22 September 2017 
Title:
Progress in ErrorCorrection: A Survey
Date: 22 September 2017 (Friday)
Time:
Refreshments: 12.45pm to 1.30pm
Colloquium : 1.30pm to 2.30pm
Venue: Refreshments: Empty Space in front of noticeboard (Near LT)
Colloquium : LT 5, (SPMS0308),
School of Physical and Mathematical Sciences
Speaker: Visiting Professor Venkatesan Guruswami
Division of Mathematical Sciences
School of Physical and Mathematical Sciences
Abstract:
Errorcorrecting codes play a crucial role in safeguarding data against the adverse effects of noise during communication and storage. They are also versatile tools in the arsenal of theoretical computer science and combinatorics. The central challenge in coding theory is to construct codes with minimum possible redundancy for different error models and requirements on the decoder, along with efficient algorithms for errorcorrection using those codes. Much progress has been made toward this quest in the nearly seven decades since the birth of coding theory. Several fundamental problems, however, are still outstanding, and exciting new directions continue to emerge to address current technological demands as well as connections to computational complexity and cryptography.
This talk will survey some of our works over the years on errorcorrection in various models, such as:
 Worstcase errors, where we construct list decodable codes with redundancy as small as the target error fraction;
 i.i.d. errors, where we show polar codes enable efficient errorcorrection even as the redundancy approaches Shannon capacity;
 bit deletions, where we give codes that can correct the largest known fraction of deletions;
 Single symbol erasure, a model of renewed importance for tackling node failures in distributed storage, where we give novel repair algorithms for ReedSolomon codes.
Speaker Biography:
Venkatesan Guruswami (Venkat) is a Professor in the Computer Science Department at Carnegie Mellon University. He received his Bachelor's degree from the Indian Institute of Technology at Madras in 1997 and his Ph.D. in Computer Science from the Massachusetts Institute of Technology in 2001. He is currently on sabbatical as a visiting professor in the School of Physical and Mathematical Sciences at NTU.
Venkat's research interests span several topics including coding and information theory, complexity of approximate optimization and constraint satisfaction, pseudorandomness, and computational complexity. Venkat currently serves as the EditorinChief of the ACM Transactions on Computation Theory, and on the editorial boards of the Journal of the ACM, SIAM Journal on Computing, and Research in the Mathematical Sciences. He served as the program committee chair for the 2015 IEEE Symposium on Foundations of Computer Science, and is a Technical Program cochair of the 2018 IEEE International Symposium on Information Theory. He was an invited speaker at the 2010 International Congress of Mathematicians on the topic of ``Mathematical Aspects of Computer Science.'' Venkat is a recipient of the EATCS Presburger Award, Packard and Sloan Fellowships, the ACM Doctoral Dissertation Award, and the IEEE Information Theory Society Paper Award.
Host:
Division of Mathematical Sciences, School of Physical and Mathematical Sciences

31 May 2017 
Title:
Identities
Date: 31 May 2017 (Friday)
Time:
11.00am to 12.00pm
Venue: LT 5, (SPMS0308), School of Physical and Mathematical Sciences
Speaker: Prof Bruce C. Berndt
Department of Mathematics University of Illinois at UrbanaChampaign
Abstract: As the title suggests, this lecture features mathematical identities. The identities we have chosen (we hope) are interesting, fascinating, surprising, and beautiful! Many of the identities are due to Ramanujan. Topics behind the identities include partitions, continued fractions, sums of squares, theta functions, Bessel functions, qseries, other infinite series, and infinite integrals.
Speaker Biography:
Born in St. Joseph, Michigan, Bruce Carl Berndt graduated from Albion College in 1961, where he studied mathematics and physics, and ran track. He received his doctoral degree from the University of WisconsinMadison in 1966. After lecturing for one year at the University of Glasgow, in 1967, he assumed a position at the University of Illinois at UrbanaChampaign, where he has remained since. Berndt is an analytic number theorist who for over 40 years has devoted almost all of his research efforts toward proving the claims left behind by Ramanujan in his earlier notebooks and lost notebook. In 1996, Berndt was awarded the Steele Prize for Exposition from the American Mathematical Society for his volumes on Ramanujan's notebooks. With George Andrews, he is currently preparing volumes on Ramanujan's lost notebook. Berndt is especially proud of and thankful for the thirtyfive students who have completed their Ph.D. degrees under his direction.
