--- a/project/templates/talk/conf_schedule.html	Wed Nov 30 13:25:05 2011 +0530
+++ b/project/templates/talk/conf_schedule.html	Wed Nov 30 15:39:35 2011 +0530
@@ -24,11 +24,12 @@
 <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>
 <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>
 <tr><td class="right">14:45-14:55</td><td class="left"></td><td class="left"><b>Lightning Talks</b></td></tr>
-<tr><td class="right">14:55-15:25</td><td class="left"></td><td class="left"><b>Tea</b></td></tr>
-<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>
-<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>
-<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>
-<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>
+<tr><td class="right">14:55-15:25</td><td class="left"></td><td class="left"><b>Tea Break</b></td></tr>
+<tr><td class="right">15:25-16:10</td><td class="left">Prabhu Ramachandran</td><td class="left"><b>Invited Talk</b></td></tr>
+<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>
+<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>
+<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>
+
 </tbody>
 </table>
 
@@ -44,22 +45,20 @@
 <tr><th scope="col" class="right">Time</th><th scope="col" class="left">Speaker</th><th scope="col" class="left">Title</th></tr>
 </thead>
 <tbody>
-<tr><td class="right">09:00-09:45</td><td class="left">Prabhu Ramachandran</td><td class="left"><b>Invited</b></td></tr>
-<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>
-<tr><td class="right">10:05-10:15</td><td class="left"></td><td class="left"><b>Lightning Talks</b></td></tr>
+<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>
+<tr><td class="right">09:45-10:15</td><td class="left">Kannan Moudgalya</td><td class="left"><b>Invited</b></td></tr>
 <tr><td class="right">10:15-10:45</td><td class="left"></td><td class="left"><b>Tea</b></td></tr>
 <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>
 <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>
 <tr><td class="right">11:25-12:10</td><td class="left">Emmanuelle</td><td class="left"><b>Invited</b></td></tr>
 <tr><td class="right">12:10-13:10</td><td class="left"></td><td class="left"><b>Lunch</b></td></tr>
-<tr><td class="right">13:10-13:55</td><td class="left">Asokan</td><td class="left"><b>Invited</b></td></tr>
-<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>
-<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>
-<tr><td class="right">14:45-14:55</td><td class="left"></td><td class="left"><b>Lightning  Talks</b></td></tr>
-<tr><td class="right">14:55-15:25</td><td class="left"></td><td class="left"><b>Tea</b></td></tr>
-<tr><td class="right">15:25-16:05</td><td class="left">Ole Nielsen</td><td class="left"><b>Invited</b></td></tr>
-<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>
-<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>
+<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>
+<tr><td class="right">13:30-14:10</td><td class="left">Ole Nielsen</td><td class="left"><b>Invited</b></td></tr>
+<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>
+<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>
+<tr><td class="right">15:00-15:30</td><td class="left"></td><td class="left"><b>Tea</b></td></tr>
+<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>
+<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>
 <tr><td class="right">16:10-16:40</td><td class="left">Jarrod Millman</td><td class="left"><b>Invited</b></td></tr>
 </tbody>
 </table>
@@ -381,7 +380,7 @@
 computational challenges that are well-known in large data analytics. The
 <b>scipy</b> stack, including <b>Cython</b> and <b>scikit-learn</b>, used with care, can
 provide a high-performance environment, matching dedicated solutions. I
-will highlight how the *scikit-learn* performs efficient data analysis in
+will highlight how the <b>scikit-learn</b> performs efficient data analysis in
 Python. 
 </p>
 <p>