Vindula Jayawardana

vindula [AT] mit [DOT] edu

I am a PhD student in computer science at MIT. I am fortunate to be advised by Prof. Cathy Wu in the Laboratory for Information and Decision Systems (LIDS). Earlier, I completed my bachelors at University of Moratuwa in Sri Lanka.

Before graduate school, I was a part of Mobility, Algorithms, and Society Lab at Cornell University and Data Science, Engineering & Analytics Research Hub of University of Moratuwa. I have also spent time at NVIDIA, Toyota, WSO2, PickMe, and Trancite24.

I am broadly interested in learning-enabled autonomy. In light of this, I am interested in making multi-agent reinforcement learning seamlessly generalize across problem variations (solving Contextual MDPs). Real-world application-wise, I am interested in planning for autonomous vehicles with problem variations. In the past, I have also worked on mathematical programming for combinatorial optimizations and natural language processing for information extraction. Currently, I am also leading the project Greenwave.

[New] I'm on the job market 2024-2025. Feel free to contact me if you know of a position for which I could be a fit.

Resume | Google Scholar | LinkedIn | Github | Blog

News

Sep	2024	Our paper "Model-Based Transfer Learning for Contextual Reinforcement Learning" was accepted at NeurIPS 2024 .
Aug	2024	Our latest work on AI-driven eco-driving at scale is featured in NewScientist.
Jun	2024	I am interning at NVIDIA this summer.
Jun	2024	Three papers accepted at TRC30. See you in Greece!
Apr	2024	Honored to be selected as a Rising Star in Cyber-Physical Systems research.
Apr	2024	I gave a talk at MIT CEE Graduate Student Seminar Series.
Mar	2024	I am co-organizing AVAS: Autonomous Vehicles Across Scales workshop in RSS 2024 in Delft, Netherlands.
Feb	2024	Our paper "Generalizing Cooperative Eco-driving via Multi-residual Task Learning" was accepted at ICRA. See you in Japan!
Jan	2024	Honored to receive the IEEE ITSS WiE/YP Workshop and Annual Forum Fellowship.
Dec	2023	Our paper "Model-free Learning of Corridor Clearance: A Near-term Deployment Perspective" was accepted at T-ITS.
Nov	2023	I gave a talk at LIDS Climate Tea Talks.
Oct	2023	Our paper "Robust Driving Across Scenarios via Multi-residual Task Learning" was accepted at Generalization in Planning workshop at NeurIPS 2023 and Machine Learning for Autonomous Driving Symposium.
Sep	2023	My interview with ADAS & Autonomous Vehicle International magazine is featured in the September issue.
Jul	2023	Our paper "Multi-behavior Learning for Socially Compatible Autonomous Driving" was accepted at ITSC 2023.
Jun	2023	I gave a talk at Toyota R&D in Mountain View.
Jun	2023	I am interning at Toyota in Mountain View this summer.
Dec	2022	"The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning" was accepted at NeurIPS 2022 .
Dec	2022	Our paper "Learning Surrogates for Diverse Emission Models" was accepted at Climate Change AI at NeurIPS 2022.
Apr	2022	Our recent work has been featured in National Public Radio (NPR) in The Loh Down on Science Podcast.
Jun	2022	I am excited to receive 2022 Harold L. Hazen Teaching Award from EECS MIT.
Jun	2022	Our paper "The Braess Paradox in Dynamic Traffic" was accepted at ITSC 2022.
May	2022	Our recent work on eco-driving was featured as a spotlight on MIT front page.
Apr	2022	Our paper "Reinforcement Learning for Eco-Lagrangian Control at Intersections" was accepted at RCC at ICRA 2022.
Feb	2022	I presented our work at the MIT CEE Annual Research Day.
Feb	2022	Our paper "Learning Eco-Driving Strategies at Signalized Intersections" was accepted at ECC 2022.
Jan	2022	I gave an invited talk at Univerity of Moratuwa.
Jun	2021	Our paper "Mixed Autonomous Supervision in Traffic Signal Control" was accepted at ITSC 2021.
Apr	2021	Our paper "Fleet management for ride-pooling with meeting points at scale: a case study in the five boroughs of New York City" is now on arXiv.
Jan	2021	I presented a poster at MIT-IBM Watson AI Lab Virtual Open House 2021.

