-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathindex.html
462 lines (364 loc) · 29.9 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
<!DOCTYPE html>
<html>
<head>
<meta http-equiv="refresh" content="0;url=http://dyerlab.gatech.edu/" />
<!--
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Eva Dyer</title>
<link href="assets/css/bootstrap.min.css" rel="stylesheet">
<link href="assets/css/main.css" rel="stylesheet">
<link href="assets/font-awesome/css/font-awesome.min.css" rel="stylesheet" type="text/css">
<link rel="shortcut icon" href="assets/ocp_main3.ico">
-->
</head>
<!--
<body id="page-top">
<div class="header-navbar">
<h1 class="header-navbar-logo">Making sense of big neural data...</h1>
<div class="header-navbar-hamburger"></div>
<ul class="header-navbar-items">
<li class="header-navbar-item"><a href="#about" class="header-navbar-link">About</a></li>
<li class="header-navbar-item"><a href="#projects" class="header-navbar-link">Projects</a></li>
<li class="header-navbar-item"><a href="#papers" class="header-navbar-link">Papers</a></li>
<li class="header-navbar-item"><a href="#code" class="header-navbar-link">Code</a></li>
</ul>
</div>
<div class="header">
<div class="header-grad"></div>
<div class="header-pcb"></div>
<div class="header-bg"></div>
<div class="header-text">
<h3 class="header-text-headline">Making sense of big neural data...
</h3>
</div>
</div>
<div id="about" class="about">
<div class="container">
<div class="row">
<div class="col-lg-12 text-center">
<h1 class="descr-title info-heading">About me...</h1>
<p class="lead info-para">
[ <a href="http://dyerlab.gatech.edu/wp-content/uploads/sites/630/2017/09/Dyer_CV.pdf" style="color: rgb(139,0,139)"> cv</a> ] [ <a href="https://scholar.google.com/citations?user=Sb_jcHcAAAAJ&hl=en" style="color: rgb(139,0,139)"> scholar</a> ] [ <a href="http://www.github.com/evadyer" style="color: rgb(139,0,139)"> github</a> ] [ <a href="http://docs.neurodata.io/xbrain" style="color: rgb(139,0,139)"> xbrain web </a>]
<br>I am currently an Assistant Professor in the Department of Biomedical Engineering at the Georgia Institute of Technology and Emory University. At Georgia Tech, I run the Neural Data Science (NerDS) Lab, focused on developing new machine learning and data science approaches for analyzing and making sense of neural datasets. Before this, I was a Research Scientist in the <a href="http://kordinglab.com/" style="color: rgb(139,0,139)">Bayesian Behavior Lab</a> at Northwestern University, where I worked with Konrad Kording (now at UPenn). I completed my Ph.D in Electrical & Computer Engineering at Rice University, under the supervision of <a href="http://web.ece.rice.edu/richb/" style="color: rgb(139,0,139)">Richard Baraniuk</a> in 2014. While at Rice, I co-developed the edX MOOC <a href="https://www.edx.org/course/discrete-time-signals-systems-part-1-ricex-elec301-1x" style="color: rgb(139,0,139)">Discrete-Time Signals and Systems</a>. Before that, I received a BSEE from the University of Miami, where I completed a double major in Audio Engineering & Physics while working as an assistant sound designer for the documentary <a href="http://www.onewater.org/movie" style="color: rgb(139,0,139)"> One Water: A collaborative effort for a sustainable future</a>.
</p>
</div>
</div>
</div>
</div>
<div id="projects" class="faq">
<h2 class="heading-faq">Projects</h2>
<p class="faq-additional-questions"><Want to get in touch? Send me an email at evadyer{at}gatech{dot}edu</a></p>
<div class="faq-column">
<div class="faq-box">
<h3>High-throughput methods for quantifying neuroanatomy</h3>
<p>With a team of researchers at Argonne National Laboratory (led by Bobby Kasthuri), I have developed the first open data analysis pipeline to convert X-ray image volumes to dense micron-scale brain maps of cell bodies, blood vessels, and myelinated axons.
Our results demonstrate that X-ray sources can be used with image parsing techniques to rapidly quantify neuroanatomy at the mesoscale. We are currently exploring ways in which our techniques can be combined with electron microscopy to obtain multi-modal brain maps.
