1. Dr. Justin S. Feigelman
Oberwiesenstrasse
59,
8050
Zürich,
Switzerland
Phone:
+41786169588
Feigelman@gmail.com
Professional Summary
Skilled
researcher
with
expertise
in
modeling,
simulation,
inference
and
high
performance
computing
with
domain
knowledge
in
life
sciences.
Experienced
C++/Matlab/R/Python
programmer.
Broad
background
in
physics,
mathematics,
molecular
biology
and
healthcare.
Skills
• Advanced
knowledge
of
Matlab,
C++,
R
• Proficiency
in
Python,
SQL
• Expertise
with
statistics,
parameter
inference
and
Bayesian
methods
• Knowledge
of
molecular
biology
and
NGS
techniques
• Advanced
knowledge
of
Linux
systems
• Experience
with
machine
learning
methods
• Familiarity
with
collaborative
tools
• Expertise
in
data
analysis
and
visualization
• Quantitative
background
in
computer
science
and
physics
• Knowledge
of
mathematical
techniques:
dynamical
systems,
stochastic
processes,
statistical
models,
optimization
• Summarization
and
presentation
of
research
findings
Work History
05/2016
to
present
Postdoctoral
Researcher
ETH
Zürich
–
Zürich,
Switzerland
• Independently
develop,
implement
and
test
novel
algorithms
for
modeling,
simulation
and
inference
of
biological
processes
• Utilize
a
variety
of
analytical,
scientific
and
development
software
applications
• Prepare
reports,
manuscripts,
proposals
and
technical
documents
08/2011
to
05/2015
Research
Assistant
(Ph.D.)
Helmholtz
Zentrum
München
–
Munich,
Germany
• Deterministic
and
stochastic
modeling
and
simulation
of
embryonic
stem
cells
• Scientific
communication
including
authoring
manuscripts,
attending
conferences,
scientific
presentations,
etc.
• Data
analysis
including
developing
novel
tools
for
data
visualization
• Establish
cross-­‐‑disciplinary
collaborations
with
research
partners
09/2007
to
07/2009
Associate
Scientist
II
Archimedes,
Inc.
–
San
Francisco,
CA,
USA
• Modeling
and
simulation
of
physiology
and
healthcare
processes
for
consulting
in
clinical
trial
design
&
public
health
• Software
development
in
SmallTalk,
R
and
Python
• Research
relevant
scientific
publications
• Create
scientific
reports
for
internal
and
client-­‐‑facing
use
Education
2016
Ph.D.:
Mathematical
modeling
of
biological
systems
Technical
University
of
Munich
–
Munich,
Germany
2011
M.Sc.:
Computational
Biology
and
Bioinformatics
Swiss
Federal
Institute
of
Technology
Zürich
(ETH)
–
Zurich,
Switzerland
2005
B.A.:
Physics
University
of
California,
Berkeley
–
Berkeley,
CA,
USA

2. Research Experience
2016
ETH
Zurich.
Zurich,
Switzerland
Postdoctoral
researcher
• Implement
high
performance
stochastic
simulation
software
(C++/Matlab)
suitable
for
Bayesian
parameter
inference
in
stochastic
chemical
reaction
networks
• Perform
tissue
culture
and
investigations
of
cholesterol
signaling
• Analysis/modeling
of
RNA-­‐‑seq
data
2011-­‐‑2016,
Helmholtz
Zentrum
München/Technische
Universität
München.
Munich,
Germany
Research
assistant
/
Ph.D.
candidate
• Coursework
in
information
theory,
Bayesian
methods,
computational
biology
• Development
of
novel
visualization
methods
for
single-­‐‑cell
gene
expression
data
• Analysis
and
stochastic/deterministic
modeling
of
mouse
embryonic
stem
cell
protein
expression
dynamics
including
parameter
inference,
signaling
processing,
and
optimization
of
models
• Development
of
novel
Bayesian
particle
filter-­‐‑based
inference
methodology
and
application
to
stem
cell
data
• Presentation
at
international
conferences
• Implementation
of
stochastic
models
for
gene
expression
• Development
of
high
performance
software
in
C++/Matlab/Python
for
the
linear
noise
approximation
of
the
chemical
master
equation
2009-­‐‑2011,
ETH
Zurich.
