Sedigheh (Nasim) Zolaktaf

Department of Computer Science
University of British Columbia
201-2366 Main Mall
Vancouver, B.C., Canada, V6T 1Z4

I am an Applied Machine Learning Scientist at Microsoft. I completed my PhD in Computer Science at the University of British Columbia (UBC) in 2020. During my PhD, I was part of the Machine Learning and Algorithms groups under the supervision of Prof. Anne Condon and Prof. Mark Schmidt, and was also fortunate to collaborate closely with Prof. Erik Winfree and Dr. Frits Dannenberg from Caltech. My PhD research focused on predicting nucleic acid kinetics modeled as large Markov chains. I completed my masters in Computer Science at UBC in 2015, under the supervision Prof. Gail C. Murphy from UBC. My masters thesis was focused on developing recommendation techniques to improve programmers productivity in integrated development environments (IDEs). Before joining UBC, I completed my undergraduate degree in Software Engineering from Sharif University of Technology, Iran in 2013.

Publications

S. Zolaktaf*, F. Dannenberg*, M. Schmidt, A. Condon, E. Winfree, The Pathway Elaboration Method for Mean First Passage Time Estimation in Large Continuous-Time Markov Chains with Applications to Nucleic Acid Kinetics, arXiv, 2021.
S. Zolaktaf, F. Dannenberg, E. Winfree, A. Bouchard-Côté, M. Schmidt, A. Condon, Efficient Parameter Estimation for DNA Kinetics Modeled as Continuous-Time Markov Chains, 25th International Conference on DNA Computing and Molecular Programming, 2019.
S. Zolaktaf, F. Dannenberg, X. Rudelis, A. Condon, J. Schaeffer, M. Schmidt, C. Thachuk, E. Winfree, Inferring Parameters for an Elementary Step Model of DNA Structure Kinetics with Locally Context-Dependent Arrhenius Rates, 23rd International Conference on DNA Computing and Molecular Programming, 2017. (Best Student Paper Award)
S. Zolaktaf, G. Murphy, What to Learn Next: Recommending Commands in a Feature-rich Application, 15th IEEE International Conference on Machine Learning and Applications (ICMLA), 2015.

Posters

S. Zolaktaf, F. Dannenberg, X. Rudelis, A. Condon, J. Schaeffer, M. Schmidt, C. Thachuk, E. Winfree, A Computational Approach for Inferring Parameters for Nucleic Acid Kinetic Models, Women in Machine Learning Workshop at NIPS, 2017.
S. Zolaktaf, X. Rudelis, A. Condon, J. Schaeffer, M. Schmidt, C. Thachuk, E. Winfree. Inferring Parameters for a New Model of the Kinetics of Interacting DNA Strands, International Conference on DNA Computing and Molecular Programming, 2016.

Theses

S. Zolaktaf, Efficiently Estimating Kinetics of Interacting Nucleic Acid Strands Modeled as Continuous-Time Markov Chains, PhD Thesis, University of British Columbia, 2020.

S. Zolaktaf, What to Learn Next: Recommending Commands in a Feature-rich Application, M.Sc. Thesis, University of British Columbia, 2015.