</p>
<h4><strong>Related publications:</strong></h4>
<ul>
<li>
<p>Dyer et al., 2016. Quantifying mesoscale neuroanatomy using X-ray microtomography (<a href="http://docs.neurodata.io/xbrain" style="color: rgb(0,206,209)">Web</a>, <a href="http://arxiv.org/abs/1604.03629" style="color: rgb(0,206,209)">Paper</a>)</p>
</li></ul>
</div>
<div class="faq-box">
<h3>Large-scale optimization</h3>
<p>Optimization problems are ubiquitous in machine learning and neuroscience. I am currently working on two projects in this domain.
First, with Azalia Mirhoseini and Farinaz Koushanfar, I am developing frameworks for data-aware distributed learning. In a recent paper, we showed how the low rank and multi-subspace structure of large datasets can be leveraged to accelerate a broad class of iterative optimization methods.
Second, with Mohammad Gheshlaghi Azar and Konrad Kording, I am developing approaches for non-convex and black-box optimization. In a recent paper at UAI 2016, we introduced a provable black-box approach for global optimization that learns a convex envelope from samples of the function.
<h4><strong>Related publications:</strong></h4>
<ul>
<li><p>A. Mirhoseini, <strong>E.L. Dyer</strong>, E. Songhori, R.G. Baraniuk, and F. Koushanfar, <em>RankMap: A platform-aware framework for distributed learning from dense datasets</em>, accepted to IEEE Trans. on Neural Networks and Learning Systems, 2016. (<a href="http://arxiv.org/abs/1503.08169" style="color: rgb(0,206,209)">Paper</a>, <a href="https://github.com/azalia/RankMap" style="color: rgb(0,206,209)">Code</a>)</p>
<li><p>M Gheshlaghi Azar, <strong>E.L. Dyer</strong>, Konrad Kording, <em>Convex Relaxation Regression (CoRR): Black-box optimization of a smooth function by learning its convex envelope</em>, Proc. of the Conference on Uncertainity in Artificial Intelligence, 2016. (<a href="https://arxiv.org/abs/1602.02191" style="color: rgb(0,206,209)">Paper</a>) </p></li>
</ul>
</div>
</div>
<div class="faq-column">
<div class="faq-box">
<h3>Low-dimensional signal models </h3>
<p> Unions of subspaces (UoS) are a generalization of single subspace models that approximate data points as living on multiple subspaces, rather than assuming a global low-dimensional model (as in PCA). Modeling data with mixtures of subspaces provides a more compact and simple representation of the data, and thus can lead to better partitioning (clustering) of the data and help in compression and denoising.
<h4><strong>Related publications:</strong></h4>
<ul>
<li><p><strong>E.L. Dyer</strong>, A.C. Sankaranarayanan, and R.G. Baraniuk, <em>Greedy feature selection for subspace clustering</em>, The Journal of Machine Learning Research 14 (1), 2487-2517, September, 2013. (<a href="https://www.dropbox.com/s/ll13utoiezvnbc6/Dyer_JMLR13.pdf?dl=0" style="color: rgb(0,206,209)">Paper</a>)</p></li>
<li><p><strong>E.L. Dyer</strong>, T.A. Goldstein, R. Patel, K.P. Körding, and R.G. Baraniuk, <em>Sparse self-expressive decompositions for dimensionality reduction and clustering</em> (<a href="http://arxiv.org/pdf/1505.00824v1.pdf" style="color: rgb(0,206,209)">Paper</a>)</p></li>
<li><p>R.J. Patel, T.A. Goldstein, <strong>E.L. Dyer</strong>, A. Mirhoseini, and R.G. Baraniuk, <em>Deterministic column sampling for low rank approximation: Nystrom vs. Incomplete Cholesky Decomposition</em>, SIAM Data Mining (SDM) Conference, May 2016. (<a href="https://www.dropbox.com/s/o4wl96k2hdxxuhf/Patel_SDM2016.pdf?dl=0" style="color: rgb(0,206,209)">Paper</a>, <a href="https://bitbucket.org/rjp2/oasis/" style="color: rgb(0,206,209)">Code</a>)</p></li>
</ul>
</div>
<div class="faq-box">
<h3>Analyzing the activity of neuronal populations</h3>
<p>
Advances in monitoring the activity of large populations of neurons has provided new insights into the collective dynamics of neurons. I am working on methods that learn and exploit low-dimensional structure in neural activity for decoding, denoising, and deconvolution.
