[Colloquium] REMINDER: 7/16 TTIC Colloquium: Shang-Hua Teng, University of Southern California

[Mary Marre via Colloquium]

 *When: *     Monday, July 16th at 10:30 am

*Where: *    TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526


*Who: *       Shang-Hua Teng, University of Southern California


*Title:       *Scalable Algorithms in the Age of Big Data and Network
Sciences: Characterization, Primitives, and Techniques

*Abstract:* In the age of network sciences and machine learning, efficient
algorithms are now in higher demand more than ever before. Big Data
fundamentally challenges the classical notion of efficient algorithms:
Algorithms that used to be considered efficient, according to
polynomial-time characterization, may no longer be adequate for solving
today's problems. It is not just desirable, but essential, that efficient
algorithms should be scalable. In other words, their complexity should be
nearly linear or sub-linear with respect to the problem size. Thus,
scalability, not just polynomial-time computability, should be elevated as
the central complexity notion for characterizing efficient computation.
Using several basic tasks in network analysis, social influence modeling,
machine learning, and optimization as examples - in this talk - I will
highlight a family of fundamental algorithmic techniques for designing
provably-good scalable algorithms.

*Bio: *Shang-Hua Teng is the University Professor and Seeley G. Mudd
Professor of Computer Science and Mathematics at University of Southern
California. He has twice won the prestigious Gödel Prize in theoretical
computer science, first in 2008, for developing the theory of smoothed
analysis , and then in 2015, for designing the groundbreaking nearly-linear
time Laplacian solver for network systems.  Citing him as, ``one of the
most original theoretical computer scientists in the world'', the Simons
Foundation named Teng a 2014 Simons Investigator, for pursuing long-term
curiosity-driven fundamental research. Prior to joining USC in 2009, he was
a professor at Boston University. He has also taught at MIT, the University
of Minnesota, and the University of Illinois at Urbana-Champaign. He has
worked at Xerox PARC, NASA Ames Research Center, Intel Corporation, IBM
Almaden Research Center, Akamai Technologies, Microsoft Research Redmond,
Microsoft Research New England and Microsoft Research Asia. Teng is a
Fellow of the Association for Computing Machinery (ACM), as well as an
Alfred P. Sloan fellow.



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

For more information on the colloquium series or to subscribe to the
mailing list,please see http://www.ttic.edu/colloquium.php




Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 523*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*

On Sun, Jul 15, 2018 at 7:02 PM, Mary Marre <mmarre at ttic.edu> wrote:

