Leila Zahedi

Florida International University Knight Foundation School of Computing and Information Sciences

Lecture Information:
  • June 29, 2022
  • 9:00 AM
  • CASE 349 & https://fiu.zoom.us/j/7868288302 Passcode:Zahedi22

Speaker Bio

Leila Zahedi is currently a Ph.D. Candidate at the Knight Foundation School of Computing and Information Sciences at Florida International University, working with Prof. M. Hadi Amini at Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). She holds a B.S. in Computer Engineering from the University of Isfahan, an M.S. in Information Technology Management: Advanced Information Systems from Yazd University, and her second M.S. in Computer Science from FIU. Her research interests include optimization algorithms for automated machine learning (AutoML), data science, and artificial intelligence.

Leila received the “2022 Outstanding Student Life Award” (Graduate Student Leader of the Year award) from the Division of Academic and Student Affairs at FIU, “Dissertation Year Fellowship” from the university graduate school at FIU, Graduate Student Scholarship from Student Government Association (SGA), IAAP Award from Iranian American Academics and Professionals organization, and GHC and TAPIA scholarships among others. She is recipient of 6 travel scholarships, and she published 18 journal and conference papers. Besides her academic activities, she also served as the chair of the Graduate and Professional Student Committee (GPSC), graduate senator at SGA and vice president of Iranian Students Organization.


Building a useful machine learning model is a challenging process, requiring human expertise to perform various proper tasks and ensure that the machine learning’s primary objective –determining the best and most predictive model — is achieved. These tasks include pre-processing, feature selection, and model selection. Even experts need the time and resources to create good predictive machine learning models. The idea of automated machine learning (AutoML) is to automate the pipeline to release the burden of substantial development costs and manual processes.

The algorithms leveraged in these systems have different hyper-parameters. On the other hand, different input datasets have various features. In both cases, the final performance of the model is closely related to the final selected configuration of features and hyper-parameters. Hence, they are considered as crucial tasks in the AutoML. The challenges regarding the computationally expensive nature of tuning hyper-parameters and optimally selecting features create significant opportunities for filling the research gaps in the AutoML field. We explore how to select the features and tune the hyper-parameters of classical machine learning algorithms efficiently and automatically.

To address some of the challenges in the AutoML field, novel algorithms for hyper-parameter tuning and feature selection are proposed. The hyper-parameter tuning algorithm aims to provide the optimal set of hyper-parameters in three classical machine learning models. On the other hand, the feature selection algorithm searches for the optimal subset of features to achieve the highest performance. Afterward, a hybrid framework is proposed for hyper-parameter tuning and feature selection. This framework is an attempt to alleviate the challenges of hyper-parameter tuning and feature selection by using an evolutionary algorithm.

Where: CASE 235 & Zoom

Meeting ID: 786 828 8302

Passcode: Zahedi22