[Theory] REMINDER: 1/28 Talks at TTIC: Ivan Stelmakh, Carnegie Mellon University

[Mary Marre]

*When:        *Friday, January 28 at *9:30am CT*

*Where:*       Zoom Virtual Talk (*register in advance here
<https://uchicagogroup.zoom.us/webinar/register/WN_f5MT0eRXSgK9bA2DRI6k6g>*)


*Who: *         Ivan Stelmakh, Carnegie Mellon University



*Title:  *Towards Principled Algorithmic Support of Human Decision-Making

*Abstract: *
Many important applications such as hiring, healthcare, and scientific peer
review rely on human decision-making. Recently, the scale of many of these
applications has increased dramatically which is both an opportunity and a
challenge. On the one hand, the large amount of data generated in these
applications opens up an *opportunity* to take a novel data-centric
perspective on the classical problems of human decision-making. On the
other hand, the large scale makes it hard or even impossible for humans to
do all the work manually; hence, there is a *challenge* of developing
principled algorithmic tools to support human decision-makers. In this
talk, I will discuss my work on exploring the opportunities and addressing
the challenges in the context of scientific peer review. In that, I will
talk about empirical and theoretical work that has impacted major Computer
Science conferences such as NeurIPS and ICML. I will then outline ideas for
future work.

*Bio: *
Ivan is a fifth-year PhD student in the Machine Learning Department at
Carnegie Mellon University advised by Nihar Shah and Aarti Singh. His
research interests lie in the area of learning from people with a focus on
building a principled approach towards large-scale human decision-making in
high-stake applications. In his thesis research, Ivan works on making
scientific peer review scientific by developing tools, techniques, and
experiments to support fair, equitable, and efficient peer review.

*Host*: *Avrim Blum* <avrim at ttic.edu>


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


On Thu, Jan 27, 2022 at 1:09 PM Mary Marre <mmarre at ttic.edu> wrote:

