If you want to wrap your mind around the concept of blockchain and cryptocurrency, you should look at the history and usage of the “rai stone,” according to Pranay Anchuri, data scientist at Princeton University.
In the Micronesia area of the Pacific Ocean, above Australia and Papua New Guinea, there is the tropical island Yap where people used large, disk-shaped rocks, a rai stone, as a form of currency until the early 20th century, said Anchuri, who’s appointed to the DataX program, partly overseen by the Center for Statistics and Machine Learning (CSML); and also to the Center for Information Technology Policy (CITP).
These limestone boulders, weighing as much as 8,800 pounds and having a hole gouged in the middle, were too heavy to movie, so people used oral history as a way to keep track of ownership – a kind of early ledger that functions similarly to blockchain technology. Anchuri used these stones as an entry point to his two-part workshop, “Introduction to Blockchain and Decentralized Finance,” which was part of Princeton University’s 2022 Wintersession.
That workshop plus two others featured CSML-affiliated personnel and were held the week of January 10th. An introductory workshop on machine learning led by Peter Ramadge, director of CSML, was also part of Wintersession. (Read more about that January 11th event here.) While these four workshops were structured as introductory bite-size discussions, they also showcased the resources and know how at CSML.
“Not only do we have interesting research projects, engaged faculty and researchers, and thriving undergraduate and graduate certificates at CSML, but we also have extremely knowledgeable people who are advancing educational opportunities and research on campus and are important to our overall scholarly community, said Ramadge.
Besides Anchuri, CSML-affiliated facilitators for Wintersession included Jose Garrido Torres, a DataX data scientist jointly appointed to the departments of chemistry and computer science, and Vineet Bansal, a senior research software engineer, who’s jointly appointed to the Princeton Institute for Computational Science and Engineering (PICSciE).
Torres’ workshop, Data Visualization in Python, was held on January 12th and was co-sponored by PICSciE. Bansal held his workshop on January 11th and was co-sponsored by PICSciE as well. Anchuri, whose workshop was co-sponsored by CITP, held his workshop on January 11th and the 13th.
For his workshop, Anchuri talked at length about the history of cryptocurrency and precursor forms of electronic cash, differences between physical cash and decentralized currency such as Bitcoin, the perceived benefits of cryptocurrency, and the blockchain technologies that undergirds cryptocurrency. Blockchain is the decentralized and distributed digital database that records every cryptocurrency transaction. Anchuri also gave a technical overview of how Bitcoin and other cryptocurrencies work.
In another part of his talk, Anchuri discussed the rise of Ether, another cryptocurrency, and, mostly importantly, Ethereum, the blockchain technology behind Ether. Different sectors of the economy, from finance to the art world, have become interested in Ethereum because this type of blockchain can store smart contracts.
“I think it’s important to know about cryptocurrency, blockchain, and the larger decentralized finance world, because they are already having an impact on finance, national governments, politics, the environment and more,” said Anchuri. “They can seem like intimidating topics, but hopefully my talk demystified these concepts and served as an entry point into learning more about decentralized finance.”
Anchuri studies blockchain as part of his work at CITP, which focuses on issues in technology, engineering, public policy, and the social sciences. Read more about his work here.
For Bansal’s talk, he went over the basics of NumPy, an open-source and popular software library for Python. Bansal said NumPy “underlies most scientific computing done in Python.” NumPy is attractive to scientists because it enables them to work with matrices and linear algebra in any scientific domain that involves number crunching. Python was originally not made for numerical computing, so engineers created the NumPy package, which is short for “Numerical Python.”
Bansal explained to participants the NumPy array, the principal data type in the NumPy package, and how it differs from similar Python structures. He also discussed how certain features of Numpy arrays can be used to write scientific code in Python and enable the creation of powerful research software.
“The workshop was geared towards people who have used Python, and are new to the NumPy or have some familiarity with the library but want a deeper understanding of it,” said Bansal.
Bansal is an expert on Python and NumPy and has been instrumental in applying these tools to create and optimize code for several research projects with CSML-affiliated faculty. Read more about his work here.
For Torres’ Wintersession workshop, he also talked about a Python-related subject, data visualization. Python has been particularly useful in data visualization because it has several libraries that enables researchers to visually present and analyze information in different ways, whether in a static form, an animation or with an interactive element.
Torres discussed with participants different software packages such as Matplotlib, Seaborn, and Plotly that can be used to visualize data or make plots. He showed several examples that used real world data, which included static 1D plots, 2D contour maps, heat maps, violin plots and box plots.
In Torres’ own work, he is part of a team devoted to using machine learning to optimize chemical reactions in heterogeneous catalysis. The eventual goal of this work is to discover novel molecular structures in the field of medicinal chemistry. A major component of his work is using the tools he introduced in the Wintersession workshop in order to build and visualize machine learning models for chemical reaction predictions. Read more about his work here.
“A key component of research is displaying and explaining your results or data in a clear and concise manner to your colleagues and peers, and that is why data visualization is so important,” said Torres. “I hope they come away from the workshop knowing the basic tools and mechanics to create research-quality plots using Python.”