Saket Navlakha

Saket Navlakha

Associate Professor

Ph.D., University of Maryland College Park, 2011

navlakha@cshl.edu | 516-367-5540

Navlakha Lab Website   Faculty Profile

Biological systems must solve problems to survive, and their solutions can be viewed as “algorithms.” Our goal is to uncover these algorithms, translate them to improve computer science, and use them to spark new hypotheses about biological function and dysfunction.

Saket Navlakha’s lab studies “algorithms in nature,” i.e., how collections of molecules, cells, and organisms process information and solve interesting computational problems critical for survival. Indeed, there are many shared goals and constraints faced by biological and engineered systems, including: (1) the use of distributed networks as a backbone for information processing and communication; (2) trade-­offs between optimization criteria, including efficiency, robustness, and adaptability; and (3) the need to develop low­-cost, scalable solutions that conserve important metabolic or physical resources. An algorithmic perspective on biological problem-solving can lead to two ends: (1) new biological algorithms that are simple, flexible, and robust for use in computer science applications, and (2) quantitative frameworks to predict behavior, raise testable hypotheses, and guide experiments. Our lab has most recently focused on studying neural circuit computation and plant architecture optimization from this perspective.

Machine learning helps plant science turn over a new leaf

To detect new odors, fruit fly brains improve on a well-known computer algorithm

Age is more than just a number: machine learning may be able to predict if you’re in for a healthy old age

Fruit fly brains inform search engines of the future

How plant architectures mimic subway networks

How plants grow like human brains

The Internet and your brain are more alike than you think

Brain-based algorithms make for better networks

See all Navlakha news

All Publications

BATMAN: Improved T cell receptor cross-reactivity prediction benchmarked on a comprehensive mutational scan database

25 Jan 2024 | bioRxiv
Banerjee, Amitava, Pattinson, David, Wincek, Cornelia, Bunk, Paul, Chapin, Sarah, Navlakha, Saket, Meyer, Hannah

Effects of stochastic coding on olfactory discrimination in flies and mice

Oct 2023 | PLoS Biology | 21(10):e3002206
Srinivasan, Shyam, Daste, Simon, Modi, Mehrab, Turner, Glenn, Fleischmann, Alexander, Navlakha, Saket, Benton, Richard

Reducing Catastrophic Forgetting With Associative Learning: A Lesson From Fruit Flies

19 Sep 2023 | Neural Computation | :1-23
Shen, Yang, Dasgupta, Sanjoy, Navlakha, Saket

A neural theory for counting memories

10 Oct 2022 | Nature Communications | 13(1):5961
Dasgupta, Sanjoy, Hattori, Daisuke, Navlakha, Saket

Special Issue: Biological Distributed Algorithms 2021

7 Apr 2022 | Journal of Computational Biology | 29(4):305
Emek, Yuval, Navlakha, Saket

A feedback control principle common to several biological and engineered systems

Mar 2022 | Journal of the Royal Society Interface | 19(188):20210711
Suen, Jonathan, Navlakha, Saket

Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans

9 Nov 2021 | PLoS Computational Biology | 17(11):e1009591
How, Javier, Navlakha, Saket, Chalasani, Sreekanth

Better tired than lost: Turtle ant trail networks favor coherence over short edges

21 Oct 2021 | PLoS Computational Biology | 17(10):e1009523
Chandrasekhar, Arjun, Marshall, James, Austin, Cortnea, Navlakha, Saket, Gordon, Deborah

Branch-pipe: Improving graph skeletonization around branch points in 3D point clouds

22 Sep 2021 | Remote Sensing | 13(19)
Ziamtsov, I, Faizi, K, Navlakha, S

A Correspondence between Normalization Strategies in Artificial and Biological Neural Networks.

30 Aug 2021 | Neural Computation | :1-25
Shen, Yang, Wang, Julia, Navlakha, Saket

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