Host: Associate Professor Chan Song Heng
Division of Mathematical Sciences, School of Physical and Mathematical Sciences 
21 April 2017 
Title: Intersecting families of permutations and perfect matchings
Date: 21 April 2017 (Friday)
Time:
1.30pm to 2.30pm
Venue: LT 5, (SPMS0308), School of Physical and Mathematical Sciences
Speaker: Dr Ku Cheng Yeaw
Division of Mathematical Sciences School of Physical and Mathematical Sciences
Abstract:
Speaker Biography:
Ku Cheng Yeaw is currently a senior lecturer in the Division of Mathematical Sciences at Nanyang Technological University. Most of his research is focused on applying combinatorial and algebraic methods to extremal problems for a variety of combinatorial structures
Host: Division of Mathematical Sciences, School of Physical and Mathematical Sciences 
10 March 2017 
Title:
Sketching as a Tool for Linear Algebra
Date: 10 March 2017 (Friday)
Time:
1.30pm to 2.30pm
Venue: LT 5, (SPMS0308), School of Physical and Mathematical Sciences
Speaker: Dr David Woodruff
IBM Almaden Research Center San Jose, CA, USA
Abstract: We give near optimal algorithms for regression, low rank approximation, and robust variants of these problems. Our results are based on the sketch and solve paradigm, which is a tool for quickly compressing a problem to a smaller version of itself, for which one can then run a slow algorithm on the smaller problem. These lead to the fastest known algorithms for fundamental machine learning and numerical linear algebra problems, which run in time proportional to the number of nonzero entries of the input. We first give algorithms for least squares regression, and robust variants such as l_p regression and MEstimator loss functions. Then we give algorithms for approximate singular value decomposition, and robust variants such as minimizing sum of distances to a subspace, rather than sum of squared distances, as well as minimizing entrywise l_1distance, etc.
Speaker Biography:
David Woodruff joined IBM Almaden Research Center in 2007 after completing his Ph.D. at MIT in theoretical computer science. He has been at IBM Almaden ever since. His research interests include data streams, machine learning, numerical linear algebra, sketching, and sparse recovery. He is the recipient of the 2014 Presburger Award and Best Paper Awards at STOC 2013 and PODS 2010. At IBM he is a member of the Academy of Technology and a Master Inventor.
Host: Division of Mathematical Sciences, School of Physical and Mathematical Sciences 
3 February 2017 
Title:
Topological modeling and analysis
of complex data in biomolecules
Date: 3 February 2017 (Friday)
Time:
1.15pm to 2.15pm
Venue: LT 5, (SPMS0308), School of Physical and Mathematical Sciences
Speaker: Assistant Professor Xia Kelin
Division of Mathematical Sciences School of Physical and Mathematical Sciences
Abstract: The understanding of biomolecular structure, flexibility, function, and dynamics is one
of the most challenging tasks in biological science. We introduce persistent homology
for extracting molecular topological fingerprints (MTFs) based on the persistence of
molecular topological invariants. MTFs are utilized for protein characterization,
identification, and classification. The multidimensional persistent homology is
proposed and further used to quantitatively predict the stability of protein folding
configurations generated by steered molecular dynamics. An excellent consistence
between my persistent homology prediction and molecular dynamics simulation is
found. Further, we introduce multiresolution persistent homology to handle complex
biomolecular data. By appropriately tuning the resolution of a density function, we are
able to focus the topological lens on the scale of interest. The proposed
multiresolution topological method has potential applications in arbitrary data sets,
such as social networks, biological networks and graphs.
Speaker Biography:
Dr. Kelin Xia obtained his PhD degree from the Chinese Academy of Sciences in Jan
2013. He was a visiting scholar in the Department of Mathematics, Michigan State
University from Dec 2009Dec 2012. From Jan 2013 to May 2016, he worked as a
visiting assistant professor at Michigan State University. He joined Nanyang
Technological University at Jun 2016. His research focused on scientific computation,
mathematical molecular biology, and topological data analysis (TDA), particularly
complex data in biomolecular systems.
Host: Division of Mathematical Sciences, School of Physical and Mathematical Sciences 