An interview with renowned computer science author Steven Skiena

Author: Sacha Billett

Steven Skiena is a Distinguished Teaching Professor of Computer Science and Founding Director of the Institute for AI-Driven Discovery and Innovation at Stony Brook University, The State University of New York. Skiena is the author of several popular books in the fields of algorithms, programming, and data science.

Can you introduce yourself and your primary focus of research?

I am a Professor of Computer Science at Stony Brook University, where I have taught for 31 years and counting!

My early work was in algorithm design, specifically graph, string, and geometric algorithms. But my research interests have grown over time, and today most of my work is in data science and natural language processing (NLP). These are the topics of my most popular Springer books, “The Algorithm Design Manual” and “The Data Science Design Manual”.

Your books, Algorithm Design Manual and Data Science Design Manual have proven very popular, how are they used in teaching and learning? 

I have been very gratified by the popularity of my Algorithm and Data Science “Design Manuals”. Although they are written as classical textbooks with homework problems and associated lecture notes/videos, my primary readership is people who want to learn on their own.

My Algorithm book is particularly popular among people interviewing for jobs at high-tech companies like Google and Facebook. Getting a job at such places is tough, and algorithm problems are a classic (and much feared) part of the interview process. My book is regarded by many as the best preparation for such technical interviews. I regularly get emails from people thanking me for helping them to get a job at Google!

In both books, I focus on providing intuition for the right way to think about problems - in algorithm design or data science modeling. My books tend to be less intimidating than other books on the subject - minimizing formal proofs and technical detail in favor of a broad view of what is important and why. After you understand the big picture, it is much easier to appreciate the details if you need to. 

It’s over 10 years since you wrote the second edition of “The Algorithm Design Manual” what are the significant updates we can expect to see for the third edition?

Somehow I find it takes me longer to produce a new edition of my books than it did to write it in the first place! I want to do several things in the third edition, particularly coverage of topics of growing importance like randomized algorithms and quantum computing. It will add new War Stories in dynamic programming, divide and conquer, and randomization. It will provide more interview resources and better exercises. And all the figures will be redrawn to exploit color printing - I loved how effective that proved in “The Data Science Design Manual” and want to do the same for my algorithms book.

You are the founding director of the Institute for AI-Driven Discovery and Innovation at Stony Brook – how does your data science research play into this, and how does your book on that topic contribute to this critical, interdisciplinary field?

My Institute for AI-Driven Discovery and Innovation works to advance the research and educational activities at Stony Brook University in core areas of AI including computer vision, natural language processing (NLP), and neuroscience. And of course data science! Data science is largely applied machine learning, and machine learning is what makes for the revolution in AI. The data science course I teach at Stony Brook using the book attracts 250 students per semester, which is a tribute to how popular this material is today. It helps make Stony Brook a great place to study and do research in these areas.

One thing “The Data Science Design Manual” focuses on is how data science is more than just machine learning. It stresses the somewhat less glamorous issues which really impact the quality of any model much more than which machine learning algorithm you use, things like data cleaning and visualization and statistical significance. Unlike most other data science books, this one stresses the way to think about working with data more than technical issues of programming languages and libraries. The material in this book is fundamental, and will hold up for many years to come.

You recently spoke at an event on the Augmented Age – how do you envision this new phase affecting our lives and what would you recommend students study in response to these developments?

Turing Award winner Niklaus Wirth wrote a famous book whose title (almost) was

“Algorithms + Data = Programs”

Understanding both algorithms and data are more fundamental to computer science than they ever have been. I like that my two Springer books cover this spectrum of topics that every computer science student needs to know.

What encouraged you to choose Springer Nature as your publisher for your textbooks?

I’ve been with Springer (an imprint of Springer Nature) for over 25 years now. I’ve published three different books with Springer, and all of them have done well. I’ve appreciated Springer’s global reach: my Springer books have been translated into Chinese, Japanese, Korean, Polish, Russian, and Spanish.

I first got involved with Springer when my first editor (Allan Wylde) moved there in the mid-1990’s. I’ve been working with my current editor Wayne Wheeler and Simon Rees for many years now. Publishing is a business where people and relationships matter, and I have been very happy in my dealings with them.

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