[Theory] TODAY: 8/29 Thesis Defense: Ben Lai, TTIC

[Mary Marre via Theory]

*When*:    Thursday, August 29th from* 9**:00am - 10:00am CT*

*Where*:   Talk will be given *live, in-person* at
               TTIC, 6045 S. Kenwood Avenue
               5th Floor, *Room 530*

*Virtually*: via *Zoom*
<https://uchicagogroup.zoom.us/j/91558909586?pwd=naSjIsRrL2Gj4ksOL0i3ZAa3miUjab.1>


*Who:  *    Ben Lai, TTIC


*Title:* Leveraging deep generative model for computational protein design
and optimization
*Abstract: *Recent advancements in deep learning have significantly
improved in silico protein modeling. While these developments are
promising, there still lacks a comprehensive computational framework for
protein design. Such a framework would aim to address a broad spectrum of
design challenges. I will introduce a novel generative model for protein
structures, and an iterative design framework for computational protein
engineering. Applications demonstrate success in challenging protein design
problems such as de novo fold generation, functional protein optimization,
motif scaffolding, and antibody design within a flexible, machine-driven
approach.

*Thesis Committee:* Jinbo Xu (Thesis Advisor), Avrim Blum, Aly Khan


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 Wed, Aug 28, 2024 at 3:15 PM Mary Marre <mmarre at ttic.edu> wrote:

> *When*:    Thursday, August 29th from* 9**:00am - 10:00am CT*
>
> *Where*:   Talk will be given *live, in-person* at
>                TTIC, 6045 S. Kenwood Avenue
>                5th Floor, *Room 530*
>
> *Virtually*: via *Zoom*
> <https://uchicagogroup.zoom.us/j/91558909586?pwd=naSjIsRrL2Gj4ksOL0i3ZAa3miUjab.1>
>
>
> *Who:  *    Ben Lai, TTIC
>
>
> *Title:* Leveraging deep generative model for computational protein
> design and optimization
> *Abstract: *Recent advancements in deep learning have significantly
> improved in silico protein modeling. While these developments are
> promising, there still lacks a comprehensive computational framework for
> protein design. Such a framework would aim to address a broad spectrum of
> design challenges. I will introduce a novel generative model for protein
> structures, and an iterative design framework for computational protein
> engineering. Applications demonstrate success in challenging protein design
> problems such as de novo fold generation, functional protein optimization,
> motif scaffolding, and antibody design within a flexible, machine-driven
> approach.
>
> *Thesis Committee:* Jinbo Xu (Thesis Advisor), Avrim Blum, Aly Khan
>
>
>
>
> 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, Aug 27, 2024 at 1:00 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When*:    Thursday, August 29th from* 9**:00am - 10:00am CT*
>>
>> *Where*:   Talk will be given *live, in-person* at
>>                TTIC, 6045 S. Kenwood Avenue
>>                5th Floor, *Room 530*
>>
>> *Virtually*: via *Zoom*
>> <https://uchicagogroup.zoom.us/j/91558909586?pwd=naSjIsRrL2Gj4ksOL0i3ZAa3miUjab.1>
>>
>>
>> *Who:  *    Ben Lai, TTIC
>>
>>
>> *Title:* Leveraging deep generative model for computational protein
>> design and optimization
>> *Abstract: *Recent advancements in deep learning have significantly
>> improved in silico protein modeling. While these developments are
>> promising, there still lacks a comprehensive computational framework for
>> protein design. Such a framework would aim to address a broad spectrum of
>> design challenges. I will introduce a novel generative model for protein
>> structures, and an iterative design framework for computational protein
>> engineering. Applications demonstrate success in challenging protein design
>> problems such as de novo fold generation, functional protein optimization,
>> motif scaffolding, and antibody design within a flexible, machine-driven
>> approach.
>>
>> *Thesis Committee:* Jinbo Xu (Thesis Advisor), Avrim Blum, Aly Khan
>>
>>
>>
>> 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 Fri, Aug 23, 2024 at 11:24 AM Mary Marre <mmarre at ttic.edu> wrote:
>>
>>> *When*:    Thursday, August 29th from* 9**:00am - 10:00am CT*
>>>
>>> *Where*:   Talk will be given *live, in-person* at
>>>                TTIC, 6045 S. Kenwood Avenue
>>>                5th Floor, *Room 530*
>>>
>>> *Virtually*: via *Zoom*
>>> <https://uchicagogroup.zoom.us/j/91558909586?pwd=naSjIsRrL2Gj4ksOL0i3ZAa3miUjab.1>
>>>
>>>
>>> *Who:  *    Ben Lai, TTIC
>>>
>>>
>>> *Title:* Leveraging deep generative model for computational protein
>>> design and optimization
>>> *Abstract: *Recent advancements in deep learning have significantly
>>> improved in silico protein modeling. While these developments are
>>> promising, there still lacks a comprehensive computational framework for
>>> protein design. Such a framework would aim to address a broad spectrum of
>>> design challenges. I will introduce a novel generative model for protein
>>> structures, and an iterative design framework for computational protein
>>> engineering. Applications demonstrate success in challenging protein design
>>> problems such as de novo fold generation, functional protein optimization,
>>> motif scaffolding, and antibody design within a flexible, machine-driven
>>> approach.
>>>
>>> *Thesis Committee:* Jinbo Xu (Thesis Advisor), Avrim Blum, Aly Khan
>>>
>>>
>>>
>>>
>>> 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>*
>>>
>>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.cs.uchicago.edu/pipermail/theory/attachments/20240829/bd3a68bc/attachment-0001.html>