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<!DOCTYPE html>
<html lang="en-us">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="theme" content="hugo-academic">
<meta name="generator" content="Hugo 0.55.6" />
<meta name="author" content="Sancar Adali">
<meta name="description" content="Scientist">
<link rel="stylesheet" href="/css/highlight.min.css">
<link rel="stylesheet" href="/css/bootstrap.min.css">
<link rel="stylesheet" href="/css/font-awesome.min.css">
<link rel="stylesheet" href="/css/academicons.min.css">
<link rel="stylesheet" href="//fonts.googleapis.com/css?family=Lato:400,700%7CMerriweather%7CRoboto+Mono">
<link rel="stylesheet" href="/css/hugo-academic.css">
<link rel="alternate" href="https://adalisan.github.io/index.xml" type="application/rss+xml" title="Sancar Adali - personal website">
<link rel="feed" href="https://adalisan.github.io/index.xml" type="application/rss+xml" title="Sancar Adali - personal website">
<link rel="icon" type="image/png" href="/img/icon.png">
<link rel="apple-touch-icon" type="image/png" href="/img/apple-touch-icon.png">
<link rel="canonical" href="https://adalisan.github.io/">
<title>Sancar Adali - personal website</title>
</head>
<body id="top" data-spy="scroll" data-target="#navbar-main" data-offset="71">
<nav class="navbar navbar-default navbar-fixed-top" id="navbar-main">
<div class="container">
<div class="navbar-header">
<button type="button" class="navbar-toggle collapsed" data-toggle="collapse"
data-target=".navbar-collapse" aria-expanded="false">
<span class="sr-only">Toggle navigation</span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="/">Sancar Adali - personal website</a>
</div>
<div class="collapse navbar-collapse">
<ul class="nav navbar-nav navbar-right">
<li class="nav-item">
<a href="/#about" data-target="#about">
<span>Home</span>
</a>
</li>
<li class="nav-item">
<a href="/#publications_selected" data-target="#publications_selected">
<span>Publications</span>
</a>
</li>
<li class="nav-item">
<a href="/#posts" data-target="#posts">
<span>Posts</span>
</a>
</li>
<li class="nav-item">
<a href="/#projects" data-target="#projects">
<span>Projects</span>
</a>
</li>
<li class="nav-item">
<a href="/#links" data-target="#links">
<span>Links</span>
</a>
</li>
<li class="nav-item">
<a href="/#contact" data-target="#contact">
<span>Contact</span>
</a>
</li>
</ul>
</div>
</div>
</nav>
<span id="homepage" style="display: none"></span>
<section id="about" class="home-section">
<div class="container">
<div class="row" itemprop="author" itemscope itemtype="http://schema.org/Person" itemref="person-email person-address">
<div class="col-xs-12 col-md-4">
<div id="profile">
<div class="portrait" style="background-image: url('https://adalisan.github.io/img/self.png');">
</div>
<meta itemprop="image" content="https://adalisan.github.io/img/self.png">
<div class="portrait-title">
<h2 itemprop="name">Sancar Adali</h2>
<h3 itemprop="jobTitle">Scientist</h3>
<h3 itemprop="worksFor" itemscope itemtype="http://schema.org/Organization">
<span itemprop="name">Raytheon-BBN Technologies</span>
</h3>
</div>
<link itemprop="url" href="https://adalisan.github.io/">
<ul class="social-icon" aria-hidden="true">
<li>
<a itemprop="sameAs" href="mailto:[email protected]" target="_blank">
<i class="fa fa-envelope big-icon"></i>
</a>
</li>
<li>
<a itemprop="sameAs" href="//twitter.com/adalisan" target="_blank">
<i class="fa fa-twitter big-icon"></i>
</a>
</li>
<li>
<a itemprop="sameAs" href="https://scholar.google.com/citations?user=hcZ700AAAAJ" target="_blank">
<i class="ai ai-google-scholar big-icon"></i>
</a>
</li>
<li>
<a itemprop="sameAs" href="//github.com/adalisan" target="_blank">
<i class="fa fa-github big-icon"></i>
</a>
</li>
<li>
<a itemprop="sameAs" href="//linkedin.com/pub/sancar-adali/3/a06/57b/" target="_blank">
<i class="fa fa-linkedin big-icon"></i>
</a>
</li>
</ul>
</div>
</div>
<div class="col-xs-12 col-md-8" itemprop="description">
<h1 id="biography">Biography</h1>
<p>My <a href="/resume.pdf">resume</a></p>
<p>I am a machine learning researcher at Raytheon BBN Technologies. I have worked on computer vision, bioinformatics and statistical analysis problems. I am interested in different approaches to learning from disparate modalities of data collected from different sensors. I am also interested in applications of data fusion to graphs and in general, learning from graph data.</p>
<p>I have a PhD in Applied Mathematics and Statistics from Johns Hopkins University. My PhD advisor was <a href="http://www.ams.jhu.edu/~priebe/" target="_blank">Carey Priebe</a>. We have worked on projects involving data in dissimilarity representation in addition to graphs. My dissertation research involved using dissimilarity data from disparate sources in order to solve learning problems. These type of problems are examples of multiview learning and my approach is a dissimilarity-centric method to find a common representation for the disparate data from different views. An extension of my approach allows the solution of the seeded graph matching problem, a variant of the graph matching problem where a portion of the vertex correspondences are known.</p>
