Tag: computer science

 

Meet Mathew Pregasen. Mathew is a Columbia junior studying computer science who founded a startup with Anuke Ganegoda (CC ’18), Sahir Jaggi (SEAS ’17) and Rikhav Shah (MIT ’19). Named Parsegon Inc, the company implements a new method of transcribing English descriptions of math into mathematical script. For example, Parsegon’s technology could take a sentence “integral from 0 to 10 in region D of 2x squared + 3x cubed – the square root of x” and convert it into visual, textbook-formatted math.

How did you come up with the idea of Parsegon? What experience made you want to start your own business?

The way it started was pretty accidental. It was first a small project that we had no intention of turning into a company, but as it developed we realized it had more potential. Soon, we started to think of this project in a business context. We did Almaworks, raised some funding, hired some people for the summer, and further developed our business. In the ending, it is a technology project.

How did Almaworks facilitate your business development process?

I think the most beneficial part is that it connects you with incredibly helpful mentors. At first, you might not know too much about design, planning, or the law associated with a startup business, but as long as you get close to a mentor, you will get proper advice on business direction, project development, and especially important legal services.

What’s the current entrepreneurial environment at Columbia like? How does it compare to other schools?

I think in the last two years, there has been some significant changes, where the administration—especially entrepreneurship administration—has been putting a lot of resources into the entrepreneurship community. They raised the amount of provided grants and have organized the Columbia Entrepreneurship Competition for the last four years.  Alongside that, you have clubs like CORE (Columbia Organization of Rising Entrepreneurs) and ADI (Application Development Initiative) that push this culture. I think ultimately the culture should be self-accelerating instead of accessory, but you need to have some initial velocity at the beginning.

 

Mathew Pregasen

Image via Mathew Pregasen

So back to Parsegon. It seems to be designed for people who are not fast at mathematical typing. How do you attract people who are already proficient at mathematical expression in typing packages such as LaTeX?

We are not competing with LaTeX and we don’t expect people to write papers in Parsegon. That being said, we do have a very user-friendly environment that reduces time and difficulty in typing. Parsegon is also educational in the sense that it makes teaching more accessible to students and enables the entire classroom to engage in interactive math.

 

You have been trying to integrate Parsegon into classrooms. What is the feedback from teachers and students?

We primarily focus on high schools, and we’ve been having very strong feedback.

What do you think is the biggest challenge for Parsegon?

I think the greatest challenge for us is to make a technology that provides a number of services for very diverse classroom environments. Some people might not be familiar with computer typing and some do prefer a very traditional and structured typing style, so although we are making it more accessible to people, it is still a big challenge to build the technology that accommodates the needs of everyone and strikes a proper balance between accessibility and formality.

Are there any computer science classes at Columbia that have helped you in this process?

Namely Operating Systems (W4118) with Jason Nieh. I also took a class called Computer Theory with Alfred Aho which was useful for the theoretical angle.

What do you think is the future of Parsegon?

We want to build the best tool for educational practices in the America. We believe that there is a big gap between the technology side of users and the technology provided for educational professionals, and we believe that our implementation will not only complement the traditional learning method, but also improve it. The importance of Parsegon is that it teaches students to understand the language of math. If you can understand the language of math, you usually also understand the theory of math much more coherently. And we believe that is the best way Parsegon could improve the learning process of math on a more cognitive level.

Photo Courtesy of Color Code

On Thursday, the ColorCode committee learned that Columbia University Computer Science professor Satyen Kale assigned to his Machine Language (COMS 4117) class a competition “to produce the eponymous cyborg law enforcer.” Drawing on data from the NYPD’s “Stop, Question and Frisk” records, students have been asked to create a machine learning algorithm to “decide when a suspect it has stopped should be arrested” based on characteristics ranging from “sex” and “race” to “suspect was wearing unseasonable attire”, “suspicious bulge”, and “change direction at sight of officer”. Stop­ and ­Frisk is a violently racist program that allows police to stop, question, and frisk any pedestrian who arouses “reasonable suspicion.” Numerous studies and investigations of the NYPD’s own data have shown that Stop­ and ­Frisk disproportionately targets Black people. It has torn apart Black communities in the city and contributes to a system of mass incarceration and policing that brutalizes, incarcerates, and kills Black people across the nation. The program has even been deemed unconstitutional by federal courts.

That a Columbia professor would ask students to implement a program that reproduces and aids Stop­ and Frisk policing with zero acknowledgement of the violence and harm inflicted by the actual program­­–and in fact suggest that machine learning algorithms like this constitute “the future” of machine learning applications— is an egregious example of racist, ahistorical, and irresponsible pedagogy. Data are not apolitical. Algorithms are not objective. To teach technical skills without also teaching anti­racist, anti­oppression developing principles is unforgivable, despicable, and dangerous. For us, as students of color who also are coders, entrepreneurs, and engineers, assignments like this confirm feelings of exclusion and isolation accumulated over many semesters here–­­­being one in a only handful of Black students in a lecture hall, for example, or graduating from SEAS not having had even a single Black professor. It confirms the department and university’s disregard for our wellbeing as students of color, which always is intertwined with the wellbeing of our communities.

Moving forward, ColorCode demands that this Machine Learning assignment be revoked, and that the professor issue an apology addressing the concerns above. We demand that students in the class be provided with alternate ways to receive credit. We demand that the professor and the department acknowledge these concerns, apologize, and make significant, structural changes to ensure this does not happen again. Finally, we support the demands of Mobilized African Diaspora/BCSN and in particular add our voices to demand that the School of Engineering commit to hiring more Black professors and underrepresented professors of color.

ColorCode is a group focused on getting people of color into the technology sector. To respond to this op-ed or submit one of your own, email submissions@columbialion.com