Videos available from the IPAM National Meeting of Women in Financial Math

You can find the Videos and PDFs on the IPAM website.

Thursday, April 27, 2017

9:35 – 10:00
Tanya Beder (SBCC Group)

The Here-to-Stay Roles of Big Data and Machine Learning
PDF Presentation

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10:05 – 10:55
Karyn Williams (Two Sigma Investments)

Panel Discussion on Predictions for FinTech & Asset Management with Darcy Pauken and Anjun Zhou

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11:05 – 11:55
Monique Miller (Wilshire Funds Management)

Panel Discussion on Predictions for Portfolios and the Role of Robo Advisors with Cleo Chang, Tina Singh, and Jia Ye

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2:00 – 2:50
Rosemary Macedo (QS Investors)

Panel Discussion on The Outlook for Quantitative Investing with Cristina Polizu, Gita Rao, and Elizabeth Smith

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3:00 – 3:50
Tanya Beder (SBCC Group)

Panel Discussion on New Directions in Financial Mathematics –Risk/Algorithmic Trading/ETFs and Beyond with Natalia Bandera, Lisa Borland, and Dinah Chowayou

Friday, April 28, 2017

9:30 – 10:30
Xin Guo (University of California, Berkeley (UC Berkeley))

General Research Directions in Financial Mathematics
PDF Presentation

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11:00 – 11:25
11:30 – 11:55
Alexandra Chronopoulou (University of Illinois at Urbana-Champaign)

Recent Advances in Factional Stochastic Volatility Models
PDF Presentation

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12:00 – 12:50
Linda Kreitzman (University of California, Berkeley (UC Berkeley))

The Impact of Fintech and Data Science on Financial Institutions: The Need for New Skill sets.
PDF Presentation

2:00 – 2:25
Kim Weston (University of Texas at Austin)

Equilibrium with Transaction Costs
PDF Presentation

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2:30 – 2:55
Rohini Kumar (Wayne State University)

Portfolio optimization in a short time horizon

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3:10 – 3:35
Deniz Sezer (University of Calgary)

Illiquidity, Credit risk and Merton’s model
PDF Presentation

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3:40 – 4:05
Yuchong Zhang (Columbia University)

Optimal Reward and Mean Field Game of Racing
PDF Presentation

Why I attend Mathematical Problems in Industry (MPI) workshops


by Hangjie Ji, Duke University

I have found that it is very easy for graduate students to get immersed in their own filtered and narrowed research area without thinking about how to use their expertise in other applications. During my Ph.D. study in mathematics I was an active participant in several industrial workshops where graduate students have the opportunity to work in teams on real problems. In this post I will share my experiences at these workshops.

First of all, these workshops provide an industrial-like environment quite different from the usual academic setting. In the Mathematical Problems in Industry (MPI) series organized by Duke University, University of Delaware, NJIT and other institutes, engineers and scientists from industry introduce challenges that they face in business and the workshop participants form groups to tackle the problems. The initial proposed problems could be ill-posed in the mathematical sense, and require careful clarification and formulation to build workable models. During the weeklong workshop professors, postdocs, and graduate students work together, and usually spend at least the first two days on resolving the confusions of the problem setup and terminologies by communicating with the industrial participants. Once the mathematical model is established, various methods that we have learnt from our own research can then be applied to the problem, and the group members usually form smaller teams to tackle the problem from different perspectives. While at first it seems a little intimidating to speak up in front of a group of senior professors, observing the way people think and discuss is actually a very good learning experience for me, and junior mathematicians do contribute lots of fresh insights to the problems. At the end of the week, each team would present their results and it always amazes me how much work can be accomplished in a single week. One of my favorite moments of the final presentation is a display of Colemanballs quotes or bloopers that happen during the workshop like “F is so big it has to be zero!”


