Team

We link your experience with artificial intelligence

Team

Sebastian Thiel

Head of Strategy, Managing Director

    • MSc (Computer Science) Technical University of Berlin
    • Computer Vision: Object-Recognition,- detection and -tracking  / Pattern Recognition
    • Research and implementation of Machine Learning methods on image and video data (2D and 3D)
    • 5 Years experience as Research Assistant at GfAI Berlin, developing and implementing solutions for various industry partners
    • Research and Teaching  Assistant at TU Berlin (Methods of Artificial Intelligence, Prof. Opper), assisting  Machine Learning/AI lectures and supervising a Master thesis

eMail: sebastian.thiel(at)alm.gmbh

Philipp Batz

Head of Machine Learning, Managing Director

    • MSc (Statistics) Humboldt University of Berlin; MSc (Computer Science) Siegen University
    • Machine Learning / Fast approximate algorithms for Big Data
    • Research and implementation on time series algorithms / Reinforcement Learning and Control  on mechanical systems (with focus on robotics)
    • Research Assistant at TU Berlin (Methods of Artificial Intelligence, Prof Opper), member of EU-funded Project CompLACS

eMail: philipp.batz(at)alm.gmbh

Scientific Publications

P.Batz, T.Will, S.Thiel, T.M.Ziesche, C.Joachim (2023)
From identification to forecasting: the potential of image recognition and artificial intelligence for aphid pest monitoring,
Frontiers in Plant Science,  Section: Sustainable and Intelligent Phytoprotection, Volume 14

P.Batz, A.Ruttor, S.Thiel, J.Wegener, F.Zautke, C.Schwekendiek, K.Bienefeld (2022)
Semi-automatic detection of honeybee brood hygiene—an example of artificial learning to facilitate ethological studies on social insects,
Biology Methods and Protocols, Volume 7, Issue 1, Oxford University Press OUP

S.Thiel, A.Ruttor, P.Batz (2018)
Beehaviour – Computerunterstützte Erkennung von Verhaltensmustern in Bienenvideos,
65. Jahrestagung der Institute für Bienenforschung e.V., Koblenz

P.Batz, A.Ruttor, M.Opper (2018)
Approximate Bayes learning of stochastic differential equations,
Physical Review E, 98/2, American Physical Society APS

P.Batz, A.Ruttor, M.Opper (2016)
Variational estimation of the drift for stochastic differential equations from the empirical density,
Journal of Statistical Mechanics: Theory and Experiment, 2016/8, Institute of Physics Press IOP

A.Iwainsky, S.Thiel (2016)
A Mixed Reality Environment for Educating Nurses (Best Paper Award),
Future Education and Training in Computing (FETCH), BMBF-Verbundprojekt AKOLEP

A.Ruttor, P.Batz, M.Opper(2013)
Approximate Gaussian process inference for the drift function in stochastic differential equations,
Advances in Neural Information Processing Systems 26 (NIPS), p.2040-2048