-
Notifications
You must be signed in to change notification settings - Fork 0
/
index.html
189 lines (174 loc) · 7.17 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
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<title>Michael Eickenberg</title>
<!-- <meta http-equiv="refresh" content="0;URL='https://www.linkedin.com/pub/michael-eickenberg/88/626/351'" /> -->
</head>
<body>
<div style="width: 980px; margin: 0 auto; overflow: hidden;">
<div style="float: left; width: 75%;">
<p><h1>Michael Eickenberg</h1>
</p>
<!-- <p>PhD student under the supervision of
<a href="https://team.inria.fr/parietal/bertrand-thirions-page/">
Bertrand Thirion</a>
in the
<a href="https://team.inria.fr/parietal/">Inria Parietal Team</a>
at
<a href="http://dsv.cea.fr/dsv/i2bm/Pages/NeuroSpin.aspx">
Neurospin, CEA Saclay</a>
</p> -->
<p>Postdoc in the <a href="http://gallantlab.org">gallantlab</a> at UC Berkeley.</p>
<p>
My work consists in using machine learning methods towards forward and reverse modeling of fMRI brain activity
following sensory stimulation.
<br/>
The main approaches I take to create predictive models from and to BOLD fMRI brain imaging data lie in regularized empirical risk minimization methods, often with non-smooth convex regularizers, which lead to convex optimization problems for which iterative algorithms can be devised.
<br/>
Recently I have had success in forward modelling brain activity from features
extracted from convolutional networks. This project was one of the reasons to
create <a href="http://sklearn-theano.github.io">sklearn-theano</a>,
an open-source software package which makes the use of
powerful convolutional nets very easy. It has also sparked my general
interest in this type of learning architecture.
</p>
<p>
<h3>Open Source Contributions</h3>
<ul>
<li><a href="http://sklearn-theano.github.io">sklearn-theano</a>
- bringing state of the art neural networks to your scikit-learn pipeline
</li>
<li><a href="http://github.com/tensorlib/tensorlib">tensorlib</a>
A library for all sorts of tensor decompositions, to be extended :)
</li>
<li><a href="http://github.com/nilearn/nilearn">nilearn</a>
- do your volume based neuroimaging in python with this.<br/>
(Come and try, we have a beautiful
<a href="http://nilearn.github.io/auto_examples/manipulating_visualizing/plot_demo_plotting_glass_brain.html">
Glass Brain
</a>
to convince you)
</li>
<li><a href="http://github.com/scikit-learn/scikit-learn">
scikit-learn</a>
With this, everybody can now call themselves a "data scientist"
</li>
</ul>
</p>
<p>
<h3>Teaching</h3>
<ul>
<li> data8 cognitive neuroscience connector class (<a href="http://data8.org/cogneuro-connector">psych 88</a>)
</ul>
Teaching assistant for
<ul>
<li>Summer semester 2015: Fourier Series and Transformation
for Physics, Paris-Sud University</li>
<li>Winter semester 2014/2015: Applied linear algebra with matlab,
math dept., Paris-Sud University</li>
<li>Winter semester 2013/2014: Linear Algebra,
<li>Summer semester 2013: Analysis,
Institut Universitaire Technologique d'Orsay</li>
</ul>
</p>
<p>
<h3>Awards</h3>
(a one element list, so I can easily add others as they come streaming in
by the dozens)
<ul>
<li>
<a href="http://www.nncn.de/en/news/nachrichten-en/brainsforbrains2012">
Bernstein Association for Computational Neuroscience "Brains for Brains Young Researchers' Computational Neuroscience Award"
<a>
</li>
