We are always looking for talent. If you would like to join the IBS Lab, please see the “Join the Team” below.
Researchers
Dr.-Ing. Alexander von Lühmann
Head of Independent Research Group
R 4.045, Marchstr. 23, D-10587 Berlin
Alexander von Lühmann is currently the head of the independent research group “Intelligent Biomedical Sensing (IBS)” at BIFOLD, Machine Learning Dept, TU Berlin. He is also a visiting researcher at the Neurophotonics Center of Boston University (BU NPC) and Chief Scientific Officer at NIRx Medical Technologies. Previously, he was R&D director at NIRx, post-doc at BU NPC, visiting researcher at the Martinos Center of Harvard Medical School in Boston, USA, and Chief Technology Officer of Crely, a US-Singapore-based healthcare startup. He received his PhD (Dr.-Ing.) with distinction in 2018 from Technische universität Berlin (TU Berlin) and the M.Sc. and B.Sc. degrees in Electrical Engineering from Karlsruhe Institute of Technology (KIT) in 2014/11.
At the Intelligent Biomedical Sensing(IBS) Lab, Alex' research focuses on machine learning and instruments for comprehensive brain-body monitoring. The IBS lab develops miniaturized wearable neurotechnology and body-worn sensors for unobtrusive monitoring of the embodied brain in the everyday world. It uses machine learning on the multimodal sensor data, together with environmental context information, to contribute to a paradigm shift in individualized comprehensive understanding of physical and mental health: Toward intelligent assessment and treatment of physical and mental states and risk factors. Current focus is on instrumentation for diffuse optics and biopotentials (fNIRS, DOT, EEG, ExG) and novel approaches for modelling of physiology and physiological transfer functions using machine learning.
Dr. Eike Middell
Postdoctoral Researcher
R 4.050, Marchstr. 23, D-10587 Berlin
I graduated in astroparticle physics and gained much of my experience while working on neutrino telescopes in Siberia and Antarctica.
These experiments instrumented large bodies of water and glacial ice with optical sensors to detect the faint light created by cosmic-ray particles interacting with matter.
Subsequently, I transitioned to the field of neuroimaging with a focus on improving the digital signal processing of functional near-infrared spectroscopy (fNIRS) imagers, specifically in the context of assessing side-effects and potential improvements of deep brain stimulation for Parkinson's disease. Eventually, my role in a small team involved overseeing the software development of a portable near-infrared spectrometer from conception to market release. Further on, as a freelancing scientific software developer I contributed to diverse projects, such as forecasting financial time series and improving the safety of industrial plants by using spectroscopic measurements from remote sensors to infer the distribution of leaked gas clouds.
My research interests lie at the intersection of the physical measurement process in functional near-infrared spectroscopy (fNIRS), the inverse problem of image reconstruction as well as the application of machine learning methods on multimodal sensor data to improve the utilization of physiological information during data analysis. As the team's lead software engineer, I am particularly interested in constructing reproducible data processing pipelines that can ensure the reliability and replicability of fNIRS data analysis.
Dr. Tomas Codina
Postdoctoral Researcher
R 4.050, Marchstr. 23, D-10587 Berlin
I received my PhD (Dr. rer.nat) in theoretical high-energy physics (hep-th) in 2023 at Humboldt University, Berlin, and a “Licenciatura” degree in physics (equivalent to a bachelor plus master’s degree) in 2019 at “Universidad de Buenos Aires”, Argentina. During those years, my research focused on higher-derivative corrections in string theory, a candidate theory to describe gravity at a quantum regime, particularly restricted to cosmological and black-hole backgrounds. In the search of changing my scientific career path to a field with concrete real-world applications, my programming and AI enthusiasm combined with my interest in biomedical applications helped me transition into the field of neuroimaging data analysis.
I am interested in machine and deep learning applications for multimodal signal processing. In particular, I am engaged in developing and implementing source-decomposition methods that combine fNIRS/DOT and EEG modalities, together with physiological and environmental data to extract reliable brain activity.
Dr. Theekshana Dissayanake
Postdoctoral Researcher
R 4.050, Marchstr. 23, D-10587 Berlin
Dr. Theekshana Dissanayake holds a PhD from the Queensland University of Technology (QUT), Brisbane, Australia. His PhD is associated with the Signal Processing, Artificial Intelligence and Vision Technologies (SAIVT) group led by Professor Sridha Sridharan. He holds the prestigious Google PhD Fellowship Award 2021 for his research proposal on Domain Generalization for Biosignal Data. His thesis on Deep Machine Learning for Biosignal Analysis was recommended for the Outstanding Thesis Award 2024, and he holds the Higher-Achiver PhD Student Award nominated by Prof. Clinton Fookes and Prof. Sridha Sridharan for publishing three Q1 journals within the first year of his PhD.
