[Theory] NOW: 2/18 Talks at TTIC: Eric Wong, Massachusetts Institute of Technology

[Mary Marre]

*When:*        Friday, February 18th at* 10:30 am CT*


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

                   TTIC, 6045 S. Kenwood Avenue

                   5th Floor, Room 530


*Where:*       Zoom Virtual Talk (*register in advance here
<https://uchicagogroup.zoom.us/webinar/register/WN_MIgNiZOeTyGM0M5MF3d75w>*)


*Who: *         Eric Wong, Massachusetts Institute of Technology




*Title:      *     Building the Reliability Stack for Machine Learning

*Abstract:*
Currently, machine learning (ML) systems have impressive performance but
can behave in unexpected ways. These systems latch onto unintuitive
patterns and are easily compromised, a source of grave concern for deployed
ML in settings such as healthcare, security, and autonomous driving. In
this talk, I will discuss how we can redesign the core ML pipeline to
create reliable systems. First, I will show how to train provably robust
models, which enables formal robustness guarantees for complex deep
networks. Next, I will demonstrate how to make ML models more debuggable.
This amplifies our ability to diagnose failure modes, such as hidden biases
or spurious correlations. To conclude, I will discuss how we can build upon
this ``reliability stack'' to enable broader robustness requirements, and
develop new primitives that make ML debuggable by design.

*Bio:*
Eric Wong is a postdoctoral researcher in the Computer Science and
Artificial Intelligence Laboratory at Massachusetts Institute of
Technology. His research focuses on the foundations for reliable systems:
methods that allow us to diagnose, create, and verify robust systems. He is
a 2020 Siebel Scholar and received an honorable mention for his thesis on
the robustness of deep networks to adversarial examples at Carnegie Mellon
University.


