About

Daniar H. Kurniawan is a highly skilled Ph.D. student at the University of Chicago with a focus on machine learning and systems. He is currently working on developing a new caching algorithm and caching architecture to improve inference-system performance. Daniar has previously worked on cutting millisecond tail latency for data-parallel applications affected by Java's Garbage Collection. He has also worked on a variety of projects, including detecting distributed concurrency bugs, improving performance/fault tolerance in distributed systems, and improving resource allocation strategy. Daniar is experienced in hacking large-scale systems, such as Cassandra, Apache Ignite, Voldemort, and Spark. He is currently a Research Intern at Seagate, where he is working on improving the availability and reliability of CORTX systems. Daniar has also previously worked as a Research Intern at VMware, where he developed a cloud-based spreadsheet platform for browsing, displaying, and analyzing very large data sets (billions of rows). Daniar holds a Bachelor of Engineering in Informatics from Institut Teknologi Bandung and is currently pursuing a Doctor of Philosophy in Computer Science from the University of Chicago, which he is expected to complete in 2023. He has a strong tech stack, including research scientist, Spark, Java, ML, and Cassandra, which he has utilized in his research and work experiences. With relevant experience of 0.78 years, Daniar is a highly skilled and knowledgeable individual who is dedicated to advancing the field of machine learning and systems.