</p>
<h4><strong>Related publications:</strong></h4>
<ul>
<li><p><strong>E.L. Dyer</strong>, M. Azar, H.L. Fernandes, M. Perich, L.E. Miller, and K.P. Körding: <em>A cryptography-based approach to brain decoding</em> (<a href="http://kordinglab.com/DAD/" style="color: rgb(0,206,209)">Web</a>, <a href="http://dx.doi.org/10.1101/080861" style="color: rgb(0,206,209)">Paper</a>)</p></li>
<li><p><strong>E.L. Dyer</strong>, C. Studer, J.T. Robinson, and R.G Baraniuk, <em>A robust and efficient method to recover neural events from noisy and corrupted data</em>, IEEE EMBS NER Conference, 2013. (<a href="https://www.dropbox.com/s/9bse7aly4bqh2d0/Dyer_EMBS2014.pdf?dl=0" style="color: rgb(0,206,209)">Paper</a>, <a href="https://github.com/KordingLab/nerds" style="color: rgb(0,206,209)">Code</a>)</p></li>
</div>
</div>
</div>
<div id="papers" class="schedule">
<div class="container">
<div class="row">
<h2 class="section-apply-heading text-center">Papers</h2>
<div class="col-lg-8 col-lg-offset-2">
<h3><a id="In_the_pipeline_0"></a>In the pipeline…</h3>
<ul>
<p><li> X. Yang, V. De Andrade, F. De Carlo, <strong>E.L. Dyer</strong>, N. Kasthuri, D. Gürsoy, <em>Seeing the structure of objects at the nanoscale through low dose computed X-ray tomography</em>, in review, 2017.</p>
</li>
<li>
<p><strong>E.L. Dyer</strong>, W.G. Roncal, J.A. Prasad, H.L. Fernandes, D. Gürsoy, V. De Andrade, K. Fezzaa, X. Xiao, J.T. Vogelstein, C. Jacobsen, K.P. Körding and N. Kasthuri, <em>Quantifying mesoscale neuroanatomy using X-ray microtomography</em>, in review at eNeuro, 2017. (<a href="https://arxiv.org/abs/1604.03629" style="color: rgb(0,206,209)">Paper</a>, <a href="http://github.com/neurodata/xbrain/tree/master/code" style="color: rgb(0,206,209)">Code</a>, <a href="http://github.com/neurodata/xbrain/tree/master/data" style="color: rgb(0,206,209)">Data</a>)</p>
</li>
<li>
<p><strong>E.L. Dyer</strong>, M. Azar, H.L. Fernandes, M. Perich, L.E. Miller, and K.P. Körding: <em>A cryptography-based approach to brain decoding</em>, to appear in Nature Biomedical Engineering, 2016. (<a href="http://dx.doi.org/10.1101/080861" style="color: rgb(0,206,209)">Paper</a>, <a href="http://kordinglab.com/DAD/" style="color: rgb(0,206,209)">Code</a>)</p>
</li>
<li>
<p><strong>E.L. Dyer</strong>, W.G. Roncal, D. Gürsoy, K.P. Körding, N. Kasthuri: <em>From sample to knowledge: Towards an integrated approach for neuroscience discovery</em>, arXiv:1604.03199 [q-bio.QM], 2016. (<a href="https://arxiv.org/pdf/1604.03199" style="color: rgb(0,206,209)">Paper</a>)</p>
</li>
<li>
<p><strong>E.L. Dyer</strong>, T.A. Goldstein, R.J. Patel, K.P. Körding, and R.G. Baraniuk: <em>Sparse self-expressive decompositions for matrix approximation and clustering</em>, arXiv:1505.00824 [cs.IT], 2015. (<a href="http://arxiv.org/abs/1505.00824" style="color: rgb(0,206,209)">Paper</a>, <a href="http://www.github.com/KordingLab/seed" style="color: rgb(0,206,209)">Code</a>)</p>
</li>
</ul>
<h3><a id="Publications_12"></a>Publications</h3>
<ul>
<li>
<p>A. Mirhoseini, <strong>E.L. Dyer</strong>, E. Songhori, R.G. Baraniuk, and F. Koushanfar, <em>RankMap: A platform-aware framework for distributed learning from dense datasets</em>, IEEE Transactions on Neural Networks and Learning Systems, 2017. (<a href="http://arxiv.org/abs/1503.08169" style="color: rgb(0,206,209)">Paper</a>, <a href="https://github.com/azalia/RankMap" style="color: rgb(0,206,209)">Code</a>)</p>
