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Ackerman, Maya

Biography

Professor Ackerman is a leading expert on Artificial Intelligence and Computational Creativity, with unique insight into the commercialization of cutting-edge research on human-AI creative collaboration. Her research has earned awards from the Association for Computational CreativityUS Office of Naval ResearchNatural Sciences and Engineering Research Council of Canada, and many more. Ackerman is CEO/Co-founder of WaveAI, today’s most advanced musical AI startup. Their app, ALYSIA, allows everyone, everywhere to create original songs in minutes, through AI-powered assistance on the creation of original lyrics, melodies, and vocals. A sought-after speaker, Dr. Ackerman had been an invited speaker at the United Nations, Google, IBM Research, Stanford University, amongst other prestigious venues. She earned her PhD from the University of Waterloo, held postdoctoral fellowships at Caltech and UC San Diego and is  on the faculty of the Computer Engineering Department at Santa Clara University and an Associate Editor of the Journal of Computational Creativity. Maya is also an opera singer and music producer.

Education

2008-2012 Ph.D. in Computer Science, University of Waterloo, ON, Canada.
2006-2007 M.Math in Computer Science, University of Waterloo, ON, Canada. Winner of the outstanding graduate student award.
2001-2006 B.Math in Computer Science and Combinatorics and Optimization, University of Waterloo, ON, Canada. Cooperative program. Graduated with distinction.
 

Courses Taught

2020 Abstract Data Types and Structures (Undergraduate), Santa Clara University.
2019 Computational Creativity (Graduate), Santa Clara University.
2019 Artificial Intelligence (Senior undergraduate), Santa Clara University.
2019 Computational Creativity (Graduate), Santa Clara University.
2018 Abstract Data Types and Structures (Undergraduate), Santa Clara University.
2018 Machine Learning (Graduate), Santa Clara University.
2018 Artificial Intelligence (Senior undergraduate), Santa Clara University.
2017 Machine Learning (Graduate), Santa Clara University.
 

Awards

2015-2019 Unsupervised Learning (Clustering) of Odontocete Echolocation Clicks. Collaborative grant. Office of Naval Research, 2015-2018. $400,000.

2019 Women in Tech Awards. Data Scientist Finalist. DTS Women in Tech Awards.

2017 Runner-up Best Paper Award. The Association for Computational Creativity.

2015 PI. First-Year Assistant Professor Award. Foundations of Incremental Clustering. Florida State University. 2015. $20,000.

2012-2014 Postdoctoral Fellowship. Natural Sciences and Engineering Research Council of Canada (NSERC), 2012-2014. $80,000.

2009-2012 Alexander Bell Canadian Graduate Scholarship. Natural Sciences and Engineering Research Council of Canada (NSERC), 2009-2012. $140,000.

2006-2011 President’s Graduate Scholarship. University of Waterloo, 2006- 2011. $50,000.

2010-2011 David R. Cheriton Scholarship. University of Waterloo, 2010-2011. $20,000.

2006-2009 Ontario graduate scholarship. Ontario Ministry of Training Colleges and Universities, 2006-2009. $51,000.

Publications

*indicates student co-authors.

ICCC ’19 M. Ackerman and Rafael Perez y Perez. Field Work in Computational Creativity. International Conference on Computational Creativity (ICCC), 2019.

Pattern Rec. ’19 Andreas Adolfsson*, M. Ackerman, and Naomi Brownstein. To Cluster, or Not to Cluster: How to Answer the Question. Pattern Recognition, 2019.

ICCC ’19 Rachel Goldstein*, Andy Vainauskas*, Margareta Ackerman, Robert Keller. BrainControlled Musical Improvisation. International Conference on Computational Creativity (ICCC), 2019.

MUME ’19 Andreas Adolfsson*, Jon Bernal*, M. Ackerman, and Julia Scott. Musical Mandala Mindfulness: A Generative Biofeedback Experience Musical MetaCreation (MuMe), 2019.

DATA BRIEF ’19 Naomi Brownstein, Andress Adolfsson*, and M. Ackerman. Descriptive Statistics and Visualization of Data from the R Datasets Package with Implications for Clusterability. Data Brief, 2019.

CSR ’19 Hugo Oliveira, Tiago Mendes, Ana Boavida, Ai Nakamura*, and M. Ackerman. CoPoeTryMe: Interactive Poetry Generation. Cognitive Systems Research, 2019.

ICCC ’18 M. Ackerman, James Morgan, and Christopher Cassion. Co-Creative Conceptual Art. International Conference on Computational Creativity (ICCC), 2018.

ICCC ’17 Divya Singh*, M. Ackerman, and Rafael Perez y Perez. A Ballad of the Mexicas: Automated Lyrical Narrative Writing. International Conference on Computational Creativity (ICCC), 2017.

ICCC ’17 M. Ackerman, Ashok Goel, Colin Johnson, Anna Jordanous, Carlos Leon, Rafael Perez y Perez, Hannu Toivonen, and Dan Ventura. Teaching Computational Creativity. International Conference on Computational Creativity (ICCC), 2017. Runner-up Best Paper Award.