<p>I also have a M.Sc in Engineering from Brown University. I worked in computer vision problems at <a href="http://vision.lems.brown.edu/" target="_blank">LEMS lab</a>.</p>
<p>Also at Johns Hopkins (2007-2009), I was a research assistant in the <a href="http://www.cvrgrid.org/" target="_blank">Cardiovascular Research Grid</a> project, developed code for predicting sudden cardiac death or VT/VF events for people implanted with introverter cardiovascular defibrillator(ICD) devices.</p>
<p>In addition to becoming familiar with different statistical methods, I also try to gain as much computing skills as I can. Learning R was an arduous journey and I learned to love R at the end by repeatedly failing and occasionally succeding. See the <a href="#software">software</a> section for the R packages I am working on.</p>
<p>I also work with Matlab and Python for scientific computing. Here’s my <a href="https://github.com/adalisan/" target="_blank">github page</a> by the way.</p>
<p>I like learning new things and solving practical data problems. My two career goals have been to improve my software development skills and learn about new mathematical methods and approaches. You could also say I am a typeface and design enthusiast.</p>
<p>Like the best of statistics/data science practitioners, I am adept at learning new computer languages.</p>
<p>I used C++ for a long time working with <a href="https://github.com/vxl/vxl" target="_blank">VXL</a> library. I picked up Python during my PhD which I would say is my (current) favorite computer language.</p>
<div class="row">
<div class="col-sm-5">
<h3>Interests</h3>
<ul class="ul-interests">
<li>Statistical Programming</li>
<li>Data analysis</li>
<li>Extensive R and Matlab programming, scientific programming in various computer languages (Python, C++)</li>
<li>Analyzing small-sample, high-dimensional data (bioinformatics and medical data)</li>
<li>Experience handling censored and missing data (survival analysis, missing data problem)</li>
<li>Manifold Learning and Dimensionality Reduction methods</li>
</ul>
</div>
<div class="col-sm-7">
<h3>Education</h3>
<ul class="ul-edu fa-ul">
<li>
<i class="fa-li fa fa-graduation-cap"></i>
<div class="description">
<p class="course">PhD in Applied Math and Statistics, 2014</p>
<p class="institution">Johns Hopkins University</p>
</div>
</li>
<li>
<i class="fa-li fa fa-graduation-cap"></i>
<div class="description">
<p class="course">M.Sc. in Applied Math and Statistics, 2008</p>
<p class="institution">Johns Hopkins University</p>
</div>
</li>
<li>
<i class="fa-li fa fa-graduation-cap"></i>
<div class="description">
<p class="course">M.Sc. in Engineering, 2005</p>
<p class="institution">Brown University</p>
</div>
</li>
<li>
<i class="fa-li fa fa-graduation-cap"></i>
<div class="description">
<p class="course">BSc in Electrical and Electronics Engineering, 2003</p>
<p class="institution">Bogazici University</p>
</div>
</li>
</ul>
</div>
</div>
</div>
</div>
</div>
</section>
<section id="publications_selected" class="home-section">
<div class="container">
<div class="row">
<div class="col-xs-12 col-md-4 section-heading">
<h1>Selected Publications</h1>
</div>
<div class="col-xs-12 col-md-8">
<div class="pub-list-item" itemscope itemtype="http://schema.org/CreativeWork">
<div class="row">
<div class="col-md-12">
<a href="https://adalisan.github.io/publication/sgm/">
<img src="/img/sgm_textart49.png" class="pub-banner"
itemprop="image">
</a>
</div>
<div class="col-md-12">
<h3 class="article-title" itemprop="name">
<a href="https://adalisan.github.io/publication/sgm/" itemprop="url">Seeded Graph Matching</a>
</h3>
<div class="pub-abstract" itemprop="text">
Given two graphs, the graph matching problem is to align the two vertex sets so as to minimize the number of adjacency disagreements between the two graphs. The seeded graph matching problem is the graph matching problem when we are first given a partial alignment that we are tasked with completing. In this paper, we modify the state-of-the-art approximate graph matching algorithm *FAQ* of Vogelstein et al. (2015) to make it a fast approximate seeded graph matching algorithm, adapt its applicability to include graphs with differently sized vertex sets, and extend the algorithm so as to provide, for each individual vertex, a nomination list of likely matches. We demonstrate the effectiveness of our algorithm via simulation and real data experiments; indeed, knowledge of even a few seeds can be extremely effective when our seeded graph matching algorithm is used to recover a naturally existing alignment that is only partially observed.
