17 December 2018 
Title: Mathematical deep learning for drug discovery
Date: 17 December 2018 (Monday)
Time: 10.30am to 11.30am
Venue: MAS Executive Classroom 2, MAS0307 School of Physical and Mathematical Sciences
Speaker : Professor Guowei Wei Department of Mathematics Michigan State University, USA
Abstract: Designing efficient drugs for curing diseases is of essential importance for the 21st century's life science. Computeraided drug design and discovery has obtained a significant recognition recently. However, the geometric complexity of proteindrug complexes remains a grand challenge to conventional computational methods, including machinelearning algorithms. We assume that the intrinsic physics of interest of proteindrug complexes lies on lowdimensional manifolds or subspaces embedded in a highdimensional data space. We devise topological abstraction, geometric simplification, graph reduction, and multiscale modeling to encode highdimensional, massive and diverse biological data into lowdimensional representations. These representations are integrated with advanced deep learning algorithms for the predictions of proteinligand binding affinity, drug toxicity, drug solubility, drug partition coefficient and mutation induced protein stability change, and for the discrimination of active ligands from decoys. I will briefly discuss how this approach became the top performer in D3R Grand Challenges, a worldwide competition series in computeraided drug design and discovery (http://users.math.msu.edu/users/wei/D3R_GC3.pdf).
Host: Division of Mathematical Sciences, School of Physical and Mathematical Sciences

03 December 2018

Title: Lie group and homogeneous space variational integrators applied to geometric optimal control theory
Date: 3 December 2018 (Monday)
Time: 4.00pm to 5.00pm
Venue: Lecture Theatre 4 (SPMS0309)
Speaker : Professor Melvin Leok Department of Mathematics University of California, San Diego
Abstract: The geometric approach to mechanics serves as the theoretical underpinning of innovative control methodologies in geometric control theory. These techniques allow the attitude of satellites to be controlled using changes in its shape, as opposed to chemical propulsion, and are the basis for understanding the ability of a falling cat to always land on its feet, even when released in an inverted orientation. We will discuss the application of geometric structurepreserving numerical schemes to the optimal control of mechanical systems. In particular, we consider Lie group variational integrators, which are based on a discretization of Hamilton's principle that preserves the Lie group structure of the configuration space. In contrast to traditional Lie group integrators, issues of equivariance and orderofaccuracy are independent of the choice of retraction in the variational formulation. The importance of simultaneously preserving the symplectic and Lie group properties is also demonstrated . Recent extensions to homogeneous spaces yield intrinsic methods for Hamiltonian flows on the sphere, and have potential applications to the simulation of geometrically exact rods, structures and mechanisms. Extensions to Hamiltonian PDEs and uncertainty propagation on Lie groups using noncommutative harmonic analysis techniques will also be discussed. Host: Professor Bernhard Schmidt Division of Mathematical Sciences, School of Physical and Mathematical Sciences

26 September 2018 
Title: The beauty of mathematics shows itself to patient followers: The work of Maryam Mirzakhani
Date: 26 September 2018
Time: 1.30pm to 2.30pm
Venue: Lecture Theatre 2 (SPMS0303)
Speaker: Dr Daniel Mathews School of Mathematical Sciences Monash University
Abstract: Maryam Mirzakhani was a brilliant, trailblazing mathematician, and the first woman to win a Fields Medal. She proved many incredible theorems across a range of fields on the cutting edge of pure mathematics. She was also an allround excellent human being. Tragically, she passed away last year at the age of 40. In this talk I will discuss her life and work, and attempt to explain some of her mathematics and its implications. Along the way we'll see such things as moduli spaces, hyperbolic surfaces, the art of MC Escher, and fun facts about billiards. I will not assume any technical knowledge of these fields.
Host: Associate Professor Andrew James Kricker Division of Mathematical Sciences, School of Physical and Mathematical Sciences

14 September 2018 
Title: DataDriven Approach to Pricing Optimization
Date: 14 September 2018 (Friday)
Time: 1.30pm to 2.30pm
Venue: Lecture Theatre 3 (SPMS0302)
Speaker: Dr Yan Zhenzhen Division of Mathematical Sciences School of Physical and Mathematical Sciences
Abstract: We have developed an estimation and optimization framework for the multiproduct pricing problem and network pricing problem. The key feature is to develop a convex model to approximate customer’s choice response to the change of prices. The convex model exploits properties of the marginal distributions of the random shock in customer’s utility function. We have shown that the multiproduct pricing problem becomes a convex optimization problem with the proposed choice model under a logconcavity assumption of each marginal probability density function. With this approach, we used aggregate sales information from a set of pricing experiments to guide us to the appropriate consumer choice model without presuming a structural choice model. This has partially addressed the problem of model misspecification for pricing problems. Extensive tests using both simulated and experimental data for two companies' multiproduct pricing problems demonstrates clearly the benefits of the data driven pricing approach.
Host: Division of Mathematical Sciences, School of Physical and Mathematical Sciences

29 June 2018 
Title: Stories vs Statistics
Date: 29 June 2018 (Friday)
Time: 4.00pm to 5.00pm
Venue: Public Lecture : Lecture Theatre 4 (SPMS0309) Speaker : Professor John Allen Paulos PhD (University of Wisconsin in Madison) USA
Abstract: The talk will discuss the complex relationship between stories and statistics or, to vary the alliteration, between narratives and numbers. It will then go on to the most common mathematical mistakes in news reports and the media generally.
No mathematical background will be needed, just a bit of arithmetic, a little logic, and maybe a feel for probability. The talk is in two parts, the first a bit more theoretical, the second more topical and newsrelated.
Host: Division of Mathematical Sciences, School of Physical and Mathematical Sciences

28 February 2018 
Title: Branching diffusion representation for nonlinear Cauchy problems and Monte Carlo approximation
Date: 28 February 2018 (Wednesday)
Time: 1.30pm to 2.30pm
Venue: Lecture Theatre 4 (SPMS0309)
School of Physical and Mathematical Sciences
Speaker: Professor Nizar Touzi Ecole Polytechnique France
Abstract: We provide a probabilistic representations of the solution of some semilinear hyperbolic and highorder PDEs based on branching diffusions. These representations pave the way for a MonteCarlo approximation of the solution, thus bypassing the curse of dimensionality. We illustrate the numerical implications in the context of some popular PDEs such as the Burger's equation, the nonlinear KleinGordon equation, a simplified scalar version of the YangMills equation, a fourthorder nonlinear beam equation and the GrossPitaevskii PDE as an example of nonlinear Schrödinger equations.
Speaker Biography: Nizar Touzi is Professor of Applied Mathematics at Ecole Polytechnique (France) since 2006. He was previously Chair Professor at Imperial College London. He was an invited session speaker at the International Congress of Mathematicians (Hyderabad 2010). He received the Louis Bachelier prize of the French Academy of Sciences in 2012, the Paris Europlace prize of Best Young Researcher in Finance in 2007, and is presently holding an Advanced ERC grant 20132018. He is Coeditor and Associate Editor in various international journals in the fields of financial mathematics, applied probability, and control theory.
Host: Associate Professor Nicolas Privault Division of Mathematical Sciences, School of Physical and Mathematical Sciences 