Openings:
We are looking for creative, hard-working scientists at any level. If interested, please email me your CV and a brief statement of how our research interests overlap. Thanks.Research interests
- We work at the interface of theoretical computer science, machine learning, and systems biology.
- We primarily study "algorithms in nature", i.e., how collections of molecules, cells, and organisms process information and solve interesting computational problems critical for survival.
Publications
+2024
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S. Dasgupta, Y. Meirovitch, X. Zheng, I. Bush, J. W. Lichtman, and S. Navlakha (2024). A neural algorithm for computing bipartite matchings. Proc. Natl. Acad. Sci. USA, 121(37):e2321032121.
[abstract] [pdf] [press release] [code]
+2023
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S. Srinivasan, S. Daste, M. Modi, G. Turner, A. Fleischmann, and S. Navlakha (2023). Effects of stochastic coding on olfactory discrimination in flies and mice. PLoS Biol., 21, 10:e3002206.
[abstract] [pdf] [press release] -
Y. Shen, S. Dasgupta, and S. Navlakha (2023). Reducing catastrophic forgetting with associative learning --- a lesson from fruit flies. Neural Comp., 35, 11:1797-1819.
[abstract] [pdf] [code]
+2022
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S. Dasgupta, D. Hattori, and S. Navlakha (2022). A neural theory for counting memories. Nature Commun., 13, 5961.
[abstract] [pdf] [press release] [code] -
J. Y. Suen and S. Navlakha (2022). A feedback control principle common to several biological and engineered systems. J. R. Soc. Interface,19, 188:20210711.
[abstract] [pdf] [press release]
+2021
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J. J. How, S. Navlakha, and S. H. Chalasani (2021). Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans. PLoS Comput. Biol., 17, 11:e1009591.
[abstract] [pdf] [press release] -
A. Chandrasekhar, J. A. R. Marshall, C. Austin, S. Navlakha, and D. M. Gordon (2021). Better tired than lost: turtle ant trail networks favor coherence over short edges. PLoS Comput. Biol., 17, 10:e1009523.
[abstract] [pdf] -
I. Ziamtsov, K. Faizi, and S. Navlakha (2021). Branch-Pipe: Improving graph skeletonization around branch points in 3D point clouds. Remote Sens., 13(19), 3802.
[abstract] [pdf] -
Y. Shen, J. Wang, and S. Navlakha (2021). A correspondence between normalization strategies in artificial and biological neural networks. Neural Comput., 33(12): 3179-3203.
[abstract] [pdf] -
S. Navlakha, S. Morjaria, R. Perez-Johnston, A. Zhang, and Y. Taur (2021). Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning. BMC Infect. Dis. 21, 1:391.
[abstract] [pdf] [press release] [yahoo] [medical research] -
Y. Liang et al. (2021). Can a fruit fly learn word embeddings? To appear in the Intl. Conf. on Learning Representations (ICLR), 2021.
[abstract] [pdf] [demo] [discover magazine]
+2020
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S. Sultan, J. Snider, A. Conn, M. Li, C. N. Topp, and S. Navlakha (2020). A statistical growth property of plant root architectures. Plant Phenomics, 2020, 1-11.
[abstract] [pdf] -
Y. Shen, S. Dasgupta, and S. Navlakha (2020). Habituation as a neural algorithm for online odor discrimination. Proc. Natl. Acad. Sci. USA, 117, 22:12402-12410.
[abstract] [pdf] [code] [press release] [inverse] [tbr news media] -
I. Ziamtsov and S. Navlakha (2020). Plant 3D (P3D): A plant phenotyping toolkit for 3D point clouds. Bioinformatics, 36(12): 3949-3950.
[abstract] [pdf] [code] -
M. J. Wilkinson et al. (2020). Ten-hour time-restricted eating reduces weight, blood pressure, and atherogenic lipids in patients with metabolic syndrome. Cell Metab., 31, 1:92-104.e5.
[abstract] [pdf] [press release]
+2019
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I. Ziamtsov and S. Navlakha (2019). Machine learning approaches to improve three basic plant phenotyping tasks using 3D point clouds. Plant Physiol., 181, 4:1425-1440.
[abstract] [pdf] [press release] -
A. Conn, A. Chandrasekhar, M. van Rongen, O. Leyser, J. Chory, and S. Navlakha (2019). Network trade-offs and homeostasis in Arabidopsis shoot architectures. PLoS Comput. Biol., 15, 9:e1007325.
[abstract] [pdf] [code and data] -
J. Y. Suen and S. Navlakha (2019). Travel in city road networks follows similar trade-off transport principles as neural and plant arbors. J. R. Soc. Interface, 16, 154:20190041.
