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diff --git a/Schedule-old.md b/Schedule-old.md
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+## Weekly Schedule
+
+Note that all of the readings are accessible from the original repositories I have linked to if you access them from the University (or use VPN into SCS if you are accessing from home).
+
+
+| Week              | Date | Topic | Speaker | Readings | 
+| :----------------: | :------: | :---- | :---- | :---- |
+| 1 | 1/9 | Introduction to Disaggregated & Heterogeneous Platforms | M. Tamer Özsu | R. Wang et al., [The Case for Shared-Memory Databases with RDMA-Enabled Memory Disaggregation](https://www.vldb.org/pvldb/vol16/p15-wang.pdf), _Proc. VLDB Endowment_, 2022. <br/> I. Blagodurov et al., [The time is ripe for disaggregated systems](https://www.sigarch.org/the-time-is-ripe-for-disaggregated-systems/). Computer Architecture Today – ACM SIGARCH Blog, 2021. <br/> S. Ghandeharizadeh et al., [Disaggregated database management systems](https://doi.org/10.1007/978-3-031-29576-8_3). In _Performance Evaluation and Benchmarking_, 2023.|
+| 2 | 1/16 | Introduction to Graph Processing | M. Tamer Özsu|  M. Besta et al., [Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries](https://doi.org/10.1145/3604932), _ACM Comput. Surv._ 56(2): 31:1-31:40, 2024. <br/> M.T. Özsu and P. Valduriez, [Big Data Processing](https://doi.org/10.1007/978-3-030-26253-2_10). In _Principles of Distributed Database Systems_. Springer, 2022. (Focus on Section 10.4) <br/> M.T. Özsu and P. Valduriez, [Big Data Processing](https://doi.org/10.1007/978-3-030-26253-2_10). In _Principles of Distributed Database Systems_. Springer, 2022. (Focus on Section 12.6) |
+| 3 | 1/23 | Networking infrastructure |  | R. Recio, [A Tutorial of the RDMA Model](https://www.hpcwire.com/2006/09/15/a_tutorial_of_the_rdma_model-1/), HPC Wire, 2006. <br/> InfiniBand Trade Organization, [Enabling the Modern Data Center – RDMA for the Enterprise](https://www.infinibandta.org/wp-content/uploads/2019/05/IBTA_WhitePaper_May-20-2019.pdf), 2019. <br/> A. Lerner et al., [Databases on modern networks: A decade of research that now comes into practice](https://doi.org/10.14778/3611540.3611579). _Proc. VLDB Endowment_, 16(12):3894–3897, 2023. <br/> 9. D. Gouk et al., [Direct access, High-Performance memory disaggregation with DirectCXL](https://www.usenix.org/conference/atc22/presentation/gouk). In _Proc. USENIX 2022 Annual Technical Conf._, pages 287–294, 2022. 
+ |
+| 4 | 1/30 | Storage disaggregation | Student 1 <br/> Student 2 | A. Verbitski et al., [Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases](https://doi.org/10.1145/3035918.3056101), In _Proc. ACM SIGMOD Int. Conf. Management of Data_, pages 1041–1052, 2017. <br/> P. Antonopoulos, et al., [Socrates: The New SQL Server in the Cloud](https://dl.acm.org/doi/10.1145/3299869.3314047), In _Proc. ACM SIGMOD Int. Conf. Management of Data_, pages 1743–1756, 2019. |
+| 5 | 2/6 | Storage disaggregation |  Student 3 <br/> Student 4| W. Cao et al., [PolarFS: An Ultra-low Latency and Failure Resilient Distributed File System for Shared Storage Cloud Database](https://doi.org/10.14778/3229863.3229872), _Proc. VLDB Endowment_, 11(12): 1849-1962, 2018. <br/> M. Vuppalapati et al., [Building An Elastic Query Engine on Disaggregated Storage](https://www.usenix.org/conference/nsdi20/presentation/vuppalapati), In _Proc. 17th USENIX Symp. on Networked Systems Design & Implementation,_ pages 449-462, 2020. |
