Christoffer Löffler

I am a PhD student at Björn Eskofier's MaD Lab and Fraunhofer IIS where I work on machine learning for time series data in the ADA Lovelace Center.

At Fraunhofer I've worked on optical positioning, complex event processing, and large scale virtual reality. I did my Bachelors and Masters at Friedrich-Alexander University Erlangen-Nürnberg (FAU), where I was advised by Christopher Mutschler, back then researching at Michael Philippsen's Programming Systems lab.

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Research

I'm interested in machine learning with few labeled data, e.g., semi-supervised or active learning. I also like building automatic ML pipelines and embedded classification.

Recipes for Post-training Quantization of Deep Neural Networks
Ashutosh Mishra, Christoffer Löffler, Axel Plinge
EMC^2: Workshop on Energy Efficient Machine Learning and Cognitive Computing, 2020
Axel's video / Ashutosh's video / pdf / project

We show that post-training quantization (done greedily) benefits from an optimal global bit-width and evaluate this on VGG, ResNet, UNet and our tool tracking FCN.

A Sense of Quality for Augmented Reality Assisted Process Guidance
Anes Redzepagic, Christoffer Löffler, Tobias Feigl, Christopher Mutschler
IEEE Intl. Symposium on Mixed and Augmented Reality (ISMAR), poster track, 2020
video presentation / pdf / project

We combine inertial sensors, mounted on work tools, with AR-headsets to enrich modern assistance systems with a sense of process quality, powered by machine learning.

IALE: Imitating Active Learner Ensembles
Christoffer Löffler, Christopher Mutschler
Preprint, 2020
code / arXiv / pdf

We propose an imitation learning approach that learns a policy for active learning from an ensemble of deep active learners.

Automated Quality Assurance for Hand-held Tools via Embedded Classification and AutoML
Christoffer Löffler, Christian Nickel, Christopher Sobel, Daniel Dzibela, Jonathan Braat, Benjamin Gruhler, Philipp Woller, Nicolas Witt, Christopher Mutschler
European Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), demo track, 2020
demo video / video presentation / project / pdf

We describe an AutoML system for our custom hardware and classify multivariate data using deep and shallow methods.

ViPR: Visual-Odometry-aided Pose Regression for 6DoF Camera Localization
Felix Ott, Tobias Feigl, Christoffer Löffler, Christopher Mutschler,
Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshops, workshop track, 2020
video / pdf

Learning to fuse absolute poses (6 degrees of freedom) with optical flow (e.g., FlowNet) to improve a mobile agent's self positioning.

Localization Limitations of ARCore, ARKit, and Hololens in Dynamic Large-scale Industry Environments.
Tobias Feigl, Andreas Porada, Steve Steiner, Christoffer Löffler, Christopher Mutschler, Michael Philippsen
15th Intl. Conf. on Computer Graphics Theory and Applications (GRAPP), 2020
pdf

An evaluation of the big AR systems for real use-cases.

Evaluation criteria for inside-out indoor positioning systems based on machine learning
Christoffer Löffler, Sascha Riechel, Janina Fischer, Christopher Mutschler
IEEE Intl. Conf. on Indoor Positioning and Indoor Navigation (IPIN), 2018
project / warehouse dataset / pdf

Using reference positioning systems, we record a multi-camera dataset with exact labels and propose criteria for evaluating indoor positioning.

Optical Camera Communication for Active Marker Identification in Camera-based Positioning Systems
Lorenz Gorse, Christoffer Löffler, Christopher Mutschler, Michael Philippsen
IEEE 15th Workshop on Positioning, Navigation and Communications (WPNC), 2018
pdf

How to build a cheap but reliable optical positioning system with Raspberry Pi with active LED markers and continuously identify them.

Approximative event processing on sensor data streams
Christoffer Löffler, Christopher Mutschler, Michael Philippsen
Proc. of the 9th ACM Intl. Conf. on Distributed Event-Based Systems (DEBS), poster track, best demo/poster award, 2015
pdf

Event-Based Systems (EBS) can efficiently analyze large streams of sensor data in near-realtime. But they struggle with noise or incompleteness that is seen in the unprecedented amount of data generated by the Internet of Things

Predictive load management in smart grid environments
Christopher Mutschler, Christoffer Löffler, Nicolas Witt, Thorsten Edelhäußer, Michael Philippsen
Proc. of the 8th ACM Intl. Conf. on Distributed Event-Based Systems (DEBS), 2014
pdf

We've won the DEBS 2014 Grand Challenge with our hidden Markov model approach.

Simulating the energy management on smartphones using hybrid modeling techniques
Ibrahim Alagöz, Christoffer Löffler, Vitali Schneider, Reinhard German
GI/ITG Intl. Conf. on Measurement, Modelling, and Evaluation of Computing Systems and Dependability and Fault Tolerance (MMB & DFT), best student paper award, 2014
pdf

We model a smartphone playing back music and its energy management.

Evolutionary algorithms that use runtime migration of detector processes to reduce latency in event-based systems
IEEE NASA/ESA Conf. on Adaptive Hardware and Systems (AHS), 2013
Christoffer Löffler, Christopher Mutschler, Michael Philippsen
pdf

When running a distributed low-latency event processing system, you may want to optimize latency - heuristically.

Teaching
DRL seminar, deep reinforcement learning, summer 2019
Machine Learning for Timeseries (project), 2019-2021

SemML, seminar on machine learning, winter 2015-2021

The name Konishi is borrowed from the inspirational novel Diaspora by Greg Egan, where Konishi polis refers to one of many systems that simulate realities, inhabited by sentient software. This website is forked from here, thank you Jon Barron.