Zurich,
Switzerland
Research
assistant
/
Master’s
in
Computational
Biology
&
Bioinformatics
• Coursework
in
statistical
inference,
systems
biology,
computational
biology,
spatiotemporal
modeling,
stochastic
processes,
simulation,
optimization
techniques
(convex,
combinatorial,
genetic
algorithms),
synthetic
biology,
computational
statistics
(machine
learning)
• Excellence
Scholarship
&
Opportunity
Program:
investigating
modular
modules
for
metabolic
networks/graph
theoretical
methods
• Thesis
in
multiscale
stochastic
simulation
methods
• Extensive
use
of
Matlab/Mathematica
2007-­‐‑2009
Archimedes,
Inc.
San
Francisco,
CA
Associate
Scientist
I/II
• Agent
based
stochastic/deterministic
modeling
of
biomarkers
in
the
context
of
diabetes
and
metabolic
disorder
• Implementation
of
pharmacological/surgical
intervention/screening
policy
study
models
• Develop/implement
models
(Python,
R,
SmallTalk)
and
perform
simulations
in
context
of
human
health
and
disease
2007
Tucson,
AZ
Premedical
preparation.
>99%
quantile
in
medical
college
admissions
test.
2006-­‐‑2007
University
of
Arizona.
Tucson,
AZ
Graduate
assistant
(Ph.D.
candidate),
Biomedical
Engineering.
• Graduate
research
rotations
in:
o MRI
contrast
agents
(biochemistry)/imaging
of
tumor
vasculature
o Non-­‐‑linear
optics
imaging
techniques
o Signal
processing
for
electrocardiology/ventricular
fibrillation
post
myocardial
infarction
(Matlab)

3. • Coursework
in
electronics,
physiology/anatomy,
cellular
signaling,
signal
processing,
statistics
2004-­‐‑2005
UC
Berkeley.
Berkeley,
CA
Research
assistant
in
experimental
single
molecule
biophysics
• Electron
microscopy/image
analysis
• Assist
in
force
measurement
experiments
• Preparation
of
microfluidic
devices
• Data
analysis
• Coursework
in
physics
(B.A.),
chemistry/organic
chemistry,
mathematics,
biology,
electronics,
etc.
2004
Lawrence
Berkeley
National
Labs.
Berkeley,
CA
Research
assistant
in
particle
physics
• Develop
image
processing
software
• GUI
development
in
Qt/C++
Languages
English
(Native),
German
(fluent)
Publications
Feigelman,
J.,
Ganscha,
S.
and
Claassen,
M.
matLeap:
A
fast
adaptive
Matlab-­‐‑ready
tau-­‐‑leaping
implementation
suitable
for
Bayesian
inference.
Arxiv
preprint:
https://arxiv.org/pdf/1608.07058v1.pdf
Feigelman,
J.,
Ganscha,
S.,
Hastreiter,
S.,
Schwarzfischer,
M.,
Filipczyk,
A.,
Schroeder,
T.,
Theis,
F.J.,
Marr,
C.
and
Claassen,
M..
Exact
Bayesian
lineage
tree-­‐‑based
inference
identifies
Nanog
negative
autoregulation
in
mouse
embryonic
stem
cells.
In
revision
at
Cell
Systems.
bioRxiv
preprint:
http://biorxiv.org/content/early/2016/05/13/053231
Feigelman,
J..
Stochastic
and
deterministic
methods
for
the
analysis
of
Nanog
dynamics
in
mouse
embryonic
stem
cells.
Ph.D.
