forked from himalakkaraju/himalakkaraju.github.io
-
Notifications
You must be signed in to change notification settings - Fork 0
/
postdoc.html
131 lines (117 loc) · 7.04 KB
/
postdoc.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
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<!-- The above 3 meta tags *must* come first in the head; any other head content must come *after* these tags -->
<meta name="Hima Lakkaraju" content="">
<meta name="Hima Lakkaraju" content="">
<title>Postdoctoral Fellowship Position</title>
<link href="https://fonts.googleapis.com/css?family=Nunito+Sans" rel="stylesheet">
<link href="https://fonts.googleapis.com/css?family=Patrick+Hand+SC" rel="stylesheet">
<!-- Bootstrap core CSS -->
<link href="css/bootstrap.min.css" rel="stylesheet">
<!-- Bootstrap theme -->
<link href="css/bootstrap-theme.min.css" rel="stylesheet">
<!-- font awesome icons -->
<link href="css/icon.css" rel="stylesheet">
<!-- IE10 viewport hack for Surface/desktop Windows 8 bug -->
<link href="css/ie10-viewport-bug-workaround.css" rel="stylesheet">
<!-- Overriding personal style -->
<link href="css/style.css" rel="stylesheet">
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">
<script type="text/javascript">
function visibility_on(id) {
var e = document.getElementById(id+"_text");
if(e.style.display == 'none')
e.style.display = 'block';
var e = document.getElementById(id+"_img");
if(e.style.display == 'none')
e.style.display = 'block';
}
function visibility_off(id) {
var e = document.getElementById(id+"_text");
if(e.style.display == 'block')
e.style.display = 'none';
var e = document.getElementById(id+"_img");
if(e.style.display == 'block')
e.style.display = 'none';
}
function toggle_visibility(id) {
var e = document.getElementById(id+"_text");
if(e.style.display == 'inline')
e.style.display = 'block';
else
e.style.display = 'inline';
var e = document.getElementById(id+"_img");
if(e.style.display == 'inline')
e.style.display = 'block';
else
e.style.display = 'inline';
}
function toggle_vis(id) {
var e = document.getElementById(id);
if (e.style.display == 'none')
e.style.display = 'inline';
else
e.style.display = 'none';
}
</script>
</head>
<body>
<!-- Fixed navbar -->
<nav class="navbar navbar-default navbar-fixed-top">
<div class="container">
<div class="navbar-header">
<button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false" aria-controls="navbar">
<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="#">Hima Lakkaraju</a>
</div>
<div id="navbar" class="navbar-collapse collapse">
<ul class="nav navbar-nav">
<li><a href="https://himalakkaraju.github.io">Home</a></li>
<li><a href="https://himalakkaraju.github.io#news">News</a></li>
<li><a href="https://himalakkaraju.github.io#research">Research</a></li>
<li><a href="https://himalakkaraju.github.io#students">Advising</a></li>
<li><a href="https://himalakkaraju.github.io#teaching">Teaching</a></li>
<!--<li><a href="#papers">Service</a></li>-->
</ul>
</div><!--/.nav-collapse -->
</div>
</nav>
<div class="container theme-showcase" role="main">
<div class="row">
<h3>Postdoctoral Felllowship Position</h3>
<font size="3">
<p><a href="https://himalakkaraju.github.io">Prof. Hima Lakkaraju</a> invites applications for a Postdoctoral Research Fellowship position at Harvard University starting in the Summer or Fall of 2021. </p>
<p>
The selected candidate will be expected to lead research in novel machine learning methods. More specifically, this fellowship will focus on making theoretical and methodological advances at the intersection of explainable, fair, adversarial, and differentially private ML.
</p>
<p>
We seek highly-motivated applicants with background in one or more of the following areas: machine learning, explainable ML, adversarial ML, fairness and differential privacy, statistical learning theory. Successful applicants will be strong technically as well as have an inclination towards real-world problems.
</p>
<p>
We are looking for applicants with demonstrably strong research skills, ideally, with multiple publications in top venues in machine learning, artificial intelligence (e.g., ICML, NeurIPS, ICLR, KDD, AAAI, IJCAI, UAI, FAccT, AIES), and/or top-tier interdisciplinary journals (e.g., Nature family of journals, PNAS).
</p>
<p>
Candidates must have a Ph.D. or equivalent degree in computer science, statistics, or a closely related field. Strong programming skills and experience with machine learning and its applications are required.
<h3>Application Process</h3>
The position is available immediately and can be renewed annually. Interested applicants should apply via <a href="https://forms.gle/yCv6ZQ5mp76YyyuT7">this form</a> and submit the following documents:
<p></p>
<ul>
<li>Curriculum Vitae (please include links to your academic webpage and any software you developed) </li>
<li> Two representative publications (preprints are acceptable) </li>
<li> Statement of Research (2 pages) describing prior research experience and future research plans </li>
<li> Three letters of recommendation (will be solicited after the initial review) </li>
</ul>
We are currently reviewing applications for this position. Interested candidates are encouraged to submit their applications as soon as possible and preferably by <font color="#cc0000"><b>April 10th, 2021</b></font>. We will continue accepting applications after this deadline if the position is not filled.
<h3>Advisor Bio</h3>
<a href="https://himalakkaraju.github.io">Prof. Hima Lakkaraju</a> is an Assistant Professor at Harvard University focusing on explainability, fairness, and robustness of machine learning models. At the core of her research lie rigorous computational techniques spanning ML and data mining. She has published extensively on the topics of fair, robust, and interpretable ML in various top-tier ML and AI conferences including NeurIPS, ICML, AISTATS, KDD, AAAI, and AIES. Hima has recently been named one of the 35 innovators under 35 by MIT Tech Review, and was featured as one of the innovators to watch by Vanity Fair. She has received several awards including the best paper awards at SIAM International Conference on Data Mining (SDM) and INFORMS. Her research has also been covered by popular media outlets including the New York Times, MIT Tech Review, Harvard Business Review, TIME, Forbes, Business Insider, and Bloomberg.
</font size="3">
</body>
</html>