Machine Learning Seminar by Prof. William Stafford Noble – Deep learning applications in mass spectrometry proteomics and single-cell genomics

  Short Bio William Stafford Noble is a Professor in the Department of Genome Sciences and in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. He received the Ph.D. in computer science...

A New Supercomputer for Scaling Up Machine Learning and Artificial Intelligence at Waipapa Taumata Rau / The University of Auckland

The School of Computer Science at Waipapa Taumata Rau / The University of Auckland (UoA), has invested in and installed a new GPU supercomputer, aiming at building capability of large-scale machine learning and artificial intelligence research and teaching.  The newly...

Machine Learning Seminar by Prof. Christian S. Jensen – New Vehicle Routing Paradigms Enabled by Big Vehicle Trajectory Data

  Short Bio Christian S. Jensen is Professor of Computer Science at Aalborg University, Denmark. His research concerns primarily analytics, including machine learning, data mining, and query processing, and data management, with...

Machine Learning Seminar by Dr. Xia Ning – Deep Generative Models for Molecule Optimization

  Short Bio Dr. Xia Ning is an Associate Professor in the Biomedical Informatics Department, and the Computer Science and Engineering Department, The Ohio State University. She received her Ph.D. in Computer Science and...

Machine Learning Seminar by Alex Gavryushkin – Online Algorithms for Evolutionary and Systems Biology

  Short Bio Alex Gavryushkin is an associate professor and Rutherford Discovery Fellow at the University of Canterbury, where he leads the Biological Data Science lab, which he founded in 2017. The lab is primarily interested in...

Machine Learning Seminar by Pat Langley – Computational Scientific Discovery: Heuristic Search for Communicable Laws and Models

  Short Bio Dr. Pat Langley serves as Director of the Institute for the Study of Learning and Expertise and as a Research Scientist at Stanford University's Center for Design Research. He has contributed to AI and cognitive...

ML Student Seminar on Dec 2 2021: Adversarial Learning on Time Series Forecasting

Where and when: Thursday, Dec 2 at 2-3pm in Google Meets (see the calendar invitation for the link) Speaker: Mark Chen, supervised by Joerg Wicker and Gill Dobbie Abstract: Time series forecasting is one of the main topics which involves time series data. It predicts...

AI Reading Group on Nov 25 2021: The neural architecture of language: Integrative modeling converges on predictive processing

Where and when: Thursday, Nov 25 at 2-3pm in Google Meet (see the calendar invite for the link). Abstract The neuroscience of perception has recently been revolutionized with an integrative modeling approach in which computation, brain function, and behavior are...

ML Student Seminar on Nov 18 2021: Beyond Recall: Teaching High Quality Language Models to Generalise to Unseen Compositional Questions

Where and when: Thursday, Nov 18 at 2-3pm in Google Meets (see the calendar invitation for the link) Speaker: Tim Hartill, supervised by Michael Witbrock and Pat Riddle Abstract: Sequence-to-sequence Transformer-based Language Models pretrained on large text corpora...

Machine Learning Seminar by Zachary Lipton – RATT: Leveraging Unlabeled Data to Guarantee Generalization

  Short Bio Zachary Chase Lipton is the BP Junior Chair Assistant Professor of Operations Research and Machine Learning at Carnegie Mellon University and a Visiting Scientist at Amazon AI. He directs the Approximately Correct...

Adversarial Learning: Robust and Reliable Machine Learning Models

Kia Ora! I am Luke Chang and I am passionate about building more reliable machine learning models, an artificial intelligence people can trust. I started my machine learning journey by designing a transmission controller for an engine in my third-year undergraduate project. I was fascinated by how artificial intelligence can be integrated into everyday systems. When I learnt a state-of-the-art machine learning model is vulnerable to malicious attacks, even when trained on a large amount of...

Knowledge-Driven Text Generation

I am Beryl Qi and my research topic is ‘Knowledge-Driven Text Generation’. Natural language generation (NLG) is an important research direction in the field of natural language processing. This technology can be applied to various information processing tasks, such as QA (Question Answering), IE (Information Extraction), PD (Problem Decomposition), etc. The wide application of NLG benefits from its learning and processing capabilities when facing to explosion of data. The overall system we...

Identification and Mitigation of Selection Bias

Kia Ora! I am Katharina Dost, a PhD student in my second year with the School of Computer Science. My research topic is “Identification and Mitigation of Selection Bias” and I would like to use this post to talk about my research and my experiences, so read on! Our world runs on data. We gather whatever we can and use it to answer a variety of questions. These can be something as simple as “What is the average age of my customers?” but also as critical as “Which treatment is best suited for...

University of Auckland Machine Learning Group

Machine Learning is a field of Computer Science that aims to develop algorithms that learn from experience. This is in contrast to traditional computer science algorithms where behavior is directly coded into the algorithms. Machine Learning algorithms take experience in form of data and observe patterns in these data to make decisions and predictions. Machine Learning became widely popular in recent years and Machine Learning methods are widely used, having applications in every industry...