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Uniform Manifold Approximation and Projection (UMAP) is a nonlinear dimensionality reduction technique developed by McIness et al. in 2018. Though its use is often called into question in the biological sciences, it has become a key visualization tool for many computational biologists wanting to tease apart important differences between cellular transcription or genetic profiles. While UMAP has been widely adopted as the state-of-the-art in nonlinear dimensionality reduction, it is often poorly understood by its users, leading to its misuse. The difficulty in understanding UMAP is in part due to the tremendous effort by the original authors in exposing mathematical ideas that ground UMAP as the first graph-based approach with theoretical understanding of its functionality. The authors use ideas from Riemannian geometry and algebraic topology to construct the theoretical framework on which UMAP is built. It is then good news that UMAP can be thoroughly understood from an entirely computational perspective.
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Essential genes are those which are crucial for survival of an organism in a given context. This post will introduce manifold and metric learning to characterize and classify essential genes from the chaos game representation of a genetic sequence.
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Published in Journal of Fluid Mechanics, 2017
Recommended citation: A. Hasnain, E. Segura, K. Alba. (2017) " Buoyant displacement flow of immiscible fluids in inclined pipes. " Journal of Fluid Mechanics. 824 (661-687). http://aqibhasnain.github.io/files/hasnain2017buoyantexp.pdf
Published in Physics of Fluids, 2017
Recommended citation: A. Hasnain, K. Alba. (2017) " Buoyant displacement flow of immiscible fluids in inclined ducts: A theoretical approach." Physics of Fluids. 29, 5 (052102). http://aqibhasnain.github.io/files/hasnain2017buoyanttheory.pdf
Published in Physical Review Fluids, 2019
Recommended citation: O. Oladasu, A. Hasnain, ..., K. Alba. (2019) Density-stable displacement flow of immiscible fluids in inclined pipes. "." Physical Review Fluids. 4, 4 (044007). http://aqibhasnain.github.io/files/oladasu2019density.pdf
Published in 2019 IEEE Biomedical Circuits and Systems Conference, Nara, Japan, 2019
Recommended citation: A. Hasnain, ..., E. Yeung. (2019). "A data-driven method for quantifying the impact of a genetic circuit on its host." 2019 IEEE Biomedical Circuits and Systems Conference. 1-4. http://aqibhasnain.github.io/files/hasnain2019impact.pdf
Published in 2019 IEEE Conference on Decision and Control, Nice, France, 2019
Recommended citation: N. Boddupalli, A. Hasnain, E. Yeung. (2019). "Koopman Operators for Generalized Persistence of Excitation Conditions for Nonlinear Systems." 2019 IEEE Conference on Decision and Control. 8106-8111. http://aqibhasnain.github.io/files/boddupalli2019koopman.pdf
Published in 2019 IEEE Conference on Decision and Control, Nice, France, 2019
Recommended citation: A. Hasnain, N. Boddupalli, E. Yeung. (2019). "Optimal reporter placement in sparsely measured genetic networks using the Koopman operator." 2019 IEEE Conference on Decision and Control. 19-24. http://aqibhasnain.github.io/files/hasnain2019optimal.pdf
Published in 2020 IEEE American Control Conference, Virtual Conference, 2020
Recommended citation: A. Hasnain, N. Boddupalli, E. Yeung. (2020). "Steady state programming of controlled nonlinear systems via deep dynamic mode decomposition." 2020 IEEE American Control Conference. arXiv preprint arXiv:1909.13385. http://aqibhasnain.github.io/files/hasnain2020steady.pdf
Published in 2020 IEEE American Control Conference, Virtual Conference, 2020
Recommended citation: S. Balakrishnan, A. Hasnain, ..., E. Yeung. (2020). "Prediction of fitness in bacteria with causal jump dynamic mode decomposition." 2020 IEEE American Control Conference. arXiv preprint arXiv:2006.12726. http://aqibhasnain.github.io/files/balakrishnan2020prediction.pdf
Published in Preprint, 2021
Recommended citation: S. Balakrishnan, A. Hasnain, ..., E. Yeung. (2021). "The Effect of Sensor Fusion on Data-Driven Learning of Koopman Operators ." arXiv preprint arXiv:2106.15091v.
Published in In Review, Nature Communications, 2022
Recommended citation: A. Hasnain, S. Balakrishnan, ..., E. Yeung. (2022). "Learning transcriptome dynamics for discovery of optimal genetic reporters of novel compounds." bioRXiv preprint https://doi.org/10.1101/2022.05.27.493781.
Published in submitted to Automatica, 2022
Recommended citation: S. Balakrishnan, A. Hasnain, ..., E. Yeung. (2021). " Data-Driven Observability Decomposition with Koopman Operators for Optimization of Output Functions of Nonlinear Systems." arXiv preprint arXiv:2210.09343.
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
Course, UC Santa Barbara, Department of Mechanical Engineering, 2017
TA’d for the three quarter-series of courses on thermodynamics and heat transfer. Main responsibilities were answering student’s questions, hosting office hours, and grading. I was also responsible for delivering two lectures, the first on thermodynamic lookup tables and how to perform single and double interpolation, and the second on radiative heat transfer analysis using view factors.
Course, UC Santa Barbara, Department of Mechanical Engineering, 2020
This course taught dynamical systems analysis of biological systems and design of synthetic biological circuits to both graduate and undergraduate students. Main responsibilities were answering student’s questions, hosting office hours, and grading. I was also responsible for delivering two lectures on recently developed techniques for gene regulatory network reconstruction e.g. dynamic mode decomposition.