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Title: Entrepreneurship in academia: fueling the next commercial revolutions
Abstract: We discuss how to build a successful ecosystem for entrepreneurship in academia, aligning goals and expectation to create successful startups.
We discuss the advantage of startups, academia and industry jobs and how to make them best choice given your propensity for innovation.
We propose a model and guidelines for founding successful startup incubators and mentoring peer network.
(Thursday Pre-Workshop Presentation)
(Thursday Pre-Workshop Presentation)
Title: Mathworks and Machine Learning
Title: Mathworks and Machine Learning
Bring your laptops with to follow real time demonstrations. Demo will be done using MATLAB Online.
Bring your laptops with to follow real time demonstrations. Demo will be done using MATLAB Online.
Abstract: Engineers and data scientists work with large amounts of data in a variety of formats such as sensor, image, video, telemetry, databases, and more. They use machine learning to find patterns in data and to build models that predict future outcomes based on historical data.
In this session, we explore the fundamentals of machine learning using MATLAB. We introduce techniques available in MATLAB to quickly explore your data, evaluate machine learning algorithms, compare the results, and apply the best technique to your problem.
Highlights:
• Training, evaluating, and comparing a range of machine learning models
• Using refinement and reduction techniques to create models that best capture the predictive power of your data
• Running predictive models in parallel using multiple processors to expedite your results
• Deploying your models to production in a variety of formats
(Friday Presentation)
(Friday Presentation)
Title: Digital Twins: The Mathematician Angle
Title: Digital Twins: The Mathematician Angle
Abstract: The rise of connected devices has enabled the streaming of information and optimization of operational behavior throughout a device's lifetime. Digital twin strategies involve creating virtual representations of operating devices to enhance their performance or usage.
We will explore how to leverage mathematical modeling using MATLAB® and Simulink® as part of digital twin components on a variety of systems in applications such as anomaly detection, predictive maintenance, and control optimization.
Both data-driven and physics-based approaches for digital twin modeling will be covered and demonstrate how these models are applied in real-world operations. We will also highlight how mathematicians can play a crucial role in this cutting-edge subject.
Bio (Mike Michailidis): Mike Michailidis is the mathematics academic discipline manager at MathWorks. He received his B.Sc. in Mathematics and M.Sc. in Applied Mathematics from the Aristotle University of Thessaloniki, Greece. He was awarded a Ph.D. in Electrical Engineering from the University of Denver, conducting research on modeling and control of unmanned aerial vehicles with time-varying aerodynamic uncertainties. After serving as a postdoc and adjunct professor at the University of Denver for a couple of years, Mike has been on a mission to harmoniously bridge the worlds of mathematics and computation for faculty and students ever since.
Bio (Gen Sasaki): Gen Sasaki is a Principal Customer Success Engineer at the MathWorks, managing a team to ensure university educators and students in the Midwest and Southwest USA get the most out of MATLAB. He holds a BSME and MSME with a focus on control systems. He has worked in automotive and aerospace applications for nearly 30 years, in powertrain, various embedded controls, and functional safety.
(Thursday Pre-Workshop Presentation)
Title: Formulating Effective Models, Methods, and Conceptual Frameworks for the Geosciences
Abstract: Can waves transport significant amounts of ocean heat and tracers great distances, thus affecting Earth’s climate? This question was the basis for a project which culminated in the wave-driven circulation model and a concrete answer to how this process takes place. Moreover, the project also showed that wave-generated transport was most intense in the nearshore, leading to an examination of the impact of wave-generated transport on important nearshore processes, such as movement of ocean pollution and nutrients in coastal areas. In my talk, I will describe how the vortex-force conceptualization led to the formulation of the model and a theoretical basis for how waves and currents interact at scales larger than the wave scales.
The ever-present noise in natural processes and in the instruments used to measure them motivated me to create computational methods that could combine models, such as the wave circulation model and models for climate and weather, and observations in a probabilistic framework to make better predictions. While optimal estimate methods for linear problems existed, the focus of my work was instead on developing algorithms that could handle the more common noisy nonlinear processes in the geosciences. I will detail some of the strategies I used to create methods and algorithms that assimilate observations, rational models, and machine-learned data-driven constructs to improve forecasts in time-dependent problems, arising in the geosciences and beyond.
Finally, I will discuss my more recent work, which employs mathematical arguments to guide in quantifying and understanding resilience in the context of a changing climate and biological systems response via adaptation to stresses.
(Friday Presentation)
Title: Careers outside of academia and the preparation you need for these
Bio: Juan M. Restrepo is a Distinguished Member of the R&D Staff at Oak Ridge National Laboratory (ORNL) and section head of Mathematics in Computation. He is also a joint faculty member in the mathematics department at the University of Tennessee, Knoxville. He is a statistical physicist by training and works on data-driven methods and the application of probabilistic methods to dynamics problems. He also works on ocean and climate dynamics. Prior to coming to ORNL he was a professor of mathematics at Oregon State University and at the University of Arizona. He was elected fellow at SIAM for his work on Bayesian estimation, and fellow of APS for his work on climate dynamics.