</div>
<div class="pub-authors" itemprop="author">
Donniell Fishkind, Sancar Adali, Heather Patsolic, Lingyao Meng, Vince Lyzinski, Carey E. Priebe
</div>
<div class="pub-publication">
PR
</div>
<div class="pub-links">
<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/publication/sgm/">
Details
</a>
<a class="btn btn-primary btn-outline btn-xs" href="https://doi.org/10.1016/j.patcog.2018.09.014">
PDF
</a>
<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/">
Slides
</a>
<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/">
Video
</a>
<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/">
Code
</a>
<a class="btn btn-primary btn-outline btn-xs" href="http://www.cis.jhu.edu/~parky/D3M/SGM/">
Dataset
</a>
<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/project/SGM">
Project
</a>
</div>
</div>
</div>
</div>
<div class="pub-list-item" itemscope itemtype="http://schema.org/CreativeWork">
<div class="row">
<div class="col-md-12">
<a href="https://adalisan.github.io/publication/fidcommjofc/">
<img src="/img/JOFC_textart.png" class="pub-banner"
itemprop="image">
</a>
</div>
<div class="col-md-12">
<h3 class="article-title" itemprop="name">
<a href="https://adalisan.github.io/publication/fidcommjofc/" itemprop="url">Fidelity-Commensurability Tradeoff in Joint Embedding of Disparate Dissimilarities</a>
</h3>
<div class="pub-abstract" itemprop="text">
In various data settings, it is necessary to compare observations from disparate data sources. We assume the data is in the dissimilarity representation (Pękalska and Duin, 2005) and investigate a joint embedding method (Priebe et al., 2013) that results in a commensurate representation of disparate dissimilarities. We further assume that there are “matched” observations from different conditions which can be considered to be highly similar, for the sake of inference. The joint embedding results in the joint optimization of fidelity (preservation of within-condition dissimilarities) and commensurability (preservation of between-condition dissimilarities between matched observations). We show that the tradeoff between these two criteria can be made explicit using weighted raw stress as the objective function for multidimensional scaling. In our investigations, we use a weight parameter, w, to control the tradeoff, and choose match detection as the inference task. Our results show weights that are optimal (with respect to the inference task) are different than equal weights for commensurability and fidelity and the proposed weighted embedding scheme provides significant improvements in statistical power.