[abstract] [pdf] [code] -
A. Chandrasekhar and S. Navlakha (2019). Neural arbors are Pareto optimal. Proc. R. Soc. B., 286(1902):20182727.
[abstract] [pdf] -
S. Rashid et al. (2019). Adjustment in tumbling rates improves bacterial chemotaxis on obstacle-laden terrains. Proc. Natl. Acad. Sci. USA, 116, 24:11770-11775.
[abstract] [pdf] [press release] [naked scientists] [nature reviews microbiology] -
S. Rashid, S. Singh, S. Navlakha, and Z. Bar-Joseph (2019). A bacterial based distributed gradient descent model for mass scale evacuations. Swarm Evol. Comput., 46, 97-103.
[abstract] [pdf]
+2018
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S. Dasgupta, T. C. Sheehan, C. F. Stevens, and S. Navlakha (2018). A neural data structure for novelty detection. Proc. Natl. Acad. Sci. USA, 115, 51:13093-13098.
[abstract] [pdf] [press release] [video] [nautilus] -
J. G. Fleischer, R. Schulte, H. H. Tsai, S. Tiyagi, A. Ibarra, M. N. Shokhirev, L. Huang, M. W. Hetzer, and S. Navlakha (2018). Predicting age from the transcriptome of human dermal fibroblasts. Genome Biol., 19, 1:221.
[abstract] [pdf] [code] [press release] [san diego union tribune] [tech times] [pew trusts] -
S. Navlakha, Z. Bar-Joseph, and A. L. Barth (2018). Network design and the brain. Trends Cogn. Sci., 22, 1:64-78.
[abstract] [pdf] -
A. Chandrasekhar, D. M. Gordon, and S. Navlakha (2018). A distributed algorithm to maintain and repair the trail networks of arboreal ants. Nature Sci. Rep., 8, 1:9297.
[abstract] [pdf] [bbc podcast] [wired] -
J. How and S. Navlakha (2018). Evidence for Rentian scaling of functional modules in diverse biological networks. Neural Comput., 30, 8:2210-2244.
[abstract] [pdf] -
P. Beukema et al. (2018). TrpM8-mediated somatosensation in mouse neocortex. J. Comp. Neurol., 526(9):144-1456.
[abstract] [pdf] [press release]
+2017
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S. Dasgupta, C. F. Stevens, and S. Navlakha (2017). A neural algorithm for a fundamental computing problem. Science, 358, 6364:793-796.
[abstract] [pdf] [biorxiv] [press art] [press release] [forbes] [scientific american] [the verge] [sd union tribune] [nautilus] [smithsonian] [seeker] [cbc news] [medium w/ code] [quanta magazine] [science in the classroom] [video] -
A. Conn, U. Pedmale, J. Chory, and S. Navlakha (2017). High-resolution laser scanning reveals plant architectures that reflect universal network design principles. Cell Syst., 5, 1:53-62.e3.
[abstract] [pdf] [code and data] [journal cover] [press art] [press release] [popular science] [sd union tribune] [video] -
A. Conn, U. Pedmale, J. Chory, C. F. Stevens, and S. Navlakha (2017). A statistical description of plant shoot architecture. Curr. Biol., 27, 14:2078-2088.e3.
[abstract] [pdf] [code and data] [press art] [press release] [video] -
J. Y. Suen and S. Navlakha (2017). Using inspiration from synaptic plasticity rules to optimize traffic flow in distributed engineered networks. Neural Comput., 29, 5:1204-1228.
[abstract] [pdf] [press release] [press art] [science daily] [daily mail] -
S. Navlakha (2017). Learning the structural vocabulary of a network. Neural Comput., 29, 2:287-312.
[abstract] [pdf] [journal cover]
+2016
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S. Singh, S. Rashid, S. Navlakha, and Z. Bar-Joseph (2016). Distributed gradient descent in bacterial food search. Proc. 20th Intl. Conf. on Research in Computational Molecular Biology (RECOMB).
[abstract] [pdf]
+2015
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S. Navlakha, A. L. Barth, and Z. Bar-Joseph (2015). Decreasing-rate pruning optimizes the construction of efficient and robust networks. PLoS Comput. Biol., 11(7): e1004347.
[abstract] [pdf] [press release] [fermat's library] -
S. Navlakha and Z. Bar-Joseph (2015). Distributed information processing in biological and computational systems. Commun. ACM, 58(1), 94–102.
[abstract] [pdf] [video] -
S. Navlakha, C. Faloutsos, Z. Bar-Joseph (2015). MassExodus: Modeling evolving networks in harsh environments. Proc. of the European Conf. on Machine Learning and Principles and Practices of Knowledge Discovery and Databases (ECML/PKDD). Journal version in Data Mining and Knowledge Discovery (DAMI), 29(5), 1-22.
