[Theory] REMINDER: 4/24 Talks at TTIC: Aleksandrina Goeva, Broad Institute of MIT and Harvard

[Mary Marre]

*When:*        Monday, April 24, 2023 at* 11:30** a**m CT   *


*Where:       *Talk will be given *live, in-person* at

                   TTIC, 6045 S. Kenwood Avenue

                   5th Floor, Room 530


*Virtually:*  *via *Panopto (*livestream
<https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=d09c990d-316a-4fba-9884-afe80148e235>*
)

*Access limited to TTIC/UChicago (see info below)


*Who: *         Aleksandrina Goeva, Broad Institute of MIT and Harvard


------------------------------

*Title:* Machine Learning Methods for Inferring Changes in Cells Across
Space, Time, Health and Disease


*Abstract:* The dream of generations of scientists and physicians, to be
able to ask any question about cellular function or tissue organization, is
becoming reality through single cell and tissue profiling technologies.
However, our ability to render these technological advancements into
actionable information in research or in the clinic necessitates the
development of rigorous, efficient, robust, testable, and interpretable
analytical methods. In this talk, I will provide a survey of my
contributions to the fields of molecular cell and tissue biology in the
development of (1) interpretable models of spatial transcriptomics data
leveraging prior domain knowledge from annotated single-cell atlases, (2)
principled statistical and machine learning methods for discovering
transcriptional changes across conditions, and (3) systems biology
approaches and hypothesis testing frameworks for modeling cell-cell
communication.


*Bio:* Aleksandrina Goeva is a Postdoctoral Fellow in the Macosko Lab at
the Stanley Center for Psychiatric Research within the Broad Institute of
MIT and Harvard. She received her PhD in Statistics at Boston University,
where she worked with Henry Lam and Eric Kolaczyk on complexity penalized
methods for structured and unstructured data. During her postdoc,
Aleksandrina has developed a deep interest in complex biological systems,
for which we typically have only an incomplete and noisy set of
measurements, and for the impact that expanding our knowledge of basic
biological mechanisms can have on improving human health. Her current
research is focused on ill-posed inverse problems in biology, where she
combines domain expert knowledge with modeling approaches applied to
single-cell RNA-seq and spatial transcriptomics data to answer questions
about the mechanisms of interaction between cells and the function of
tissues in health and disease. In addition to striving to gain useful
biological insights, Aleksandrina also works on theoretical problems in
statistics that arise from her applied work. Aleksandrina has extensive
experience in math, stats, and science education centered around her
passion for conveying complex concepts in simple and accurate terms. She
also has extensive leadership experience in building enduring structured
environments that foster connections within and across fields and gather
large local and global scientific communities illustrated by her service as
a co-chair of the Models, Inference & Algorithms seminar series at the
Broad Institute, as an Assistant Editor at the Harvard Data Science Review
journal, and as an organizer of the Learning Meaningful Representations of
Life workshop at NeurIPS.

*Host: *Jinbo Xu <j3xu at ttic.edu>


*Access to this livestream is limited to *TTIC / UChicago* (press panopto
link and login to your UChicago account).



Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue, Rm 517*
*Chicago, IL  60637*
*773-834-1757*
*mmarre at ttic.edu <mmarre at ttic.edu>*


On Tue, Apr 18, 2023 at 4:27 PM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*        Monday, April 24, 2023 at* 11:30** a**m CT   *
>
>
> *Where:       *Talk will be given *live, in-person* at
>
>                    TTIC, 6045 S. Kenwood Avenue
>
>                    5th Floor, Room 530
>
>
> *Virtually:*  *via *Panopto (*livestream
> <https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=d09c990d-316a-4fba-9884-afe80148e235>*
> )
>
> *Access limited to TTIC/UChicago (see info below)
>
>
> *Who: *         Aleksandrina Goeva, Broad Institute of MIT and Harvard
>
>
> ------------------------------
>
> *Title:* Machine Learning Methods for Inferring Changes in Cells Across
> Space, Time, Health and Disease
>
>
> *Abstract:* The dream of generations of scientists and physicians, to be
> able to ask any question about cellular function or tissue organization, is
> becoming reality through single cell and tissue profiling technologies.
> However, our ability to render these technological advancements into
> actionable information in research or in the clinic necessitates the
> development of rigorous, efficient, robust, testable, and interpretable
> analytical methods. In this talk, I will provide a survey of my
> contributions to the fields of molecular cell and tissue biology in the
> development of (1) interpretable models of spatial transcriptomics data
> leveraging prior domain knowledge from annotated single-cell atlases, (2)
> principled statistical and machine learning methods for discovering
> transcriptional changes across conditions, and (3) systems biology
> approaches and hypothesis testing frameworks for modeling cell-cell
> communication.
>
>
> *Bio:* Aleksandrina Goeva is a Postdoctoral Fellow in the Macosko Lab at
> the Stanley Center for Psychiatric Research within the Broad Institute of
> MIT and Harvard. She received her PhD in Statistics at Boston University,
> where she worked with Henry Lam and Eric Kolaczyk on complexity penalized
> methods for structured and unstructured data. During her postdoc,
> Aleksandrina has developed a deep interest in complex biological systems,
> for which we typically have only an incomplete and noisy set of
> measurements, and for the impact that expanding our knowledge of basic
> biological mechanisms can have on improving human health. Her current
> research is focused on ill-posed inverse problems in biology, where she
> combines domain expert knowledge with modeling approaches applied to
> single-cell RNA-seq and spatial transcriptomics data to answer questions
> about the mechanisms of interaction between cells and the function of
> tissues in health and disease. In addition to striving to gain useful
> biological insights, Aleksandrina also works on theoretical problems in
> statistics that arise from her applied work. Aleksandrina has extensive
> experience in math, stats, and science education centered around her
> passion for conveying complex concepts in simple and accurate terms. She
> also has extensive leadership experience in building enduring structured
> environments that foster connections within and across fields and gather
> large local and global scientific communities illustrated by her service as
> a co-chair of the Models, Inference & Algorithms seminar series at the
> Broad Institute, as an Assistant Editor at the Harvard Data Science Review
> journal, and as an organizer of the Learning Meaningful Representations of
> Life workshop at NeurIPS.
>
> *Host: *Jinbo Xu <j3xu at ttic.edu>
>
>
> *Access to this livestream is limited to *TTIC / UChicago* (press panopto
> link and login to your UChicago account).
>
>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue, Rm 517*
> *Chicago, IL  60637*
> *773-834-1757*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
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