</div>
<div class="pub-authors" itemprop="author">
Sancar Adali, Carey Priebe
</div>
<div class="pub-publication">
In <em>JoC</em>
</div>
<div class="pub-links">
<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/publication/fidcommjofc/">
Details
</a>
<a class="btn btn-primary btn-outline btn-xs" href="https://link.springer.com/article/10.1007/s00357-016-9214-6">
PDF
</a>
<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/">
Slides
</a>
<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/">
Video
</a>
<a class="btn btn-primary btn-outline btn-xs" href="https://github.com/adalisan/JOFC-MatchDetect">
Code
</a>
<a class="btn btn-primary btn-outline btn-xs" href="http://www.cis.jhu.edu/~parky/Data/Wiki/">
Dataset
</a>
<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/project/JOFC">
Project
</a>
</div>
</div>
</div>
</div>
<div class="pub-list-item" itemscope itemtype="http://schema.org/CreativeWork">
<div class="row">
<div class="col-md-12">
<a href="https://adalisan.github.io/publication/thesis/">
<img src="/img/JOFC_textart.png" class="pub-banner"
itemprop="image">
</a>
</div>
<div class="col-md-12">
<h3 class="article-title" itemprop="name">
<a href="https://adalisan.github.io/publication/thesis/" itemprop="url">Joint Optimization of Fidelity and Commensurability for Manifold Alignment and Graph Matching</a>
</h3>
<div class="pub-abstract" itemprop="text">
In this thesis, we investigate how to perform inference in settings in which the data consist of different modalities or views. For effective learning utilizing the information available, data fusion that considers all views of these multiview data settings is needed. We also require dimensionality reduction to address the problems associated with high dimensionality, or “the curse of dimensionality.” We are interested in the type of information that is available in the multiview data that is essential for the inference task. We also seek to determine the principles to be used throughout the dimensionality reduction and data fusion steps to provide acceptable task performance. Our research focuses on exploring how these queries and their solutions are relevant to particular data problems of interest.
</div>
<div class="pub-authors" itemprop="author">
Sancar Adali
</div>
<div class="pub-publication">
</div>
<div class="pub-links">
<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/publication/thesis/">
Details
</a>
<a class="btn btn-primary btn-outline btn-xs" href="https://jscholarship.library.jhu.edu/bitstream/handle/1774.2/37006/ADALI-DISSERTATION-2014.pdf?sequence=1&isAllowed=y">
PDF
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Slides
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Dataset
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<div class="pub-list-item" itemscope itemtype="http://schema.org/CreativeWork">
<div class="row">
<div class="col-md-12">
<h3 class="article-title" itemprop="name">
<a href="https://adalisan.github.io/publication/infracv/" itemprop="url">Learning spatiotemporal features for infrared action recognition with 3d convolutional neural networks</a>
</h3>
<div class="pub-abstract" itemprop="text">
Infrared (IR) imaging has the potential to enable more robust action recognition systems compared to visible spectrum cameras due to lower sensitivity to lighting conditions and appearance variability. While the action recognition task on videos collected from visible spectrum imaging has received much attention, action recognition in IR videos is significantly less explored. Our objective is to exploit imaging data in this modality for the action recognition task. In this work, we propose a novel two-stream 3D convolutional neural network (CNN) architecture by introducing the discriminative code layer and the corresponding discriminative code loss function. The proposed network processes IR image and the IR-based optical flow field sequences. We pretrain the 3D CNN model on the visible spectrum Sports-1M action dataset and finetune it on the Infrared Action Recognition (InfAR) dataset. To our best knowledge, this is the first application of the 3D CNN to action recognition in the IR domain. We conduct an elaborate analysis of different fusion schemes (weighted average, single and double-layer neural nets) applied to different 3D CNN outputs. Experimental results demonstrate that our approach can achieve state-of-the-art average precision (AP) performances on the InfAR dataset:(1) the proposed two-stream 3D CNN achieves the best reported 77.5% AP, and (2) our 3D CNN model applied to the optical flow fields achieves the best reported single stream 75.42% AP.
</div>
<div class="pub-authors" itemprop="author">
Zhoulin Jiang, Viktor Rozgic, Sancar Adali
</div>
<div class="pub-publication">
Proc. CVPR
</div>
<div class="pub-links">
<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/publication/infracv/">
Details
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<a class="btn btn-primary btn-outline btn-xs" href="https://doi.org/10.1016/j.patcog.2018.09.014">
PDF
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Slides
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Video
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Code
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<a class="btn btn-primary btn-outline btn-xs" href="http://www.cis.jhu.edu/~parky/SGM/">
Dataset
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<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/project/SGM">
Project
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<div class="pub-list-item" itemscope itemtype="http://schema.org/CreativeWork">
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<a href="https://adalisan.github.io/publication/sgmviajofc/">
<img src="/img/JOFC_textart.png" class="pub-banner"
itemprop="image">
</a>
</div>
<div class="col-md-12">
<h3 class="article-title" itemprop="name">
<a href="https://adalisan.github.io/publication/sgmviajofc/" itemprop="url">Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability</a>
</h3>
<div class="pub-abstract" itemprop="text">
We present a novel approximate graph matching algorithm that incorporates seeded data into the graph matching paradigm. Our Joint Optimization of Fidelity and Commensurability (JOFC) algorithm embeds two graphs into a common Euclidean space where the matching inference task can be performed. Through real and simulated data examples, we demonstrate the versatility of our algorithm in matching graphs with various characteristics--weightedness, directedness, loopiness, many-to-one and many-to-many matchings, and soft seedings.
