22 <tr><td class="right">12:10-13:10</td><td class="left"></td><td class="left"><b>Lunch</b></td></tr> |
22 <tr><td class="right">12:10-13:10</td><td class="left"></td><td class="left"><b>Lunch</b></td></tr> |
23 <tr><td class="right">13:10-13:55</td><td class="left">Ajith Kumar</td><td class="left"><b>Invited</b></td></tr> |
23 <tr><td class="right">13:10-13:55</td><td class="left">Ajith Kumar</td><td class="left"><b>Invited</b></td></tr> |
24 <tr><td class="right">13:55-14:15</td><td class="left">Bala Subrahmanyam Varanasi</td><td class="left"><a href="#sec2.6">Sentiment Analysis</a></td></tr> |
24 <tr><td class="right">13:55-14:15</td><td class="left">Bala Subrahmanyam Varanasi</td><td class="left"><a href="#sec2.6">Sentiment Analysis</a></td></tr> |
25 <tr><td class="right">14:15-14:45</td><td class="left">Vishal Kanaujia</td><td class="left"><a href="#sec2.7">Exploiting the power of multicore for scientific computing in Python</a></td></tr> |
25 <tr><td class="right">14:15-14:45</td><td class="left">Vishal Kanaujia</td><td class="left"><a href="#sec2.7">Exploiting the power of multicore for scientific computing in Python</a></td></tr> |
26 <tr><td class="right">14:45-14:55</td><td class="left"></td><td class="left"><b>Lightning Talks</b></td></tr> |
26 <tr><td class="right">14:45-14:55</td><td class="left"></td><td class="left"><b>Lightning Talks</b></td></tr> |
27 <tr><td class="right">14:55-15:25</td><td class="left"></td><td class="left"><b>Tea</b></td></tr> |
27 <tr><td class="right">14:55-15:25</td><td class="left"></td><td class="left"><b>Tea Break</b></td></tr> |
28 <tr><td class="right">14:25-15:55</td><td class="left">Jayneil Dalal</td><td class="left"><a href="#sec2.8">Building Embedded Systems for Image Processing using Python</a></td></tr> |
28 <tr><td class="right">15:25-16:10</td><td class="left">Prabhu Ramachandran</td><td class="left"><b>Invited Talk</b></td></tr> |
29 <tr><td class="right">15:55-16:25</td><td class="left">Kunal Puri</td><td class="left"><a href="#sec2.9">Smoothed Particle Hydrodynamics with Python</a></td></tr> |
29 <tr><td class="right">16:10-16:40</td><td class="left">Jayneil Dalal</td><td class="left"><a href="#sec2.8">Building Embedded Systems for Image Processing using Python</a></td></tr> |
30 <tr><td class="right">16:25-16:45</td><td class="left">Nivedita Datta</td><td class="left"><a href="#sec2.10">Encryptedly yours : Python & Cryptography</a></td></tr> |
30 <tr><td class="right">16:40-17:10</td><td class="left">Kunal Puri</td><td class="left"><a href="#sec2.9">Smoothed Particle Hydrodynamics with Python</a></td></tr> |
31 <tr><td class="right">16:45-17:30</td><td class="left">Gael</td><td class="left"><a href="#sec2.23"><b>Machine learning as a tool for Neuroscience</b></td></tr> |
31 <tr><td class="right">17:10-17:30</td><td class="left">Nivedita Datta</td><td class="left"><a href="#sec2.10">Encryptedly yours : Python & Cryptography</a></td></tr> |
|
32 |
32 </tbody> |
33 </tbody> |
33 </table> |
34 </table> |
34 |
35 |
35 |
36 |
36 <h2 id="sec-2">Day 2 </h2> |
37 <h2 id="sec-2">Day 2 </h2> |
42 </colgroup> |
43 </colgroup> |
43 <thead> |
44 <thead> |
44 <tr><th scope="col" class="right">Time</th><th scope="col" class="left">Speaker</th><th scope="col" class="left">Title</th></tr> |
45 <tr><th scope="col" class="right">Time</th><th scope="col" class="left">Speaker</th><th scope="col" class="left">Title</th></tr> |
45 </thead> |
46 </thead> |
46 <tbody> |
47 <tbody> |
47 <tr><td class="right">09:00-09:45</td><td class="left">Prabhu Ramachandran</td><td class="left"><b>Invited</b></td></tr> |
48 <tr><td class="right">09:00-09:45</td><td class="left">Gael</td><td class="left"><a href="#sec2.23">Invited Speaker: <b>Machine learning as a tool for Neuroscience</b></td></tr> |
48 <tr><td class="right">09:45-10:05</td><td class="left">Mahendra Naik</td><td class="left"><a href="#sec2.13">Large amounts of data downloading and processing in python with facebook data as reference</a></td></tr> |
49 <tr><td class="right">09:45-10:15</td><td class="left">Kannan Moudgalya</td><td class="left"><b>Invited</b></td></tr> |
49 <tr><td class="right">10:05-10:15</td><td class="left"></td><td class="left"><b>Lightning Talks</b></td></tr> |
|
50 <tr><td class="right">10:15-10:45</td><td class="left"></td><td class="left"><b>Tea</b></td></tr> |
50 <tr><td class="right">10:15-10:45</td><td class="left"></td><td class="left"><b>Tea</b></td></tr> |
51 <tr><td class="right">10:45-11:05</td><td class="left">Hrishikesh Deshpande</td><td class="left"><a href="#sec2.14">Higher Order Statistics in Python</a></td></tr> |
51 <tr><td class="right">10:45-11:05</td><td class="left">Hrishikesh Deshpande</td><td class="left"><a href="#sec2.14">Higher Order Statistics in Python</a></td></tr> |