</li>
<li>
<p>M. Azar, <strong>E.L. Dyer</strong>, and K.P. Körding, <em>Convex relaxation regression: Black-Box optimization of smooth functions by learning their convex envelopes</em>, Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), June 2016. (<a href="http://arxiv.org/abs/1602.02191" style="color: rgb(0,206,209)">Paper</a>, <a href="https://dl.dropboxusercontent.com/u/1260288/Posters/Dyer_UAI2016.png" style="color: rgb(0,206,209)">Poster</a>, <a href="https://dl.dropboxusercontent.com/u/1260288/Talks/Dyer_UAI2016.pdf" style="color: rgb(0,206,209)">Slides</a>)</p>
</li>
<li>
<p>R.J. Patel, T.A. Goldstein, <strong>E.L. Dyer</strong>, A. Mirhoseini, and R.G. Baraniuk, <em>Deterministic column sampling for low rank approximation: Nystrom vs. Incomplete Cholesky Decomposition</em>, SIAM Data Mining (SDM) Conference, May 2016. (<a href="http://dx.doi.org/10.1137/1.9781611974348.67" style="color: rgb(0,206,209)">Paper</a>, <a href="https://bitbucket.org/rjp2/oasis/" style="color: rgb(0,206,209)">Code</a>)</p>
</li>
<li>
<p><strong>E.L. Dyer</strong>, A.C. Sankaranarayanan, and R.G. Baraniuk, <em>Greedy feature selection for subspace clustering</em>, The Journal of Machine Learning Research 14 (1), 2487-2517, September, 2013. (<a href="http://www.jmlr.org/papers/v14/dyer13a.html" style="color: rgb(0,206,209)">Paper</a>)</p>
</li>
<li>
<p><strong>E.L. Dyer</strong>, C. Studer, J.T. Robinson, and R.G Baraniuk, <em>A robust and efficient method to recover neural events from noisy and corrupted data</em>, IEEE EMBS Neural Engineering (NER) Conference, 2013. (<a href="http://dx.doi.org/10.1109/NER.2013.6696004" style="color: rgb(0,206,209)">Paper</a>, <a href="https://github.com/KordingLab/nerds" style="color: rgb(0,206,209)">Code</a>)</p>
</li>
<li>
<p><strong>E.L. Dyer</strong>, C. Studer, and R.G Baraniuk, <em>Subspace clustering with dense representations</em>, IEEE International Conf. on Signal Processing (ICASSP) 2013 Proceedings, Vancouver, BC, 2013. (<a href="https://www.dropbox.com/s/7yr34ifdhbbp4h7/Dyer_ICASSP2013.pdf?dl=0" style="color: rgb(0,206,209)">Paper</a>)</p>
</li>
<li>
<p><strong>E.L. Dyer</strong>, M. Majzoobi, F. Koushanfar, <em>Hybrid modeling of non-stationary process variations</em>, IEEE/ACM Design and Automation Conference (DAC) 2011 Proceedings, San Diego, CA, 2011. (<a href="https://www.dropbox.com/s/ez16ijczply4fvq/Dyer_DAC2011.pdf?dl=0" style="color: rgb(0,206,209)">Paper</a>)</p>
</li>
<li>
<p>M. Majzoobi, <strong>E.L. Dyer</strong>, A. Enably, and F. Koushanfar, <em>Rapid FPGA characterization using clock synthesis and signal sparsity</em>, IEEE International Test Conference (ITC) 2010 Proceedings, Austin, TX, November 2010. (<a href="https://www.dropbox.com/s/0llytz2o5iw47mq/Majzoobi_ITC2010.pdf?dl=0" style="color: rgb(0,206,209)">Paper</a>)</p>
</li>
<li>
<p><strong>E.L. Dyer</strong>, M.F. Duarte, D.H. Johnson, and R.G. Baraniuk, <em>Recovering spikes from noisy neuronal calcium signals via structured sparse approximation</em>, Lecture Notes in Computer Science, LVA/ICA 2010, Volume 6365/2010, 604-611. (<a href="https://www.dropbox.com/s/8qy8n8yeozsyqxf/Dyer_LVA2010.pdf?dl=0" style="color: rgb(0,206,209)">Paper</a>)</p>
</li>
<li>
