The Rutgers Professional Science Master’s program has a thrilling new addition. We are excited to welcome Lars Sorensen as a new faculty member! Sorensen is joining us as an Assistant Teaching Professor. He brings a wealth of knowledge and experience in computer science and education to our program.  

Headshot of new faculty
Lars Sorensen

Current MBS students may recognize Sorensen as the instructor of our course Python Methodologies for Data Science. He also teaches the Rutgers School of Arts and Sciences course Introduction to Computer Science. In addition to these roles, Sorensen is pursuing a PhD in educational psychology here at Rutgers.  

Sorensen was born and raised in New Jersey. He grew up in Metuchen and received a bachelor’s degree in computer science from St. John’s University. After graduation, he entered the private sector as a computer programmer and consultant, holding different professional IT roles. Eventually, he sought new opportunities to use his expertise.  

“Like a lot of people with computer science backgrounds,” said Sorensen, “when I was in my thirties, I picked my head up and said, ‘Okay, what do I want to be when I grow up?’” 

He returned to academia for a role at the Laboratory for Computer Science Research (LCSR).  

At the LCSR, he worked with a graduate student with vision impairment to use the technology available. This kindled Sorensen’s interest in educational technology and educational psychology. He is currently working on his dissertation in educational psychology, where he works in gaming and education to use video games and other media as a non-traditional source of knowledge. 

“It’s funny,” said Sorensen. “I’m faculty over in the computer science department. Everyone assumes my PhD is in computer science, but it’s not. It’s in educational psychology.” 

With his extensive background in educational psychology, Sorensen is an excellent professor who brings a unique perspective to the classroom.  

“My work with my PhD is more of a psychological take,” he added. “What people don’t realize about the introductory computer science courses is that it’s less about the computer knowledge and more about convincing the students that they can do it.” In his introductory classes, students come from all different skill levels and with differing needs. Sorensen ensures that all students achieve learning objectives.  

Sorensen is thrilled to join our team, seeing great value in our fundamental goal of preparing students with a solid academic background and relevant business skills.  

“The MBS program mixes in things that have utility as well as classic academic issues,” said Sorensen.  

Our degree provides students with a strong theory and foundational model, as well as practical tools used in the workforce. 

“You can tend to theory and the academic work that university is truly for,” said Sorensen, “and you can bring in things like Tableau.” Tableau is a piece of software crucial for a career in data science.  

Sorensen has been using this mindset while teaching his course Python Methodologies for Data Science. He developed the course to prepare students to use Python professionally. Students not only learn the language, but they also learn concepts and skills that are essential for success in the workforce. 

In his new role, Sorensen will continue to maintain our Python course. He also has plans to examine and improve our Cybersecurity curriculum—and that’s just the beginning.   

Sorensen describes himself as student-based, having worked closely with students while serving in the Rutgers Computer Science department. He’s the faculty advisor for the RU Security Club and Creation of Games Society. He also serves as a judge for HackRU and HackHERS

“Working with the students is why we’re here. It’s why I like being teaching faculty,” said Sorensen. 

Sorensen is an exciting addition to our program, and we are eager to see the contributions that he will bring to the MBS community! 

Author(s): Julianna Rossano Published on: 11/09/2023
Tags: PSM faculty, Faculty Highlight, Python Methodologies for Data Science