Research Focus
Naturalistic motor control decoded from high-resolution surface electromyography. I build estimation and sequence models that recover movement from soft printed electrode array recordings under unconstrained conditions, including real-time gesture decoding, for assistive interfaces and movement disorders.
Publication record: 4 peer-reviewed papers (3 first-author) and 2 manuscripts in preparation — full list.
Position:
Ph.D. Candidate, School of Electrical and Computer Engineering, Tel Aviv University
Email:
luxembourg@mail.tau.ac.il
ORCID:
0009-0008-7462-7339
Google Scholar:
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ResearchGate:
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LinkedIn:
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Education
Ph.D., Electrical and Computer Engineering
2024–present
Institution:
Tel Aviv University
Supervisors:
Prof. Yael Hanein (TAU) and Prof. Hava Siegelmann (UMass Amherst)
M.Sc., Brain Sciences (Computation & Information Processing)
2020–2024
Institution:
The Hebrew University of Jerusalem
Advisor:
Prof. Idan Segev
Grades:
GPA 90.4 · Thesis 92.5 · Final examination 94
B.Sc., Computer Science (Minor: Computational Neuroscience)
2017–2020
Institution:
The Hebrew University of Jerusalem
Grades:
GPA 90.3 · Class rank 92 / 369
Research Experience
Naturalistic Motor Control Using High-Resolution sEMG
2024–presentTel Aviv University
- • Built and validated an estimation pipeline that recovers finger joint angles and discrete gestures from soft printed electrode array recordings under dynamic hand position, benchmarked against computer-vision hand tracking.
- • Developing sequence models that segment continuous sEMG–IMU recordings into variable-duration tokens, testing whether natural movement decomposes into a reusable vocabulary of elementary actions; manuscript in preparation.
- • Building a real-time decoding pipeline that carries this offline estimation work into online operation, for low-latency gesture-driven interaction; early stage.
- • Developing an IMU-referenced decomposition algorithm for sEMG based on coupled non-negative matrix factorization, validated on lower-limb recordings in Parkinson's disease with and without freezing of gait; manuscript in preparation.
Single-Neuron Functional Analysis: An Information-Theoretic Approach
2021–2024Segev Lab, The Hebrew University of Jerusalem
- • Showed that neurons are not computationally interchangeable: human L2/3 pyramidal cells carry measurably more information than rat L5b, dendritic morphology accounts for part of the gap, and removing NMDA nonlinearity collapses complexity in every model — evidence that single-cell biophysics, not only circuit wiring, sets a neuron's computational capacity.
- • Built the measures that make such comparisons possible — a firing-rate-invariant entropy and statistical-complexity measure of output spike trains, plus an objective network-depth benchmark replacing subjective model comparison — letting any neuron model be placed on a common complexity scale.
Neural Decoding of Visual Attention from EEG
2019–2020The Hebrew University of Jerusalem
- • Applied machine-learning classifiers to EEG recordings to decode covert visual attention; undergraduate research project, exploratory.
Conference Presentations
- • Poster — 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Copenhagen, Denmark, 2025.
- • Talk — "Finger Joint Angle and Gesture Estimation in Dynamic Hand Postures Using a Soft Printed Electrode Array." IEEE International Conference on the Science of Electrical Engineering (ICSEE), Jerusalem, June 2026 (presented by a colleague); Physical Electronics Department Meetup, Tel Aviv University, February 2026.
- • Poster — Karniel Computational Motor Control Workshop (KCMCW), Tel Aviv University, 2026.
- • Poster — 14th Annual Nano Workshop, Jan Koum Center for Nanoscience and Nanotechnology, Tel Aviv University, 2026.
Teaching
Teaching Assistant — Dynamical Systems and the Neuron
The Hebrew University of Jerusalem · 2021–2022
Teaching Assistant — Brain–Computer Interfaces as Assistive Technology
The Hebrew University of Jerusalem · 2021–2022
Technical Skills
Programming
Python
C
C++
Java
Machine Learning
PyTorch
Deep Learning
Transformers
Temporal Convolutional Models
Autoregressive Sequence Models
Non-Negative Matrix Factorization
Signals & Instrumentation
Surface EMG
EEG
IMU
High-Density Soft Electrode Arrays
Arduino / Embedded Prototyping
Computer-Vision Motion Capture
Interactive Systems
Unity
Real-Time Gesture-Driven Interfaces
Low-Latency Decoding Pipelines
Domain
Motor Control
Computational Neuroscience
Brain–Computer Interfaces
Information Theory / Entropy Estimation
Biophysical Compartmental Modeling
Full CV
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