NUS School of Computing - Graduate Student Tutorials 2018

 

The two-day tutorial sessions organised by NUS School of Computing provides an excellent opportunity for student participants to explore as well as to gain exposure and foundations to the latest computing research. This forum, which is particularly useful for students who wish to embark on computing research, also aims to facilitate interactions and discussions of ideas amongst student participants, researchers and faculty members.


Graduate students and final-year undergraduate students are welcome to attend the tutorials. Participants from external institutions or organizations may also apply to attend the sessions. 


Date & Time
10 & 11 January 2018

9.00am to 5.00pm

Breaks: Morning - 10.30am to 11.00am
             Lunch – 1.00pm to 2.00pm
             Afternoon – 3.30pm to 4.00pm

Venue
NUS School of Computing (COM1 Level 2)

Registration
Registration Period        : 1 to 30 December 2017
Registration Fees           : S$200 per day (Industry participants only)
Registration Link            : http://blog.nus.edu.sg/sumsch/

 
Due to limited places, attendance is strictly by registration only. Only notified registered participants will be allowed to attend the sessions.

Programme Outline

DAY 1 – 10 JANUARY 2018

Session

CS-1

Title

Big Data Systems on Future Hardware

Speaker

He Bingsheng

Associate Professor, Dept of Computer Science

Venue

Video Conference Room (VC)

Abstract

Big data has become a buzz word. Among various big-data challenges, high performance is a must, not an option. We are facing the challenges (and also opportunities) at all levels ranging from sophisticated algorithms and procedures to mine the gold from massive data to high-performance computing (HPC) techniques and systems to get the useful data in time. How to leverage emerging hardware has become a hot research topic to tame the performance challenges of big data applications. Our research has been on the novel design and implementation of big data systems on emerging hardware (many-core CPUs, GPUs, and FPGAs etc). Interestingly, we have also observed the interplay between emerging hardware and big data systems. In this talk, I will present our research efforts in the past ten years and outline our research agenda on future hardware. More details about our research can be found at http://www.comp.nus.edu.sg/~hebs/.

Session

CS-2

Title

Discrete Sampling and Integration in High Dimensional Spaces

Speaker

Kuldeep Meel

Assistant Professor, Dept of Computer Science

Venue

Seminar Room (SR) 3

Abstract

Discrete sampling and integration are fundamental problems in Artificial Intelligence, with a wide variety of applications spanning probabilistic inference, statistical machine learning, network reliability analysis and the like.  This tutorial will introduce the audience to the extensive theoretical and practical research work done in this area over the past few decades. We will discuss in detail the promising recent approach of using universal hashing to integrate and sample with strong formal guarantees, while also scaling to large and realistic problem sizes.

Session

IS-1

Title

Mining Clinical Data

Speaker

Vaibhav Rajan

Assistant Professor, Dept of Information Systems and Analytics

Venue

Seminar Room (SR) 10

Abstract

The availability of digital clinical data through Electronic Medical Records (EMR) in hospitals is increasing throughout the world. This data presents an unprecedented opportunity to study and gain deeper understanding of diseases, develop new personalized treatments and improve healthcare systems. However, many hurdles remain in the analysis and clinically-relevant interpretation of this data and new models and algorithms are required to address these challenges. This course will begin with a description of the typical contents of EMR data and an overview of potential clinical applications of mining such data. A detailed description of the challenges of modeling clinical data will follow. These data-related challenges include heterogeneity, complex dependencies, censored data, missing and noisy measurements, sparsity and irregularity of temporal clinical data. Methods, from biostatistics, epidemiology and machine learning, developed to address these challenges for the design of exploratory and predictive models shall be outlined. Recent machine learning based methods for specific applications (like predicting complications, computational phenotyping and estimation of physiological states from temporal data) and some open research problems shall be discussed.

DAY 2 – 11 JANUARY 2018

Session

CS-3

Title

Algorithms in Genomics

Speaker

Sung Wing-Kin

Professor, Dept of Computer Science

Venue

Seminar Room (SR) 10

Abstract

Due to the advance in technology, we can sequence our DNA efficiently and cost-effectively. Currently, we can sequence our full genome using US$1000. In the future, we expect sequencing cost US$100 per genome. Although the sequencing technology is mature, one problem is that the datasets are big (in term of hundreds of Giga bytes). The computational methods for analyzing these datasets have lagged behind. In this full day course, we will introduce background of genomics and the current methods available. Then, we will cover the possible future research topics.

 

Session

CS-4

Title

Algorithmic Foundations of Privacy

Speaker

Reza Shokri
Assistant Professor, Dept of Computer Science

Venue

Video Conference Room (VC)

Abstract

This short course covers algorithmic foundations of computation computational privacy. It provides a thorough methodology for analysis of privacy against inference attacks using techniques from statistics, probability theory, and machine learning. Students will learn how to reason quantitatively about privacy, and evaluate it using the appropriate metrics. The course will provide the students with the foundations to design privacy-preserving mechanisms for a range of systems from data analytics to machine learning. After this course, students should be able to identify privacy vulnerabilities in a system, design basic inference attacks, and propose countermeasures in a systematic manner.