Tutorial Speakers

Monday, June 17

Claudia Rave

Gerente Fluir, Laboratorio de Decisión, Medellín, Colombia

Bridging Sectoral Modeling and Institutions Reengineering: Empowering OR Practitioners to Collaborate with the Public Power Design

Public institutions, servicing to both productive and social services (such as transportation, infrastructure, utilities, land issues, among others), often adhere to decision-making frameworks based on administrative regions or conventional functional areas (like human resources, operations, management). However, these frameworks may not always reflect the underlying dynamics pertinent to the organization’s goals.

Advancing and implementing Operations Research (OR) modeling solutions for development necessitates aligning the organization’s decision-making processes with its array of services and capabilities. OR practitioners are increasingly required to delve into the intricacies of political and policy dialogues to effectively assess either or both processes modeling and institutional reengineering.

During the tutorial, we will delve into 2 case studies involving land management and transportation institutions, exploring their journey towards enhanced performance and service modernization.

Bio

Planning Consultant and Practitioner for the Energy and Infrastructure Sectors with over 20 years of experience in Global Development issues and territories in transformation. Focused on Latin America and the Caribbean, I’ve successfully led entire project cycles with cross-functional teams of experts for governments, the private sector, and multilateral or donor institutions. In addition, with solid scientific training and real-world political and managerial experience, I’ve deepened my expertise in Public Institutions’ assessment and reengineering to build capacity and align them with its current challenges and SDGs.

Ruslan Shaydulin

JPMorgan Chase, USA

Tamás Terlaky

Lehigh University, USA

Quantum Computing Optimization: From Foundations to Combinatorial and Conic Optimization

The tutorial begins with a gentle introduction to the foundations of quantum computing, including a brief review of the state of the current state of existing quantum computers and development trends.

Then we review how combinatorial optimization problems are solved in the quantum computing paradigm using a commonly considered near-term quantum optimization heuristic called the Quantum Approximate Optimization Algorithm (QAOA). We discuss both the theoretical aspects of QAOA as well as its implementation on hardware.

The last part of the tutorial presets variants of Inexact-Feasible-Quantum-Interior-Poit-Methods enhanced with Iterative Refinement for solving Linear Optimization Problems. Extensions to Semidefinite ans Second-order Conic Optimization will be fleshed.

Ruslan Shaydulin Bio

Ruslan Shaydulin is a quantum algorithms researcher at the Global Technology Applied Research center at JPMorgan Chase. Ruslan’s research centers on applying quantum algorithms to classical problems, with a focus on optimization and machine learning. Prior to joining JPMorgan Chase, Ruslan was a Maria Goeppert Mayer fellow at Argonne National Laboratory. You can follow him on LinkedIn at https://www.linkedin.com/in/rshaydu

Tamás Terlaky 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. He will be a Plenary Speaker at ISMP’2024 in Montreal.

Tuesday, June 18

Emilio Carrizosa

Universidad de Sevilla

Mathematical Optimization Models for Group Counterfactual Explanations

Counterfactual Analysis has shown to be a powerful tool for Explainable Artificial Intelligence. In Supervised Classification, this means associating with each record a so-called counterfactual explanation, i.e., an instance close to the record (closeness measured by an appropriate and context-dependent dissimilarity) and whose probability of being classified by a given classifier in the positive class is high.

While the literature focuses on the problem of finding one counterfactual for one record, in this talk a stakeholder perspective is taken, and we address the more general setting in which a group of counterfactual explanations is sought for a group of instances. Mathematical optimization models are introduced to illustrate different instance-counterfactual allocation rules. A number of research challenges for the Operations Research community are identified.

Bio
Emilio Carrizosa is a Professor of Statistics & Operations Research, IMUS and Faculty of Mathematics, at the University of Seville. He is also President of math-in, Red Española Matemática-Industria and President of PET MSO-ED, Plataforma Española de Tecnologías de Modelización, Simulación y Optimización en un Entorno Digital.

You are kindly invited to have a look at:

– My research:
- My most recent publications in open access
– My outreach services:
- A recent (11.29.23) interview in Correo de Andalucía (in Spanish)
- A recent (10.23.23) interview en Diario de Sevilla (in Spanish)
- A recent (05.03.23) interview in the Spanish TV program “Una matemática viene a verme” (in Spanish)
- My slot in the (virtual and physical) exhibition “Sevilla también es Ciencia” (in Spanish)
- A recent (03.14.23) roundtable participation: III Foro Económico en Andalucía El Español (in Spanish)
- A less recent (09.30.22) interview in El Correo de Andalucía (in Spanish)
- “Matemáticas, la fórmula del éxito” (in Spanish TV)
– Some academic activities:
- Online Seminar Series “Machine Learning NeEDS Mathematical Optimization” https://congreso.us.es/mlneedsmo/
- EURO 2024, https://euro2024cph.dk/

Kannan Ramanathan

University of Texas at Dallas, USA

How to use Visual Basic for Applications (VBA) in Excel – For Beginners

This is not a lecture – it is a workshop.

