Machine Learning Student Seminars

Do you find yourself wondering what great research is being done in our group? Our ML Student Seminar series is intended to give us all a chance to see and hear about what others work on and thereby gives the chance to identify where collaboration is possible. For the students, this is a great chance to present their work and get some feedback and ideas.

In every session, a PhD (or postgrad) student will give a talk on her current work. We will then give feedback, discuss potential problems the student is facing, provide ideas on how to solve challenges and let our creativity flow. The talks are not intended to be perfect and could be quite short. However, the student needs to provide us all with enough information to understand the research and the particular problem she is aiming to solve.

Where and when?

The ML Student Seminars will be held bi-weekly (alternating with the AI Reading Group!) on Thursdays 2pm-3pm in 303S-561. We strongly encourage all members of our group to participate – students and lecturers.

Speakers

In order to have an interesting seminar series, we aim for diverse talks and therefore iterate through supervisors. The supervisors will be asked to nominate students that should give a talk soon — preferably new students that still search for their path, students struggling with a specific problem, but also students that already have a lot of interesting research to report. Please avoid nominating students that just had their Provisional Year Reviews as many of us will already have seen their current work.  Since we meet bi-weekly, every student will be presenting about once a year.

ML Student Seminar on Sep 9 2021: Machine Learning in Julia

Where and when: Thursday, Sep 9 at 2-3pm in 303S-561 Speaker: Anthony Blaom, https://ablaom.github.io Abstract: Julia is a young but mature programming language which allows one to write fast code fast. Over a two year period, we have developed a Julia toolbox, called MLJ, for training, evaluating, tuning, and composing a large number of machine learning models written in Julia and other...

ML Student Seminar on Aug 12 2021: Reinforcement Learning Control for Vapour Compression Refrigeration System

Where and when: Thursday, Aug 12 at 2-3pm in 303S-561 Speaker: Tech Logg Ding, Ph.D. student, Department of Mechanical Engineering Abstract: Refrigeration systems are essential for various cooling applications in modern society. The majority of the refrigeration systems in the world uses the Vapour Compression Refrigeration Cycle (VCRC) technology due to its’ efficiency. In the literature,...

ML Student Seminar on July 29 2021: SymbioLCD: Ensemble-Based Loop Closure Detection using CNN-Extracted Objects and Visual Bag-of-Words

Where and when: Thursday, July 29 at 2-3pm in 303S-561 Speaker: Jonathan Kim, PhD student, supervised by Pat Riddle and Joerg Wicker Abstract: Loop closure detection is an essential tool of Simultaneous Localization and Mapping (SLAM) to minimize drift in its localization. Many state-of-the-art loop closure detection (LCD) algorithms use visual Bag-of-Words (vBoW), which is robust against...

ML Student Seminar on July 15 2021: Machine Learning for Detecting Vision Problem in Young Children

Where and when: Thursday, July 15 at 2-3pm in 303S-561 Speakers: Jason Turuwhenua is a Senior Research Fellow and Principal Investigator for the Eye Health and Diagnostics Lab at the Auckland Bioengineering Institute. Jason is interested in the application of engineering techniques to solve problems in the diagnosis of vision. Mohammad Norouzifard is Postdoctoral Research Fellow in Eye Health...

ML Student Seminar on June 17 2021: Defining Explainability Via Mental Models

Where and when: Thursday, June 17 at 2-3pm in 303S-561 Speaker: Mike Merry, PhD student, supervised by Pat Riddle, and Jim Warren Abstract: Wide-ranging concerns exist regarding the use of black-box artificial intelligence (AI) in sensitive contexts. Despite performance gains and substantial hype, uptake of AI is still hindered by these concerns. The ability to explain the inner workings of a...

ML Student Seminar June 3 2021: Data has shape and shape has meaning

Where and when: Thursday, June 3 at 2-3pm in 303S-561 Speaker: Neset Tan, PhD student, supervised by Mark Gahegan and Michael Witbrock Abstract: Geometric and topological tools can be effective for understanding complex data. Topological data analysis is a young and growing area that provides strong tools from a combination of algebraic topology and statistics to capture useful patterns in...

ML Student Seminar May 20 2021: Multivariate Sequential Analytics for Long-term Chronic Condition Management

Speaker: William Hsu, PhD student, supervised by Jim Warren and Pat Riddle Abstract: Chronic conditions place a considerable burden on modern healthcare systems. Within New Zealand and worldwide cardiovascular disease (CVD) affects a significant proportion of the population and is the leading cause of death. An enduring challenge in population health is to accurately identify at-risk...

Oct
07

Machine Learning Student Seminar

From April 22, 2021 to December 16, 2021, Every 2 weeks at 2:00 pm
Oct
21

Machine Learning Student Seminar

From April 22, 2021 to December 16, 2021, Every 2 weeks at 2:00 pm
Nov
04

Machine Learning Student Seminar

From April 22, 2021 to December 16, 2021, Every 2 weeks at 2:00 pm
Nov
18

Machine Learning Student Seminar

From April 22, 2021 to December 16, 2021, Every 2 weeks at 2:00 pm
Dec
02

Machine Learning Student Seminar

From April 22, 2021 to December 16, 2021, Every 2 weeks at 2:00 pm

Organization

Katharina Dost

Katharina Dost