Research

For a complete list of publications, please visit my Google Scholar page.

Reinforcement Learning

Learning for Control

Combinatorial Optimizations

Intelligent Transportation Systems

Natural Language Processing

	IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning Vindula Jayawardana, Baptiste Freydt, Ao Qu, Cameron Hickert, Zhongxia Yan, Cathy Wu In review 2024 arXiv \| code \| project page \|
	Model-Based Transfer Learning for Contextual Reinforcement Learning JungHoon Cho, Vindula Jayawardana, Sirui Li, Cathy Wu Advances in Neural Information Processing Systems (NeurIPS) 2024 European Workshop on Reinforcement Learning (EWRL) 2024 arXiv \|
	Mitigating Metropolitan Carbon Emissions with Dynamic Eco-driving at Scale Vindula Jayawardana, Baptiste Freydt, Ao Qu, Cameron Hickert, Edgar Sanchez, Catherine Tang, Mark Tylor, Blaine Leonard, Cathy Wu In review 2024 project page \| arXiv \| Media: New Scientist \| The Cool Down
	Generalizing Cooperative Eco-driving via Multi-residual Task Learning Vindula Jayawardana, Sirui Li, Cathy Wu, Yashar Farid, Kentaro Oguchi International Conference on Robotics and Automation (ICRA) 2024 Generalization in Planning workshop at NeurIPS 2023 Machine Learning for Autonomous Driving Symposium 2023 project page \| arXiv \| poster \| slides \| video \|
	Learning-Augmented Vehicle Dispatching with Slack Times for High-Capacity Ride-Pooling Youngseo Kim, Vindula Jayawardana, Samitha Samaranayake Transportation Research Part C: Emerging Technologies 2024 (major revision) arXiv \|
	Model-free Learning of Corridor Clearance: A Near-term Deployment Perspective Dajiang Suo, Vindula Jayawardana, Cathy Wu (* equal contribution) IEEE Transactions on Intelligent Transportation Systems (T-ITS) 2024 arXiv \|
	The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning Vindula Jayawardana, Catherine Tang, Sirui Li, Dajiang Suo, Cathy Wu Advances in Neural Information Processing Systems (NeurIPS) 2022 project page \| arXiv \| video \| poster \| slides \|
	Learning Eco-Driving Strategies at Signalized Intersections (MIT Homepage Spotlight) Vindula Jayawardana, Cathy Wu, European Control Conference (ECC) 2022 Robotics for Climate Change at ICRA 2022 project page \| arXiv \| video \| poster \| slides \| Media: MIT News \| Tech Crunch \| NPR \|
	Multi-behavior Learning for Socially Compatible Autonomous Driving Sanjula Jayawardana, Vindula Jayawardana, Kaneeka Vidanage, Cathy Wu (* equal supervision) IEEE Intelligent Transportation Systems Conference (ITSC) 2023 paper \| poster \|
	The Braess Paradox in Dynamic Traffic Dingyi Zhuang, Yushu Huang, Vindula Jayawardana, Jinhua Zhao, Dajiang Suo, Cathy Wu IEEE Intelligent Transportation Systems Conference (ITSC) 2022 arXiv \|
	Mixed Autonomous Supervision in Traffic Signal Control Vindula Jayawardana, Anna Landler, Cathy Wu IEEE Intelligent Transportation Systems Conference (ITSC) 2021 paper \|
	Fleet Management for Ride-Pooling with Meeting Points at Scale: A Case Study in the Five Boroughs of New York City Motahare Mounesan, Vindula Jayawardana, Yaocheng Wu, Samitha Samaranayake, Huy T. Vo arXiv \| code \|
	Word Vector Embeddings and Domain Specific Semantic-based Semi-supervised Ontology Instance Population Vindula Jayawardana, Dimuthu Kariyawasam, Nisansa de Silva, Shehan Perera Keet Sugathadasa, Buddhi Ayesha, Madhavi Perera International Journal on Advances in ICT for Emerging Regions 2017 Seventeenth International Conference on Advances in ICT for Emerging Regions 2017 paper \|
	Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings Vindula Jayawardana, Dimuthu Kariyawasam, Nisansa de Silva, Shehan Perera Keet Sugathadasa, Buddhi Ayesha, Seventh International Conference on Innovative Computing Technology (INTECH) 2017 paper \|
	Legal Document Retrieval using Document Vector Embeddings and Deep Learning Keet Sugathadasa, Buddhi Ayesha, Nisansa de Silva, Shehan Perera Vindula Jayawardana, Dimuthu Kariyawasam, Madhavi Perera Computing Conference 2017 arXiv \|
	Synergistic Union of Word2vec and Lexicon for Domain Specific Semantic Similarity Keet Sugathadasa, Buddhi Ayesha, Nisansa de Silva, Shehan Perera Vindula Jayawardana, Dimuthu Kariyawasam, Madhavi Perera IEEE International Conference on Industrial and Information Systems (ICIIS) 2017 paper \|

