Curriculum Vitae

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• Curriculum Vitae
• Resume

Education

Pennsylvania State University

Ph.D., Informatics
Aug 2022 - Aug 2025
Dissertation: Application of Deep Reinforcement Learning to Solve Optimization Problems in Transportation Domains

University of Houston (Transferred to Pennsylvania State University)

Ph.D., Computer Science
Aug 2019 - Aug 2022

University of Houston

M.S., Computer Science
Aug 2019 - Aug 2022

University of Moratuwa

B.S. (Hons) Engineering, Computer Science and Engineering
Jan 2014 - Jan 2018
Final Year Project: Sentimental Analysis of Twitter using Semi-Supervised Approaches

Research Interests

• Reinforcement Learning
• Optimization
• Operations Research
• Cyber-Physical Systems

Skills

Languages: Python, C/C++, Java
AI/ML Libraries: PyTorch, TensorFlow, Keras, Hugging Face, Scikit-learn
Optimization Tools: CPLEX, Gurobi, Mosek, Google OR-Tools
Data Processing & Visualization: pandas, Spark, matplotlib
Miscellaneous Libraries: SciPy, NumPy, Ray, RLLib, OpenAI Gym
Databases: SQLite, MySQL, OracleDB, MongoDB
Deployment & DevOps: Git, Docker, AWS (SageMaker, EC2, Lambda, S3)

Research Experience

Pennsylvania State University

Graduate Research Assistant, Applied Artificial Intelligence Lab
Aug 2022 - May 2025

• Lead research projects funded by the U.S. Department of Energy (DOE) and the National Science Foundation (NSF), introducing cost- and energy-efficient solutions to tackle real-world problems.
• Analyzed current state-of-the-art and identified research gaps in prior works, leading to the development of innovative algorithms for real-time decision-making.
• Introduced novel problem formulations and mathematical models to address real-world challenges while ensuring inherent spatio-temporal and resource constraints.
• Proposed artificial intelligence–based solutions to tackle challenging real-world problems and successfully deployed them in relevant industries.
• Published research findings in AI/ML conferences (ICML) and assisted in preparing slides for presenting results at DOE and NSF meetings.

The University of Houston

Graduate Research Assistant, Resilient Networks and Systems Lab
Sep 2019 - Aug 2022

• Spearheaded research projects funded by DOE and NSF, leading to advancements in energy-efficient technologies.
• Focused on understanding real-world decision-making problems and identified gaps in existing solution approaches, leading to the development of novel methodologies.
• Proposed novel problem formulations and mathematical models to effectively address research limitations in prior studies.
• Applied AI-based solution approaches to real-world problems and successfully deployed them in relevant industries.
• Published research findings in AI conferences (AAAI, IJCAI) and assisted in preparing slides for DOE and NSF presentations.

Software Engineering Experience

LSEG Technology

Software Engineer, Post Trade Team
Jan 2018 - Jul 2019

• Developed application software following object-oriented design principles, enhancing code maintainability and scalability.
• Followed Agile development practices (Scrum) to improve team collaboration and project delivery speed.
• Developed software solutions using Java, Python, and C++, improving system performance and reliability.
• Modified SQL queries and tested against BDD, ensuring correctness of system functionality.
• Enhanced back-end regression to achieve proper code coverage based on unit testing, ensuring compatibility and reducing testing time.
• Developed a report generation system to assess the state of the testing framework, automating email notifications at the end of regression.
• Contributed to code integration and deployment plans, ensuring seamless software updates and minimizing downtime.
• Participated in professional training programs conducted by Millennium IT Software and Post Trade Team.
• Worked on front-end development for both product and solution, including enhancements, bug fixing, merging, and introducing new features.

WSO2 Lanka PVT Ltd

Software Engineering Intern, Data Analytics Team
Jul 2016 – Dec 2016

• Developed an HL7 Monitoring Solution by integrating WSO2 ESB, DAS, and BAM for real-time and batch processing of healthcare data.
• Built an alert generation system analyzing HL7/FHIR data to monitor disease outbreaks, triggering timely email and SMS notifications.
• Created Spark scripts for batch analytics and Siddhi execution plans for real-time alerts, including disease outbreak and patient wait-time notifications.
• Engineered a mechanism to evaluate hospital functionality by assessing admission and discharge messages, including bed and oxygen cylinder availability.
• Implemented interactive dashboards using Jaggery, JavaScript, jQuery, Leaflet.js, and DataTables, featuring charts, maps, and tables.
• Utilized HAPI test panel to simulate HL7 v2 messages and validate the monitoring pipeline end-to-end.
• Packaged the monitoring solution as a Carbon Application (CApp), bundling event streams, receivers, publishers, and visualization artifacts.
• Attended workshops and gained hands-on experience with Git, MSF4J (Microservices for Java), and WSO2 product architecture for enterprise middleware solutions.

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

Sentimental Analysis of Twitter using Semi-Supervised Approaches

HL7 Monitoring Solution

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