*Host: **David McAllester* <mcallester 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, Feb 18, 2022 at 9:50 AM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*        Friday, February 18th at* 10:30 am CT*
>
>
> *Where:       *Talk will be given *live, in-person* at
>
>                    TTIC, 6045 S. Kenwood Avenue
>
>                    5th Floor, Room 530
>
>
> *Where:*       Zoom Virtual Talk (*register in advance here
> <https://uchicagogroup.zoom.us/webinar/register/WN_MIgNiZOeTyGM0M5MF3d75w>*
> )
>
>
> *Who: *         Eric Wong, Massachusetts Institute of Technology
>
>
>
>
> *Title:      *     Building the Reliability Stack for Machine Learning
>
> *Abstract:*
> Currently, machine learning (ML) systems have impressive performance but
> can behave in unexpected ways. These systems latch onto unintuitive
> patterns and are easily compromised, a source of grave concern for deployed
> ML in settings such as healthcare, security, and autonomous driving. In
> this talk, I will discuss how we can redesign the core ML pipeline to
> create reliable systems. First, I will show how to train provably robust
> models, which enables formal robustness guarantees for complex deep
> networks. Next, I will demonstrate how to make ML models more debuggable.
> This amplifies our ability to diagnose failure modes, such as hidden biases
> or spurious correlations. To conclude, I will discuss how we can build upon
> this ``reliability stack'' to enable broader robustness requirements, and
> develop new primitives that make ML debuggable by design.
>
> *Bio:*
> Eric Wong is a postdoctoral researcher in the Computer Science and
> Artificial Intelligence Laboratory at Massachusetts Institute of
> Technology. His research focuses on the foundations for reliable systems:
> methods that allow us to diagnose, create, and verify robust systems. He is
> a 2020 Siebel Scholar and received an honorable mention for his thesis on
> the robustness of deep networks to adversarial examples at Carnegie Mellon
> University.
>
>
> *Host: **David McAllester* <mcallester 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, Feb 17, 2022 at 3:29 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When:*        Friday, February 18th at* 10:30 am CT*
>>
>>
>> *Where:       *Talk will be given *live, in-person* at
>>
>>                    TTIC, 6045 S. Kenwood Avenue
>>
>>                    5th Floor, Room 530
>>
>>
>> *Where:*       Zoom Virtual Talk (*register in advance here
>> <https://uchicagogroup.zoom.us/webinar/register/WN_MIgNiZOeTyGM0M5MF3d75w>*
>> )
>>
>>
>> *Who: *         Eric Wong, Massachusetts Institute of Technology
>>
>>
>>
>>
>> *Title:      *     Building the Reliability Stack for Machine Learning
>>
>> *Abstract:*
>> Currently, machine learning (ML) systems have impressive performance but
>> can behave in unexpected ways. These systems latch onto unintuitive
>> patterns and are easily compromised, a source of grave concern for deployed
>> ML in settings such as healthcare, security, and autonomous driving. In
>> this talk, I will discuss how we can redesign the core ML pipeline to
>> create reliable systems. First, I will show how to train provably robust
>> models, which enables formal robustness guarantees for complex deep
>> networks. Next, I will demonstrate how to make ML models more debuggable.
>> This amplifies our ability to diagnose failure modes, such as hidden biases
>> or spurious correlations. To conclude, I will discuss how we can build upon
>> this ``reliability stack'' to enable broader robustness requirements, and
>> develop new primitives that make ML debuggable by design.
>>
>> *Bio:*
>> Eric Wong is a postdoctoral researcher in the Computer Science and
>> Artificial Intelligence Laboratory at Massachusetts Institute of
>> Technology. His research focuses on the foundations for reliable systems:
>> methods that allow us to diagnose, create, and verify robust systems. He is
>> a 2020 Siebel Scholar and received an honorable mention for his thesis on
>> the robustness of deep networks to adversarial examples at Carnegie Mellon
>> University.
>>
>>
>> *Host: **David McAllester* <mcallester 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, Feb 11, 2022 at 4:41 PM Mary Marre <mmarre at ttic.edu> wrote:
>>
>>> *When:*        Friday, February 18th at* 10:30 am CT*
>>>
>>>
>>> *Where:       *Talk will be given *live, in-person* at
>>>
>>>                    TTIC, 6045 S. Kenwood Avenue
>>>
>>>                    5th Floor, Room 530
>>>
>>>
>>> *Where:*       Zoom Virtual Talk (*register in advance here
>>> <https://uchicagogroup.zoom.us/webinar/register/WN_MIgNiZOeTyGM0M5MF3d75w>*
>>> )
>>>
>>>
>>> *Who: *         Eric Wong, Massachusetts Institute of Technology
>>>
>>>
>>>
>>>
>>> *Title:      *     Building the Reliability Stack for Machine Learning
>>>
>>> *Abstract:*
>>> Currently, machine learning (ML) systems have impressive performance but
>>> can behave in unexpected ways. These systems latch onto unintuitive
>>> patterns and are easily compromised, a source of grave concern for deployed
>>> ML in settings such as healthcare, security, and autonomous driving. In
>>> this talk, I will discuss how we can redesign the core ML pipeline to
>>> create reliable systems. First, I will show how to train provably robust
>>> models, which enables formal robustness guarantees for complex deep
>>> networks. Next, I will demonstrate how to make ML models more debuggable.
>>> This amplifies our ability to diagnose failure modes, such as hidden biases
>>> or spurious correlations. To conclude, I will discuss how we can build upon
>>> this ``reliability stack'' to enable broader robustness requirements, and
>>> develop new primitives that make ML debuggable by design.
>>>
>>> *Bio:*
>>> Eric Wong is a postdoctoral researcher in the Computer Science and
>>> Artificial Intelligence Laboratory at Massachusetts Institute of
>>> Technology. His research focuses on the foundations for reliable systems:
>>> methods that allow us to diagnose, create, and verify robust systems. He is
>>> a 2020 Siebel Scholar and received an honorable mention for his thesis on
>>> the robustness of deep networks to adversarial examples at Carnegie Mellon
>>> University.
>>>
>>>
>>> *Host: **David McAllester* <mcallester 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>*
>>>
>>
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