Curriculum Vitae

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Education

• Ph.D. in Informatics, Pennsylvania State University, Summer 2025
• M.S. in Computer Science, The University of Houston, Summer 2022
• B.S. in Computer Science and Engineering, University of Moratuwa, 2018

Research Focus

Reinforcement Learning, Optimization, Operational Research

Tech Stack

Languages:

Libraries:

CI/CD and Tools:

Research Experience

• 2022/08 - 2025/05 - Graduate Research Assistant
  • Pennsylvania State University
  • Supervisor: Dr. Aron Laszka
  • Duties:
    • Spearheaded research projects funded by the U.S. Department of Energy (DOE) and the National Science Foundation (NSF).
    • Studied real-world decision-making problems and identified gaps in existing solution approaches.
    • Developed mathematical models and formulated problem statements to effectively address these challenges.
    • Applied AI-based solution approaches to real-world problems and successfully deployed them in relevant industries.
    • Published research findings in top-tier AI conferences (AAAI, IJCAI) and assisted preparing slides for presenting results at DOE and NSF meetings.
    • Introduced heuristics that reduced annual energy costs by $140K for public transit agencies operating mixed fleets of buses.
    • Implemented a deep reinforcement learning approach that reduced operational costs by 20% by enabling online booking for traditionally offline Vehicle Routing Problems (VRPs).
    • Collected and analyzed real-world data using Python to identify trends—such as changes in paratransit operations before and after COVID-19 and the impact of vulnerability reward programs.
• 2019/09 - 2022/08 - Graduate Research Assistant
  • The University of Houston
  • Supervisor: Dr. Aron Laszka
  • Duties:
    • Spearheaded research projects funded by the U.S. Department of Energy (DOE) and the National Science Foundation (NSF).
    • Studied real-world decision-making problems and identified gaps in existing solution approaches.
    • Developed mathematical models and formulated problems to address these challenges.
    • Applied artificial intelligence–based solutions to tackle real-world problems and successfully deployed them in relevant industries.
    • Published research findings in top-tier AI conferences (ICML) and assisted preparing slides for presenting results at DOE and NSF meetings.
    • Developed a deep reinforcement learning (DRL) method for proactive responder repositioning in emergency management, achieving response times 1000× faster while reducing operational delays.
    • Applied DRL to solve online vehicle routing with advance booking, enabling real-time confirmations within seconds.

Software Engineering Experience

• 2018/01 - 2019/07: Software Engineer
  • LSEG Technology (formerly MillenniumIT)
  • Duties:
    • Writing application software in the Object-Oriented manner.
    • Practicing agile development practices.
    • Working experience with languages such as Java, Python, and C++
    • Introduce Unit Testing for Libraries in Post Trade C++ Code
    • Worked on Making changes in DB and Tested with BDD based Testing.
    • Worked on Duplicated Error Codes Identification and Replacement with valid New Error Codes.
    • Developed DSO for Front-End End to End Testing.
    • Analyzed and Changed Back-End Regression Script to work with both Old and New Testing Framework
    • Developed a New Report Generation for Testing Framework, which used to be emailed at the end of regression.
    • Completed the Code Integration works related to Back-End Regression
    • Updated automatic updates to auto-generated codes based on Database changes using Integration Plans.
    • Participated in Code Integration and Code Deployment Plans.
    • Participated in professional training programs conducted by Millennium IT Software and Post Trade Team.
    • Worked on Front-End Development for both Product and Solution which consists of Enhancement, Bug Fixing, Merging and Introducing new features.
• 2016/07 - 2016/12: Software Engineering Intern
  • WSO₂ Lanka PVT Limited
  • Duties:
    • Implemented alert generation mechanism, which provides email and SMS when a disease spreads wide by analyzing the description.
    • Implemented alert generation mechanism, which could determine the functional state of hospitals (i.e., number of beds available, number of Oxygen cylinders available) based on the admission and discharge messages.