<p>G. Fischer, <strong>E.L. Dyer</strong>, C. Csoma, A. Deguet, and G. Fichtinger, <em>Validation system for MR image overlay and other needle insertion techniques</em>, Medicine Meets Virtual Reality 15- in vivo, in vitro, in silico: Designing the Next in Medicine, IOS Press, 2007. (<a href="https://www.dropbox.com/s/qkaaae97gafxug5/Fischer_MMVR15.pdf?dl=0" style="color: rgb(0,206,209)">Paper</a>)</p>
</li>
</ul>
<h3><a id="Abstracts_33"></a>Abstracts</h3>
<ul>
<li>
<p>A. Bleckert, A. Bodor, J. Borseth, D. Brittain, D. Bumbarger, D. Castelli, <strong>E.L. Dyer</strong>, T. Keenan,
Y. Li, F. Long, J. Perkins, D. Reid, D. Sullivan, M. Takeno, R. Torres, D. Williams, C. Reid, N. da
Costa: Linking functional and anatomical circuit connectivity using fast parallelized TEM imaging,
Society for Neuroscience Annual Meeting (SFN), November 2016.</p></li>
<li>
<p>R. Vescovi, E. Miqueles, D. Gursoy, V. De Andrade, <strong>E.L. Dyer</strong>, K. Kording, M. Cardoso, F. De Carlo, C. Jacobsen, N. Kasthuri. <em>TOMOSAIC: Towards Terabyte Tomography</em>, International X-ray Microscopy (XRM) Conference, 2016.</p>
</li>
<li>
<p><strong>E.L. Dyer</strong>, H.L. Fernandes, X. Xiao, W. Gray Roncal, J.T. Vogelstein, C. Jacobsen, K.P. Körding and N. Kasthuri, <em>Quantifying mesoscale neuroanatomy using X-ray microtomography</em>, presented at the Society for Neuroscience (SFN) Annual Meeting in October 2015 and the Annual Statistical Analysis of Neural Data (SAND) Meeting in May 2015.(<a href="https://www.dropbox.com/s/dcp0gp8bttgf3bz/Dyer_SFN2015.pdf?dl=0" style="color: rgb(0,206,209)">Abstract</a>)</p>
</li>
<li>
<p><strong>E.L. Dyer</strong>, T.A. Goldstein, R. Patel, K.P. Körding, and R.G. Baraniuk, <em>Sparse Self-Expressive Decompositions for Dimensionality Reduction and Clustering</em>, Signal Processing with Adaptive Sparse Structured Representations (SPARS), July, 2015. (<a href="https://www.dropbox.com/s/vle719pfb6os1cy/Dyer_SPARS2015.pdf?dl=0" style="color: rgb(0,206,209)">Abstract</a>)</p>
</li>
<li>
<p><strong>E.L. Dyer</strong>, D.B. Murphy, R.G. Baraniuk, and J.T Robinson, <em>Compressive neural circuit reconstruction using patterned optical stimulation</em>, Society for Neuroscience (SFN) Annual Meeting, 2013.</p>
</li>
<li>
<p><strong>E.L. Dyer</strong>, C. Studer, and R.G Baraniuk, <em>Subspace clustering with dense representations</em>, Signal Processing with Adaptive Sparse Structured Representations (SPARS) 2013 Proceedings, Lausanne, Switzerland, 2013.</p>
</li>
<li>
<p><strong>E.L. Dyer</strong>, U. Rutishauser, and R.G Baraniuk, <em>Group sparse coding with collections of winner-take-all (WTA) circuits</em>, Organization for Computational Neurosciences (OCNS), BMC Neuroscience, 2012.</p>
</li>
<li>
<p><strong>E.L. Dyer</strong>, A.C. Sankaranarayanan, and R.G. Baraniuk, <em>Learning hybrid linear models via sparse recovery</em>, Signal Processing with Adaptive Sparse Structured Representations (SPARS) 2011 Proceedings.</p>
</li>
<li>
<p><strong>E.L. Dyer</strong>, D.H. Johnson, and R.G. Baraniuk, <em>Learning modular representations from global sparse coding networks</em>, Organization for Computational Neurosciences (OCNS), BMC Neuroscience 2010, 11(1): P131.</p>
</li>
<li>
<p><strong>E.L. Dyer</strong>, D.H. Johnson, and R.G. Baraniuk, <em>Sparse coding in modular networks</em>, Computational and systems neuroscience (COSYNE), 2010.</p>
</li>
<li>
<p><strong>E.L. Dyer</strong>, D.H. Johnson, and R.G Baraniuk, <em>Sparse coding with population sketches</em>, Organization for Computational Neurosciences (OCNS), BMC Neuroscience 2009, 10(1):P132.</p>
