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Subject Area Experts
The founding members of the Data Analytics Laboratory at TU Berlin include Volker Markl, Klaus-Robert Müller, Odej Kao, Anja Feldmann, Thomas Wiegand, Thomas Sikora, and Jean-Pierre Seifert.
| Professor |
Areas of Expertise / Active Research
Areas |
| Volker Markl
[1] | The prior work of Volker Markl
lies in the area of database management systems, architectures, and
query processing algorithms. This includes in particular work on
multidimensional indexing, applying machine learning algorithms to
query optimization, statistics management, declarative specification,
compilation, optimization, and parallelization of iterative data flow
programs, and big data benchmarking. Volker is leading the
Stratosphere project, a collaborative research unit by the German
National Science Foundation (DFG) that focuses on building a big data
analytics infrastructure, as well as several European and national
research efforts related to Stratosphere. In the context of
Stratosphere Volker focuses on data analysis programming languages,
the automatic parallelization and optimization of data analysis
algorithms in the areas of machine learning, algebraic optimization
and text mining, as well as big data benchmarking. He has further
conducted work in information integration (the DAMIA project) and is
currently conducting work in new hardware architectures for
information management (the SindPad and RETHINK projects), as well as
information marketplaces (the MIA and DOPA projects). |
|
Klaus-Robert Müller
[2] | Previous work of Klaus-Robert Müller
encompasses machine learning and a number of applications. He has
worked on kernel methods, neural networks, large scale learning,
signal processing, and in application areas like the brain computer
interface, computer vision, and bioinformatics. He has studied methods
for the reliable detection of outliers in complex event streams, which
relates to the veracity aspect in this program. Computer vision and
signal processing will be important for the different media types
encountered in this project. Finally, large scale and kernel learning
provide important prior work to tackle questions of large scale
learning. |
| Odej Kao
[3] | Previous work of Odej Kao
includes massive-parallel resource management for highly efficient
processing of semi-structured data and data streams on
high-performance and cloud platforms. The major focus is set on
cross-optimized booking and assignment of resources for efficient
query and stream processing. Already developed systems such as OpenCCS
(for massively-parallel compute clusters) or Nephele (for cloud-based
platforms) are used for many years in production environments and
serve as staring points for large research projects such as HPC4U,
AssessGrid, Stratosphere and many others. During these projects
additional features such as advanced methods for fault tolerance,
failure prediction, job migration, and virtualization were developed
and integrated. Additional related work considers the platforms for
plug-and-play of medical devices and online analysis of the delivered
data streams. The results of the analysis are transferred to hospitals
for online assistance. Analogous projects are performed in the energy
of smart grids and integration of renewable energies. Odej Kao is a
member of the Stratosphere Collaborative Research
Unit. In the research unit, Odej has researched and developed a
platform for large-scale data analysis in
infrastructure-as-a-service settings from the aspects of
fault-tolerance and processing data analysis programs in these
massively-parallel settings. |
| Anja Feldmann [4]
| Working towards an improved
understanding of the Internet has been the research focus of Anja
Feldmann over the past 15 years. For example, this includes work on
understanding the Internet Ecosystem, current traffic streams in the
Internet, the nature of Internet traffic, tools for network intrusion
detection, and also work on the current architectural limitations of
the Internet and how to overcome them relying on information from the
network, and/or using software defined networking principles, and/or
relying on network virtualization. |
| Thomas Sikora
[5] | Description/analysis as well as
compression of graphics, images, audio, and video has been prime
research focus of Thomas Sikora over the last twenty years. This
includes significant work on novel concepts for compression of audio
and 2D/3D video data and on
analysis/interpretation of audio, images, and video for
multimedia retrieval and surveillance applications. As the video
chairman of the ISO MPEG group he was responsible for the development
of the MPEG-4 video compression standard as well as for the MPEG-7
visual standard targeted for visual content description. Especially,
the MPEG-7 standard can be seen as instrumental for sparking the
enormous research momentum worldwide on multimedia content
description, search, filtering, and classification. |
| Thomas Wiegand
[6] | Prior work of Thomas Wiegand has
addressed video coding, multimedia networking as well as semantic
approaches to media analysis and coding. In the area of video coding,
Wiegand made significant contributions to the video coding standards
H.264/MPEG4-AVC and H.265/MPEG-HEVC, extended the compression to
scalable formats and to stereoscopic as well as autostereoscopic 3D
video. Moreover, together with Klaus-Robert Müller, he investigated
novel methods for measuring subjective perception of visual
differences using EEG. His work on multimedia networking, includes
novel work on error-resilient transmission of real-time video,
contributions to standardization in IETF for RTP payloads,
specifications for video broadcast to televisions and handsets in DVB,
and extensions to MPEG-DASH . He has conducted pioneering research in
the field of semantic approaches to video coding. |
| Sebastian Möller
[7] | The Quality and
Usability Lab headed by Sebastian Möller is interested in
understanding human behavior and judgment when interacting with
technology. The goal is to develop methods, algorithms and tools for
improving human-machine interaction, and human-human-interaction
mediated by technology. For this purpose, data is collected either in
the laboratory or in the field, and put into relation to user
judgments on quality and usability. For data collection in the field,
mobile apps have proven to be a powerful tool, and the lab has
successfully developed apps for administering student life (Mobile
Campus Charlottenburg, MoCCha), organizing conferences or events
(Interspeech and DAGA conference apps, Long Night of Sciences app), or
supporting a user’s private life (energy-saving app Sense4En). Data
collected with these apps is currently analyzed for understanding user
behavior, and offering new services of added value. In addition,
Sebastian Möller develops algorithms for predicting speech and
audio-visual quality, and supports the international ITU-T
standardization work in this
area. |
| Klaus Obermayer [8]
| We are concerned with the
principles underlying information processing in biological systems. On
the one hand we want to understand how the brain computes, on the
other hand we want to utilize the strategies employed by biological
systems for machine learning applications. Our
research interests cover three thematic areas.
Models of neuronal
systems: In close collaboration with
neurobiologists and clinicians we study how the brain processes visual
information and perception is linked to cognitive function. Research
topics include: cortical dynamics, representation of visual
information, adaptation & plasticity, reward-based learning and
decision making in uncertain environments and how its interaction with
perception and memory. Machine
Learning and Neural Networks: Here we
investigate how machines can learn from examples in order to predict
and act. Current research topics include the learning of
representations & semi-supervised learning schemes,
prototype-based methods, and models of decision making &
reinforcement learning. Analysis of
neural data: Here we apply statistical
methods and machine learning to the analysis of multivariate
biomedical data. Research topics vary and currently include the
analysis of multimodal data, for example, correlating behavioral,
neuroanatomical, brain imaging, and genetic data. |
Information
Your application for an individual "consultation hour" with one of our "Data Science and Engineering" advisors, please send/give to:
Claudia Gantzer, +49 314 23555 / room EN-728 / email: lehre@dima.tu-berlin.de [9]
Data Science and Engineering Track Contact
Juan Soto+49 30 314 23551
Raum Office: E-N 724
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