Based on the overarching research foci of BIFOLD, the BIFOLD Graduate School is offering new PhD projects in the areas of current challenges in artificial intelligence (AI) and data science (DS), with focus on data management, machine learning, and their intersection.
Below is a brief description of the current research pursued by the BIFOLD research groups, including short lists of their main topics and foci. For more details, we recommend that you look at the respective webpages of the group leads or for more detailed information.
Contact:
Please feel free to reach out to the group leads directly or to: gsapplication@bifold.tu-berlin.de – depending of the nature of your query.
Full job posting: EN / DE
BIFOLD RESEARCH GROUPS AND THEIR TOPICS
The Distinguished Research Group of Volker Markl works on a wide range of topics and challenges in Database Systems and Information Management, with the overarching goal to address both the human and technical latencies prevalent in the data analysis process. The group investigates:
- Automatic Optimization of Data Processing on Modern Hardware.
- Automatic Optimization of Distributed ML Programs.
- Optimization of the Data Science and ML Process.
- Hardware-tailored Code Generation.
- Compliant Geo-distributed Data Analytics.
- Efficient Visualization of Big Data.
- Scalable Gathering and Processing of Distributed Streaming Data.
- Data Processing on Modern Hardware.
- Scalable State Management.
The Distinguished Research Group led by Klaus-Robert Müller tackles problems within the bigger fields of Machine Learning and Intelligent Data Analysis, with the overarching goals to develop robust and interpretable ML methods for learning from complex structured and non-stationary data, and the fusion of heterogeneous multi-modal data sources. The group works on:
- Learning from Structured, Non-stationary and Multi-modal Data.
- Incorporating Domain Knowledge and Symmetries in ML Models.
- Robust Explainable AI for Structured, Heterogeneous Data.
- Structured Anomaly Detection.
- Robust Reinforcement Learning in Complex, Partially Observed State Spaces.
- ML Applications in the Sciences.
- Deep Learning and GANs.
The Senior Research Group led by Begüm Demir works on Big Data Analytics for Earth Observation (EO) at the intersection of remote sensing, DM and ML. The group investigates and creates theoretical and methodological foundations of DM and ML for EO, with the goal to process and analyze a large amount of decentralized EO data in a scalable and privacy-aware manner and focuses on the following topics:
- Privacy-preserving Analysis of EO Data.
- Continual Learning for Large-Scale EO Data Analysis.
- Heterogeneous Multi-Source EO Data Analysis.
- Uncertainty-Aware Analysis of Large-Scale EO Data.
The Junior Research Group led by Grégoire Montavon works on advancing Explainable AI (XAI) in the context of deep neural networks (DNNs). Its research focuses on solidifying the theoretical and algorithmic foundations of XAI for DNNs and closing the gap between existing XAI methods and practical desiderata:
- From Explainable AI to trustworthy models.
- From Explainable AI to actionable systems.
- Application to historical networks and biological interaction network.
The Independent Research Group of Shinichi Nakajima focuses on probabilistic modelling and inference methods for multimodal, heterogeneous, and complex structured data analysis, providing ML tools that can incorporate multiple aspects of data samples observed under different circumstances, in efficient and theoretically grounded ways.
- Generative Models and Inference Methods.
- Applications of Generative Models and Bayesian Inference Methods.
- Practical Uncertainty Estimation Methods.
The Research Training Group led by Steffen Zeuch works on developing a data management system for the processing of heterogeneous data streams in distributed fog and edge environments. The aim is to design a data management system that unifies cloud, fog, and sensor environments at an unprecedented scale. In particular, a system that can host these environments on a unified platform, and leverages the opportunities of the unified architecture for cross-paradigm data processing optimizations, to support emerging IoT applications.
- Data Processing on Modern Hardware.
- Data Processing in a Fog/Cloud Environment.
The Research Training Group led by Stefan Chmiela focuses on Machine Learning for many-body problems, with particular focus on quantum chemistry. The group develops methods that combine fundamental physical principles with statistical modeling approaches to overcome the combinatorial challenges that manifest themselves when large numbers of particles interact. Research is centered around topics such as
- graph neural networks,
- large-scale kernel methods and
- the challenge of invariant/equivariant modelling.