> *When:        *Friday, January 28 at *9:30am CT*
>
> *Where:*       Zoom Virtual Talk (*register in advance here
> <https://uchicagogroup.zoom.us/webinar/register/WN_f5MT0eRXSgK9bA2DRI6k6g>*
> )
>
>
> *Who: *         Ivan Stelmakh, Carnegie Mellon University
>
>
>
> *Title:  *Towards Principled Algorithmic Support of Human Decision-Making
>
> *Abstract: *
> Many important applications such as hiring, healthcare, and scientific
> peer review rely on human decision-making. Recently, the scale of many of
> these applications has increased dramatically which is both an opportunity
> and a challenge. On the one hand, the large amount of data generated in
> these applications opens up an *opportunity* to take a novel data-centric
> perspective on the classical problems of human decision-making. On the
> other hand, the large scale makes it hard or even impossible for humans to
> do all the work manually; hence, there is a *challenge* of developing
> principled algorithmic tools to support human decision-makers. In this
> talk, I will discuss my work on exploring the opportunities and addressing
> the challenges in the context of scientific peer review. In that, I will
> talk about empirical and theoretical work that has impacted major Computer
> Science conferences such as NeurIPS and ICML. I will then outline ideas for
> future work.
>
> *Bio: *
> Ivan is a fifth-year PhD student in the Machine Learning Department at
> Carnegie Mellon University advised by Nihar Shah and Aarti Singh. His
> research interests lie in the area of learning from people with a focus on
> building a principled approach towards large-scale human decision-making in
> high-stake applications. In his thesis research, Ivan works on making
> scientific peer review scientific by developing tools, techniques, and
> experiments to support fair, equitable, and efficient peer review.
>
> *Host*: *Avrim Blum* <avrim at ttic.edu>
>
>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Chicago, IL  60637*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
>
> On Thu, Jan 20, 2022 at 8:21 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *1/21 Talk RESCHEDULED to 1/28 at 9:30am CT*
>> ******************************************************
>> *Old Date:*    Friday, January 21st at 9:30 am CT
>>
>> *New Date:  *Friday, January 28 at *9:30am CT*
>>
>> *Where:*       Zoom Virtual Talk (*register in advance here
>> <https://uchicagogroup.zoom.us/webinar/register/WN_f5MT0eRXSgK9bA2DRI6k6g>*
>> )
>>
>>
>> *Who: *         Ivan Stelmakh, Carnegie Mellon University
>>
>>
>>
>> *Title: *
>> Towards Principled Algorithmic Support of Human Decision-Making
>>
>> *Abstract: *
>> Many important applications such as hiring, healthcare, and scientific
>> peer review rely on human decision-making. Recently, the scale of many of
>> these applications has increased dramatically which is both an opportunity
>> and a challenge. On the one hand, the large amount of data generated in
>> these applications opens up an *opportunity* to take a novel
>> data-centric perspective on the classical problems of human
>> decision-making. On the other hand, the large scale makes it hard or even
>> impossible for humans to do all the work manually; hence, there is a
>> *challenge* of developing principled algorithmic tools to support human
>> decision-makers. In this talk, I will discuss my work on exploring the
>> opportunities and addressing the challenges in the context of scientific
>> peer review. In that, I will talk about empirical and theoretical work that
>> has impacted major Computer Science conferences such as NeurIPS and ICML. I
>> will then outline ideas for future work.
>>
>> *Bio: *
>> Ivan is a fifth-year PhD student in the Machine Learning Department at
>> Carnegie Mellon University advised by Nihar Shah and Aarti Singh. His
>> research interests lie in the area of learning from people with a focus on
>> building a principled approach towards large-scale human decision-making in
>> high-stake applications. In his thesis research, Ivan works on making
>> scientific peer review scientific by developing tools, techniques, and
>> experiments to support fair, equitable, and efficient peer review.
>>
>> *Host*: *Avrim Blum* <avrim at ttic.edu>
>>
>>
>> Mary C. Marre
>> Faculty Administrative Support
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue*
>> *Chicago, IL  60637*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>>
>> On Wed, Jan 19, 2022 at 2:29 PM Mary Marre <mmarre at ttic.edu> wrote:
>>
>>> *TALK IS RESCHEDULED TO 1/28 at 9:30am CT*
>>> ******************************************************
>>> *Canceled Date:*     Friday, January 21st at* 9:30 am CT*
>>>
>>> *Where:*    Zoom Virtual Talk (*register in advance here
>>> <https://uchicagogroup.zoom.us/webinar/register/WN_f5MT0eRXSgK9bA2DRI6k6g>*
>>> )
>>>
>>>
>>> *Who: *      Ivan Stelmakh, Carnegie Mellon University
>>>
>>>
>>>
>>> *Title: *
>>> Towards Principled Algorithmic Support of Human Decision-Making
>>>
>>> *Abstract: *
>>> Many important applications such as hiring, healthcare, and scientific
>>> peer review rely on human decision-making. Recently, the scale of many of
>>> these applications has increased dramatically which is both an opportunity
>>> and a challenge. On the one hand, the large amount of data generated in
>>> these applications opens up an *opportunity* to take a novel
>>> data-centric perspective on the classical problems of human
>>> decision-making. On the other hand, the large scale makes it hard or even
>>> impossible for humans to do all the work manually; hence, there is a
>>> *challenge* of developing principled algorithmic tools to support human
>>> decision-makers. In this talk, I will discuss my work on exploring the
>>> opportunities and addressing the challenges in the context of scientific
>>> peer review. In that, I will talk about empirical and theoretical work that
>>> has impacted major Computer Science conferences such as NeurIPS and ICML. I
>>> will then outline ideas for future work.
>>>
>>> *Bio: *
>>> Ivan is a fifth-year PhD student in the Machine Learning Department at
>>> Carnegie Mellon University advised by Nihar Shah and Aarti Singh. His
>>> research interests lie in the area of learning from people with a focus on
>>> building a principled approach towards large-scale human decision-making in
>>> high-stake applications. In his thesis research, Ivan works on making
>>> scientific peer review scientific by developing tools, techniques, and
>>> experiments to support fair, equitable, and efficient peer review.
>>>
>>> *Host*: *Avrim Blum* <avrim at ttic.edu>
>>>
>>>
>>> Mary C. Marre
>>> Faculty Administrative Support
>>> *Toyota Technological Institute*
>>> *6045 S. Kenwood Avenue*
>>> *Chicago, IL  60637*
>>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>>
>>>
>>> On Fri, Jan 14, 2022 at 2:21 PM Mary Marre <mmarre at ttic.edu> wrote:
>>>
>>>> *When:*     Friday, January 21st at* 9:30 am CT*
>>>>
>>>>
>>>>
>>>> *Where:*    Zoom Virtual Talk (*register in advance here
>>>> <https://uchicagogroup.zoom.us/webinar/register/WN_f5MT0eRXSgK9bA2DRI6k6g>*
>>>> )
>>>>
>>>>
>>>> *Who: *      Ivan Stelmakh, Carnegie Mellon University
>>>>
>>>>
>>>>
>>>> *Title: *
>>>> Towards Principled Algorithmic Support of Human Decision-Making
>>>>
>>>> *Abstract: *
>>>> Many important applications such as hiring, healthcare, and scientific
>>>> peer review rely on human decision-making. Recently, the scale of many of
>>>> these applications has increased dramatically which is both an opportunity
>>>> and a challenge. On the one hand, the large amount of data generated in
>>>> these applications opens up an *opportunity* to take a novel
>>>> data-centric perspective on the classical problems of human
>>>> decision-making. On the other hand, the large scale makes it hard or even
>>>> impossible for humans to do all the work manually; hence, there is a
>>>> *challenge* of developing principled algorithmic tools to support
>>>> human decision-makers. In this talk, I will discuss my work on exploring
>>>> the opportunities and addressing the challenges in the context of
>>>> scientific peer review. In that, I will talk about empirical and
>>>> theoretical work that has impacted major Computer Science conferences such
>>>> as NeurIPS and ICML. I will then outline ideas for future work.
>>>>
>>>> *Bio: *
>>>> Ivan is a fifth-year PhD student in the Machine Learning Department at
>>>> Carnegie Mellon University advised by Nihar Shah and Aarti Singh. His
>>>> research interests lie in the area of learning from people with a focus on
>>>> building a principled approach towards large-scale human decision-making in
>>>> high-stake applications. In his thesis research, Ivan works on making
>>>> scientific peer review scientific by developing tools, techniques, and
>>>> experiments to support fair, equitable, and efficient peer review.
>>>>
>>>> *Host*: *Avrim Blum* <avrim at ttic.edu>
>>>>
>>>>
>>>>
>>>> Mary C. Marre
>>>> Faculty Administrative Support
>>>> *Toyota Technological Institute*
>>>> *6045 S. Kenwood Avenue*
>>>> *Chicago, IL  60637*
>>>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>>>
>>>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.cs.uchicago.edu/pipermail/theory/attachments/20220128/97e0a7b1/attachment-0001.html>