He is an active reviewer in machine learning and biomedical engineering journals such as IEEE Journal of Biomedical and Health Informatics, IEEE Sensors Journal, IEEE IoT Things Journal and Computers in Biology and Medicine. During his PhD, he was affiliated as a Graduate Research Assistant at Monash University, AIM for Health Lab, and Alfred Health Neuroscience. Furthermore, he has working experience with industry collaborators, WearOptimo PVT LTD and M3DICINE PVT LTD focusing on the design of intelligent biomedical sensor devices. Before starting his PhD, he completed his bachelor's degree (first-class honors) from the Department of Computer Engineering, University of Peradeniya, Sri Lanka.
Dr. Theekshana Dissanayake's primary research focus is Medical Artificial intelligence, specifically developing deep learning algorithms to learn from biomedical signals, medical images, and clinical videos.
Nils Harmening
Doctoral Researcher
R 4.050, Marchstr. 23, D-10587 Berlin
Nils Harmening received his master's degree in Physics at Freie Universität Berlin in 2017 with a focus on nonlinear dynamics, biophysics and climate science. Since 2019 he is working on enhancing human head models for EEG and fNIRS data analysis and image reconstruction improvement using Photogrammetry and Electrical Impedance Tomography (EIT).
Brain-Computer Interfaces, Electrophysical modeling of the human head, Electrical Impedance Tomography (EIT), Inverse EIT Problem, Deep Learning for inverse problems
Shakiba Moradi
Doctoral Researcher – BIFOLD Graduate Student
R 4.050, Marchstr. 23, D-10587 Berlin
I earned my Master's degree in Biomedical Engineering from Sharif University of Technology in 2018, specializing in simulating orientation selectivity in the human visual cortex in the presence of adaptation. Since then, I have worked as a data scientist in various tech companies, analyzing diverse data sets, including customer behaviors and in-game player activities. My primary focus, however, has been on processing medical images and videos, advancing the field with innovative AI solutions.
My research interests lie in developing machine learning solutions for analyzing biomedical recordings, with a particular emphasis on medical imaging. I am passionate about exploring the intersection of AI and neuroscience, aiming to address open problems in these fields through innovative AI applications.
Bilal Siddique
Doctoral Researcher
R 4.050, Marchstr. 23, D-10587 Berlin
Bilal Siddique earned his Master’s Degree in Biomedical Engineering from Air University, Islamabad, Pakistan, with a focus on Biomedical Instrumentation and Biomedical Signal Analysis. During his studies, he developed a portable cardiac monitor designed for real-time arrhythmia diagnosis and the data sharing of cardiac bio-potentials and associated physiological parameters between patients and doctors. This project was funded by the National Center of Artificial Intelligence in Islamabad, Pakistan. Since 2022, he has served as a Lecturer in the Biomedical Engineering Department at Air University, supervising undergraduate Final Year Design Projects, two of which have secured seed grants.
My research primarily focuses on designing bio-potential acquisition hardware and using signal processing techniques to extract novel biomarkers, facilitating their application in real-world rehabilitative and diagnostic solutions.
We have several researcher positions open. You will find more PhD/PostDocs here soon.
Students
Jacky Behrendt
Student Research Assistant – Analysis & ML
R 4.039, Marchstr. 23, D-10587 Berlin
I am a Master's student in mathematics at Technische Universität Berlin. I earned a bachelor's degree in mathematics and computer science from TU Berlin. During my bachelor's, I focused on harmonic analysis and machine learning. For my Bachelor's thesis, I worked on neural networks and iterative algorithms for phase retrieval in ptychography. I also worked as a tutor in linear algebra in the Math Department.
My research interests are broadly in harmonic and functional analysis. I am also very engaged in machine learning, neural networks, and deep learning. In particular, I am interested in applications of machine learning to model-based algorithms and in solving inverse problems.
Josef Cutler
Student Research Assistant – Teaching & ML
R 4.039, Marchstr. 23, D-10587 Berlin
I am a Bachelor’s student in mathematics at TU Berlin. Previously, I studied English literature at UCLA. Before coming to IBS, I worked as a student research assistant with the Berlin Science Survey, where I programmed tools for automated data collection and analysis.
I am interested in applications of mathematics in computational perception, including signal processing and machine learning.
Thomas Fischer
Student Research Assistant – Analysis & ML
R 4.039, Marchstr. 23, D-10587 Berlin
I did my bachelor's degree in computer science at the University of Jena. In parallel, I was enrolled in mathematics and took courses in probability theory and numerics together with the mathematics students. My bachelor thesis was in the area of machine learning, in particular variational inference and some graph theory. During my Bachelor's, I also served as a tutor for discrete mathematics. I am currently pursuing a Master's degree in Computer Science at TU, focusing on machine learning and data analytics.
I am interested in theoretical computer science and applied mathematics, especially machine learning, graph theory, computability theory, and algorithms. In particular, deep learning architectures such as CNNs, transformers, and graph neural networks fascinate me. I am also very interested in medicine and the human body, so I enjoy working with all kinds of medical data.
