jpfeiffe.github.io/index.html at master · jpfeiffe/jpfeiffe.github.io · GitHub

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
<html>
<title>Joseph J. Pfeiffer III</title>
<head>
<style type="text/css">
BODY
{
border-width:5px 100px;
border-style:solid;
background-color:#FFFFFF;
border-color:#FFFFFF;
font-family:optima helvetica sans-serif;
}
TABLE
{
background-color:#FFFFFF;
padding-right: 150;
}

td.bars
{
background-color:#000000;
padding-color:#000000;
border-color:#000000;
color:#000000;
}
.heading {
  font-weight: normal;
  color: black;
  font-size: 22px;
  padding-left: 15;
  border-bottom: blue 2px solid;
}
.subheading {
  font-weight: normal;
  color: black;
  font-size: 17px;
  padding-left: 15;
  border-bottom: blue 1px solid;
}

td
{
padding: 10px 10px;
}
a:link {
  text-decoration: none;
  color: black;
}

a:visited {
  text-decoration: none;
  color: black;
  }

a:hover {
  text-decoration: underline;
  }
.pub_name {
  font-size: 14px;
  font-family: optima, verdana, sans-serif;
  color: charcoal;
  background: none;
  font-weight: bold;
  text-align: left;
  margin: 10px 0px 1px 0px;
}
.intern_name {
  font-size: 14px;
  font-family: optima, verdana, sans-serif;
  color: charcoal;
  background: none;
  font-weight: bold;
  text-align: left;
  margin: 10px 0px 1px 0px;
}
blockquote{font-size:90%;line-height:1em;padding:5px 15px;background:#f6f6f6;margin:10px 0;border:1px solid #eee}
pre{overflow-x:auto;white-space:pre-wrap;white-space:-moz-pre-wrap!important;white-space:-pre-wrap;white-space:-o-pre-wrap;word-wrap:break-word}
</style>
</head>
<body>
<script language="JavaScript" type="text/javascript">
<!--
function sizeTbl(t) {
  var tbl = document.getElementById(t);
  if (tbl.style.display == 'block') {
  tbl.style.display = 'none';
  }
  else {
  tbl.style.display = 'block';
  }
}
// -->
</script>
<table id="maintable" width="100%">
<tr>
<td width="180" valign="top" align="left">
<div align="center" style="margin: 5px 0px 15px 0px">
<img src="./pic.jpg" width="150">
</div>
<div style="font-size: x-large; font-weight: bold; border-bottom: blue 2px solid">Joel Pfeiffer</div>
<div style="margin: 2px 0px 2px 0px">
<div style="font-size: x-small; font-style: italic">Joseph J. Pfeiffer III</div>
</div>

<div style="font-size: small">jpfeiffe at gmail dot com</div>
</div>
<div style="font-size: small; margin: 5px 0px 5px 0px"><p>
<a href="https://linkedin.com/in/joel-pfeiffer-57801737/">LinkedIn</a> <br><br>
<a href="https://scholar.google.com/citations?user=ZFCbPDEAAAAJ&hl=en&oi=ao">Google Scholar</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>
</div>
</td>
<td class="padding2" rowspan="2" valign="top">
<div class="heading" id="research" style="margin: 0px 0px 10px 0px">Research</div>
<div style="margin: 0px 0px 5 px 0px">
<p> Principal ML scientist at Microsoft Bing Ads working on a variety of ML topics.  I graduated with a PhD a few years ago from Purdue, studying machine learning in relational domains with Professor <a href="http://www.cs.purdue.edu/homes/neville/">Jennifer Neville</a>.
</p>
<p>
</div>
<br><br>
<div class="heading" id="Codes" style="margin: 0px 0px 10px 0px">Codes</div>
<p> A collection of code for various models, generally written in Python for the simplest forms of representations.  Several of these are done simply for my own practice/experience and I think they could be useful for someone else looking for example code.  Some of these are implementations of other work and some from my own work above.
</p>
<div class="pub_name">Relational Machine Learning Library</div>
Collection of RML codes in Python!
<br>
<a href="http://github.com/jpfeiffe/rmllib" style="font-size: small">[RMLLib]</a>&nbsp;
<br>
<div class="pub_name">Attributed Graph Models</div>
Implementation of the above AGM model (WWW2014), using the simple FCL proposal distribution.  Uses a simple 0/1 label value.  Requires Python w/ matplotlib.  The first implementation uses the Preferential Attachment model of Barabasi/Albert to create a degree distribution for AGM/FCL, while the second version learns/samples from an observed network.
<br>
<a href="public_code/AGM_PA_Generator.py" style="font-size: small">[PA-AGM-FCL]</a>&nbsp;
<a href="public_code/AGM_Generator.py" style="font-size: small">[AGM-FCL]</a>&nbsp;
<br>
<div class="pub_name">Data Augmentation, Stochastic EM and EM</div>
Implementation of the above ICDM2014 paper on Data Augmentation.  Has some tests and comparisons between DA, Stochastic EM and EM for Naive Bayes and Logistic Regression.  Implemented in C++ and contains a distribution of liblinear and eigen3.  Please read and carry on any appropriate copyright notices for these works.
<br>
<a href="public_code/ICDM_DA_Distribute.tar.gz" style="font-size: small">[Code]</a>
<br>
<div class="pub_name">Log-Linear models</div>
A simple Python implementation for learning log-linear (maximum entropy) models.  Just uses 0/1 feature/label values, and implemented for my own practice.  Requires Python and scipy/numpy; I implemented both calling scipy's BFGS optimization, as well as my own GD method for fun.
<br>
<a href="public_code/BFGS_LogLinearModel.py" style="font-size: small">[BFGS]</a>&nbsp;
<a href="public_code/GD_LogLinearModel.py" style="font-size: small">[GD]</a>&nbsp;

<br><br>

<div class="heading" id="Publications" style="margin: 0px 0px 10px 0px">Publications</div>
Please view a complete list of publications on <a href="https://scholar.google.com/citations?user=ZFCbPDEAAAAJ&hl=en&oi=ao">[Google Scholar]</a>. <br>

Some work not found on Google scholar are the privacy proposals I've been involved with -- please view the archives at
<a href="https://github.com/WICG/privacy-preserving-ads/tree/main/archive/NoisyRanking.md">[Parkeet V2]</a> and
<a href="https://github.com/WICG/privacy-preserving-ads/blob/main/archive/MaskedLARK.md">[MaskedLARK]</a> for predecessors
to the <a href="https://learn.microsoft.com/en-us/microsoft-edge/web-platform/ad-selection-api">[Private Ad Selection API]</a>.

<br><br>
<div class="pub_name">Overcoming Uncertainty for Within-Network Relational Machine Learning</div>
Joseph J. Pfeiffer III <br>
Ph.D. Thesis<br>
 <a href="pubs/thesis.pdf" style="font-size: small">[PDF]</a>&nbsp
<a href="javascript:sizeTbl('thesis')", style="font-size: small">[BibTeX]</a>&nbsp;
<div id=thesis name=tbl1 style="overflow:hidden;display:none">
<blockquote>
<pre>
@phdthesis {thesis,
author = {Joseph J. {Pfeiffer III}},
title = {Overcoming Uncertainty for Within-Network Relational Machine Learning},
year = {2015},
month = May,
school = {Purdue University}
}
</pre>
</blockquote>
</div>


</tr>
</table>
<script type="text/javascript">
var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www.");
document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E"));
</script>
<script type="text/javascript">
try {
var pageTracker = _gat._getTracker("UA-15958418-1");
pageTracker._trackPageview();
} catch(err) {}</script>
</body>

</html>