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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

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

Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning.

4 May 2021 | BMC Infectious Diseases | 21(1):391
Navlakha, Saket, Morjaria, Sejal, Perez-Johnston, Rocio, Zhang, Allen, Taur, Ying

Reply to Semelidou and Skoulakis: "Short-term" habituation has multiple distinct mechanisms

25 Aug 2020 | Proceedings of the National Academy of Sciences of USA | 117(34):20373-20374
Shen, Y, Dasgupta, S, Navlakha, S

Habituation as a Neural Algorithm for Online Odor Discrimination

19 May 2020 | Proceedings of the National Academy of Sciences of USA | 117(22):12402-12410
Shen, Y, Dasgupta, S, Navlakha, S

Plant 3D (P3D): A Plant Phenotyping Toolkit for 3D Point Clouds

31 Mar 2020 | Bioinformatics
Ziamtsov, I, Navlakha, S

Ten-Hour Time-Restricted Eating Reduces Weight, Blood Pressure, and Atherogenic Lipids in Patients with Metabolic Syndrome.

7 Jan 2020 | Cell Metabolism | 31(1):92-104.e5
Wilkinson, Michael, Manoogian, Emily, Zadourian, Adena, Lo, Hannah, Fakhouri, Savannah, Shoghi, Azarin, Wang, Xinran, Fleischer, Jason, Navlakha, Saket, Panda, Satchidananda, Taub, Pam

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