</ul>
</p>
<p>
<h3>Publications</h3>
<ul>
<li>
Eickenberg, M., Exarchakis, G., Hirn, M., Mallat, S. - Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities
<i>NIPS 2017 (<a href="https://papers.nips.cc/paper/7232-solid-harmonic-wavelet-scattering-predicting-quantum-molecular-energy-from-invariant-descriptors-of-3d-electronic-densities.pdf">pdf</a>)</i>
</li>
<li>
Eickenberg, M., Dohmatob, E., Thirion, B., Varoquaux, G. -
Total Variation meets Sparsity: statistical learning with segmenting
penalties <i>(MICCAI 2015)</i>
</li>
<li>
Eickenberg, M., Gramfort, A., Varoquaux, G., Thirion, B. - Seeing it all: Convolutional Neural Nets Map the Function of the Human
Visual System <i>Neuoroimage 2017</i>
</li>
<li>
Pedregosa, F., Eickenberg, M., Ciuciu, P., Thirion, B., Gramfort, A. -
Data-driven HRF estimation for encoding and decoding models
<i>(<a href="http://arxiv.org/abs/1402.7015">arXiv</a>)</i>
</li>
<li>
Eickenberg, M., Pedregosa, F., Senoussi, M., Gramfort, A., Thirion, B. -
Second order scattering descriptors predict fMRI activity due to visual textures
<i>(PRNI 2013)</i>
</li>
<li>Eickenberg, M., Vaiter, S., Golbabaee, M., Gramfort, A., Peyré G. -
Sign stability in l1 MEG source estimation <i>(
<a href="http://spars2013.epfl.ch/data/_uploaded/flash/Paper%20141.pdf">
SPARS2013</a>)</i>
</li>
<li>Eickenberg, Gramfort, A., Thirion, B. -
Multilayer Scattering Image Analysis Fits fMRI Activity in Visual Areas
<i>(PRNI 2012)</i>
</li>
<li>Eickenberg, M., Rowekamp, R., Kouh, M., Sharpee, T. -
Characterizing responses of translation-invariant neurons to natural stimuli: maximally informative invariant dimensions. <i>
<a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3410933/">
(Neural Computation 2012 Sep;24)</a>
</i>
</li>
</ul>
</p>
</div>
<div style="float: right; width: 25%;">
<div style="padding-bottom:20pt">
<a href="https://www.linkedin.com/pub/michael-eickenberg/88/626/351">
<img src="In-2C-28px-R.png" alt="LinkedIn" width="28" height="28"></img>
</a>
<a href="https://twitter.com/meickenberg">
<img src="Twitter_logo_blue.png" alt="Twitter @meickenberg"
width="28" height="28">
</img>
</a>
<a href="https://github.com/eickenberg">
<img src="GitHub-Mark-32px.png" alt="Github @eickenberg"
width="28" height="28">
</img>
</a>
</div> <img src="michael_staring_at_screen.png"
alt="Michael staring at two screens"
width="250" height="166">
</img>
<br/><center>(me staring at brains,<br/>
© Inria / Photo H. Raguet)
<br/>
<a class="twitter-timeline" href="https://twitter.com/meickenberg" data-widget-id="591311711765995521">Tweets de @meickenberg</a>
<script>!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+"://platform.twitter.com/widgets.js";fjs.parentNode.insertBefore(js,fjs);}}(document,"script","twitter-wjs");</script>
</center>
</div>
</div>
</body>
</html>
<!-- --- -->
<!-- layout: default -->
<!-- --- -->
<!-- <div class="home"> -->
<!-- <h1 class="page-heading">Posts</h1> -->
<!-- <ul class="post-list"> -->
<!-- {% for post in site.posts %} -->
<!-- <li> -->
<!-- <span class="post-meta">{{ post.date | date: "%b %-d, %Y" }}</span> -->
<!-- <h2> -->
<!-- <a class="post-link" href="{{ post.url | prepend: site.baseurl }}">{{ post.title }}</a> -->
<!-- </h2> -->
<!-- </li> -->
<!-- {% endfor %} -->
<!-- </ul> -->
<!-- <p class="rss-subscribe">subscribe <a href="{{ "/feed.xml" | prepend: site.baseurl }}">via RSS</a></p> -->
<!-- </div> -->