Today, we have more data than ever before. And there are more fields of research generating this staggering amount of information. So we need people who can make sense of this data, and put it to use. That’s where Boris Landa comes in.
Landa’s research is in statistical signal processing and geometric data analysis, focusing on developing theoretical and computational tools for processing and analyzing large datasets.
In the latest edition of “Office Hours,” a Q&A series that introduces new Yale faculty members to the broader community, the assistant professor of electrical & computer engineering at Yale School of Engineering & Applied Science discusses his research, his collaborations, and offers some recommendations of hiking spots.
Title
Assistant professor of electrical & computer engineering
Research Interest
Focuses on developing theoretical and computational tools for processing and analyzing large datasets
Prior Institution
Tel-Aviv University
Started at Yale
July 1, 2024
### How would you describe your work?
**Boris Landa:** My goal is to develop algorithms and supporting theory to improve data processing and analysis, with an emphasis on the robustness and mathematical rigor of the methods.
### What have you been working on recently?
We’ve been developing methods to process large genomics datasets, like single-cell RNA sequencing, to uncover meaningful patterns and structures. These genomics experiments typically involve measuring thousands of genes across large collections of cells, with data indicating the gene counts for each cell — “this gene appeared 50 times, that gene appeared zero times…,” and so on. The goal is to extract valuable scientific insights from the data. This can be challenging since these datasets are very large and noisy. Also, you often have multiple batches of data obtained from different experiments, and you want to analyze them simultaneously. There are many complex scenarios where you need to process this data reliably, that’s why we focus on the robustness and mathematical rigor of the methods we develop.
### How did you get into this field?
**Landa:** I did my undergrad in electrical engineering and a Ph.D. in applied mathematics, where I designed advanced signal and image processing techniques. When I first came to Yale as a Gibbs Assistant Professor in the Applied Math program \[in the Faculty of Arts and Sciences\], I had the chance to collaborate with researchers from different fields and learn about a broad range of topics. I was fascinated with modern genomics research and found the computational challenges intriguing. I saw an opportunity to apply my background to solve real-world problems and make a meaningful impact. What excites me most is the combination of novel theoretical research with practical applications.
### When did you become interested in the mathematical aspects of engineering?
**Landa:** During my undergraduate studies in electrical engineering, I realized that the topics I was interested in, like signal processing and communications, relied heavily on mathematics. Math is foundational to many of the technologies we use daily, and I wanted to deepen my understanding in math to solve real-world problems more effectively. That led me to pursue a Ph.D. in applied mathematics, which I see as an integral part of engineering.
### What do you do when you’re not working?
**Landa:** I love to spend time with my family. I have two little kids. The younger one is in first grade and the older one is in third. When the weather is nice on a weekend, we go hiking together. We like to hike around lakes, such as Lake Wintergreen and Lake Chamberlain, which are fairly close. Climbing up East Rock or Sleeping Giant \[a state park in Hamden\] is always nice. Aside from hiking, I play tennis and enjoy an occasional game of chess.