Lecturer CV — page 1

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

Lecturer CV — page 2

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

Lecturer CV — page 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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