EvoMUSART ’17 M. Ackerman and David Loker. Algorithmic Songwriting with ALYSIA. The International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMUSART), 2017.

SDM ’17 Amit Dhurandhar, M. Ackerman, and Xiang Wang. Uncovering Group Level Insights with Accordant Clustering. SIAM International Conference on Data Mining (SDM), 2017.

ICCC ’16 Taylor Brockhoeft*, Jennifer Petuch*, James Bach*, Emil Djerekarov*, M. Ackerman and Gary Tyson. Interactive Projections for Dance Performance. International Conference on Computational Creativity (ICCC), 2016.

ICDM ’16 Jarrod Moore* and M. Ackerman. Foundations of Perturbation Robust Clustering. IEEE International Conference on Data Mining (ICDM), 2017.

JAAMAS ’16 M. Ackerman and Simina Branzei. Authorship Order: Alphabetical or Contribution? Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS), 2016.

JMLR ’16 M. Ackerman and Shai Ben-David. A Characterization of Linkage-Based Hierarchical Clustering. Journal of Machine Learning Research (JMLR), 2016.

NIPS ’14 M. Ackerman and Sanjoy Dasgupta. Incremental Clustering: The Case for Extra Clusters. Neural Information Processing Systems Conference (NIPS), 2014.

IJBRA ’14 M. Ackerman, Dan Brown, and David Loker. Effects of Rooting via Outgroups on Ingroup Topology in Phylogeny. International Journal of Bioinformatics Research and Application (IJBRA), 2014.

AAMAS ’14 M. Ackerman and Simina Branzei. Authorship Order: Alphabetical or Contribution? Autonomous Agents and Multiagent Systems (AAMAS), 2014.

AISTATS ’13 M. Ackerman, Shai Ben-David, David Loker, and Sivan Sabato. Clustering Oligarchies. Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), 2013.

ACM RECSYS ’13 Hossein Vahabi, M. Ackerman, David Loker, Ricardo Baeza-Yates and Alejandro Lopez-Ortiz. Orthogonal Query Recommendation. ACM Conference on Recommender Systems (ACM RECSYS), 2013.

AAAI ’12 M. Ackerman, Shai Ben-David, Simina Branzei, and David Loker. Weighted Clustering. Association for the Advancement of Artificial Intelligence Conference (AAAI), 2012.

ICCABS ’12 M. Ackerman, Dan Brown, and David Loker. Effects of Rooting via Outgroups on Ingroup Topology in Phylogeny. IEEE International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), 2012.

COGSCI ’12 Joshua Lewis, M. Ackerman, and Virginia De Sa. Human Cluster Evaluation and Formal Quality Measures. Proc. 34th Annual Conference of the Cognitive Science Society, 2012.

IJCAI ’11 M. Ackerman and Shai Ben-David. Discerning Linkage-Based Algorithms Among Hierarchical Clustering Methods. International Joint Conference on Artificial Intelligence (IJCAI oral), 2011.

CSP 2011 Daniel Maoz and M. Ackerman. Chavruta: A Novel Teaching Methodology Based on an Ancient Tradition. From Antiquity to the Post-Modern World: Contemporary Jewish Studies in Canada. Newcastle upon Tyne: Cambridge Scholars Publishing, 193-205, 2011.

NIPS ’10 M. Ackerman, Shai Ben-David, and David Loker. Towards Property-Based Classification of Clustering Paradigms. Neural Information Processing Systems (NIPS), 2010.

COLT ’10 M. Ackerman, Shai Ben-David, and David Loker. Characterization of Linkage-Based Clustering. Conference on Learning Theory (COLT), 2010.

CSTL ’10 Daniel Maoz and M. Ackerman, Chavruta and Transformative Learning: A Comparative Analysis, in Opportunities and New Directions: Canadian Scholarship of Teaching and Learning (Edited by N. Simmons. Waterloo: CTE, 2010) pp. 51-59.

COCOON ’09 M. Ackerman and Erkki Makinen. Three New Algorithms for Regular Language Enumeration. Computing and Combinatorics: 15th Annual International Conference (COCOON), Lecture Notes in Computer Science 5609, Springer-Verlag, Berlin Heidelberg, pp. 178-191, 2009.

AISTATS ’09 M. Ackerman and Shai Ben-David. Clusterability: A Theoretical Study. Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS oral), JMLR: &CP 5, pp. 1-8, 2009.

TCS ’09 M. Ackerman and Jeffrey Shallit. Efficient Enumeration of Words in Regular Languages. Theoretical Computer Science, Volume 410, Issue 37, pp. 3461-3470, 2009.

NIPS ’08 M. Ackerman and Shai Ben-David. Measures of Clustering Quality: A Working Set of Axioms for Clustering. Full oral presentation at the Neural Information Processing Systems Conference (NIPS), 2008. (Acceptance rate: 2.7%).

CIAA ’07 M. Ackerman and Jeffrey Shallit. Efficient enumeration of regular languages. Conference on Implementation and Application of Automata (CIAA), Lecture Notes in Computer Science, 4783, Springer-Verlag, Berlin Heidelberg, pp. 226-241, 2007.

Maya Ackeraman

Assistant Professor of Computer Science and Engineering