+| 6 | 2/13 | Storage/Memory disaggregation | Student 5 <br/> Student 6 | A. Agiwal et al., [Napa: Powering Scalable Data Warehousing with Robust Query Performance at Google](https://doi.org/10.14778/3476311.3476377), 14(12): 2986-2998, 2021. <br/> Y, Shan et al., [LegoOS: A Disseminated, Distributed OS for Hardware Resource Disaggregation](https://www.usenix.org/conference/osdi18/presentation/shan), In _Proc. 14th USENIX Symp. on Operating System Design and Implementation_, pages 69-87, 2018. |
+| 7 | 2/20 |  No class -- Reading week|  |  |
+| 8 | 2/27 | Memory disaggregation | Student 7 <br/> Student 8 |  Y. Zhang et al., [Towards Cost-Effective and Elastic Cloud Database Deployment via Memory Disaggregation](https://doi.org/10.14778/3467861.3467877), _Proc. VLDB Endowment_, 14(10): 1900 - 1912, 2021. <br/>  Wei Cao et al., [PolarDB Serverless: A Cloud Native Database for Disaggregated Data Centers](https://doi.org/10.1145/3448016.3457560), In _Proc. ACM SIGMOD Int. Conf. Management of Data_, pages 2477–2489, 2021. |
+| 9 | 3/5 | Memory disaggregation | Student 9 <br/> Student 10 |  Q. Zhang et al., [Understanding the Effect of Data Center Resource Disaggregation on Production DBMSs](https://doi.org/10.14778/3397230.3397249), _Proc. VLDB Endowment_, 13(9): 1568-1581, 2020. <br/> Q. Zhang et al., [Redy: Remote Dynamic Memory Cache](https://doi.org/10.14778/3503585.3503587), _Proc. VLDB Endowment_, 15(4): 766 - 779, 2022. |
+| 10 | 3/12 | Hardware accelerators | Student 11 <br/> Student 12 | C. Lutz et al., [Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects](https://doi.org/10.1145/3318464.3389705), In _Proc. ACM SIGMOD Int. Conf. Management of Data_, pages 1633–1649, 2020. <br/> L. Hu et al., [GAMMA: A Graph Pattern Mining Framework for Large Graphs on GPU](https://ieeexplore.ieee.org/document/10184586), In _IEEE 39th International Conference on Data Engineering_, 2023. |
+| 11 | 3/19 | Hardware accelerators  | Student 13 <br/> Student 14  | D. Korilija et al, [Farview: Disaggregated Memory with Operator Off-loading for Database Engines](https://www.cidrdb.org/cidr2022/papers/p11-korolija.pdf), In _Proc. 12th Conf. on Innovative Data Syst. Research_, 2022. <br/> 23 |
+| 12 | 3/26 |  Project presentations |  |  |
+| 13 | 4/2 |  Project presentations |  |  |
diff --git a/Schedule.md b/Schedule.md
@@ -1,46 +1,149 @@
 ## Weekly Schedule
 
-| Week              | Date | Topic | Speaker | Readings | 
-| :----------------: | :------: | :---- | :---- | :---- |
-| 1 | 1/9 | Introduction to Disaggregated & Heterogeneous Platforms | M. Tamer Özsu | 1, 2, 3 |
-| 2 | 1/16 | Introduction to Graph Processing | M. Tamer Özsu| 4, 5 |
-| 3 | 1/23 | Networking infrastructure |  | 6, 7, 8, 9 |
-| 4 | 1/30 | Storage disaggregation | Student 1 <br/> Student 2 | 10 <br/> 11 |
-| 5 | 2/6 | Storage disaggregation |  Student 3 <br/> Student 4| 12 <br/> 13 |
-| 6 | 2/13 | Storage/Memory disaggregation | Student 5 <br/> Student 6 | 14 <br/> 15 |
-| 7 | 2/20 |  No class -- Reading week|  |  |
-| 8 | 2/27 | Memory disaggregation | Student 7 <br/> Student 8 | 16 <br/> 17 |
-| 9 | 3/5 | Memory disaggregation | Student 9 <br/> Student 10 | 18 <br/> 19 |
-| 10 | 3/12 | Hardware accelerators | Student 11 <br/> Student 12 | 20 <br/> 21 |
-| 11 | 3/19 | Hardware accelerators  | Student 13 <br/> Student 14  | 22 <br/> 23 |
-| 12 | 3/26 |  Project presentations |  |  |
-| 13 | 4/2 |  Project presentations |  |  |
-
-## Readings
-
-Note that all of the following are accessible from the original repositories I have linked to if you access them from the University (or use VPN into SCS if you are accessing from home).
-
-1. R. Wang et al., [The Case for Shared-Memory Databases with RDMA-Enabled Memory Disaggregation](https://www.vldb.org/pvldb/vol16/p15-wang.pdf), _Proc. VLDB Endowment_, 2022
-2. I. Blagodurov et al., [The time is ripe for disaggregated systems](https://www.sigarch.org/the-time-is-ripe-for-disaggregated-systems/). Computer Architecture Today – ACM SIGARCH Blog, 2021.
