February 27, 2019

Machine Learning and Data Analytics Symposium – MLDAS 2019

Date: 1-2 April, 2019

Location: HBKU Research Complex, Multipurpose Room

Introduction

The purpose of this symposium is to bring together researchers, practitioners, students, and industry experts in the fields of machine learning, data mining, and related areas to present recent advances, to discuss open research questions, and to bridge the gap between data analytics research and industry needs on certain concrete problems.

The MLDAS symposium will serve as a platform for exchange of ideas, identification of important and challenging applications, and discovery of possible synergies.

We will address the topics of interest through both invited and contributed talks describing (1) research ideas, (2) new challenges, (3) mature research and (4) practical results. The symposium program will consist of presentations by invited speakers of both industry and academia, and by the authors of the papers submitted to the symposium. In addition, we will have a panel discussion to identify important research problems and applications.

Submissions

We would like to invite authors to send in contributed submissions - FULL papers or POSITION papers - on any of the topics of interest of the symposium: machine learning, data mining, applied machine learning techniques, data analytics solutions.

Submissions to MLDAS 2019 are due by March 1, 2019 at 11:59 PM Pacific Standard Time.

The submissions page is here.

Registration

Registration for MLDAS 2019 is open.

Researchers, students, and practitioners interested in attending the symposium are invited to register for the symposium by sending an e-mail to registration@mldas.org by March 27, 2019.

The e-mail should include the name, affiliation (institution and address), website (if available) and a brief statement of interest describing the reasons for attending the symposium. For example: "I would like to attend MLDAS 2019 because my interests are in health informatics and I would like to apply machine learning techniques on the data that we have available."

The attendance to the symposium is limited to 120 attendees and registration requests received after March 25, 2019 cannot be guaranteed.

Organization

MLDAS is organized by the Qatar Computing Research Institute (QCRI) and by The Boeing Company.

Chairs

MLDAS is co-chaired by Sanjay Chawla (QCRI), Dragos Margineantu (Boeing Research & Technology) and Zoi Kaoudi (QCRI).
Please click here to contact the MLDAS co-chairs.

AGENDA

Day 1


07:30: Registration & Coffee


08:05 - 10:00: Session 1

  • 08:05 - 08:30: Welcome and Opening Remarks
  • 08:30 - 09:15: How the periodic table got built up? [slides]
    • Shri Kulkarni | California Institute of  Technology
  • 09:15- 10:00: Robust Learning Ideas for AI Engineering
    • Dragos Margineantu | Boeing

10:00 - 10:30: Coffee Break


10:30 - 12:00: Session 2

  • 10:30 - 11:15: Neuromorphic Computing Chips for AI at the Edge [slides]
    • Ben Abdallah Abderazek | University of Aizu
  • 11:15 - 11:35: Using Advertising Data to Model Migration, Poverty and Digital Gender Gaps [slides]
    • Ingmar Weber | QCRI
  • 11:35 - 12:00: Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors [slides]
    • Amin Sadeghi | University of Tehran

12:00 - 13:30: Lunch Break


13:30 - 15:00: Session 3

  • 13:30 - 14:15: Unsupervised Video Object Segmentation for Deep Reinforcement Learning [slides]
    • Pascal Poupart | University of Waterloo
  • 14:15 - 15:00: Towards verifying neural autonomous systems [slides]
    • Alessio Lomuscio | Imperial College London

15:00 - 15:30: Coffee Break


15:30 - 17:00: Session 4

  • 15:30 - 16:15: Deep Learning in Julia [slides]
    • Deniz Yuret | Koç University
  • 16:15 - 16:35: Detecting Phishing Domains using Certificate Transparency
    • Mashael Al-Sabah | QCRI
  • 16:35 - 17:00: Hack me if you can: A rule mining-based advanced persistent threats detection system.
    • Sidahmed Benabderrahmane | University of Edinburgh

 

Day 2


08:05 - 10:00: Session 1

  • 08:05 - 08:50: Developing data-driven machine learning techniques to address biomedical classification problems: applications and challenges
    • Jiangning Song | Monash University
  • 08:50 - 9:35 Analysing GPS Trajectory Data [slides]
    • Dimitris Gunopoulos | University of Athens
  • 9:35 - 10:00 The Best of Both Worlds: Ensuring Selective Privacy with Performance in a Collaborative Filtering Framework
    • Manoj Reddy | UCLA

10:00 - 10:30: Coffee Break


10:30 - 12:00: Session 2

  • 10:30 - 10:50: Efficient Machine Learning Approach to Capture Genetic Correlation
    • Halima Bensmail | QCRI
  • 10:50 - 12:00: PANEL: "Learning from the past: how to avoid the next AI-winter"

12:00 - 13:30: Lunch Break

For more information about the different talks, click here.

SPEAKERS

Halima Bensmail

Associate Professor / Principal Scientist, Data Analytics, QCRI

Shrinivanas Kulkarni

George Ellery Hale Professor of Astronomy, Caltech

Alessio Lomuscio

Professor, Department of Computing at Imperial College London, UK

Mohammad Amin Sadeghi

Assistant Professor, ECE, University of Tehran

Ingmar Weber

Research Director, Social Computing, QCRI

Mashael Al-Sabah

Scientist, Cyber Security, QCRI

Deniz Yuret

Professor of Computer Engineering at Koç University, Istanbul, Turkey

Pascal Poupart

Professor, Health Informatics David R. Cheriton School of Computer Science, University of Waterloo, Canada

Dimitrios Gunopulos

Professor, Informatics and Telecommunications, National and Kapodistrian University of Athens

Jiangning Song

Group Leader, Cancer and Infection and Immunity Programs, Monash Biomedicine Discovery Institute (BDI) and Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Australia

Ben Abdallah Abderazek

Head of Computer Engineering Division and Director of Adaptive Systems Laboratory at the School of Computer Science and Engineering, The University of Aizu, Japan

Manoj Reddy

PhD Candidate at University of California Los Angeles

Sid Ahmed Benabderrahmane

Senior research associate, Laboratory for Foundations of Computer Science, University of Edinburgh