Welcome to my Personal Homepage!
Last updated: April 2023.
“Intelligence is the ability to adapt to change.” – Stephen Hawking
In April 2023, Neda Tavakoli obtained her Ph.D. in Computer Science at Georgia Institute of Technology, advised by Prof. Srinivas Aluru, an internationally well-known leader. She also obtained her Master’s degree in Machine Learning at Georgia Tech in 2020. She is a highly experienced researcher, with a Ph.D. specializing in designing sequential and parallel algorithms for large-scale graph problems. Her work involves creating a mathematical and algorithmic framework to solve multiple NP-hard problems in genome graphs, utilizing programming languages such as C++, Python, and shell script. Additionally, she has extensive knowledge of deep learning frameworks like TensorFlow, PyTorch, and Jax, and has designed and built several platforms and technologies for deep learning models in finance and computational biology. With a comprehensive understanding of ML accelerators such as GPUs in high-performance computing environments, she is highly skilled in using distributed training approaches for deep learning models. She has also actively contributed to the writing and publishing of research papers, many of which have been cited by other researchers, with over 1000 citations to date.
Ph.D. in Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
M.Sc in Machine Learning Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
M.Sc in Computer Science, Texas Tech University, Lubbock, TX, USA
April 2023: She successfully defended her Ph.D. in Computer Science at Georgia Tech.
Approximate Sequence Matching Algorithms to Handle Bounded Number of Errors, International Workshop on String Algorithms in Bioinformatics (StringBio) Slide, Orlando, FL, USA, 2018.
N. Tavakoli, D. Gibney, S. Aluru, Haplotype-aware variant selection for genome graphs, Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (ACM-BCB) 2022. (Link) (PDF).
L. F. Gutirrez, N. Tavakoli, S. S. Namini, A. S. Namin, Similarity Analysis of Federal Reserve Statements The Great Recession vs. COVID-19, SN Business & Economics 2022 (Link).
R. Flores, A. Siami Namin, N. Tavakoli, S. S. Namini, K. S. Jones, Using Experiential Learning to Teach and Learn Digital Forensics: Educator and Student Perspective, Computers and Education Open 2021 (Link).
C. Jain, N. Tavakoli, S. AluruA Variant Selection Framework for Genome Graphs, Accepted to appear in ISMB 2021.
L. F. Gutirrez, N. Tavakoli, S. S. Namini, A. S. Namin, A Concern Analysis of Federal Reserve Statements The Great Recession vs. COVID-19, IEEE International Conference on Big Data (Big Data) 2020.
2020.V. Nair, M. Chatterjee, N. Tavakoli, A. S. Namin, C. Snoeyink, Optimizing CNN using fast Fourier Transformation for Object Recognition, 19th IEEE International Conference on Machine Learning and Applications (ICMLA) 2020.
N. Tavakoli, S. Siami‑Namini, M. Adl Khanghah, F. Mirza Soltani, A. Siami Namin, An autoencoder‑based deep learning approach for clustering time series data, Accepted to appear in Springer Nature (SN) Applied Science, 2020. (Link) (PDF)
N. Tavakoli, D. Dai, Y. Chen, A Software-Defined QoS Provisioning Framework for HPC Applications, Accepted to appear in International Journal of Grid and High Performance Computing, 2020. (PDF)
N. Tavakoli, Modeling Genome Data Using Bidirectional LSTM, IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), Vol. 2, (pp. 183-188), 2019. (Paper)
S. S. Namini, N. Tavakoli, A. S. Namin, The Performance of LSTM and BiLSTM in Forecasting Time Series, The 2nd Workshop on Big Data Engineering and Analytics in Cyber-Physical Systems BigEACPS’19, 2019.