Thesis,
Mathematical
Modeling
of
Biological
Systems,
Technische
Universität
München
(2016).
http://doi.org/10.1101/053231
Blasi,
T.,
Feller,
C.,
Feigelman,
J.,
Hasenauer,
J.,
&
Imhof,
A.
(2016).
Combinatorial
Histone
Acetylation
Patterns
Are
Generated
by
Motif-­‐‑Specific
Reactions.
Cell
Systems,
2(1),
49–58.
http://doi.org/10.1016/j.cels.2016.01.002
Filipczyk,
A*.,
Marr,
C*.,
Hastreiter,
S*.,
Feigelman,
J.,
Schwarzfischer,
M.,
Hoppe,
P.
S.,
et
al.
(*equal
contribution).
(2015).
Network
plasticity
of
pluripotency
transcription
factors
in
embryonic
stem
cells.
Nature
Cell
Biology,
17(10),
1235–1246.
http://doi.org/10.1038/ncb3237
Feigelman,
J.,
Popović,
N.,
&
Marr,
C.
(2015).
A
case
study
on
the
use
of
scale
separation-­‐‑based
analytical
propagators
for
parameter
inference
in
models
of
stochastic
gene
regulation.
Journal
of
Coupled
Systems
and
Multiscale
Dynamics,
3(2),
164–173.
http://doi.org/10.1166/jcsmd.2015.1074
Strasser,
M.
K.,
Feigelman,
J.,
Theis,
F.
J.,
&
Marr,
C.
(2015).
Inference
of
spatiotemporal
effects
on
cellular
state
transitions
from
time-­‐‑lapse
microscopy.
BMC
Systems
Biology,
9(1),
61.
http://doi.org/10.1186/s12918-­‐‑015-­‐‑0208-­‐‑5
Feigelman,
J.,
Theis,
F.
J.,
&
Marr,
C.
(2014).
MCA:
Multiresolution
Correlation
Analysis,
a
graphical
tool
for
subpopulation
identification
in
single-­‐‑cell
gene

4. expression
data.
BMC
Bioinformatics,
15(1),
1–10.
http://doi.org/10.1016/j.jeconom.2012.08.001
Koumoutsakos,
P.,
&
Feigelman,
J.
(2013).
Multiscale
stochastic
simulations
of
chemical
reactions
with
regulated
scale
separation.
Journal
of
Computational
Physics,
244,
290–297.
http://doi.org/10.1016/j.jcp.2012.11.030
Kaltenbach,
H.-­‐‑M.,
Constantinescu,
S.,
Feigelman,
J.,
&
Stelling,
J.
(2011).
Graph-­‐‑Based
Decomposition
of
Biochemical
Reaction
Networks
into
Monotone
Subsystems.
In
Lecture
Notes
in
Computer
Science
(Vol.
6833,
pp.
139–150).
Berlin,
Heidelberg:
Springer
Berlin
Heidelberg.
http://doi.org/10.1007/978-­‐‑3-­‐‑642-­‐‑23038-­‐‑7_13
Kahn,
R.,
Alperin,
P.,
Eddy,
D.,
Borch-­‐‑Johnsen,
K.,
Buse,
J.,
Feigelman,
J.,
et
al.
(2010).
Age
at
initiation
and
frequency
of
screening
to
detect
type
2
diabetes:
a
cost-­‐‑
effectiveness
analysis.
Lancet,
375(9723),
1365–1374.
http://doi.org/10.1016/S0140-­‐‑6736(09)62162-­‐‑0
Indik,
J.
H.,
Donnerstein,
R.
L.,
Hilwig,
R.
W.,
Zuercher,
M.,
Feigelman,
J.,
Kern,
K.
B.,
et
al.
(2008).
The
influence
of
myocardial
substrate
on
ventricular
fibrillation
waveform:
a
swine
model
of
acute
and
postmyocardial
infarction.
Critical
Care
Medicine,
36(7),
2136–2142.
http://doi.org/10.1097/CCM.0b013e31817d798c