52 <tr><td class="right">11:05-11:25</td><td class="left">Shubham Chakraborty</td><td class="left"><a href="#sec2.15">Combination of Python and Phoenix-M as a low cost substitute for PLC</a></td></tr> |
52 <tr><td class="right">11:05-11:25</td><td class="left">Shubham Chakraborty</td><td class="left"><a href="#sec2.15">Combination of Python and Phoenix-M as a low cost substitute for PLC</a></td></tr> |
53 <tr><td class="right">11:25-12:10</td><td class="left">Emmanuelle</td><td class="left"><b>Invited</b></td></tr> |
53 <tr><td class="right">11:25-12:10</td><td class="left">Emmanuelle</td><td class="left"><b>Invited</b></td></tr> |
54 <tr><td class="right">12:10-13:10</td><td class="left"></td><td class="left"><b>Lunch</b></td></tr> |
54 <tr><td class="right">12:10-13:10</td><td class="left"></td><td class="left"><b>Lunch</b></td></tr> |
55 <tr><td class="right">13:10-13:55</td><td class="left">Asokan</td><td class="left"><b>Invited</b></td></tr> |
55 <tr><td class="right">13:10-13:30</td><td class="left">Mahendra Naik</td><td class="left"><a href="#sec2.13">Large amounts of data downloading and processing in python with facebook data as reference</a></td></tr> |
56 <tr><td class="right">13:55-14:15</td><td class="left">Jaidev Deshpande</td><td class="left"><a href="#sec2.18">A Python Toolbox for the Hilbert-Huang Transform</a></td></tr> |
56 <tr><td class="right">13:30-14:10</td><td class="left">Ole Nielsen</td><td class="left"><b>Invited</b></td></tr> |
57 <tr><td class="right">14:15-14:45</td><td class="left">Chetan Giridhar</td><td class="left"><a href="#sec2.19">Diving in to Byte-code optimization in Python</a></td></tr> |
57 <tr><td class="right">14:10-14:30</td><td class="left">Jaidev Deshpande</td><td class="left"><a href="#sec2.18">A Python Toolbox for the Hilbert-Huang Transform</a></td></tr> |
58 <tr><td class="right">14:45-14:55</td><td class="left"></td><td class="left"><b>Lightning Talks</b></td></tr> |
58 <tr><td class="right">14:30-15:00</td><td class="left">Chetan Giridhar</td><td class="left"><a href="#sec2.19">Diving in to Byte-code optimization in Python</a></td></tr> |
59 <tr><td class="right">14:55-15:25</td><td class="left"></td><td class="left"><b>Tea</b></td></tr> |
59 <tr><td class="right">15:00-15:30</td><td class="left"></td><td class="left"><b>Tea</b></td></tr> |
60 <tr><td class="right">15:25-16:05</td><td class="left">Ole Nielsen</td><td class="left"><b>Invited</b></td></tr> |
60 <tr><td class="right">15:305-16:00</td><td class="left">Kunal puri</td><td class="left"><a href="#sec2.21">GPU Accelerated Computational Fluid Dynamics with Python</a></td></tr> |
61 <tr><td class="right">16:05-16:35</td><td class="left">Kunal puri</td><td class="left"><a href="#sec2.21">GPU Accelerated Computational Fluid Dynamics with Python</a></td></tr> |
61 <tr><td class="right">16:00-16:10</td><td class="left">Sachin Shinde</td><td class="left"><a href="#sec2.22">Reverse Engineering and python</a></td></tr> |
62 <tr><td class="right">16:35-16:45</td><td class="left">Sachin Shinde</td><td class="left"><a href="#sec2.22">Reverse Engineering and python</a></td></tr> |
|
63 <tr><td class="right">16:10-16:40</td><td class="left">Jarrod Millman</td><td class="left"><b>Invited</b></td></tr> |
62 <tr><td class="right">16:10-16:40</td><td class="left">Jarrod Millman</td><td class="left"><b>Invited</b></td></tr> |
64 </tbody> |
63 </tbody> |
65 </table> |
64 </table> |
66 <br/><br/> |
65 <br/><br/> |
67 |
66 |
379 <p> |
378 <p> |
380 Optimal use of the data available from a brain imaging session raises |
379 Optimal use of the data available from a brain imaging session raises |
381 computational challenges that are well-known in large data analytics. The |
380 computational challenges that are well-known in large data analytics. The |
382 <b>scipy</b> stack, including <b>Cython</b> and <b>scikit-learn</b>, used with care, can |
381 <b>scipy</b> stack, including <b>Cython</b> and <b>scikit-learn</b>, used with care, can |
383 provide a high-performance environment, matching dedicated solutions. I |
382 provide a high-performance environment, matching dedicated solutions. I |
384 will highlight how the *scikit-learn* performs efficient data analysis in |
383 will highlight how the <b>scikit-learn</b> performs efficient data analysis in |
385 Python. |
384 Python. |
386 </p> |
385 </p> |
387 <p> |
386 <p> |
388 The challenges discussed here go beyond neuroscience. Imaging |
387 The challenges discussed here go beyond neuroscience. Imaging |
389 neuroscience is a test bed for advanced data analysis in science, as it |
388 neuroscience is a test bed for advanced data analysis in science, as it |