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Lecturer CV
Dr
.
Sarah
Chen
Lecturer
in
Computer
Science
Department
of
Computing
·
Imperial
College
London
London
,
UK
+44 (0)20 7594 1234
sarah
.
chen
@
imperial
.
ac
.
uk
sarahchen
.
dev
ORCID
: 0000-0002-8765-4321
PROFESSIONAL
SUMMARY
Researcher
and
lecturer
in
machine
learning
systems
,
with
a
focus
on
efficient
deep
learning
,
model
compression
,
and
federated
learning
.
Over
six
years
of
teaching
experience
at
undergraduate
and
postgraduate
levels
,
spanning
core
computer
science
and
specialised
ML
courses
.
Committed
to
inclusive
pedagogy
and
reproducible
research
.
Published
18
peer
-
reviewed
papers
with
over
1,200
citations
.
RESEARCH
INT EREST S
Efficient
Deep
Learning
Model
Compression
&
Pruning
Federated
Learning
Trustworthy
AI
ML
for
Health
Reproducible
Research
ACADEMIC
APPOINT MENT S
Lecturer
(
Assistant
Professor
)
in
Computer
Science
Sept
2021 –
Present
Imperial
College
London
,
Department
of
Computing
Postdoctoral
Research
Associate
Oct
2018 –
Aug
2021
University
of
Cambridge
,
Department
of
Engineering
Research
Intern
Summer
2017
Google
Research
,
Zurich
—
Efficient
ML
team
EDUCAT ION
PhD
in
Computer
Science
2015 – 2018
University
of
Oxford
·
Thesis
:
Efficient
Training
of
Deep
Neural
Networks
via
Structured
Pruning
MEng
in
Computer
Science
(
First
Class
Honours
)
2011 – 2015
University
of
Cambridge
·
Dissertation
:
Accelerating
CNNs
on
Edge
Devices
Lead
the
Efficient
ML
research
group
(4
PhD
students
, 2
postdocs
)
—
Module
leader
for
Machine
Learning
(
COMP
70050)
and
Advanced
Deep
Learning
(
COMP
70062)
—
Departmental
lead
for
EDI
in
Computing
—
launched
the
Women
in
ML
mentorship
programme
—
Developed
novel
pruning
algorithms
reducing
inference
cost
of
vision
transformers
by
4.2×
with
<1%
accuracy
loss
—
Co
-
supervised
3
PhD
students
and
6
MEng
projects
—
Designed
and
implemented
a
sparse
training
framework
for
large
language
models
—
1 / 3
SELECT ED
PUBLICAT IONS
[1]
S
.
Chen
,
A
.
Gupta
,
and
M
.
Patel
. "
Gradient
-
Aware
Structured
Pruning
for
Vision
Transformers
."
Proceedings
of
the
International
Conference
on
Machine
Learning
(
ICML
),
2025.
[2]
S
.
Chen
,
L
.
Wang
,
and
R
.
Kumar
. "
FedComp
:
Communication
-
Efficient
Federated
Learning
with
Adaptive
Compression
."
Advances
in
Neural
Information
Processing
Systems
(
NeurIPS
),
2024.
[3]
S
.
Chen
,
J
.
Brown
,
and
T
.
Nakamura
. "
On
the
Reproducibility
of
Pruning
Benchmarks
:
A
Large
-
Scale
Empirical
Study
."
Journal
of
Machine
Learning
Research
(
JMLR
),
2024.
[4]
S
.
Chen
,
R
.
Singh
,
and
K
.
Okafor
. "
One
-
Shot
Pruning
without
Fine
-
Tuning
:
A
Lottery
Ticket
Perspective
."
International
Conference
on
Learning
Representations
(
ICLR
),
2023.
[5]
S
.
Chen
and
M
.
Hinton
. "
Sparse
Training
from
Scratch
:
A
Practical
Framework
for
Edge
Deployment
."
Proceedings
of
Machine
Learning
and
Systems
(
MLSys
),
2022.
Full
list
available
at
sarahchen
.
dev
/
publications
·
h
-
index
: 14 ·
Citations
: 1,280
T EACHING
Module
Leader
—
Machine
Learning
2021–
Present
COMP
70050, 3
rd
-
year
undergraduate
, ~120
students
Core
ML
theory
,
supervised
/
unsupervised
learning
,
neural
networks
.
Redesigned
coursework
around
fairness
and
interpretability
.
Module
Leader
—
Advanced
Deep
Learning
2022–
Present
COMP
70062,
MSc
, ~80
students
Transformers
,
graph
neural
networks
,
generative
models
,
model
compression
.
Emphasis
on
reproducing
recent
papers
.
Lecturer
—
Research
Methods
in
Computing
2022–
Present
COMP
70001, 1
st
-
year
PhD
, ~30
students
Experimental
design
,
statistical
analysis
,
reproducible
workflows
,
academic
writing
.
Guest
Lecturer
—
Efficient
AI
2023–
Present
University
of
Cambridge
(
invited
) ·
Princeton
(
invited
)
Modular
lectures
on
pruning
,
quantisation
,
and
knowledge
distillation
for
deep
learning
.
G RANT S
&
AWARDS
UKRI
Future
Leaders
Fellowship
(
Extension
)
2025–2029
£1,185,000 — "
Trustworthy
and
Efficient
Foundation
Models
for
Healthcare
"
Royal
Society
Research
Grant
2023–2024
£74,000 — "
Communication
-
Efficient
Federated
Learning
for
Multi
-
Hospital
Studies
"
Best
Paper
Award
—
MLSys
2022
2022
For
"
Sparse
Training
from
Scratch
:
A
Practical
Framework
for
Edge
Deployment
"
Imperial
College
Excellence
in
Teaching
Award
2023
Awarded
for
innovative
use
of
peer
-
led
workshops
in
Machine
Learning
course
PROFESSIONAL
SERVICE
Senior
Program
Committee
2023–
Present
NeurIPS
,
ICML
,
ICLR
Associate
Editor
2024–
Present
Transactions
on
Machine
Learning
Research
(
TMLR
)
Workshop
Organiser
2024
"
Efficient
ML
for
Healthcare
" @
NeurIPS
2024
Equity
,
Diversity
&
Inclusion
Lead
2022–
Present
Department
of
Computing
,
Imperial
College
London
2 / 3
SKILLS
&
MET HODS
Programming
Python
,
C
++,
CUDA
,
Julia
,
R
,
SQL
ML
Frameworks
PyTorch
,
JAX
,
TensorFlow
,
Hugging
Face
,
Ray
Tools
&
Platforms
Git
,
Docker
,
SLURM
,
AWS
,
GCP
,
LaTeX
,
Quarto
Research
Methods
Experimental
design
,
reproducibility
audits
,
statistical
hypothesis
testing
,
Bayesian
optimisation
PHD
SUPERVISION
Lisa
Adegoke
— "
Model
Compression
for
Medical
Imaging
" (2023–2026,
primary
supervisor
)
—
James
O
'
Neill
— "
Federated
Learning
with
Heterogeneous
Clients
" (2023–2026,
primary
supervisor
)
—
Priya
Sharma
— "
Efficient
Fine
-
Tuning
of
Large
Language
Models
" (2024–2027,
primary
supervisor
)
—
Daniel
Kowalski
— "
Neuro
-
Symbolic
Reasoning
on
Edge
Devices
" (2024–2028,
co
-
supervisor
)
—
3 / 3
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