Title : Towards Automated Validation and Inspection of Machine Learning Pipelines

Abstract: Machine Learning (ML) is increasingly used to automate impactful decisions, and the risks arising from this wide-spread use are garnering attention from policy makers, scientists, and the media. ML applications are often very brittle with respect to their input data, which leads to concerns about their reliability, accountability, and fairness. In this lecture, I will introduce some of the practical problems in this area and give an overview over two recent approaches on tackling such issues. Deequ is a library for automating the verification of data quality at scale. It provides a declarative API, which combines common quality constraints with user-defined validation code, and thereby enables ‘unit tests’ for data. Deequ efficiently execute the resulting constraint validation workload by translating it to aggregation queries on Apache Spark, and also supports the incremental validation of data quality on growing datasets. mlinspect is a library that enables the lightweight lineage-based inspection of ML preprocessing pipelines. The key idea is to extract a directed acyclic graph representation of the dataflow from ML preprocessing pipelines in Python, and to use this representation to automatically instrument the code with predefined inspections based on a lightweight annotation propagation approach. In contrast to existing work, mlinspect operates on declarative abstractions of popular data science libraries like estimator/transformer pipelines and does not require manual code instrumentation.

Title : Humans in Online Labor Markets

Abstract: Online labor markets are increasingly becoming a destination for work. These marketplaces include freelancing platforms such as Qapa and MisterTemp' in France, and TaskRabbit and Fiverr in the USA. On those platforms, workers can find temporary jobs in the physical world such as moving furniture, or in the form of virtual micro-gigs such as helping with designing a website. I will present the results of a study of fairness on those platforms, and discuss how human factors affect algorithm design. The talk will end with a summary of open questions on the Future of Work.

Title : Video Data Management: From Data Models to Data Storage and Benchmarking

Title : Hallucinating Analytics over Real-World Data using Augmented Reality

Abstract: In addition to the virtual universe, there is a vast amount of data present in the real world. Given recent advances in computer vision, augmented reality, and cloud services, we are faced with a tremendous opportunity to augment the structured data around end-users with insights. Coinciding with these trends, the number of data-rich end-user activities is also rapidly increasing. Thus, it is useful to investigate the process of data exploration and analysis in augmented and mixed reality settings. In this talk, we describe a data exploration platform that utilizes augmented reality to enable querying over real-world data.

Title : Towards Democratizing Data Science

Abstract: Technology has been the key enabler of the current Big Data movement. Without open-source tools like TensorFlow and Spark, as well as the advent of cheap, abundant computing and storage in the cloud, the trend toward datafication of almost every field in research and industry could never have happened. However, the current Big Data tool set is ill-suited for an efficient knowledge discovery by domain experts with only limited IT skills and thus represents a major bottleneck in our data-driven society. In this talk, I will present an overview of my current research efforts to revisit the Big Data stack from the user interface to the underlying hardware for making Big Data tools more efficient and easier to use for domain experts and thus enable the democratization of data science.