VBA (Visual Basic for Applications) can be used with several Microsoft applications. It is recommended that you participate in this workshop with your laptop as we will do some hands-on exercises. There have been some discussion that AI tools will make VBA obsolete. However, VBA is going to be around for some more time. We will discuss how we can leverage the power of AI to make it easier to work with VBA.

This workshop

Is meant for beginners and will cover basics in VBA
Will focus primarily on how to use VBA in MS Excel
Will touch on its usage in MS Word and MS Powerpoint

Bio

Kannan Ramanathan is a Clinical Professor at The Naveen Jindal School of Management at The University of Texas at Dallas (UTD) in the USA. His Ph.D. is in Strategic Management from the University of Illinois at Urbana-Champaign. He worked for various businesses of General Electric for several years as a Lean Six Sigma Master Black Belt. At UTD, his research interests focus on Lean Six Sigma and its applications in healthcare.

Ko-Yang Wang

National Sun-Yat-Sen University, Taiwan

Tutorial on Generative AI and Its Responsible Applications in the Financial Industry

This lecture will explore the research and applications of generative AI in the financial industry. Topics include the latest trends, R&D opportunities, case studies, and impacts on the banking and financial industries. The complexity and challenges of responsible uses of AI and practical financial digital transformation issues will be discussed.

Bio

Ko-Yang Wang is a Visiting Professor at the School of Banking and Finance of National Sun Yat-Sen University. He is an educator, entrepreneur, consultant, and frequent keynote speaker on FinTech, AI, Web3, Blockchain, and Digital Transformation. He excels at fusing emerging technologies to drive digital transformation and innovation. He was the founding chairman of the Taiwan Fintech Association, a co-founder and an executive advisor of the Taiwan Blockchain Alliance, EVP of the Institute for Information Industry, and on advisory boards of six government ministries in Taiwan.

Earlier in the USA, he was the Practice Leader/Partner for the BPM Consulting Practice and CTO for four different consulting practices in IBM Global Business Services and the Research & Innovation Executive for the Business Transformation Executive of IBM Global Services. He was awarded an IBM Distinguished Engineer, an IBM Academy Member, and an Outstanding Technology Achievement Award.

He was elected as a Fellow of the Chinese Society for the Management of Technology in 2017 and received a 2019 Smart City Outstanding Contribution Award for his contributions to Smart Commerce and FinTech in Taiwan. He was named 2016 CS Outstanding Alumni, Purdue University, and 2017 College of Science Outstanding Alumni, National Tsing Hua University.

Wednesday, June 19

Yasmín Ríos-Solís

Tecnológico de Monterrey – Monterrey campus, Mexico

Public Transportation in Developing Countries

Have you tried to implement a known transportation algorithm from developed countries in a developing one? Most researchers and practitioners know topicalization is complex, and many features and utilities will be underused. What are the characteristics that make the developing countries’ transport systems different? This tutorial will overview these characteristics and how to include them in the models and algorithms.

Bio

Yasmín Ríos Solís studied applied mathematics at ITAM, Mexico. She completed her master’s and Ph.D. in combinatorial optimization at the Sorbonne University in France, and then she did a postdoc at the University of Bielefeld, Germany. She is currently part of the Tecnológico de Monterrey at the School of Engineering and Sciences. Her research is related to discrete optimization problems for which she proposes mathematical models and new optimization methods, both exact and heuristic.

Juan Camilo Serpa

McGill University, Canada

The Data Mangrove: Developing a world-leading Sustainability Data Hub

The Data Mangrove (datamangrove.com) is a new hub uniting sustainability researchers, allowing them to share data (through an open-data platform), promote research papers, and to implement data-driven technologies for research partnerships. This virtual hub will facilitate connections across the sustainability research community, facilitating data-driven collaboration. This tutorial introduces the hub’s features and invites researchers to join this academic hub before its late-summer launch.

Bio

Juan Camilo Serpa is an associate professor in data analytics at the Desautels Faculty of Management, a William Dawson Scholar, area head of the Operations Management Department, and the academic director of the Management Science Research Centre. His research focuses on big data analytics and structural econometrics applied to supply chain management and empirical operations. For years, Prof. Serpa has collaborated with numerous NGOs and community organizations focused on sustainability. Utilizing AI and analytics, