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Education

• Ph.D. in Informatics, The Pennsylvania State University, University Park, PA, 2022–present
• M.S. in Computer Science, University of Houston, Houston, TX, 2018–2022
• B.S. in Computer Engineering, Shahid Beheshti University, Tehran, Iran, 2013–2018

Work experience

• Aug 2022 – Present: Graduate Research Assistant, Pennsylvania State University
• Jul 2024 – Aug 2024: Machine Learning Research Intern, Triconex at Schneider Electric
• Aug 2018 – Aug 2022: Graduate Research Assistant, University of Houston
• Sep 2017–Jul 2018: Full Stack Software Engineer, Hamisystem Sharif

Skills

• Data Structures, Algorithms, Object Oriented Design, Design Patterns
• Machine Learning, Artificial Intelligence
  • Python, Tensorflow, Pytorch, Numpy, Scipy, CV2, Scikit-Learn
• Web Technologies
  • .NET
    • C#, ASP
  • JAVA
    • J2SE, J2EE, Spring, Hibernate
  • Front-End
    • NodeJS, ExpressJS, AnguarJS, jQuery
• RDBMS
  • MSSQL Server, MySQL
• NoSQL
  • Cassandra, Redis, Neo4J, MongoDB
• Computer Networks
  • CCNA, CEH
• Information Retrieval
  • Lucene, Nutch, Elastic Search
• Micro Controllers
  • Assembly, C, C++, ARM, AVR
• Hardware Design
  • FPGA, Verilog
• Linux
  • LPIC-1, Bash, Ansible, Slurm, Docker, Kubernetes
• Misc
  • Git, LaTeX, CI/CD

Publications

Cyber-attacks and Mitigation in Blockchain based Transactive Energy Systems

C. Barreto, T. Eghtesad, S. Eisele, A. Laszka, A. Dubey and X. Koutsoukos, "Cyber-Attacks and Mitigation in Blockchain Based Transactive Energy Systems," 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), Tampere, Finland, 2020, pp. 129-136, doi: 10.1109/ICPS48405.2020.9274708.

Mechanisms for Outsourcing Computation via a Decentralized market

Scott Eisele, Taha Eghtesad, Nicholas Troutman, Aron Laszka, and Abhishek Dubey. 2020. Mechanisms for outsourcing computation via a decentralized market. In Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems (DEBS '20). Association for Computing Machinery, New York, NY, USA, 61–72. DOI:https://doi.org/10.1145/3401025.3401737

Blockchains for Transactive Energy Systems: Opportunities, Challenges, and Approaches

S. Eisele et al., "Blockchains for Transactive Energy Systems: Opportunities, Challenges, and Approaches," in Computer, vol. 53, no. 9, pp. 66-76, Sept. 2020, doi: 10.1109/MC.2020.3002997.

The Hackers’ Viewpoint: Exploring Challenges and Benefits of Bug-Bounty Programs

Akgul, O., Eghtesad, T., Elazari, A., Gnawali, O., Grossklags, J., Votipka, D., & Laszka, A. The Hackers’ Viewpoint: Exploring Challenges and Benefits of Bug-Bounty Programs.

Deep Reinforcement Learning based Adaptive Moving Target Defense

Eghtesad T., Vorobeychik Y., Laszka A. (2020) Adversarial Deep Reinforcement Learning Based Adaptive Moving Target Defense. In: Zhu Q., Baras J.S., Poovendran R., Chen J. (eds) Decision and Game Theory for Security. GameSec 2020. Lecture Notes in Computer Science, vol 12513. Springer, Cham. https://doi.org/10.1007/978-3-030-64793-3_4

Safe and Private Forward-Trading Platform for Transactive microgrids

Scott Eisele, Taha Eghtesad, Keegan Campanelli, Prakhar Agrawal, Aron Laszka, and Abhishek Dubey. 2021. Safe and Private Forward-trading Platform for Transactive Microgrids. ACM Trans. Cyber-Phys. Syst. 5, 1, Article 8 (January 2021), 29 pages. DOI:https://doi.org/10.1145/3403711

Decentralized Computation Market for Stream Processing Applications

Eisele, S., Wilbur, M., Eghtesad, T., Silvergold, K., Eisele, F., Mukhopadhyay, A., Laszka, A. and Dubey, A., 2022, September. Decentralized Computation Market for Stream Processing Applications. In 2022 IEEE International Conference on Cloud Engineering (IC2E) (pp. 36-46). IEEE.

Bug Hunters’ Perspectives on the Challenges and Benefits of the Bug Bounty Ecosystem

Akgul O., Eghtesad T., Elazari A., Gnawali O., Grossklags J., Mazurek M., Votipka D. and Laszka A., 2023, August. Bug Hunters' Perspectives on the Challenges and Benefits of the Bug Bounty Ecosystem. In 32nd USENIX Security Symposium (USENIX Security 23) (pp. 1-18). USENIX Association.

Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks

Eghtesad, T., Li, S., Vorobeychik, Y. and Laszka, A., 2024 May. Hierarchical Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks. In 2024 International Conference on Autonomous Agents and Multiagent Systems (AAMAS). IFAAMAS.

Talks

Adversarial Deep Reinforcement Learning based Adaptive Moving Target Defense

October 26, 2020

Conference Proceedings at International Conference on Decision and Game Theory for Security, University of Maryland -- College Park

Adversarial Reinforcement Learning for Cyber-Attack Prevention, Detection, and Mitigation

March 15, 2024

Comprehensive Exam (Doctoral Proposal Defense) at College of Information Sciences and Technology, Pennsylvania State University -- University Park

Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks

May 07, 2024

Conference Presentation at 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Auckland, NZ