-3. S. Ghandeharizadeh et al., [Disaggregated database management systems](https://doi.org/10.1007/978-3-031-29576-8_3). In _Performance Evaluation and Benchmarking_, 2023. 
-4. M.T. Özsu and P. Valduriez, [Big Data Processing](https://doi.org/10.1007/978-3-030-26253-2_10). In _Principles of Distributed Database Systems_. Springer, 2022. (Focus on Section 10.4)
-5. M.T. Özsu and P. Valduriez, [Big Data Processing](https://doi.org/10.1007/978-3-030-26253-2_10). In _Principles of Distributed Database Systems_. Springer, 2022. (Focus on Section 12.6)
-6. R. Recio, [A Tutorial of the RDMA Model](https://www.hpcwire.com/2006/09/15/a_tutorial_of_the_rdma_model-1/), HPC Wire, 2006.
-7. InfiniBand Trade Organization, [Enabling the Modern Data Center – RDMA for the Enterprise](https://www.infinibandta.org/wp-content/uploads/2019/05/IBTA_WhitePaper_May-20-2019.pdf), 2019.
-8. A. Lerner et al., [Databases on modern networks: A decade of research that now comes into practice](https://doi.org/10.14778/3611540.3611579). _Proc. VLDB Endowment_, 16(12):3894–3897, 2023.
-9. D. Gouk et al., [Direct access, High-Performance memory disaggregation with DirectCXL](https://www.usenix.org/conference/atc22/presentation/gouk). In _Proc. USENIX 2022 Annual Technical Conf._, pages 287–294, 2022. 
-10. A. Verbitski et al., [Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases](https://doi.org/10.1145/3035918.3056101), In _Proc. ACM SIGMOD Int. Conf. Management of Data_, pages 1041–1052, 2017.
-11. P. Antonopoulos, et al., [Socrates: The New SQL Server in the Cloud](https://dl.acm.org/doi/10.1145/3299869.3314047), In _Proc. ACM SIGMOD Int. Conf. Management of Data_, pages 1743–1756, 2019.
-12. W. Cao et al., [PolarFS: An Ultra-low Latency and Failure Resilient Distributed File System for Shared Storage Cloud Database](https://doi.org/10.14778/3229863.3229872), _Proc. VLDB Endowment_, 11(12): 1849-1962, 2018.
-13. M. Vuppalapati et al., [Building An Elastic Query Engine on Disaggregated Storage](https://www.usenix.org/conference/nsdi20/presentation/vuppalapati), In _Proc. 17th USENIX Symp. on Networked Systems Design & Implementation,_ pages 449-462, 2020.
-14. A. Agiwal et al., [Napa: Powering Scalable Data Warehousing with Robust Query Performance at Google](https://doi.org/10.14778/3476311.3476377), 14(12): 2986-2998, 2021
-15. Y, Shan et al., [LegoOS: A Disseminated, Distributed OS for Hardware Resource Disaggregation](https://www.usenix.org/conference/osdi18/presentation/shan), In _Proc. 14th USENIX Symp. on Operating System Design and Implementation_, pages 69-87, 2018.
-16. Y. Zhang et al., [Towards Cost-Effective and Elastic Cloud Database Deployment via Memory Disaggregation](https://doi.org/10.14778/3467861.3467877), _Proc. VLDB Endowment_, 14(10): 1900 - 1912, 2021.
-17. Wei Cao et al., [PolarDB Serverless: A Cloud Native Database for Disaggregated Data Centers](https://doi.org/10.1145/3448016.3457560), In _Proc. ACM SIGMOD Int. Conf. Management of Data_, pages 2477–2489, 2021.
-18. Q. Zhang et al., [Understanding the Effect of Data Center Resource Disaggregation on Production DBMSs](https://doi.org/10.14778/3397230.3397249), _Proc. VLDB Endowment_, 13(9): 1568-1581, 2020.
-19. Q. Zhang et al., [Redy: Remote Dynamic Memory Cache](https://doi.org/10.14778/3503585.3503587), _Proc. VLDB Endowment_, 15(4): 766 - 779, 2022.
-20. C. Lutz et al., [Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects](https://doi.org/10.1145/3318464.3389705), In _Proc. ACM SIGMOD Int. Conf. Management of Data_, pages 1633–1649, 2020.
