Fixes

Author: anuragkh

index.md

@@ -31,8 +31,8 @@ towards supporting stateful applications on serverless architectures. To this
 end, we are exploring the ground up design of the serverless OS stack that
 facilitates stateless and stateful applications.
 
-Publications:
-* A Berkeley View on Serverless Computing [[UC Berkeley Tech Report](https://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-3.pdf)]
+_Publications:_
+* A Berkeley View on Serverless Computing \[[UC Berkeley Tech Report](https://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-3.pdf)\]
 
 **System stack for emerging hardware:** Today's system stacks were designed to
 operate with traditional hardware, e.g., with 1 Gbps links and traditional 
@@ -42,37 +42,31 @@ and optimization of these systems. To resolve these challenges, we are
 revisiting traditional system designs to bridge the gap between hardware
 capabilities and realizable system properties.
 
-Publications:
-* Distributed Monitoring & Diagnosis Stack for High Speed Networks [[NSDI'19 Paper](http://cs.berkeley.edu/~anuragk/papers/confluo.pdf)], [[Code](https://github.com/ucbrise/confluo)]
-
-**Secure cloud systems:**
-
-With web applications and services moving from self-owned servers in
-private data centers to to public cloud platforms, users must now trust the 
-cloud provider who manages the physical infrastructure that their applications 
-run on. Unfortunately, high-profile security breaches in the public cloud 
-indicate that this trust may not always be well placed. We are exploring the 
-vulnerabilities of existing system deployments hosted on the cloud and the 
-design of secure systems that no longer have to trust the cloud provider.
-
-Publications:
-* Attacking Data Center Networks [[MSR Tech Report](http://cs.berkeley.edu/~anuragk/papers/dcn.pdf)]
-
-**Queries on compressed data:**
-
-Ensuring low latency and high throughput for user-facing queries is challenging
-when the volume of data being queried grows larger than the DRAM capacity. 
-Traditionally, storage systems have resorted to spilling over such data to 
-significantly slower secondary storage, resulting in higher query latency 
-and reduced throughput. We have been exploring a fundamentally new approach
-to resolve this challenge --- enabling queries directly on a compressed
-representation of the data.
-
-Publications:
-* Succinct: Enabling Queries on compressed data [[NSDI'15 Paper](http://cs.berkeley.edu/~anuragk/papers/succinct.pdf)], [[Code: Standalone System](http://github.com/amplab/succinct-cpp)], [[Code: Succinct on Spark](https://github.com/amplab/succinct)]
-* BlowFish: Dynamic Storage-Performance Tradeoff in Data Stores [[NSDI'16 Paper](http://cs.berkeley.edu/~anuragk/papers/blowfish.pdf)]
-* Sprint: Regular Expression Queries on Compressed Data [[Tech Report](http://cs.berkeley.edu/~anuragk/papers/swift.pdf)], [[Code](https://github.com/amplab/sprint)]
-* ZipG: Serving Queries on Compressed Graphs [[SIGMOD'17 Paper](http://cs.berkeley.edu/~anuragk/papers/zipg.pdf)]
+_Publications:_
+* Distributed Monitoring & Diagnosis Stack for High Speed Networks \[[NSDI'19 Paper](http://cs.berkeley.edu/~anuragk/papers/confluo.pdf)\], \[[Code](https://github.com/ucbrise/confluo)\]
+
+**Secure cloud systems:** With web applications and services moving from 
+self-owned servers in private data centers to to public cloud platforms, users
+must now trust the cloud provider who manages the physical infrastructure that 
+their applications run on. Unfortunately, high-profile security breaches in the 
+public cloud indicate that this trust may not always be well placed. We are 
+exploring the vulnerabilities of existing system deployments hosted on the 
+cloud and the design of secure systems that no longer have to trust the cloud 
+provider.
+
+**Queries on compressed data:** Ensuring low latency and high throughput for 
+user-facing queries is challenging when the volume of data being queried grows 
+larger than the DRAM capacity. Traditionally, storage systems have resorted to 
+spilling over such data to significantly slower secondary storage, resulting in 
+higher query latency and reduced throughput. We have been exploring a 
+fundamentally new approach to resolve this challenge --- enabling queries 
+_directly_ on a compressed representation of the data.
+
+_Publications:_
+* Succinct: Enabling Queries on compressed data \[[NSDI'15 Paper](http://cs.berkeley.edu/~anuragk/papers/succinct.pdf)\], \[[Code: Standalone System](http://github.com/amplab/succinct-cpp)\], \[[Code: Succinct on Spark](https://github.com/amplab/succinct)\]
+* BlowFish: Dynamic Storage-Performance Tradeoff in Data Stores \[[NSDI'16 Paper](http://cs.berkeley.edu/~anuragk/papers/blowfish.pdf)\]
+* Sprint: Regular Expression Queries on Compressed Data \[[Tech Report](http://cs.berkeley.edu/~anuragk/papers/swift.pdf)\], \[[Code](https://github.com/amplab/sprint)\]
+* ZipG: Serving Queries on Compressed Graphs \[[SIGMOD'17 Paper](http://cs.berkeley.edu/~anuragk/papers/zipg.pdf)\]
 
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