</li>
</ul>
<h3><a id="Theses_56"></a>Theses</h3>
<ul>
<li>New Theory and Methods for Signals in Unions of Subspaces, Ph.D. Thesis, Dept. of Electrical and Computer Engineering, Rice University, September, 2014.</li>
<li>Endogenous Sparse Recovery, M.S. Thesis, Dept. of Electrical and Computer Engineering, Rice University, October, 2011.</li>
</ul>
</div>
</div>
</div>
</div>
<div id="code" class="schedule">
<div class="container">
<div class="row">
<h2 class="section-apply-heading text-center">Code</h2>
<div class="col-lg-8 col-lg-offset-2">
<ul>
<li>XBRAIN: X-ray Brain Reconstruction, Analytics, and Inference for Neuranatomy (<a href="http://github.com/neurodata/xbrain/tree/master/code" style="color: rgb(0,206,209)">code</a>, <a href="http://github.com/neurodata/xbrain/tree/master/data" style="color: rgb(0,206,209)">data</a>)</li>
<li>RankMap API for Distributed Learning (<a href="https://github.com/azalia/RankMap" style="color: rgb(0,206,209)">Code</a>, <a href="http://arxiv.org/abs/1503.08169" style="color: rgb(0,206,209)">Paper</a>)</li>
<li>Self-Expressive Decomposition (SEED) (<a href="https://github.com/KordingLab/SEED" style="color: rgb(0,206,209)">Code</a>, <a href="http://arxiv.org/abs/1505.00824" style="color: rgb(0,206,209)">Paper</a>)</li>
<li>Accelerated Sequential Incoherence Sampling (oASIS) (<a href="https://bitbucket.org/rjp2/oasis/" style="color: rgb(0,206,209)">Code</a>, <a href="http://arxiv.org/abs/1505.05208" style="color: rgb(0,206,209)">Paper</a>)</li>
<li>Neural Event Recovery and Detection via Sparsity (NERDS) (<a href="https://github.com/KordingLab/nerds" style="color: rgb(0,206,209)">Code</a>, <a href="http://www.ece.rice.edu/~eld1/pubs/Dyer_ICASSP2013.pdf" style="color: rgb(0,206,209)">Paper</a>)</li>
<li>Rapid Characterization of FPGAs with Matrix Completion (<a href="http://www.ece.rice.edu/~eld1/software/RapidFPGA.zip" style="color: rgb(0,206,209)">Code</a>)</li>
</ul>
<hr>
</div>
</div>
</div>
</div>
<div class="page-footer">
<div class="container">
<div class="row" id="footer-text">
<div class="col-lg-10 col-lg-offset-1 text-center">
<p>Want to get in touch? Contact <a href="mailto:[email protected]">[email protected]</a>!</p>
<p>Copyright © Eva Dyer 2016</p>
</div>
</div>
</div>
</div>
<script src="/assets/js/jquery.js"></script>
<script>
$(function() {
function isMobile() {
return /Android|webOS|iPhone|iPad|iPod|BlackBerry|IEMobile|Opera Mini/i.test(navigator.userAgent);
}
// http://paulirish.com/2011/requestanimationframe-for-smart-animating/
// http://my.opera.com/emoller/blog/2011/12/20/requestanimationframe-for-smart-er-animating
// requestAnimationFrame polyfill by Erik Möller. fixes from Paul Irish and Tino Zijdel
// MIT license
(function() {
var lastTime = 0;
var vendors = ['ms', 'moz', 'webkit', 'o'];
for(var x = 0; x < vendors.length && !window.requestAnimationFrame; ++x) {
window.requestAnimationFrame = window[vendors[x]+'RequestAnimationFrame'];
window.cancelAnimationFrame = window[vendors[x]+'CancelAnimationFrame'] ||
window[vendors[x]+'CancelRequestAnimationFrame'];
}
if (!window.requestAnimationFrame)
window.requestAnimationFrame = function(callback, element) {
var currTime = new Date().getTime();
var timeToCall = Math.max(0, 16 - (currTime - lastTime));
var id = window.setTimeout(function() { callback(currTime + timeToCall); },
