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Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 2

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Blog Post number 1

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publications

Tools, Methods, and Applications for Optophysiology in Neuroscience

Published in Frontiers in Molecular Neuroscience, 2013

The advent of optogenetics and genetically encoded photosensors has provided neuroscience researchers with a wealth of new tools and methods for examining and manipulating neuronal function in vivo. There exists now a wide range of experimentally validated protein tools capable of modifying cellular function, including light-gated ion channels, recombinant light-gated G protein-coupled receptors, and even neurotransmitter receptors modified with tethered photo-switchable ligands. A large number of genetically encoded protein sensors have also been developed to optically track cellular activity in real time, including membrane-voltage-sensitive fluorophores and fluorescent calcium and pH indicators. The development of techniques for controlled expression of these proteins has also increased their utility by allowing the study of specific populations of cells. Additionally, recent advances in optics technology have enabled both activation and observation of target proteins with high spatiotemporal fidelity. In combination, these methods have great potential in the study of neural circuits and networks, behavior, animal models of disease, as well as in high-throughput ex vivo studies. This review collects some of these new tools and methods and surveys several current and future applications of the evolving field of optophysiology.

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iPSC-derived neurons as a higher-throughput readout for autism: promises and pitfalls

Published in Trends in Molecular Medicine, 2014

The elucidation of disease etiologies and establishment of robust, scalable, high-throughput screening assays for autism spectrum disorders (ASDs) have been impeded by both inaccessibility of disease-relevant neuronal tissue and the genetic heterogeneity of the disorder. Neuronal cells derived from induced pluripotent stem cells (iPSCs) from autism patients may circumvent these obstacles and serve as relevant cell models. To date, derived cells are characterized and screened by assessing their neuronal phenotypes. These characterizations are often etiology-specific or lack reproducibility and stability. In this review, we present an overview of efforts to study iPSC-derived neurons as a model for autism, and we explore the plausibility of gene expression profiling as a reproducible and stable disease marker.

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All-optical Electrophysiology in Mammalian Neurons using engineered Microbial Rhodopsins

Published in Nature Methods, 2014

All-optical electrophysiology—spatially resolved simultaneous optical perturbation and measurement of membrane voltage—would open new vistas in neuroscience research. We evolved two archaerhodopsin-based voltage indicators, QuasAr1 and QuasAr2, which show improved brightness and voltage sensitivity, have microsecond response times and produce no photocurrent. We engineered a channelrhodopsin actuator, CheRiff, which shows high light sensitivity and rapid kinetics and is spectrally orthogonal to the QuasArs. A coexpression vector, Optopatch, enabled cross-talk–free genetically targeted all-optical electrophysiology. In cultured rat neurons, we combined Optopatch with patterned optical excitation to probe back-propagating action potentials (APs) in dendritic spines, synaptic transmission, subcellular microsecond-timescale details of AP propagation, and simultaneous firing of many neurons in a network. Optopatch measurements revealed homeostatic tuning of intrinsic excitability in human stem cell–derived neurons. In rat brain slices, Optopatch induced and reported APs and subthreshold events with high signal-to-noise ratios. The Optopatch platform enables high-throughput, spatially resolved electrophysiology without the use of conventional electrodes.

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Gene Expression Analysis in Fmr1 KO Mice Identifies an Immunological Signature in Brain Tissue and mGluR5-related Signaling in Primary Neuronal Cultures

Published in Molecular Case Studies, 2015

Background

Fragile X syndrome (FXS) is a neurodevelopmental disorder whose biochemical manifestations involve dysregulation of mGluR5-dependent pathways, which are widely modeled using cultured neurons. In vitro phenotypes in cultured neurons using standard morphological, functional, and chemical approaches have demonstrated considerable variability. Here, we study transcriptomes obtained in situ in the intact brain tissues of a murine model of FXS to see how they reflect the in vitro state. …

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Next-Generation Roadmap for Patient-Centered Genomics

Published in , 2016

In the era of precision medicine, understanding genetic variation has grown from a topic of research interest into a tangible source of therapeutic benefit for patients. As the list of confirmed links between genetic lesions and disease continues to grow, so does the list of actionable genetic diagnoses. …

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A Novel de novo Mutation in ATP1A3 and Childhood-onset Schizophrenia

Published in Molecular Case Studies, 2016

We describe a child with onset of command auditory hallucinations and behavioral regression at 6 yr of age in the context of longer standing selective mutism, aggression, and mild motor delays. His genetic evaluation included chromosomal microarray analysis and whole-exome sequencing. Sequencing revealed a previously unreported heterozygous de novo mutation c.385G>A in ATP1A3, predicted to result in a p.V129M amino acid change. This gene codes for a neuron-specific isoform of the catalytic α-subunit of the ATP-dependent transmembrane sodium–potassium pump. Heterozygous mutations in this gene have been reported as causing both sporadic and inherited forms of alternating hemiplegia of childhood and rapid-onset dystonia parkinsonism. We discuss the literature on phenotypes associated with known variants in ATP1A3, examine past functional studies of the role of ATP1A3 in neuronal function, and describe a novel clinical presentation associated with mutation of this gene.