Projects

Emergency Responder Stationing

Sivagnanam, A., Pettet, A., Lee, H., Mukhopadhyay, A., Dubey, A., & Laszka, A. (2024). Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing. In Proceedings of the 41 st International Conference on Machine Learning, (ICML 2024)

The Benefits of Vulnerability Discovery and Bug Bounty Programs

Atefi, S., Sivagnanam, A., Ayman, A., Grossklags, J., & Laszka, A. (2023, May). The Benefits of Vulnerability Discovery and Bug Bounty Programs: Case Studies of Chromium and Firefox. In Proceedings of the ACM Web Conference, (pp 2209–2219), ACM

Sivagnanam, A., Atefi, S., Ayman, A., Grossklags, J., & Laszka, A. (2021). On the Benefits of Bug Bounty Programs: A Study of Chromium Vulnerabilities. In Workshop on the Economics of Information Security (WEIS) (Vol. 10)

Offline Vehicle Routing Problem with Online Bookings

Sivagnanam, A., Kadir, SU., Mukhopadhyay, A., Pugliese, P., Dubey, A., Samaranayake, S., & Laszka, A. (2022, July) Offline Vehicle Routing Problem with Online Bookings: A Novel Problem Formulation with Applications to Paratransit. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (pp. 3933-3939)

Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service

Sivagnanam, A., Ayman, A., Wilbur, M., Pugliese, P., Dubey, A., & Laszka, A. (2021, May). Minimizing energy use of mixed-fleet public transit for fixed-route service. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 17, pp. 14930-14938)

Ayman, A., Sivagnanam, A., Wilbur, M., Pugliese, P., Dubey, A., & Laszka, A. (2021). Data-driven prediction and optimization of energy use for transit fleets of electric and ICE vehicles. ACM Transactions on Internet Technology (TOIT), 22(1), 1-29

Publications

Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing

Sivagnanam, A., Pettet, A., Lee, H., Mukhopadhyay, A., Dubey, A., & Laszka, A. (2024). Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing. In Proceedings of the 41 st International Conference on Machine Learning, (ICML 2024)

The Benefits of Vulnerability Discovery and Bug Bounty Programs: Case Studies of Chromium and Firefox

Atefi, S., Sivagnanam, A., Ayman, A., Grossklags, J., & Laszka, A. (2023, May). The Benefits of Vulnerability Discovery and Bug Bounty Programs: Case Studies of Chromium and Firefox. In Proceedings of the ACM Web Conference, (pp 2209–2219), ACM

Offline Vehicle Routing Problem with Online Bookings: A Novel Problem Formulation with Applications to Paratransit

Sivagnanam, A., Kadir, SU., Mukhopadhyay, A., Pugliese, P., Dubey, A., Samaranayake, S., & Laszka, A. (2022, July) Offline Vehicle Routing Problem with Online Bookings: A Novel Problem Formulation with Applications to Paratransit. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (pp. 3933-3939)

Data-Driven Prediction and Optimization of Energy Use for Transit Fleets of Electric and ICE Vehicles

Ayman, A., Sivagnanam, A., Wilbur, M., Pugliese, P., Dubey, A., & Laszka, A. (2021). Data-driven prediction and optimization of energy use for transit fleets of electric and ICE vehicles. ACM Transactions on Internet Technology (TOIT), 22(1), 1-29

Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service

Sivagnanam, A., Ayman, A., Wilbur, M., Pugliese, P., Dubey, A., & Laszka, A. (2021, May). Minimizing energy use of mixed-fleet public transit for fixed-route service. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 17, pp. 14930-14938)

Workshops

On the Benefits of Bug Bounty Programs: A Study of Chromium Vulnerabilities

Sivagnanam, A., Atefi, S., Ayman, A., Grossklags, J., & Laszka, A. (2021). On the Benefits of Bug Bounty Programs: A Study of Chromium Vulnerabilities. In Workshop on the Economics of Information Security (WEIS) (Vol. 10)

Talks

Offline Vehicle Routing Problem with Online Bookings: A Novel Problem Formulation with Applications to Paratransit

July 29, 2022

Talk at Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22), Vienna, Austria

On the Benefits of Bug Bounty Programs: A Study of Chromium Vulnerabilities

June 29, 2021

Talk at The 20th Annual Workshop on the Economics of Information Security (WEIS 2021), Virtual Workshop

Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service

May 18, 2021

Talk at Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-21), Virtual Conference