Selected Open Source Contributions

IntersectionZoo: Benchmarking Contextual Reinforcement Learning - Main contributor

Charon: SCIM 2.0 Open Source Implementation (111 stars, 159 folks on Github) - Main contributor

SCIM 2.0 Compliance Test Suite (24 stars, 19 folks on Github) - Main contributor

Open Ridepool Simulator - Co-main contributor

Selected Awards

Rising Star in Cyber-Physical Systems research (NSF and University of Virginia)

IEEE ITSS WiE/YP Fellowship 2024 (IEEE Intelligent Transportation Systems Society)

Harold L. Hazen Teaching Award 2022 (EECS MIT)

Digital Mobility Solutions Lanka Fellowship 2018 (University of Moratuwa)

Migara Ranathunga Trust Award 2018 (Insititute of Engineers Sri Lanka)

Gold Award at National Best Quality ICT Awards 2017 (Sri Lanka Sector of British Computer Society)

Global Finalist in NASA International Space Apps 2017 (NASA)

Silver Medal at Junior Science Olympiad Sri Lanka 2010 (Sri Lankan Junior Science Olympiad)

Service

Workshop Organizer	AVAS (RSS)
Conference Reviewer	NeurIPS, ICML, AAAI, ICRA, ITSC, TRC30, TRB, NLDL, MERCon
Journal Reviewer	T-RO, TNNLS, T-IST, Physica A
Workshop Reviewer	CCAI (NeurIPS), WMLMDS (AAAI), R2HCAI (AAAI)

Students Mentored

Graduate Students	Jessica Ding (MIT - M.Eng Student 2024), Baptiste Freydt (ETH Zurich - MS Student 2022)
Undergraduate Students	Sanjula Jayawardana (University of Westminster 2023), Catherine Tang (MIT-UROP 2022), Anirudh Valiveru (MIT-UROP 2022), Jiaxin He (Vanderbilt University 2022), Sunera Chandrasiri (University of Moratuwa 2022), Ammar Fayad (MIT-UROP 2021), Anna Landler (MIT-SuperUROP 2021)

Teaching

1.041/1.200/11.544: Transportation Systems Modeling: Teaching Assistant, MIT, Fall 2020/2021.

CS4622: Machine Learning: Teaching Assistant, UoM, Spring 2019.

CS2022: Data Structures and Algorithms: Instructor, UoM, Spring 2019.

CS3042: Database Systems: Teaching Assistant, UoM, Fall 2018.

CS2052: Computer Architecture: Teaching Assistant, UoM, Fall 2018.

CS2062: Object Oriented Software Development: Teaching Assistant, UoM, Spring 2018.

CS3962: Research and Report Writing : Teaching Assistant, UoM, Spring 2018.

Template adapted from 1 and 2.