</div>
<div class="pub-authors" itemprop="author">
Vince Lyzinski, Sancar Adali, Joshua T. Vogelstein, Youngser Park, Carey E. Priebe
</div>
<div class="pub-publication">
In <em>arxiv</em>
</div>
<div class="pub-links">
<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/publication/sgmviajofc/">
Details
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<a class="btn btn-primary btn-outline btn-xs" href="https://link.springer.com/article/10.1007/s00357-016-9214-6">
PDF
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Slides
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<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/">
Video
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<a class="btn btn-primary btn-outline btn-xs" href="https://github.com/adalisan/JOFC-GraphMatch">
Code
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Dataset
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Project
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</div>
</div>
</div>
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</div>
</section>
<section id="publications" class="home-section">
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<h1>Recent Publications</h1>
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<div class="col-xs-12 col-md-8">
<ul class="fa-ul">
<li itemscope itemtype="http://schema.org/CreativeWork">
<i class="fa-li fa fa-file-text-o pub-icon" aria-hidden="true"></i>
<span itemprop="name">Seeded Graph Matching</span>
<p>
<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/publication/sgm/">
Details
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<a class="btn btn-primary btn-outline btn-xs" href="https://doi.org/10.1016/j.patcog.2018.09.014">
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Code
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<a class="btn btn-primary btn-outline btn-xs" href="http://www.cis.jhu.edu/~parky/D3M/SGM/">
Dataset
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<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/project/SGM">
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</p>
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<i class="fa-li fa fa-file-text-o pub-icon" aria-hidden="true"></i>
<span itemprop="name">Fidelity-Commensurability Tradeoff in Joint Embedding of Disparate Dissimilarities</span>
<p>
<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/publication/fidcommjofc/">
Details
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<a class="btn btn-primary btn-outline btn-xs" href="https://link.springer.com/article/10.1007/s00357-016-9214-6">
PDF
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Slides
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<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/">
Video
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<a class="btn btn-primary btn-outline btn-xs" href="https://github.com/adalisan/JOFC-MatchDetect">
Code
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<a class="btn btn-primary btn-outline btn-xs" href="http://www.cis.jhu.edu/~parky/Data/Wiki/">
Dataset
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<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/project/JOFC">
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<i class="fa-li fa fa-file-text-o pub-icon" aria-hidden="true"></i>
<span itemprop="name">Joint Optimization of Fidelity and Commensurability for Manifold Alignment and Graph Matching</span>
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<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/publication/thesis/">
Details
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<a class="btn btn-primary btn-outline btn-xs" href="https://jscholarship.library.jhu.edu/bitstream/handle/1774.2/37006/ADALI-DISSERTATION-2014.pdf?sequence=1&isAllowed=y">
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Dataset
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<i class="fa-li fa fa-file-text-o pub-icon" aria-hidden="true"></i>
<span itemprop="name">Learning spatiotemporal features for infrared action recognition with 3d convolutional neural networks</span>
<p>
<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/publication/infracv/">
Details
</a>
<a class="btn btn-primary btn-outline btn-xs" href="https://doi.org/10.1016/j.patcog.2018.09.014">
PDF
</a>
<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/">
Slides
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<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/">
Video
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<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/">
Code
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<a class="btn btn-primary btn-outline btn-xs" href="http://www.cis.jhu.edu/~parky/SGM/">
Dataset
</a>
<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/project/SGM">
Project
</a>
</p>
</li>
<li itemscope itemtype="http://schema.org/CreativeWork">
<i class="fa-li fa fa-file-text-o pub-icon" aria-hidden="true"></i>
<span itemprop="name">Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability</span>
<p>
<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/publication/sgmviajofc/">
Details
</a>
<a class="btn btn-primary btn-outline btn-xs" href="https://link.springer.com/article/10.1007/s00357-016-9214-6">
PDF
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<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/">
Slides
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<a class="btn btn-primary btn-outline btn-xs" href="https://adalisan.github.io/">
Video
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