-21. L. Hu et al., [GAMMA: A Graph Pattern Mining Framework for Large Graphs on GPU](https://ieeexplore.ieee.org/document/10184586), In _IEEE 39th International Conference on Data Engineering_, 2023.
-22. xxx
-23. xxx
-
+Note that all of the readings are accessible from the original repositories I have linked to if you access them from the University (or use VPN into SCS if you are accessing from home).
+
+<table><tbody>
+<tr>
+  <th> Week </th>
+  <th> Date </th>
+  <th> Topic </th>
+  <th> Speaker </th>
+  <th> Readings </th>
+<tr>
+<tr>
+  <td>1</td>
+  <td>1/9</td>
+  <td>Introduction to Disaggregated & Heterogeneous Platforms</td>
+  <td>M. Tamer Özsu</td>
+  <td>
+
+  * R. Wang et al., [The Case for Shared-Memory Databases with RDMA-Enabled Memory Disaggregation](https://www.vldb.org/pvldb/vol16/p15-wang.pdf), _Proc. VLDB Endowment_, 2022. 
+  * I. Blagodurov et al., [The time is ripe for disaggregated systems](https://www.sigarch.org/the-time-is-ripe-for-disaggregated-systems/). Computer Architecture Today – ACM SIGARCH Blog, 2021. 
+  * Ghandeharizadeh et al., [Disaggregated database management systems](https://doi.org/10.1007/978-3-031-29576-8_3). In _Performance Evaluation and Benchmarking_, 2023.
+  </td>
+
+<tr>
+  <td>2</td>
+  <td>1/16</td>
+  <td>Introduction to Graph Processing</td>
+  <td>M. Tamer Özsu</td>
+  <td>
+
+  * M. Besta et al., [Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries](https://doi.org/10.1145/3604932), _ACM Comput. Surv._ 56(2): 31:1-31:40, 2024. 
+  * M.T. Özsu and P. Valduriez, [Big Data Processing](https://doi.org/10.1007/978-3-030-26253-2_10). In _Principles of Distributed Database Systems_. Springer, 2022. (Focus on Section 10.4)-ripe-for-disaggregated-systems/). Computer Architecture Today – ACM SIGARCH Blog, 2021. 
+  * M.T. Özsu and P. Valduriez, [Big Data Processing](https://doi.org/10.1007/978-3-030-26253-2_10). In _Principles of Distributed Database Systems_. Springer, 2022. (Focus on Section 12.6) 
+  </td>
+
+<tr>
+  <td>3</td>
+  <td>1/23</td>
+  <td>Networking infrastructure</td>
+  <td></td>
+  <td>
+
+  * R. Recio, [A Tutorial of the RDMA Model](https://www.hpcwire.com/2006/09/15/a_tutorial_of_the_rdma_model-1/), HPC Wire, 2006. 
+  * InfiniBand Trade Organization, [Enabling the Modern Data Center – RDMA for the Enterprise](https://www.infinibandta.org/wp-content/uploads/2019/05/IBTA_WhitePaper_May-20-2019.pdf), 2019. 
+  * A. Lerner et al., [Databases on modern networks: A decade of research that now comes into practice](https://doi.org/10.14778/3611540.3611579). _Proc. VLDB Endowment_, 16(12):3894–3897, 2023. 
+  * D. Gouk et al., [Direct access, High-Performance memory disaggregation with DirectCXL](https://www.usenix.org/conference/atc22/presentation/gouk). In _Proc. USENIX 2022 Annual Technical Conf._, pages 287–294, 2022. 
+  </td>
+
+<tr>
+  <td>4</td>
+  <td>1/30</td>
+  <td>Storage disaggregation </td>
+  <td>Student 1 <br/><br/><br/><br/> Student 2</td>
+  <td>
+
+  * A. Verbitski et al., [Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases](https://doi.org/10.1145/3035918.3056101), In _Proc. ACM SIGMOD Int. Conf. Management of Data_, pages 1041–1052, 2017. 
+  * P. Antonopoulos, et al., [Socrates: The New SQL Server in the Cloud](https://dl.acm.org/doi/10.1145/3299869.3314047), In _Proc. ACM SIGMOD Int. Conf. Management of Data_, pages 1743–1756, 2019. 
+  </td>
+
+<tr>
+  <td>5</td>
+  <td>2/6</td>
+  <td>Storage disaggregation </td>
+  <td>Student 3 <br/><br/><br/><br/> Student 4</td>
+  <td>
+
+  * W. Cao et al., [PolarFS: An Ultra-low Latency and Failure Resilient Distributed File System for Shared Storage Cloud Database](https://doi.org/10.14778/3229863.3229872), _Proc. VLDB Endowment_, 11(12): 1849-1962, 2018. 
