Data Science 101: CDS Student Sarvesh Patki Features Pascal Wallisch on a Recent SPodcast Episode

Pascal discusses his teaching style, tips for aspiring data scientists, and his distinct dress code

Due to inclement weather, Sarvesh Patki’s first day of class at NYU was held online. Despite the less than ideal start to the fall 2021 semester, Sarvesh said DS-GA 1001: Intro to Data Science by Clinical Associate Professor of Data Science and Psychology Pascal Wallisch left a lasting impression. “I was in awe of his unique teaching style,” said Sarvesh. “I would be lying if I said the thought of inviting him to my podcast didn’t come to my mind.” As the semester went on Pascal got to know about Sarvesh’s SPodcast YouTube channel, which currently has 1.72K subscribers, and was curious to know more. Towards the end of the semester to the enthusiasm of both parties, Sarvesh invited the data science professor to make a guest appearance on the channel.

The pair got together virtually to record a Q&A-style conversation that went live on August 1st, 2022. With assistance from Max Wang, Khevna Parikh, and Eugenia Fomitcheva in developing the podcast questions, Sarvesh started off the segment from his virtual Washington Square Park backdrop by inviting Pascal to introduce himself to the audience. Originally from Germany, the first-generation college student made his way to the University of Chicago where he graduated with a PhD. He then attended the NYU Center for Neural Science for his postdoctoral studies before switching to the Psychology department, and ultimately landing as an educator at CDS.

One of the reasons Sarvesh found Pascal’s lectures to be so engaging is due to his unique teaching style. The interviewer invited Pascal to share the motivation behind his teaching philosophy. From his background in psychology, something that has been essential to Pascal is not taking the attention of his audience for granted. His classes are deliberately designed to be engaging through his TREC method: transmitting conceptual theories through real-life examples that are True, Relevant and Rigorous, Engaging, and Clear.

His background in psychology has not only informed his teaching methods but an understanding of his intellectual interests. His academic and professional journey has been motivated by a sense of exploration rather than the pursuit of one area of expertise. “I’m excited by these newer fields of neural science and data science that didn’t exist when I was an undergraduate,” said Pascal. “It’s where I feel like I can make a genuine contribution to the field.”

Pascal explains his work in the seemingly disparate but highly interrelated fields of neural science, psychology, and data science through Isaiah Berlin’s Fox vs. Hedgehog approach to intellectual styles. Pascal explains he’s much more of a fox style that integrates perspectives from numerous fields, rather than the hedgehog which is dialed into one expertise. “Academics are very much geared towards the hedgehog approach,” said Pascal. He explains this theory was the inspiration behind NYU’s Fox Lab where he serves as Principal Investigator, which combines research at the intersection of data science, psychology, and neuroscience.

The diversity of experience coming into the field of data science is something that Pascal thoroughly enjoys. His advice for people going into the field from various backgrounds is to develop a broad-minded fox approach to data science. The ability to use data science in a variety of fields and research areas is what makes it highly valuable career-wise. It is also what he thinks the field stands to lose as it continues to have a larger impact on society. His fear is as it grows it will cluster into specialized tracks. “From a fox perspective that would be tragic,” said Pascal.

Sarvesh goes on to ask about Pascal’s future plans. “If you had told me twenty-five years ago I was going to be in the US, in New York City, you’re first going to be doing psychology and then data science which didn’t exist back then; I would have said that none of those things were going to happen,” said Pascal. He says people underestimate how transformational time is and there’s no telling where he’ll end up next. Wherever that may be, he hopes to continue making an impact in the field of data science and inspiring students down their own paths.

As the episode winds to a close Sarvesh asks one more question regarding Pascal’s dress code. The professor explains every day of the week corresponds to a colored shirt: Monday is red, Tuesday is green, Wednesday is yellow, Thursday is blue, and Friday is white. Pascal has strong associations with numbers, such as days of the week, to colors. He explains research has linked this association to a specific colored refrigerator magnet that was popular during his childhood. He also uses his colored shirts in his teaching to explain concepts such as signal processing.

Please visit the full “How to become a data scientist | Data Science 101” episode to view it in its entirety.

By Meryl Phair



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Official account of the Center for Data Science at NYU, home of the Undergraduate, Master’s, and Ph.D. programs in Data Science.