The other two modeling camps that I have participated in are designed for graduate student career development in interdisciplinary problem solving skills. At the Graduate Student Mathematical Modeling Camp (GSMMC) at Rensselaer Polytechnic Institute where graduate student teams get together to solve real problems that arise from industrial applications under the guidance of invited faculty mentors. These modeling camps are usually held one week prior to the MPI workshops, and part of the goal is to better prepare the grad students for the coming MPI workshop, as the weeklong collaboration with team members in a friendly and productive environment quickly adjusts us to more challenging projects at MPI. Another workshop that I participated in, the Industrial Mathematical and Statistical Modeling Workshop (IMSM) is sponsored by the Statistical and Applied Mathematical Science Institute (SAMSI) and NC State University and focuses more on data-driven industrial projects. In the IMSM workshop I worked with a group of seven graduate students from different universities on a project proposed by costal engineers from United States Army Corps of Engineers Costal Lab, and our goal was to estimate the underwater bathymetry in environmental flows. Using the measurement data from several sources and statistical learning methods, we successfully incorporated the data to a wave model that leads to reasonable estimates. In addition to the interesting results that we worked out for the projects, I am also really happy with the training that I received during the workshop on technological tools like Git repository and Python, handling unfiltered dirty data with simulations, and perhaps most importantly, scientific communication skills. For instance, learning how to speak with a costal engineer during the IMSM project turned out to be not so easy. Getting exposed to these skills, or at least being aware of the new tools people are using in industry, is very beneficial for PhD students who have interests in pursuing an industrial career after graduation. The projects during these workshops have always been nice talking points in my conversations with industrial people, and I believe that similar experiences could naturally contribute to a sparkling resume for potential employers as it shows interest and passion outside of one’s academic research work.

As a summary, my personal experience at these workshops has been consistently rewarding and enlightening. I have learnt a lot and have explored areas way beyond my expectation. As an extension of the MPI workshop, I worked on a side project in collaboration with Corning Inc. under the 2015 MPI Fellowship, and presented my results at the 2016 MPI workshop and in a research paper. Two years later, I am still in continuous contact with friends that I met during the first few workshops that I attended, and the connections that we built in those weeks were truly incredible and unforgettable. I am really grateful to the organizers and sponsors who made these workshops possible, and would like to encourage my fellow graduate students to grasp opportunities like these to explore the links between applied mathematics and problems of interest to industry, and to learn more about their own interests.


Photo:  Group work session

For more info:


Opportunity: GEOMETRY OF REDISTRICTING: SUMMER SCHOOL August 7-11, 2017 at Tufts University

A 5-day summer school will be offered at Tufts University from August 7-11, 2017, with the principal purpose of training mathematicians to be expert witnesses for court cases on redistricting and gerrymandering.

Topics covered in the summer school will include:

  • the legal history of the Voting Rights Act and its subsequent renewals, extensions, and clarifications;
  • an explanation of “traditional districting principles,” especially compactness;
  • a course in metric geometry and mathematical ideas for perimeter-free compactness;
  • basic rudiments of GIS and the technical side of how shapefiles work;
  • training on being an expert witness;
  • ideas for incorporating voting and civil rights into mathematics teaching.

Some of the sessions in the summer school will be open to the public, and others will be limited to official participants.  Partial funding for participants’ expenses will be available. The summer school is aimed at, but not limited to, people with doctoral training in mathematics.  Preference will be given to those who can stay for the full week.

An application form will be posted on this website, and applications will be accepted from February 15 – March 15, with responses by March 25. Please contact to be added to the mailing list.

The BIG Math Network is in its startup phase.  This collaborative effort will bring together the Mathematical Sciences community (including pure and applied mathematics, statistics, operations research and data science) to

  • Build job opportunities for mathematical scientists
  • Communicate the value of mathematical science in the workplace
  • Facilitate connections between students, faculty, recruiters and managers
  • Increase knowledge about internships and how to prepare for them
  • Provide viable models for internship logistics (timing, intellectual property, training)
  • Create regional networks

Interested in sharing your BIG career story on our blog?  See our call for posts.

Check out this new internship opportunity:

NSF Mathematical Sciences Graduate Internship Program
Deadline 8AM  March 1, 2017