Isa Musisi
Bachelor Student – PPG / HRV Analysis
R 4.039, Marchstr. 23, D-10587 Berlin
I am a bachelor’s student in Business Informatics at Techinische Universität Berlin. Currently, I am writing my bachelor’s thesis under Alexanders supervision about a preprocessing pipeline for calculating the heart rate variability from cardiovascular signals measured by wearable devices for application in machine learning models. Additionally, I am working at MCS Datalabs GmbH as a software engineer mainly focused on developing mobile applications that go hand in hand with our self-developed wearable device.
To my interests count the development of mobile applications that provide a multitude of non-trivial functionalities for an interesting, exiting and also helpful user experience. And after coming more and more into contact with machine learning, I am excited to tackle challenges revolving around bringing machine learning models onto the edge, enabling the analysis, processing, training, and prediction of data in near real time.
Christian Tesch
Master Student – Multimodal Data Fusion and Labeling
R 4.039, Marchstr. 23, D-10587 Berlin
I hold a bachelor’s degree in electrical engineering with a specialization on automation. Currently, I’m enrolled in a master’s program in data science.
Throughout my bachelor’s I worked in the R&D Electronics and Gas Detection department of MSA Safety, while also working as a tutor for C programming at my university. At MSA Safety my focus was test design, automation, and analysis of mixed signal gas sensor systems.
As a hands on engineer, I enjoy solving problems that span the entire engineering stack. This includes areas such as hardware design, firmware development, and creating software and user interfaces.
Work at the IBS lab allows me to combine many of my interests: creating hardware, performing measurements and data analysis.
We have several student assistant positions open. You will find more students here soon.
Join the Team
We are always looking for talent. If you want to join the team, there are several ways to do so:
- As a researcher doing your PhD or PostDoc
- Writing your Bachelor or Master Thesis
- In a lab rotation
- As a working student
If you want to learn more, visit the Get Involved page.
Alumni
2024
Oliver Czerwiak
Master Student – Deep Learning
R 4.039, Marchstr. 23, D-10587 Berlin
I am a Master’s student in Information Systems Management at Technische Universität Berlin with a focus on Machine Learning algorithms and database management systems. Before my Master’s, I pursued my Bachelor’s in Information Systems at University of Münster with a focus on data analytics and IT-Security. Currently I am writing my master thesis with the IBS lab on fNIRS signal processing using deep learning architectures. In addition to university, I work as a junior associate at idalab where we help biotech and healthcare companies leverage artificial intelligence in their work.
I am interested in all kinds of machine learning applications ranging from object detection in autonomous vehicles over robotics and IT-Security to biological data. I am currently focusing on applications of the transformer architecture on time series data as well as unsupervised representation learning for fNIRS signals.
Veronika Wojcik
Bachelor Student – Research Intern – Dry EEG Electrodes
R 4.039, Marchstr. 23, D-10587 Berlin
I am a Bachelor’s student studying Biomedical Engineering at the University of Calgary in Canada, graduating in 2025. During my 12-week research internship at the IBS Lab. I am working with novel dry “flower electrodes” used for EEG’s. During my time at the IBS lab, I engineered a custom grommet and amplifier connection for these electrodes, to eventually be able to run an EEG with them and compare their signal quality with that of current wet/dry electrodes. Furthermore, I got to design and replicate these electrodes so that the IBS Lab is able to manufacture their own and use them for testing and future project implementation purposes.
Masha Iudina
Rotation Student – fNIRS Analysis and Photogrammetry
R 4.039, Marchstr. 23, D-10587 Berlin
I hold a Bachelor's degree in Computer Science from the Polytechnic University of Saint-Petersburg, with a focus on Forward Error Correction coding theory. Currently, I am pursuing a Master's program in Computational Neuroscience at Technische Universität Berlin. In addition, I work as a Software Developer at NIRx Medizintechnik, contributing to the development and support of fNIRS Acquisition Software.
coming soon
2023
Filip Jenko
Master Student – Headmodelling
R 4.039, Marchstr. 23, D-10587 Berlin
I studied Electrical Engineering as my bachelor’s degree program, my master’s degree is in Biomedical Engineering. I am an Erasmus student from University of Ljubljana, Slovenia.
I worked at Jozef Stefan Institute in Ljubljana, which was my introduction to the research field. The research topic I contributed to was motor learning under different conditions. We observed how well subjects learn new movements, depending on whether they know which movement is coming next, or if they know the sequence of the next movements.
I am interested in biomedical image analysis and combining it with a neural network in order to perform image diagnostics.
I currently focus on using photogrammetry to improve fNIRS signal processing. My assignment is getting a 3D model (point cloud) of a head from a 3D scan.
My other interests are motor learning and prosthetics development.