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Dive into the research topics where Amirali Aghazadeh is active.

Publication


Featured researches published by Amirali Aghazadeh.


Science Advances | 2016

Universal microbial diagnostics using random DNA probes

Amirali Aghazadeh; Adam Y. Lin; Mona A. Sheikh; Allen L. Chen; Lisa M. Atkins; Coreen L. Johnson; Joseph F. Petrosino; Rebekah A. Drezek; Richard G. Baraniuk

A new diagnostic platform based on randomized DNA probes can screen for common human pathogens. Early identification of pathogens is essential for limiting development of therapy-resistant pathogens and mitigating infectious disease outbreaks. Most bacterial detection schemes use target-specific probes to differentiate pathogen species, creating time and cost inefficiencies in identifying newly discovered organisms. We present a novel universal microbial diagnostics (UMD) platform to screen for microbial organisms in an infectious sample, using a small number of random DNA probes that are agnostic to the target DNA sequences. Our platform leverages the theory of sparse signal recovery (compressive sensing) to identify the composition of a microbial sample that potentially contains novel or mutant species. We validated the UMD platform in vitro using five random probes to recover 11 pathogenic bacteria. We further demonstrated in silico that UMD can be generalized to screen for common human pathogens in different taxonomy levels. UMD’s unorthodox sensing approach opens the door to more efficient and universal molecular diagnostics.


bioRxiv | 2018

Systematic characterization of genome editing in primary T cells reveals proximal genomic insertions and enables machine learning prediction of CRISPR-Cas9 DNA repair outcomes

Ryan T. Leenay; Amirali Aghazadeh; Joseph Hiatt; David Tse; Judd Hulquist; Nevan J. Krogan; Zhenqin Wu; Alexander Marson; Andrew May; James Zou

The Streptococcus pyogenes Cas9 (SpCas9) nuclease has become a ubiquitous genome editing tool due to its ability to target almost any location in DNA and create a double-stranded break1,2. After DNA cleavage, the break is fixed with endogenous DNA repair machinery, either by non-templated mechanisms (e.g. non-homologous end joining (NHEJ) or microhomology-mediated end joining (MMEJ)), or homology directed repair (HDR) using a complementary template sequence3,4. Previous work has shown that the distribution of repair outcomes within a cell population is non-random and dependent on the targeted sequence, and only recent efforts have begun to investigate this further5–11. However, no systematic work to date has been validated in primary human cells5,7. Here, we report DNA repair outcomes from 1,521 unique genomic locations edited with SpCas9 ribonucleoprotein complexes (RNPs) in primary human CD4+ T cells isolated from multiple healthy blood donors. We used targeted deep sequencing to measure the frequency distribution of repair outcomes for each guide RNA and discovered distinct features that drive individual repair outcomes after SpCas9 cleavage. Predictive features were combined into a new machine learning model, CRISPR Repair OUTcome (SPROUT), that predicts the length and probability of nucleotide insertions and deletions with R2 greater than 0.5. Surprisingly, we also observed large insertions at more than 90% of targeted loci, albeit at a low frequency. The inserted sequences aligned to diverse regions in the genome, and are enriched for sequences that are physically proximal to the break site due to chromatin interactions. This suggests a new mechanism where sequences from three-dimensionally neighboring regions of the genome can be inserted during DNA repair after Cas9-induced DNA breaks. Together, these findings provide powerful new predictive tools for Cas9-dependent genome editing and reveal new outcomes that can result from genome editing in primary T cells.


Signal Processing | 2018

Insense: Incoherent Sensor Selection for Sparse Signals

Amirali Aghazadeh; Mohammad Golbabaee; Andrew S. Lan; Richard G. Baraniuk

Sensor selection refers to the problem of intelligently selecting a small subset of a collection of available sensors to reduce the sensing cost while preserving signal acquisition performance. The majority of sensor selection algorithms find the subset of sensors that best recovers an arbitrary signal from a number of linear measurements that is larger than the dimension of the signal. In this paper, we develop a new sensor selection algorithm for sparse (or near sparse) signals that finds a subset of sensors that best recovers such signals from a number of measurements that is much smaller than the dimension of the signal. Existing sensor selection algorithms cannot be applied in such situations. Our proposed Incoherent Sensor Selection (Insense) algorithm minimizes a coherence-based cost function that is adapted from recent results in sparse recovery theory. Using three datasets, including a real-world dataset on microbial diagnostics, we demonstrate the superior performance of Insense for sparse-signal sensor selection.


international conference on acoustics, speech, and signal processing | 2013

Adaptive step size selection for optimization via the ski rental problem

Amirali Aghazadeh; Ali Ayremlou; Daniel D. Calderon; Tom Goldstein; Raajen Patel; Divyanshu Vats; Richard G. Baraniuk

Optimization has been used extensively throughout signal processing in applications including sensor networks and sparsity based compressive sensing. One of the key challenges when implementing iterative optimization algorithms is to choose an appropriate step size for fast algorithms. We pose the problem of choosing step sizes as solving a ski rental problem, a popular class of problems from the computer science literature. This results in a novel algorithm for adaptive step size selection that is agnostic to the choice of the optimization algorithm. Our numerical results show the advantages of using adaptivity for step size selection.


Mechanical Systems and Signal Processing | 2019

Sparsity-based approaches for damage detection in plates

Debarshi Sen; Amirali Aghazadeh; Ali Mousavi; Satish Nagarajaiah; Richard G. Baraniuk


international conference on machine learning | 2018

MISSION: Ultra Large-Scale Feature Selection using Count-Sketches.

Amirali Aghazadeh; Ryan Spring; Daniel LeJeune; Gautam Dasarathy; Anshumali Shrivastava; Richard G. Baraniuk


international conference on machine learning | 2018

Ultra-Large Scale Feature Selection using Count-Sketches

Amirali Aghazadeh; Ryan Spring; Gautam Dasarathy; Anshumali Shrivastava; Richard G. Baraniuk


international conference on acoustics, speech, and signal processing | 2018

Insense: Incoherent Sensor Selection for Sparse Signals.

Amirali Aghazadeh; Mohammad Golbabaee; Andrew S. Lan; Richard G. Baraniuk


international joint conference on artificial intelligence | 2017

RHash: Robust Hashing via ℓ∞-norm distortion

Amirali Aghazadeh; Andrew S. Lan; Anshumali Shrivastava; Richard G. Baraniuk


international joint conference on artificial intelligence | 2017

RHash: Robust Hashing via L_infinity-norm Distortion

Amirali Aghazadeh; Andrew S. Lan; Anshumali Shrivastava; Richard G. Baraniuk

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Gautam Dasarathy

University of Wisconsin-Madison

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Mohammad Golbabaee

École Polytechnique Fédérale de Lausanne

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