N. Tavakoli, D. Dai, Y. Chen, Client-side Straggler-Aware I/O Scheduler for Object-based Parallel File Systems, Parallel Computing, Elsevier (ParCo), 2019. (Paper)
S. S. Namini, N. Tavakoli, A. S. Namin, A Comparison of ARIMA and LSTM in Forecasting Time Series, International Conference on Machine Learning Application (ICMLA), Orlando, Florida, USA, December 2018. (Link)
S. Hooshmand, N. Tavakoli, P. Abedin, Sharma V. Thankachan, On Computing Average Common Substring Over Run Length Encoded Sequence, Fundamenta Informaticae, 163(3): 267-273 (2018). (Paper)
N. Tavakoli, D. Dai, Y. Chen, A Software-Defined QoS Provisioning Framework for HPC Applications, CoRR abs/1805.06169, 2018. (Paper, PDF)
D. Dai, R. Ross, D. Khaldi, Y. Yan, D. Matthieu, N. Tavakoli, and Y. Chen, Exploiting Locality in Scientific Workflow System: A Cross-Layer Solution, SC Extended Abstract, CoRR abs/1805.06167 (2018). (Paper)
N. Tavakoli, D. Dai, J. Jenkins, P. Carns, R. Ross and Y. Chen, A Software-Defined Approach for QoS Control in High-Performance Computing Storage Systems, SC Extended Abstract CoRR abs/1805.06161 (2018). (Paper)
N. Tavakoli, D. Dai, Y. Chen, Log-Assisted Straggler-Aware I/O Scheduler for High-End Computing, ICPP-W 2016. (Paper)
N. Tavakoli, Networked Markov Chain Influence Model for Providing Cascade-Resilient Interdependent Network, CRA-W, San Francisco, CA, USA, April 2016. [Poster]
Automatic Detection of Threats and Opportunities Using Natural Language Processing, Akbar Siami Namin, Sima Siami-Namini, and Neda Tavakoli, U.S. Provisional Patent Application Serial No. 62/781,184. (Status: Filed 2018).
Honors and Awards
Travel grant for attending SC’19 as a Student Volunteer, Awarded by SC, Denver, Colorado, USA, November 2019.
Travel grant for attending SC’18 as a SCinet, Awarded by SC, Dallas, Texas, USA, November 2018.
Travel grant for attending StringBio’18 to present research work, Awarded by NSF/UCF, Orlando, Florida, USA, October 2018.
Travel grant for attending MUG’18, Awarded by NSF/OSU, Columbus, Ohio, USA, August 2018.
Travel grant for attending SC’16 as a student volunteer, Awarded by SC, Salt Lake City, Utah, USA, November 2016.
Grant Proposal of $3,000 in the form of NCWIT (National Center for Women and IT) Student Fund gift. The proposal was developed and submitted by Neda Tavakoli, a graduate student in Computer Science and the president of the EWoCS (Extraordinary Woman of Computer Science) association. The gift is generously sponsored by Google Inc. to a new chapter on ACM-W at Texas Tech University, in Fall 2016.
Google/VMware travel grant for attending FAST’16, Awarded by Google and VMware, Santa Clara, CA, USA, February 2016.
Admitted and awarded the travel scholarship to attend the CRA-W Graduate Cohort Workshop CRA-W in San Francisco, CA, USA, 2015.
Scholarship from the Dean of the Whitacre College of Engineering for the graduate program of Texas Tech University 2015-2017.
Attending and passing successfully ITA (International Teaching Assistant) Workshop, Texas Tech University, 2015.
Among the top 1% of students; Ranked 227 for the Iranian Nationwide Entrance Exam to University for undergraduate students among around 1,450,000 students, Tehran, Iran.
Admitted to attend NODET high school (National Organization for Development of Exceptional Talents–designated for the top 5% of the entire high school students in the country), Tehran, Iran.
CSE6242: Data and Visual Analytics, Graduate Teaching Assistant, Computer Science Department, Georgia Tech University, Fall 2022 (Link).
CSE6220: Introduction to High-Performance Computing, Graduate Teaching Assistant, Computer Science Department, Georgia Tech University, Spring 2021.
CSE6220: Introduction to High-Performance Computing, Graduate Teaching Assistant, Computer Science Department, Georgia Tech University, Spring 2019.
CS1411: Programming Principles I (Python programming ), Lab Instructor, Computer Science Department, Texas Tech University, Spring 2017.
CS1412: Programming Principles II (C programming ), Instructor, Computer Science Department, Texas Tech University, Fall 2015, Spring 2016.
CS5381: Analysis of Algorithms, Teaching Assistant, Computer Science Department, Texas Tech University, Fall 2016.
CS2413: Data Structure, Teaching Assistant, Computer Science Department, Texas Tech University, Fall 2016.
|School of Computer Science||Georgia Institute of Technology|