danielcmo.github.io/ap.html at main · danielcmo/danielcmo.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
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
<!DOCTYPE html>
<html lang="en">
<head>
  <!-- Google tag (gtag.js) -->
<script async src="https://www.googletagmanager.com/gtag/js?id=G-C4TVS1004P"></script>
<script>
  window.dataLayer = window.dataLayer || [];
  function gtag(){dataLayer.push(arguments);}
  gtag('js', new Date());

  gtag('config', 'G-C4TVS1004P');
</script>
  <meta charset="UTF-8">
  <title>Research Areas - Daniel de Oliveira</title>
  <meta name="description" content="Research areas of Daniel de Oliveira at UFF">
  <meta name="keywords" content="Research, Areas, Daniel de Oliveira, UFF, IC-UFF, eScience">
  <style>
    body {
      font-family: Arial, Helvetica, sans-serif;
      background-color: #ffffff;
      color: #333333;
      margin: 0;
      padding: 0;
      line-height: 1.6;
    }

    a { color: #004f9e; text-decoration: none; }
    a:hover { text-decoration: underline; }

    header {
      display: flex;
      align-items: center;
      justify-content: space-between;
      padding: 20px 40px;
      border-bottom: 1px solid #ccc;
      background: #fafafa;
    }

    header img { height: 60px; }

    nav ul {
      list-style: none;
      padding: 0;
      margin: 0;
      display: flex;
      gap: 20px;
    }

    nav ul li a { font-weight: bold; }

    main {
      padding: 40px;
      max-width: 1200px;
      margin: auto;
    }

    h1 {
      margin-bottom: 20px;
      font-size: 2em;
    }

    .areas-grid {
      display: grid;
      grid-template-columns: repeat(2, 1fr);
      gap: 25px;
    }

    .area-card {
      display: flex;
      flex-direction: column;
      background: #f9f9f9;
      padding: 20px;
      border-radius: 8px;
      box-shadow: 0 2px 5px rgba(0,0,0,0.1);
      transition: transform 0.2s;
    }

    .area-card:hover {
      transform: scale(1.02);
    }

    .area-image {
      width: 100%;
      height: 150px;
      object-fit: cover;
      border-radius: 5px;
      margin-bottom: 15px;
    }

    .area-icon {
      font-size: 40px;
      margin-bottom: 15px;
      color: #004f9e;
      text-align: center;
    }

    .area-title {
      font-weight: bold;
      font-size: 1.2em;
      margin-bottom: 10px;
      color: #004f9e;
      text-align: center;
    }

    .area-description {
      font-size: 0.95em;
      color: #444;
    }

    footer {
      display: flex;
      justify-content: space-between;
      padding: 20px 40px;
      border-top: 1px solid #ccc;
      background-color: #f1f1f1;
      font-size: 0.9em;
    }
  </style>
  <!-- Icons (Font Awesome) -->
  <script src="https://kit.fontawesome.com/6d6c3a6c4c.js" crossorigin="anonymous"></script>
</head>
<body>
  <!-- Header -->
  <header>
    <a href="http://www.uff.br"><img src="arquivos/uff.png" alt="UFF Logo"></a>
    <nav>
      <ul>
        <li><a href="https://danielcmo.github.io">Home</a></li>
        <li><a href="https://danielcmo.github.io/cv.html">Publications</a></li>
        <li><a href="https://danielcmo.github.io/books.html">Books</a></li>
        <li><a href="https://danielcmo.github.io/ap.html">Research Areas</a></li>
        <li><a href="https://danielcmo.github.io/cur.html">Teaching</a></li>
        <li><a href="https://danielcmo.github.io/collab.html">Collaborations</a></li>
        <li><a href="https://danielcmo.github.io/li.html">Students</a></li>
        <li><a href="https://danielcmo.github.io/awards.html">Awards</a></li>
<li><a href="https://danielcmo.github.io/projects.html">Projects</a></li>
        <li><a href="https://danielcmo.github.io/per.html">News</a></li>
      </ul>
    </nav>
    <a href="http://www.ic.uff.br"><img src="arquivos/logo-ic.png" alt="IC-UFF Logo"></a>
  </header>

