton Scholarship. University of Waterloo, 2010-2011. $20,000.
  • Ontario graduate scholarship. Ontario Ministry of Training Colleges and Universities, 2006-2009. $51,000.


My work in computational media benefits from extensive artistic training. I've received four years of vocal training, focusing on opera, and have studied classical piano and improvisation, as well as Meiser's acting technique.

In addition, I've written a Holocaust memoir, Running from Giants: The Holocaust Through the Eyes of a Child, which tells my grandfather's story as a child during the Holocaust. This book is currently in use in Florida public schools.