Colloquium (C)

Storage (and retrieval) of information over DNA-based media raises unique challenges in contrast to contemporary digital standards, on many levels: algorithmic, computer/data scientific, information theoretic, (bio- and electrical-)engineering, biologic (and chemical). Thus, fresh (and renewed) focus on a number of rarely-studied problems in computation and information theory is necessitated, in coordination with partners from all of these disciplines. In this talk, we take an information-theoretic perspective and outline the general channel model.

DNA storage is notable for its extraordinary data density, with the recent discovery of DNA stacks added to the method of storing memory bits in DNA. However, the exploration of dynamic DNA data structures for systematic storage and retrieval, such as trees or graphs, remains largely unexplored. These dynamic DNA data structures have the potential to enable innovative applications in information storage and retrieval. Error correction is crucial for maintaining the integrity of every DNA data structure due to the complexity of the biological processes involved.

In Evolutionary Computation (EC) core concepts from evolutionary biology—inheritance, random variation, and selection—are harnessed in algorithms that are applied to complex computational problems. Importantly, EC strengths often dovetail with weak points of machine  learning (ML) algorithms, which has resulted in an increasing number of works that fruitfully combine the fields of EC with ML  and deep learning (DL). This talk surveys a number of works by my group, which are at the intersection of EC, ML, and DL.

Machine learning promises to accelerate scientific theory development and discovery in a data-driven approach. However, to fulfil this promise, methods have to a) provide an explicit human-readable form of the modelled relations and b) extrapolate well to unseen cases from only a few expensive data points.

Recent advancements in audio and language processing have yielded significant progress in audio analysis and synthesis. In the realm of audio analysis, researchers are addressing the crucial challenges of Automatic Speech Recognition (ASR), Sound Localization, Event Detection, Emotion Recognition, Speaker Diarization, and Speaker Identification. Meanwhile, in the synthesis domain efforts are focused on Speech Synthesis, Speech Separation, Audio Vocoders, and Speech-Bots.

Natural language processing (NLP) has been revolutionised in recent years to the point where it is an inseparable part of our daily lives. The transition to transformer-based models allows us to train models on vast amounts of text efficiently, proving that scale plays a crucial role in improving performance. Unfortunately, many people worldwide are marginalised from getting access to high-quality NLP models, as the language they speak and the domains they are interested in count for only a tiny fraction of current state-of-the-art models' training sets.

Machine learning algorithms increasingly influence our lives, making fairness critical to prevent discrimination based on gender, ethnicity, or other factors. In this talk I am going to focus on two important topics in algorithmic fairness; what is a fair algorithm, and how to achieve notions of fairness.
 

As more people work from home or during travel, new opportunities and challenges arise around mobile office work. They may need to work in makeshift spaces with less than optimal working conditions; applications are not flexible to their physical and social context, and remote collaboration does not account for the difference between the user's conditions and capabilities.

Hyperproperties are system properties that relate multiple execution traces to one another. Hyperproperties are essential to express a wide range of system requirements such as information flow and security policies; epistemic properties like knowledge in multi-agent systems; fairness; and robustness. 
With the aim of verifying program correctness, the two major challenges are (1) providing a specification language that can precisely express the desired properties; and (2) providing scalable verification algorithms. 

The advancements in deep learning models and their ability to excel in various fields are awe-inspiring, but practical applications still face several challenges. From a data-centric perspective, Deep Neural Networks (DNNs) require a vast amount of precisely labeled data.