HKUST CSE Special Seminar

Title: "HKUST CSE Special Seminar"

Date: 14 April (Friday), 2023

Time: 10:00 - 13:00 (HKT)

Venue: Zoom (https://hkust.zoom.us/j/94531153482?pwd=UUU4QlpLT3p0bmdSKzVoYlhKWFd6Zz09)

Meeting ID: 945 3115 3482
Passcode: 328985


Host: Dr. Amir GOHARSHADY and Dr. Xiaojuan MA
=============================================


*** Speaker 1 ***

Dr. Huan Zhao
Senior Researcher of 4Paradigm Inc.

Time:   10:00 -11:00
Topic:  Advances in Automated Graph Neural Networks: Research and Applications from an AI Startup
Q&A:    11:00-11:30



*** Speaker 2 ***

Prof. Umang Mathur
Assistant Professor
School of Computing at the National University of Singapore

Time:   11:30-12:30
Topic:  Rethinking Timestamping in Distributed Computing Through a Data-Structure Lens
Q&A:    12:30-13:00


*************
Dr. Huan Zhao

Topic: Advances in Automated Graph Neural Networks: Research and Applications from an AI Startup

Abstract:

In recent years, graph neural networks (GNNs), e.g., GCN, GAT, and
JK-Network, have been the state-of-the-art (SOTA) methods for various
tasks in GSD, e.g., product recommendation, and molecular property
prediction. However, due to diverse graphs in real-world applications,
currently, no human-designed GNNs can consistently perform well, and thus
a huge amount of human expertise and computational resources have to be
invested to obtain well-performing GNN architectures. In this talk, I will
introduce our exploration of automated graph neural networks (AutoGraph).
Specifically, we use the recently popular neural architecture search (NAS)
for graph architecture design. Specifically, two works based on
differentiable neural architecture search algorithms will be introduced,
which can search for better GNN architectures on different datasets
including node-level and graph-level tasks, and show that they can
effectively and efficiently obtain well-performing data-specific GNN
architectures. Moreover, the proposed method for graph classification has
achieved 1st place in the well-known open graph benchmark (OGB). These two
works have been published in top-tier data mining conferences, i.e., CIKM
2021 and WebConf 2022. Finally, a real-world application of AutoGraph to
biomedicine scenario, i.e., predicting Double-strand DNA breaks, will be
briefly introduced.


Biography:

Dr. Huan Zhao is a senior researcher in 4Paradigm Inc., an AI startup in
China, leading the research on automated graph neural networks (AutoGraph)
in the company. Prior to 4Paradigm, He worked as a research intern and
senior algorithm engineer at Alibaba from November 2017 to July 2019. He
obtained his Doctor Degree at the Department of Computer Science and
Engineering, HKUST in Jan. 2019. His research interests include
recommender system, graph representation learning, and automated machine
learning. He has published more than 30 top-tier conference and journal
papers, including KDD, WebConf, ACL, and AAAI etc. Besides research, he
also leads a team in 4Paradigm to deliver AutoGraph algorithms to
real-world applications,  e.g., retailing recommendation, financial fraud
detection, bioinformatics, intelligent manufacturing, etc., from the
business partners of 4Paradigm.


******************
Prof. Umang Mathur

Topic: Rethinking Timestamping in Distributed Computing Through a Data-Structure Lens

Abstract:

Many applications in distributed and concurrent computing crucially rely
on logical timestamping. This involves assigning timestamps to different
events in the execution of a system so that the ordering induced on the
assigned timestamps is isomorphic to the underlying causal ordering on
events in the execution. In other words, the timestamps of two events can
be used to infer the causal ordering between them.

In this talk I will first argue that timestamping (i.e., the process of
computing and assigning timestamps to all events) should be viewed from a
data structure perspective. This perspective allows us to ask questions
like "what is the most optimal data structure for timestamping"?

The most popular data structure is the vector clock data structure, which
stores an integer for each node/thread in a flat array. The two basic
updates on vector clocks, namely join and copy, perform exactly k integer
operations, where k is the number of threads/processes. In applications
where timestamps need to be computed at every event, such vector clock
updates can be a computational bottleneck, especially when k is large.

Next, I will describe tree clocks, a new data structure that replaces
vector clocks for computing causal orderings in program executions.
Joining and copying tree clocks takes time that is roughly proportional to
the number of entries being modified, and hence the two operations do not
suffer the a-priori I?(k) cost per application. I will then discuss an
optimality result in the context of an application of my interest, namely
data race detection. In shared memory multi-threaded programs
synchronising over locks, the happens-before (HB) partial order on events
in an execution is a classic partial order used to infer the presence of
data races dynamically. For computing the HB partial order, tree clocks
are optimal, in the sense that no other data structure can lead to smaller
asymptotic running time. Finally, for the practically inclined, I will
present empirical evidence that this data structure can significantly
speed up applications such as data race detection, and for the
theoretically inclined, I will discuss some future extensions and
applications.


Biography:

Umang Mathur is an Assistant Professor at the National University of
Singapore. He received his PhD from the University of Illinois at Urbana
Champaign and was an NTT Research Fellow at the Simons Institute for the
Theory of Computing at Berkeley. His research broadly centres on
developing techniques inspired from formal methods and logic for answering
design, analysis and implementation questions in programming languages,
software engineering and systems. He has recieved a Google PhD Fellowship,
an ACM SIGSOFT Distinguished Paper Award at ESEC/FSE'18. Best Paper Award
at ASPLOS'22 and an ACM SIGPLAN Distirnguished Paper Award at POPL'23 for
his work on designing techniques and tools for analyzing concurrent
software. More details can be found
at: https://www.comp.nus.edu.sg/~umathur/