  <!-- Main -->
  <main>
    <h1>Research Areas</h1>
    <p>
      My research focuses on a diverse set of themes that lie at the intersection of
      data management, scalable infrastructures, scientific discovery, and intelligent systems.
      These areas combine theoretical advances with practical applications, aiming to build tools,
      methods, and systems that empower science and technology in the era of big data and artificial intelligence.
    </p>

    <div class="areas-grid">

      <div class="area-card">
        <img class="area-image" src="arquivos/SciCumulus2.png" alt="Scientific Workflow Management">
        <div class="area-icon"><i class="fas fa-project-diagram"></i></div>
        <div class="area-title">Scientific Workflow Management</div>
        <div class="area-description">
          This area investigates how to model, execute, and optimize large-scale scientific workflows.
          Research focuses on automation, reproducibility, and fault tolerance to enable scientists
          to orchestrate complex experiments seamlessly, reducing human effort and improving scientific productivity.
        </div>
      </div>

      <div class="area-card">
        <img class="area-image" src="arquivos/Cloud.jpeg" alt="Cloud Computing">
        <div class="area-icon"><i class="fas fa-cloud"></i></div>
        <div class="area-title">Cloud Computing</div>
        <div class="area-description">
          Research in cloud computing addresses elasticity, scalability, and cost-effectiveness
          in distributed environments. By leveraging virtualized resources and on-demand infrastructures,
          the goal is to optimize performance and reduce barriers for executing data-intensive scientific experiments.
        </div>
      </div>

      <div class="area-card">
        <img class="area-image" src="arquivos/Provenance.png" alt="Provenance Data Management">
        <div class="area-icon"><i class="fas fa-database"></i></div>
        <div class="area-title">Provenance Data Management</div>
        <div class="area-description">
          Provenance, or data lineage, ensures that scientific results are transparent and reproducible.
          My work develops models, storage systems, and analysis techniques that make it possible to
          capture, query, and reason about the origin, evolution, and trustworthiness of data at scale.
        </div>
      </div>

      <div class="area-card">
        <img class="area-image" src="arquivos/Bioinformatics2.jpeg" alt="Bioinformatics">
        <div class="area-icon"><i class="fas fa-dna"></i></div>
        <div class="area-title">Bioinformatics</div>
        <div class="area-description">
          Bioinformatics integrates computing and biology to address challenges in genomics, transcriptomics,
          and molecular biology. Research focuses on designing scalable data pipelines, efficient algorithms,
          and workflow solutions that support the analysis of massive and heterogeneous biological datasets.
        </div>
      </div>

      <div class="area-card">
        <img class="area-image" src="arquivos/DataManagement.png" alt="Data Management for Machine Learning">
        <div class="area-icon"><i class="fas fa-brain"></i></div>
        <div class="area-title">Data Management for Machine Learning</div>
        <div class="area-description">
          Preparing data for machine learning is a complex and costly process.
          This research area explores methods for feature engineering, data cleaning, integration, and sampling,
          ensuring that ML models are trained with high-quality data while optimizing efficiency and scalability.
        </div>
      </div>

      <div class="area-card">
        <img class="area-image" src="arquivos/MLDB.png" alt="Machine Learning for Data Management">
        <div class="area-icon"><i class="fas fa-robot"></i></div>
        <div class="area-title">Machine Learning for Data Management</div>
        <div class="area-description">
          Instead of only preparing data for ML, this area investigates how ML techniques
          can improve traditional data management tasks. Applications include intelligent indexing,
          adaptive query optimization, and workload prediction, enabling smarter and more efficient data systems.
        </div>
      </div>

      <div class="area-card">
        <img class="area-image" src="arquivos/Bioinformatics.webp" alt="eScience">
        <div class="area-icon"><i class="fas fa-flask"></i></div>
        <div class="area-title">eScience</div>
        <div class="area-description">
          eScience is the use of advanced computational methods to accelerate scientific discovery.
          This area involves designing infrastructures, data repositories, and collaborative platforms
          that enable interdisciplinary research, promote open science, and help scientists turn vast
          amounts of data into knowledge and innovation.
        </div>
      </div>

    </div>
  </main>

  <!-- Footer -->
  <footer>
    <div>
      <b>Phone: +55 (21) 2629-5