> *When: *     Monday, July 16th at 10:30 am
>
> *Where: *    TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>
>
>
> *Who: *       Shang-Hua Teng, University of Southern California
>
>
> *Title:       *Scalable Algorithms in the Age of Big Data and Network
> Sciences: Characterization, Primitives, and Techniques
>
> *Abstract:* In the age of network sciences and machine learning,
> efficient algorithms are now in higher demand more than ever before. Big
> Data fundamentally challenges the classical notion of efficient algorithms:
> Algorithms that used to be considered efficient, according to
> polynomial-time characterization, may no longer be adequate for solving
> today's problems. It is not just desirable, but essential, that efficient
> algorithms should be scalable. In other words, their complexity should be
> nearly linear or sub-linear with respect to the problem size. Thus,
> scalability, not just polynomial-time computability, should be elevated as
> the central complexity notion for characterizing efficient computation.
> Using several basic tasks in network analysis, social influence modeling,
> machine learning, and optimization as examples - in this talk - I will
> highlight a family of fundamental algorithmic techniques for designing
> provably-good scalable algorithms.
>
> *Bio: *Shang-Hua Teng is the University Professor and Seeley G. Mudd
> Professor of Computer Science and Mathematics at University of Southern
> California. He has twice won the prestigious Gödel Prize in theoretical
> computer science, first in 2008, for developing the theory of smoothed
> analysis , and then in 2015, for designing the groundbreaking nearly-linear
> time Laplacian solver for network systems.  Citing him as, ``one of the
> most original theoretical computer scientists in the world'', the Simons
> Foundation named Teng a 2014 Simons Investigator, for pursuing long-term
> curiosity-driven fundamental research. Prior to joining USC in 2009, he was
> a professor at Boston University. He has also taught at MIT, the University
> of Minnesota, and the University of Illinois at Urbana-Champaign. He has
> worked at Xerox PARC, NASA Ames Research Center, Intel Corporation, IBM
> Almaden Research Center, Akamai Technologies, Microsoft Research Redmond,
> Microsoft Research New England and Microsoft Research Asia. Teng is a
> Fellow of the Association for Computing Machinery (ACM), as well as an
> Alfred P. Sloan fellow.
>
>
>
> *Host:* Avrim Blum <avrim at ttic.edu>
>
> For more information on the colloquium series or to subscribe to the
> mailing list,please see http://www.ttic.edu/colloquium.php
>
>
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 523*
> *Chicago, IL  60637*
> *p:(773) 834-1757*
> *f: (773) 357-6970*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
> On Tue, Jul 10, 2018 at 8:26 AM, Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When: *     Monday, July 16th at 10:30 am
>>
>> *Where: *    TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>>
>>
>>                                         *Who: *       Shang-Hua Teng,
>> University of Southern California
>>
>>
>> *Title:       *Scalable Algorithms in the Age of Big Data and Network
>> Sciences: Characterization, Primitives, and Techniques
>>
>> *Abstract:* In the age of network sciences and machine learning,
>> efficient algorithms are now in higher demand more than ever before. Big
>> Data fundamentally challenges the classical notion of efficient algorithms:
>> Algorithms that used to be considered efficient, according to
>> polynomial-time characterization, may no longer be adequate for solving
>> today's problems. It is not just desirable, but essential, that efficient
>> algorithms should be scalable. In other words, their complexity should be
>> nearly linear or sub-linear with respect to the problem size. Thus,
>> scalability, not just polynomial-time computability, should be elevated as
>> the central complexity notion for characterizing efficient computation.
>> Using several basic tasks in network analysis, social influence modeling,
>> machine learning, and optimization as examples - in this talk - I will
>> highlight a family of fundamental algorithmic techniques for designing
>> provably-good scalable algorithms.
>>
>> *Bio: *Shang-Hua Teng is the University Professor and Seeley G. Mudd
>> Professor of Computer Science and Mathematics at University of Southern
>> California. He has twice won the prestigious Gödel Prize in theoretical
>> computer science, first in 2008, for developing the theory of smoothed
>> analysis , and then in 2015, for designing the groundbreaking nearly-linear
>> time Laplacian solver for network systems.  Citing him as, ``one of the
>> most original theoretical computer scientists in the world'', the Simons
>> Foundation named Teng a 2014 Simons Investigator, for pursuing long-term
>> curiosity-driven fundamental research. Prior to joining USC in 2009, he was
>> a professor at Boston University. He has also taught at MIT, the University
>> of Minnesota, and the University of Illinois at Urbana-Champaign. He has
>> worked at Xerox PARC, NASA Ames Research Center, Intel Corporation, IBM
>> Almaden Research Center, Akamai Technologies, Microsoft Research Redmond,
>> Microsoft Research New England and Microsoft Research Asia. Teng is a
>> Fellow of the Association for Computing Machinery (ACM), as well as an
>> Alfred P. Sloan fellow.
>>
>>
>>
>> Host: Avrim Blum <avrim at ttic.edu>
>>
>> For more information on the colloquium series or to subscribe to the
>> mailing list,please see http://www.ttic.edu/colloquium.php
>>
>>
>>
>>
>> Mary C. Marre
>> Administrative Assistant
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue*
>> *Room 504*
>> *Chicago, IL  60637*
>> *p:(773) 834-1757*
>> *f: (773) 357-6970*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>
>
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