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Mars Mission Minder

Mars Mission Minder is a data visualization and analysis application designed to support NASA's Mars missions, currently focused on the Mars 2020 Perseverance Rover. The app enables users to analyze three key datasets: PIXL (Planetary Instrument for X-ray Lithochemistry), LIBS (Laser Induced Breakdown Spectroscopy), and SHERLOC (Scanning Habitable Environments with Raman & Luminescence for Organics and Chemicals). These datasets provide insights into elemental composition, mineral abundance, and potential biosignatures on Mars. Analytical tools such as heatmaps, principal component analyses, and ternary diagrams help visualize the data, while Mars Explorer, an interactive map, tracks the rover's path and data collection sites.

Risky Business? Deep Dives into DeFi

We seek to investigate current patterns of usage in DeFi lending protocols, and quantify risk and user behaviors across various protocols. Our strategy is to exploit powerful AI models and technology developed for transaction data such as those arising in health and commerce. For example, we can utilize temporal clustering to characterize different types of users and then use these in a dashboard to understand how usage of lending protocols changes over time.

Samuel Chabot

Research Engineer
Sam is a researcher at EMPAC. Over the past ten years, he has been foundational in establishing RPI’s immersive environments as educational assets. From architecture to game design to environmental engineering, he recognizes the necessity of a cross-disciplinary approach to these environments and education, and champions deep collaboration between EMPAC and the greater RPI community.

"R for Python (and other...) Muggles!" RPIrates Monthly Meeting (Weds, 10 Sep, 6p, AE217)

Posted September 5, 2025

"R for Python (and other...) Muggles!" RPIrates Monthly Meeting (Weds, 10 Sep, 6p, AE217)

RPI R users are invited to the September 2025 meeting of RPIrates: The RPI R Users Group! Our topic for Weds, 10 Sep (6p, AE217) will be "R for Python Muggles!" led by Dr. John S. Erickson, Director of Research Operations of the Future of Computing Institute (FOCI) at Rensselaer and Chief Instigator of RPIrates.

Thilanka Munasinghe

Sr. Lecturer
Dr. Thilanka Munasinghe is a Senior Lecturer in the Lally School of Management, in the Information Technology & Web Science (ITWS) Area at Rensselaer Polytechnic Institute (RPI), with a focus on teaching areas related to Data Science, Data Analytics, Informatics, Database Systems, and Web Systems. He was the Lead Research Specialist at the Rensselaer Institute for Data Exploration and Applications (IDEA) within the Future of Computing Institute (FOCI) at RPI. 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. Dr. Munasinghe 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 research intern during his PhD, at NASA Goddard Space Flight Center, he developed quantum and classical ML applications and knowledge graphs for climate and weather-related analytics using Earth observational NASA 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 was instrumental in providing technical expertise and mentorship to student entrepreneurs of early-stage student-initiated start-ups at WVU. Thilanka’s current research interests focus on data-driven analytics using classical and quantum computing methods, utilizing big data to address societal challenges in diverse areas, including multidisciplinary engineering applications, energy systems, urbanization, social networks, and health issues that impact society, which involves building digital twins, novel early warning systems, and Internet of Things (IoT) applications.

Lei Yu

Assistant Professor
Lei Yu is a Tenure-Track Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute. Before that, he was a Staff Research Scientist at IBM Research, IBM Thomas J. Watson Research Center. His research interests include security and privacy, big data analytics & distributed systems, the security and privacy of machine learning, and cloud and mobile computing. He received his Ph.D. in Computer Science from Georgia Institute of Technology. Additional information can be found on his homepage https://leiyucs.github.io/ .