[abstract] [pdf] -
S. Chandrasekaran, S. Navlakha, N. J. Audette, D. D. McCreary, J. Suhan, Z. Bar-Joseph, and A. L. Barth (2015). Unbiased, high-throughput electron microscopy analysis of experience-dependent synaptic changes in the neocortex. J. Neurosci., 35(50): 16450-16462.
[abstract] [pdf] [press art] [journal cover] [science daily]
+2014
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S. Navlakha, X. He, C. Faloutsos, Z. Bar-Joseph (2014). Topological properties of robust biological and computational networks. J. R. Soc. Interface, 11(96):20140283.
[abstract] [pdf] [science daily] [motherboard vice]
+2013
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S. Navlakha, P. Ahammad, E. W. Myers (2013). Unsupervised segmentation of noisy electron microscopy images using salient watersheds and region merging. BMC Bioinformatics, 14:294.
[abstract] [pdf] [code] -
S. Navlakha, J. Suhan, A. L. Barth, and Z. Bar-Joseph (2013). A high-throughput framework to detect synapses in electron microscopy images. Proc. 21st Intl. Conf. on Intelligent Systems for Molecular Biology/12th European Conference on Computational Biology (ISMB/ECCB), 2013. Journal version in Bioinformatics, 29(13):i9-17.
[abstract] [pdf] [code] [video of talk]
+2012
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S. Navlakha, A. Gitter, and Z. Bar-Joseph (2012). A network-based approach for predicting missing pathway interactions. PLoS Comput. Biol., 8(8):e1002640.
[abstract] [pdf] [code]
+2011
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S. Navlakha and Z. Bar-Joseph (2011). Algorithms in nature: the convergence of systems biology and computational thinking. Nature/EMBO Mol. Syst. Biol., 7:546.
[abstract] [pdf] [website] -
S. Navlakha and C. Kingsford (2011). Network archaeology: uncovering ancient networks from present-day interactions. PLoS Comput. Biol., 7(4):e1001119. Presented at the 6th RECOMB Systems Biology Satellite Conference, 2010.
[abstract] [pdf] [press art] [website] [mit tech review] [engadget] -
R. Patro, E. Sefer, J. Malin, G. Marçais, S. Navlakha, and C. Kingsford (2011). Parsimonious reconstruction of network evolution. Workshop on Algorithms in Bioinformatics (WABI). Journal version in Algorithms Mol. Biol., 7(1):25, 2012.
[abstract] [pdf] -
A. Thor et al. (2011). Link prediction for annotation graphs using graph summarization. Proc. 10th Intl. Semantic Web Conference (ISWC), 7031:714-729.
[abstract] [pdf]
+2010
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S. Navlakha and C. Kingsford (2010). The power of protein interaction networks for associating genes with diseases. Bioinformatics, 26(8):1057-63.
[abstract] [pdf] [website] [faculty of 1000 review] -
S. Navlakha and C. Kingsford (2010). Exploring biological network dynamics with ensembles of graph partitions. Proc. 15th Intl. Pacific Symposium on Biocomputing (PSB), 166-177.
[abstract] [pdf] -
G. Duggal, S. Navlakha, M. Girvan, and C. Kingsford (2010). Uncovering many views of biological networks using ensembles of graph partitions. Proc. 1st Intl. Workshop on Discovering, Summarizing, and Using Multiple Clusterings (KDD MultiClust), 2010:9.
[abstract] [pdf] [website] -
J. R. White, S. Navlakha, N. Nagarajan, M. Ghodsi, C. Kingsford, and M. Pop (2010). Alignment and clustering of phylogenetic markers - implications for microbial diversity studies. BMC Bioinformatics, 11:152.
[abstract] [pdf]
+2009
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S. Navlakha, J. White, N. Nagarajan, M. Pop, and C. Kingsford (2009). Finding biologically accurate clusterings in hierarchical tree decompositions using the variation of information. Proc. 14th Intl. Conf. on Research in Computational Molecular Biology (RECOMB), 5541:400–417. Journal version in J. Comput. Biol., 17(3):503–516, 2010.
[abstract] [pdf] [website] -
S. Navlakha, M.C. Schatz, and C. Kingsford (2009). Revealing biological modules via graph summarization. J. Comput. Biol., 16(2):253-64. Presented at the 5th RECOMB Systems Biology Satellite Conference, 2008.
[abstract] [pdf] [video of talk]
+2008
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S. Navlakha, R. Rastogi, and N. Shrivastava (2008). Graph
summarization with bounded error. Proc. 34th Intl. Conf. on Management of Data (SIGMOD), 419-432.
[abstract] [pdf] [press art] [code]
+2007
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H. Haidarian-Shahri, G. M. Namata, S. Navlakha, A. Deshpande, and N. Roussopoulos (2007). A graph-based approach to vehicle tracking in traffic camera video streams. Proc. 5th Intl. Workshop on Data Management for Sensor Networks (VLDB DMSN).
[abstract] [pdf]