Lead Research Specialist
Dr. Thilanka Munasinghe is the Lead Research Specialist at the Rensselaer Institute for Data Exploration and Applications (IDEA) within the Future of Computing Institute (FOCI). His research spans classical artificial intelligence and machine learning (AI/ML), as well as quantum computing applications, leveraging RPI’s state-of-the-art 127-qubit IBM Quantum System One. He holds a Ph.D. in Information Science from the University at Albany (SUNY), where his dissertation on applying classical and quantum machine learning (QML) to human dynamics problems earned him the Distinguished Doctoral Dissertation Award, the SUNY Chancellor’s Award, and the University at Albany President’s Distinguished Scholar-Leader Award.
From 2018 to 2024, he served as a Lecturer and Senior Data Scientist in RPI’s Information Technology & Web Science (ITWS) program. During this time, he designed and taught graduate and undergraduate courses in data science, analytics, and informatics, and led over 40 student-driven data science and informatics projects. These efforts produced peer-reviewed publications and led to fruitful collaborations with NASA centers, including Goddard, Langley, Marshall, and JPL, as well as Oak Ridge National Laboratory (ORNL).
He also holds an M.Sc. in Mechanical Engineering and a B.Sc. in Aerospace Engineering from West Virginia University. His background in engineering, data science, machine learning, and information science provides a unique interdisciplinary perspective, bridging engineering and applied science with over a decade of experience uniting projects across diverse domains. He has contributed to the open-source community through Google Summer of Code with MIT App Inventor and served as a visiting research student at the MIT Media Lab (Human Dynamics and Connection Science groups). As a NASA GES-DISC research intern during his Ph.D., he developed quantum and classical ML applications and knowledge graphs for climate and weather analytics using Earth observation satellite data. Earlier in his career, he served as a CodeLab Instructor at WVU LaunchLab, where he supported entrepreneurship and innovation through mobile and IoT application development for startups and business clients.
He also holds an M.Sc. in Mechanical Engineering and a B.Sc. in Aerospace Engineering from West Virginia University. His background in engineering, data science, machine learning, and information science provides a unique interdisciplinary perspective, bridging engineering and applied science with over a decade of experience uniting projects across diverse domains. He has contributed to the open-source community through Google Summer of Code with MIT App Inventor and served as a visiting research student at the MIT Media Lab (Human Dynamics and Connection Science groups). As a NASA GES-DISC research intern during his Ph.D., he developed quantum and classical ML applications and knowledge graphs for climate and weather analytics using Earth observation satellite data. Earlier in his career, he served as a CodeLab Instructor at WVU LaunchLab, where he supported entrepreneurship and innovation through mobile and IoT application development for startups and business clients.