timeToCall);
lastTime = currTime + timeToCall;
return id;
};
if (!window.cancelAnimationFrame)
window.cancelAnimationFrame = function(id) {
clearTimeout(id);
};
}());
if(!isMobile()) {
(function(window, document, undefined) {
$(document).ready(function() {
var running = false,
$header = $('.header-navbar');
function updateHeaderScroll() {
if(running) {
return;
}
running = true;
window.requestAnimationFrame(function() {
$header.toggleClass('header-navbar-scrolled', $(window).scrollTop() > 20);
running = false;
});
}
updateHeaderScroll();
$(window).scroll(updateHeaderScroll);
});
})(window, window.document);
}
$('a[href*=#]:not([href=#])').click(function() {
if (location.pathname.replace(/^\//,'') == this.pathname.replace(/^\//,'') && location.hostname == this.hostname) {
var target = $(this.hash);
target = target.length ? target : $('[name=' + this.hash.slice(1) +']');
if (target.length) {
$('html,body').animate({
scrollTop: target.offset().top - $('.header-navbar').height(),
}, 500);
$('html').removeClass('nav-open');
return false;
}
}
});
$('.header-navbar-logo').click(function() {
$('html,body').animate({scrollTop: 0}, 500);
$('html').removeClass('nav-open');
});
$('.header-navbar-hamburger').click(function() {
$('html').toggleClass('nav-open');
});
$('body').click(function(e) {
if($('html').hasClass('nav-open') && $(e.target).parents('.header-navbar').length === 0) {
$('html').removeClass('nav-open');
}
});
if(isMobile()) {
$('.header-navbar').addClass('header-navbar-scrolled');
}
});
</script>
<script>
$(function() {
var ekg = document.getElementById('ekg'),
path = ekg.getElementsByTagName('path')[0],
totalLength = path.getTotalLength()
thumper = document.getElementById('thumper'),
style = document.createElement('style'),
resizeTimeout = null;
document.getElementsByTagName('head')[0].appendChild(style);
var stylesheet = style.sheet;
function resizeEkg() {
resizeTimeout = null;
var width = document.body.clientWidth + 200;
ekg.setAttribute('width', width);
var viewBox = ekg.getAttribute('viewBox').split(' ');
viewBox[2] = width;
ekg.setAttribute('viewBox', viewBox.join(' '));
var side = ((width - 61.045) / 2).toFixed(3);
path.setAttribute('d', 'M0 39h' + side + 'l2.798 -5.740l5.525 10.763l12.629 -48.956l13.705 71.031l12.315 -35.070l4.691 14.717l5.727 -13.101l3.655 5.925h' + side);
totalLength = path.getTotalLength();
path.setAttribute('stroke-dasharray', (totalLength - 100).toFixed(3) + ' 100');
while(stylesheet.cssRules.length > 0) {
stylesheet.deleteRule(0);
}
var animation = 'ekg { 0% { stroke-dashoffset: ' + totalLength.toFixed(3) + '; } 100% { stroke-dashoffset: 0; } }'
try { stylesheet.insertRule('@-webkit-keyframes ' + animation, 0); } catch(e) {}
try { stylesheet.insertRule('@keyframes ' + animation, 0); } catch(e) {}
var duration = totalLength / 350;
path.style.webkitAnimationDuration = duration + 's';
path.style.animationDuration = duration + 's';
}
resizeEkg();
path.style.webkitAnimation = 'ekg 5.7s ease-in infinite';
path.style.animation = 'ekg 5.7s ease-in infinite';
$(window).on('resize', function() {
if(resizeTimeout !== null) {
clearTimeout(resizeTimeout);
}
resizeTimeout = setTimeout(resizeEkg, 30);
});
});
</script>
<script>
(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
})(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
ga('create', 'UA-86302750-1', 'auto');
ga('send', 'pageview');
</script>
</body>
-->
</html>