OSCAR Student Jad Rayes Applyies Large Scale Data Driven Techniques to Insider Trading

Insider trading is something that we’ve heard about time and again in the news. It made headlines back in 2001 when Enron executives were charged with insider trading and accounting fraud. It has been a significant challenge for the financial regulatory body of the U.S government, the SEC, to both prevent and identify. I became fixated on applying data mining techniques to aid financial regulators in identifying and preventing illegal insider trading. I feel that using recent advances in big data to make the market safer and fairer, is both a noble and important pursuit. I know that I want to pursue a career as a data scientist, and being able to explore and invent techniques to solve problems with big data is crucial. It is also important to my future career goals that I am able to use my technical background and skill set to solve problems of vastly different types, in different domains. My work this summer is a continuation of the work I have been doing since September of last year. I am working with insider transaction data from about 400 different companies in different sectors. Each week I make it a goal to read a paper related to my project. I write new scripts to perform different analysis on my datasets; and will often experiment with different mathematical techniques to summarize and describe new trends and correlations I observe. I spend a fair amount of time using different python packages to both build creative plots and figures, and summarize my results concisely. I find that being able to explain your results clearly is impossible without the use of a good graphing tool. Since my project began, I have discovered how information propagates between insiders, how pricing data on stock is affected by financial news, and how certain network topologies are useful for representing relationships between insiders. The most important resources I have discovered are the faculty and graduate students who have taught me how to form a hypothesis and conduct research.

OSCAR Student Bryce Kushmerick-Mccune Researches Living & Working in Restricted Housing Units in Pennsylvania State Prisons

I am part of the Change the Hole Mind team. Essentially, we’re researching what it’s like to live and work in Restricted Housing Units in Pennsylvania State prisons. Over the course of the summer and into the upcoming fall term, our team is traveling to four different institutions, and at these institutions we interview the inmates and corrections officers living and working in these units. There are three teams, and each team chose a topic to research. My team chose to research how inmates cope while living in RHUs, and how they go about accessing this support. I became interested in the CTHM project because I’ve always wanted to do something in the Criminal Justice field, and the use of RHUs (solitary confinement) has been something that really interested me since I visited Alcatraz prison when I was 13. Working on this project sets me up on the path towards working as a researcher in the Criminal Justice field, and I hope to apply this research to prison reform in the States. The workload of this project really picks up once we’ve visited the institutions, so in the week following the visits we transcribe all of the field notes we’ve taken, analyze/code all of the data, and then query the results. During this term, I’ve had to re-define solitary confinement. Whenever I thought of solitary confinement in the past, I pictured inmates alone in small cells with little to no light and limited social interaction. In the prisons that we’ve visited, however, most of the inmates are doubled bunked (meaning they share a room with another person), and they can easily talk to the inmates in the other cells. Although the initial view that I had of solitary does exist in America, it hasn’t been present in the institutions that I’ve visited, so I’ve had to rethink my definition of the word. Overall, I’m just really excited to be getting the chance to research what I’m interested in so early in my college career, and I can’t wait to continue this type of research in the future!

OSCAR Student Michael Norton Researches Turning Ambiguous Traffic Scenarios Into Autonomous Vehicles’ Intelligence

My name is Michael Norton, and I am working on the OSCAR summer impact grant Turning Ambiguous Traffic Scenarios Into Autonomous Vehicles’ Intelligence, which is an interdisciplinary research project between computer science and psychology undergraduates. The main goal of our project is to create an annotated dataset of dash camera footage for objects, action, and lane classifiers to be used by an autonomous vehicle. I first learned about the project last semester through working with Professor Lee, a psychology professor who is heading the project along with Professor Kan (physics) and Professor Lin (computer science). Professor Lee is in the Human Factors department, which focuses on human interaction with technology. I find this to be an important field, as our society’s current explosion of technological innovation requires research on how to design that technology to maximize human usability and minimize potential hazards or health concerns.

In this project, I spend most of my time reading research articles, writing our research paper, and exchanging information and ideas with my co-researchers. Overall, working on this project has been a personally enriching experience for me. I found some of the computer science literature to be impenetrable at first, but learned that persistence and collaboration with those more knowledgeable is a great way to learn difficult new material. I specifically learned a lot regarding datasets, computer vision, and neural networks.  I have also gained greater insight into autonomous vehicle technology, accident analysis techniques, and the overall transportation infrastructure. This project has affirmed my beliefs in the benefits of conducting research for the long term greater good of society.