PhD Student CV — page 1

Elena

Vasquez

PhD

Candidate

in

Computational

Neuroscience

elena

.

vasquez

@

stanford

.

edu

+1 (650) 555-0192

Stanford

,

CA

elenavasquez

.

io

·

github

.

com

/

evasquez

RESEA RCH

INT EREST S

Neural

coding

Population

dynamics

Reinforcement

learning

High

-

density

electrophysiology

Computational

psychiatry

Causal

inference

EDUCAT ION

PhD

in

Neuroscience

Expected

June

2026

Stanford

University

,

Stanford

,

CA

Advisor

:

Prof

.

Maya

Chen

·

Dissertation

:

Neural

population

dynamics

underlying

credit

assignment

in

reward

learning

BSc

in

Cognitive

Science

,

summa

cum

laude

May

2020

University

of

California

,

Berkeley

,

CA

Minor

in

Computer

Science

·

GPA

: 3.98/4.0 ·

Highest

Honors

in

Cognitive

Science

PUBLICAT IONS

Cortical

population

dynamics

reflect

task

-

specific

credit

assignment

signals

.

Vasquez

,

E

.

,

Chen

,

M

.

Nature

Neuroscience

, 2025.

Featured

Article

A

framework

for

dissociable

value

and

policy

learning

in

recurrent

neural

networks

.

Kim

,

J

.,

Vasquez

,

E

.

,

Chen

,

M

.

Advances

in

Neural

Information

Processing

Systems

(

NeurIPS

), 2024.

Spotlight

Neural

manifolds

of

uncertainty

during

probabilistic

reversal

learning

.

Vasquez

,

E

.

,

Park

,

S

.,

Chen

,

M

.

Proceedings

of

the

National

Academy

of

Sciences

, 2024.

Hierarchical

inference

in

prefrontal

cortex

during

volatile

environments

.

Vasquez

,

E

.

,

Chen

,

M

.

Computational

and

Systems

Neuroscience

(

COSYNE

)

Abstract

, 2023.

RESEA RCH

EXPERIENCE

Graduate

Research

Assistant

Sep

2020 –

Present

Chen

Lab

,

Wu

Tsai

Neurosciences

Institute

,

Stanford

University

Undergraduate

Research

Assistant

Jan

2019 –

Aug

2020

Redwood

Center

for

Theoretical

Neuroscience

,

UC

Berkeley

Research

Intern

Jun

2018 –

Dec

2018

Janelia

Research

Campus

,

HHMI

T EACHING

EXPERIENCE

Teaching

Assistant

,

NSCI

200 —

Neural

Data

Science

Spring

2023, 2024

Stanford

University

Designed

and

implemented

closed

-

loop

optogenetics

+

Neuropixels

recordings

in

behaving

mice

to

dissect

prefrontal

-

subcortical

circuits

during

reward

-

guided

decision

making

—

Developed

a

variational

autoencoder

framework

for

demixing

mixed

-

selectivity

neural

populations

,

reducing

dimensionality

from

~800

neurons

to

12

latent

factors

—

Built

and

maintained

a

shared

analysis

pipeline

(

Python

,

DataJoint

)

adopted

by

4

lab

members

for

spike

sorting

,

behavioral

alignment

,

and

statistical

modeling

—

Modeled

hierarchical

Bayesian

inference

in

prefrontal

cortex

using

probabilistic

programming

(

Pyro

)

and

compared

model

predictions

to

human

fMRI

data

—

Co

-

authored

a

first

-

author

preprint

on

uncertainty

representation

in

prefrontal

cortex

during

reversal

learning

—

Developed

a

real

-

time

calcium

imaging

analysis

pipeline

(

Suite

2

p

+

custom

Python

)

for

larval

zebrafish

whole

-

brain

recordings

—

Identified

a

novel

population

code

for

prey

capture

initiation

in

the

optic

tectum

,

presented

at

Janelia

Annual

Meeting

—

1 / 2

PhD Student CV — page 2

Developed

course

materials

on

dimensionality

reduction

,

GLMs

,

and

spike

train

analysis

for

45

graduate

students

.

Held

weekly

office

hours

and

led

two

computational

lab

sections

.

Received

TA

excellence

award

(

Spring

2024).

Guest

Lecturer

,

PSYC

150 —

Computational

Psychiatry

Fall

2023

Stanford

University

Delivered

a

90-

minute

lecture

on

reinforcement

learning

models

of

anhedonia

and

their

neural

correlates

.

Designed

an

in

-

class

coding

exercise

using

hierarchical

Bayesian

models

.

SKILLS

&

MET HODS

Experimental

:

Neuropixels

,

two

-

photon

calcium

imaging

,

optogenetics

,

stereotaxic

surgery

,

mouse

behavior

(

operant

,

reversal

learning

,

foraging

),

chemogenetics

,

histology

,

whole

-

cell

patch

clamp

(

experience

)

Computational

:

Python

,

MATLAB

,

R

,

C

++ (

intermediate

),

PyTorch

,

JAX

,

GPyTorch

,

DataJoint

,

Suite

2

p

,

Kilosort

,

Stan

,

probabilistic

programming

(

Pyro

,

NumPyro

)

Analysis

:

Dimensionality

reduction

(

PCA

,

LDS

,

VAE

),

GLMs

,

reinforcement

learning

models

,

Bayesian

inference

,

time

-

series

analysis

,

spectral

analysis

,

spike

sorting

,

decoding

,

representational

similarity

analysis

Other

:

Git

,

Docker

,

SLURM

/

HPC

,

AWS

(

EC

2,

S

3),

scientific

writing

,

peer

review

(

reviewed

for

JNeurosci

,

eLife

,

NeurIPS

),

mentorship

of

3

rotation

students

HONORS

&

AWA RDS

Wu

Tsai

Interdisciplinary

Scholar

Award

—

Stanford

University

2024

NSF

Graduate

Research

Fellowship

(

GRFP

)

—

Honorable

Mention

2021, 2022

TA

Excellence

Award

—

Stanford

Department

of

Neurobiology

2024

Summa

Cum

Laude

&

Highest

Honors

in

Cognitive

Science

—

UC

Berkeley

2020

Goldwater

Scholar

—

Barry

Goldwater

Scholarship

Foundation

2019

SERVICE

&

OUT REACH

Co

-

Organizer

,

Stanford

NeuroData

Journal

Club

2022 –

Present

Co

-

founded

a

weekly

journal

club

bridging

computational

and

experimental

neuroscience

.

Manage

speaker

schedule

,

moderate

discussions

,

and

mentor

first

-

year

graduate

presenters

. ~25

regular

attendees

across

6

departments

.

Mentor

,

BRAINS

Program

for

Underrepresented

Undergraduates

2023 –

Present

Mentor

2

undergraduate

researchers

from

underrepresented

backgrounds

on

independent

computational

projects

.

One

student

received

a

Cota

-

Robles

fellowship

;

both

presented

at

SACNAS

.

REFERENCES

Available

upon

request

.

2 / 2

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