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De novo ATP1A3 and compound heterozygous NLRP3 mutations in a child with autism spectrum disorder, episodic fatigue and somnolence, and muckle-wells syndrome

Published in Molecular Genetics and Metabolism Reports, 2018

Complex phenotypes may represent novel syndromes that are the composite interaction of several genetic and environmental factors. We describe an 9-year old male with high functioning autism spectrum disorder and Muckle-Wells syndrome who at age 5  years of age manifested perseverations that interfered with his functioning at home and at school. After age 6, he developed intermittent episodes of fatigue and somnolence lasting from hours to weeks that evolved over the course of months to more chronic hypersomnia. Whole exome sequencing showed three mutations in genes potentially involved in his clinical phenotype. The patient has a predicted pathogenic de novo heterozygous p.Ala681Thr mutation in the ATP1A3 gene (chr19:42480621C>T, GRCh37/hg19). Mutations in this gene are known to cause Alternating Hemiplegia of Childhood, Rapid Onset Dystonia Parkinsonism, and CAPOS syndrome, sometimes accompanied by autistic features. The patient also has compound heterozygosity for p.Arg490Lys/p.Val200Met mutations in the NLRP3 gene (chr1:247588214G>A and chr1:247587343G>A, respectively). NLRP3 mutations are associated in an autosomal dominant manner with clinically overlapping auto-inflammatory conditions including Muckle-Wells syndrome. The p.Arg490Lys is a known pathogenic mutation inherited from the patient’s father. The p.Val200Met mutation, inherited from his mother, is a variant of unknown significance (VUS). Whether the de novoATP1A3mutation is responsible for or plays a role in the patient’s episodes of fatigue and somnolence remains to be determined. The unprecedented combination of two NLRP3 mutations may be responsible for other aspects of his complex phenotype.

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Fair and Useful Cohort Selection

Published in Arxiv, 2020

As important decisions about the distribution of society’s resources become increasingly automated, it is essential to consider the measurement and enforcement of fairness in these decisions. In this work we build on the results of Dwork and Ilvento in [1], which laid the foundations for the study of fair algorithms under composition. In particular, we study the cohort selection problem, where we wish to use a fair classifier to select k candidates from an arbitrarily ordered set of size n > k, while preserving individual fairness and maximizing utility. We define a linear utility function to measure performance relative to the behavior of the original classifier. We develop a fair, utility-optimal O(n)-time cohort selection algorithm for the offline setting, and our primary result, a solution to the problem in the streaming setting that keeps no more than O(k) pending candidates at all time.

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Generator Surgery for Compressed Sensing

Published in NeurIPS 2020 Workshop Deep Inverse, 2020

Recent work has explored the use of generator networks with low latent dimension as signal priors for image recovery in compressed sensing. However, the recovery performance of such models is limited by high representation error. We introduce a method to reduce the representation error of such generator signal priors by cutting one or more initial blocks at test time and optimizing over the resulting higher-dimensional latent space. Experiments demonstrate significantly improved recovery for a variety of architectures. This approach also works well for out-of-training-distribution images and is competitive with other state-of-the-art methods. Our experiments show that test-time architectural modifications can greatly improve the recovery quality of generator signal priors for compressed sensing.

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Geometric Analysis of Uncertainty Sampling for Dense Neural Network Layer

Published in IEEE Signal Processing Letters, 2021

For model adaptation of fully connected neural network layers, we provide an information geometric and sample behavioral active learning uncertainty sampling objective analysis. We identify conditions under which several uncertainty-based methods have the same performance and show that such conditions are more likely to appear in the early stages of learning. We define riskier samples for adaptation, and demonstrate that, as the set of labeled samples increases, margin-based sampling outperforms other uncertainty sampling methods by preferentially selecting these risky samples. We support our derivations and illustrations with experiments using Meta-Dataset, a benchmark for few-shot learning. We compare uncertainty-based active learning objectives using features produced by SimpleCNAPS (a state-of-the-art few-shot classifier) as input for a fully-connected adaptation layer. Our results indicate that margin-based uncertainty sampling achieves similar performance as other uncertainty based sampling methods with fewer labelled samples as discussed in the novel geometric analysis.

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Inference in Network-Based Epidemiological Simulations with Probabilistic Programming

Published in AI for Public Health Workshop, ICLR, 2021

Accurate epidemiological models require parameter estimates that account for mobility patterns and social network structure. This work applies probabilistic programming to infer parameters in agent-based models. We represent mobility networks as degree-corrected stochastic block models and estimate their parameters from cell-phone co-location data. We use these networks in probabilistic programs to simulate the evolution of an epidemic, and condition on reported cases to infer disease transmission parameters. Our experiments demonstrate that the resulting models improve the accuracy-of-fit in multiple geographies relative to baselines that do not model network topology.

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teaching

Head Teaching Assistant, Database Design

Undergraduate course, Northeastern University, Khoury College of Computer Science, 2018

As head teaching assistant for CS3200 Database Design, I created and graded homeworks, held office hours and review sessions, helped create exam material, and helped organize my fellow teaching assistants.

Teaching Assistant, Algorithms

Undergraduate course, Northeastern University, Khoury College of Computer Science, 2019

As teaching assistant for CS5800 Algorithms, I helped create and grade homeworks, and held office hours and review sessions.

Research Mentor

Undergraduate research, Northeastern University, Khoury College of Computer Science, 2020

I mentored an undergraduate researcher in an independent project on semi-supervised classification. I taught him how to train neural networks in PyTorch, and we investigated image classification using label propagation in the latent space of a ResNet model. We examined the effect of different choices of distance metric/kernel function when forming the graph, and also compared the use of a simple affinity matrix against a normalized graph Laplacian matrix. We compared the performance of these models to a variety of baselines applied to a synthetic latent space constructed by dimensionality reduction on the input data.