+  * M. Vuppalapati et al., [Building An Elastic Query Engine on Disaggregated Storage](https://www.usenix.org/conference/nsdi20/presentation/vuppalapati), In _Proc. 17th USENIX Symp. on Networked Systems Design & Implementation,_ pages 449-462, 2020. 
+  </td>
+
+<tr>
+  <td>6</td>
+  <td>2/13</td>
+  <td>Storage/Memory disaggregation </td>
+  <td>Student 5 <br/><br/><br/><br/> Student 6</td>
+  <td>
+
+  * A. Agiwal et al., [Napa: Powering Scalable Data Warehousing with Robust Query Performance at Google](https://doi.org/10.14778/3476311.3476377), 14(12): 2986-2998, 2021. 
+  * Y, Shan et al., [LegoOS: A Disseminated, Distributed OS for Hardware Resource Disaggregation](https://www.usenix.org/conference/osdi18/presentation/shan), In _Proc. 14th USENIX Symp. on Operating System Design and Implementation_, pages 69-87, 2018.  
+  </td>
+
+<tr>
+  <td>7</td>
+  <td>2/20</td>
+  <td>No class -- Reading Week </td>
+  <td></td>
+  <td></td>
+
+<tr>
+  <td>8</td>
+  <td>2/27</td>
+  <td>/Memory disaggregation </td>
+  <td>Student 7 <br/><br/><br/><br/> Student 8</td>
+  <td>
+
+  * Y. Zhang et al., [Towards Cost-Effective and Elastic Cloud Database Deployment via Memory Disaggregation](https://doi.org/10.14778/3467861.3467877), _Proc. VLDB Endowment_, 14(10): 1900 - 1912, 2021. 
+  * Wei Cao et al., [PolarDB Serverless: A Cloud Native Database for Disaggregated Data Centers](https://doi.org/10.1145/3448016.3457560), In _Proc. ACM SIGMOD Int. Conf. Management of Data_, pages 2477–2489, 2021.  
+  </td>
+
+<tr>
+  <td>9</td>
+  <td>3/5</td>
+  <td>/Memory disaggregation </td>
+  <td>Student 9 <br/><br/><br/><br/> Student 10</td>
+  <td>
+
+  *Q. Zhang et al., [Understanding the Effect of Data Center Resource Disaggregation on Production DBMSs](https://doi.org/10.14778/3397230.3397249), _Proc. VLDB Endowment_, 13(9): 1568-1581, 2020. 
+  * Q. Zhang et al., [Redy: Remote Dynamic Memory Cache](https://doi.org/10.14778/3503585.3503587), _Proc. VLDB Endowment_, 15(4): 766 - 779, 2022.  
+  </td>
+
+<tr>
+  <td>10</td>
+  <td>3/12</td>
+  <td>/Hardware accelerators </td>
+  <td>Student 11 <br/><br/><br/><br/> Student 12</td>
+  <td>
+
+  *C. Lutz et al., [Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects](https://doi.org/10.1145/3318464.3389705), In _Proc. ACM SIGMOD Int. Conf. Management of Data_, pages 1633–1649, 2020. 
+  * L. Hu et al., [GAMMA: A Graph Pattern Mining Framework for Large Graphs on GPU](https://ieeexplore.ieee.org/document/10184586), In _IEEE 39th International Conference on Data Engineering_, 2023.   
+  </td>
+
+<tr>
+  <td>11</td>
+  <td>3/19</td>
+  <td>/Hardware accelerators </td>
+  <td>Student 13 <br/><br/><br/><br/> Student 14</td>
+  <td>
+
+  *. Korilija et al, [Farview: Disaggregated Memory with Operator Off-loading for Database Engines](https://www.cidrdb.org/cidr2022/papers/p11-korolija.pdf), In _Proc. 12th Conf. on Innovative Data Syst. Research_, 2022. 
+  * One more   
+  </td>
+
+<tr>
+  <td>12</td>
+  <td>3/26</td>
+  <td>/Project presentations </td>
+  <td></td>
+  <td></td>
+
+<tr>
+  <td>13</td>
+  <td>4/2</td>
+  <td>/Project presentations </td>
+  <td></td>
+  <td></td>
+
+<tbody></table>
+Week              | Date | Topic | Speaker | Readings