Title: Using Advanced Analytics to Accelerate Drug Development
Abstract: The drug development process is both time-consuming and costly. Appropriate study designs and data analyses are critical to make the optimal decision and speed up the timelines. In this presentation, I will first provide an overview of the roles and the daily activities of clinical statisticians at Eli Lilly and Company. Then, some examples on how statisticians can help improve the efficiency of drug development will be shared.
Bio: Yongming Qu is currently a Vice President at Eli Lilly and Company. He received his PhD in Statistics from Iowa State University in 2002. He has made significant contributions in all phases of clinical development at Lilly and has been active in research for using novel analytics and statistical methods in drug development. He has published more than 100 articles in statistical and medical journals. He is a Fellow of American Statistical Association.
Title: Quantum Computing, Quantum Optimization, and Quantum Interior Point Method
Abstract: This talk briefly reviews the current state of quantum computing (QC) hardware, and the opportunities and challenges quantum computing offers in solving optimization problems. The Quantum Computing (QC) and the Interior Point Methods (IPM) revolutions inspire novel challenges and novel methodologies. Optimization is in the heart of the quest to evidence quantum advantage. However, the inexactness and condition number dependence characteristics of NISQ (Noisy Intermediate-Scale Quantum) devices forced us to think differently about solving optimization problems.
Considering IPMs for linear and semi-definite optimization (LO and SDO) problems, QC inspired to design Inexact Infeasible and Inexact Feasible Primal-Dual, and Inexact Dual IPM variants. These are novel algorithms in both the QC and classic computing environments. Enhancing Quantum Interior Point Methods (QIPMs) with Iterative Refinement (IR) leads to exponential improvements in the worst-case overall running time of QIPMs, compared to previous best-performing QIPMs. We also discuss how the proposed IR scheme can be used in classical inexact IPMs with conjugate gradient methods. Further, the proposed IR scheme exhibits quadratic convergence for LO and SDO towards an optimal solution without any assumption on problem characteristics. On the practical side, IR can be useful to find precise solutions while using inexact LO and SDO solvers.
Bio: Dr. Terlaky has published four books, edited over ten books and journal special issues and published over 200 research papers. Topics include theoretical and algorithmic foundations of mathematical optimization; nuclear reactor core reloading, oil refinery, VLSI design, radiation therapy treatment, and inmate assignment optimization; quantum computing.
Dr. Terlaky is Editor-in-Chief of the Journal of Optimization Theory and Applications. He has served as associate editor of ten journals and has served as conference chair, conference organizer, and distinguished invited speaker at conferences all over the world. He was general Chair of the INFORMS 2015 Annual Meeting, a former Chair of INFORMS’ Optimization Society, Chair of the ICCOPT Steering Committee of the Mathematical Optimization Society, Chair of the SIAM AG Optimization, and Vice President of INFORMS. He received the MITACS Mentorship Award; Award of Merit of the Canadian Operational Research Society, Egerváry Award of the Hungarian Operations Research Society, H.G. Wagner Prize of INFORMS, Outstanding Innovation in Service Science Engineering Award of IISE. He is Fellow of INFORMS, SIAM, IFORS, The Fields Institute, and elected Fellow of the Canadian Academy of Engineering.
Title: Career Opportunities in Bloomberg Engineering
Abstract: In this talk, I will first give an overview about Bloomberg and Engineering at Bloomberg. I will dive deeper into the life of an engineer in Bloomberg that touches on the tech culture in Bloomberg Engineering, the technologies and the opportunities. Then I will talk about the interview process. At the end, I will talk about my journey in Bloomberg after graduating from Purdue.
Bio: I received my B.S. in Mathematics from Peking University in China and joined Purdue to pursue my Ph.D. in Mathematics in 2003. I graduated from Purdue in 2009 and joined Bloomberg as a financial software developer. I am currently an Engineering Manager in the Platform Service department in Bloomberg Engineering.
Title: AI for Good: Applying Academic Knowledge to Real-World Problems
Abstract: I work on projects that aim to solve pressing global issues using artificial intelligence as a Senior Applied Research Scientist at Microsoft AI for Good Lab. In this talk, I will discuss the technical challenges involved in these projects, such as developing robust models and collaborating with interdisciplinary teams. This presentation will provide insights into the types of technical questions that arise in my daily work and how academic training can be leveraged to make a positive impact.
Bio: Yixi Xu is a Senior Applied Research Scientist at Microsoft AI for Good Lab. Yixi holds a Ph.D. in Statistics from Purdue University and a B.A. in Applied Mathematics from the University of Science and Technology of China. At Microsoft, Yixi's research is dedicated to applying artificial intelligence to tackle real-world challenges, with a primary focus on AI for health.
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