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Hire Dr. Felipe G.

United States

PhD-level expert in (Bio)statistics, MATLAB, Modeling

Profile Summary

Ph.D. in computational neuroscience with 5+ years post-graduate experience in data science and machine learning. Development and implementation of algorithms for high-dimensional biological data (images, videos, time series, event data, biomarkers). Proven track record of research accomplishments in computational physics and statistical neuroscience. Proficiency in MATLAB and Python. 10 years of experience in statistics, signal processing, causal inference, multi-variate time-series modeling, nonlinear regression (GLMs), network theory, complex dynamical systems, theoretical and experimental neuroscience.

Subject Matter Expertise

• ★ Data Science
• ★ Machine Learning
• ★ Matlab
• ☆ Biostatistics
• ☆ Biomarkers
• ☆ Neuroscience
• ☆ Computational Neuroscience
• ☆ Signal Processing
• ☆ Software Engineering
• ☆ Data Analysis
• ☆ Predictive Modeling & Analytics
• ☆ Data Processing
• ☆ Data Visualization
• ☆ Neural Networks
• ☆ Artificial Intelligence
• ☆ Deep Learning
• ☆ Data Security
• ☆ Statistics
• ☆ Applied Statistics
• ☆ Regression Analysis
• ☆ Multivariate Analysis
• ☆ Nonlinear Regression
• ☆ Trend Analysis
• ☆ Computational Physics
• ☆ Mathematical Models
• ☆ Mathematical Optimization
• ☆ Computational Sciences
• ☆ Python

Services

• Research
• Data & AI

Research Fact Checking, Gray Literature Search, Systematic Literature Review, Secondary Data Collection

Data & AI Statistical Analysis, Algorithm Design-Non ML, Data Visualization

Work Experience

Computational Neuroscientist

Q-State Biosciences (United States)

November 2016 - April 2019

Postdoctoral Research Associate

Brown University

April 2014 - August 2016

Education

PhD in Neuroscience (Neuroscience)

École Polytechnique Fédérale de Lausanne

2009 - February 2014

Master of Science in Computational Science (Physics)

Goethe-Universität Frankfurt am Main

2007 - 2009

Bachelor of Science in Physics (Physics)

Universität Siegen

2004 - 2007

Certifications

• Certification details not provided.

Publications

JOURNAL ARTICLE

Felipe Gerhard, Tilman Kispersky, Gabrielle J. Gutierrez, Eve Marder, Mark Kramer, Uri Eden, Olaf Sporns(2013). Successful Reconstruction of a Physiological Circuit with Known Connectivity from Spiking Activity Alone . {PLoS} Comput Biol. 9. (7). p. e1003138. Public Library of Science ({PLoS})

Gerhard, F., Kispersky, T., Gutierrez, G.J., Marder, E., Kramer, M., Eden, U.(2013). Successful Reconstruction of a Physiological Circuit with Known Connectivity from Spiking Activity Alone . PLoS Computational Biology. 9. (7).

Lütcke, H., Gerhard, F., Zenke, F., Gerstner, W., Helmchen, F.(2013). Inference of neuronal network spike dynamics and topology from calcium imaging data . Frontiers in Neural Circuits. 7. (DEC).

Felipe Gerhard, Henry Lütcke, Friedemann Zenke, Wulfram Gerstner, Fritjof Helmchen(2013). Inference of neuronal network spike dynamics and topology from calcium imaging data . Frontiers in Neural Circuits. 7. Frontiers Media {SA}

Gerhard, F., Szegletes, L.(2012). Spline- and wavelet-based models of neural activity in response to natural visual stimulation . Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. p. 4611-4614.

Felipe Gerhard, Robert Haslinger, Gordon Pipa(2011). Applying the Multivariate Time-Rescaling Theorem to Neural Population Models . Neural Computation. 23. (6). p. 1452--1483. {MIT} Press - Journals

Felipe Gerhard, Gordon Pipa, Bruss Lima, Sergio Neuenschwander, Wulfram Gerstner(2011). Extraction of Network Topology From Multi-Electrode Recordings: Is there a Small-World Effect? . Front. Comput. Neurosci. 5. Frontiers Media {SA}

Naud, R., Gerhard, F., Mensi, S., Gerstner, W.(2011). Improved similarity measures for small sets of spike trains . Neural Computation. 23. (12). p. 3016-3069.

Gerhard, F., Pipa, G., Lima, B., Neuenschwander, S., Gerstner, W.(2011). Extraction of network topology from multi-electrode recordings: Is there a small-world effect? . Frontiers in Computational Neuroscience. 5.

Gerhard, F., Haslinger, R., Pipa, G.(2011). Applying the multivariate time-rescaling theorem to neural population models . Neural Computation. 23. (6). p. 1452-1483.

Felipe Gerhard, Wulfram Gerstner(2011). Efficient modeling of neural activity using coupled renewal processes . {BMC} Neuroscience. 12. (Suppl 1). p. P123. Springer Nature

Felipe Gerhard, Richard Naud, Skander Mensi, Wulfram Gerstner(2011). Improved Similarity Measures for Small Sets of Spike Trains . Neural Computation. 23. (12). p. 3016--3069. {MIT} Press - Journals

Felipe Gerhard, Robert Haslinger, Gordon Pipa(2010). Goodness-of-fit tests for neural population models: the multivariate time-rescaling theorem . {BMC} Neuroscience. 11. (Suppl 1). p. P46. Springer Nature

Felipe Gerhard, Julia Schiemann, Jochen Roeper, Gaby Schneider(2009). A simple Hidden Markov Model for midbrain dopaminergic neurons . {BMC} Neuroscience. 10. (Suppl 1). p. P235. Springer Nature

CONFERENCE PAPER

Gerhard, F., Szegletes, L.(2012). Spline- and wavelet-based models of neural activity in response to natural visual stimulation . Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. p. 4611-4614.

Gerhard, F., Gerstner, W.(2010). Rescaling, thinning or complementing? on goodness-of-fit procedures for point process models and Generalized Linear Models . Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010.

Gerhard, F., Savin, C., Triesch, J.(2009). A robust biologically plausible implementation of ICA-like learning . ESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning. p. 147-152.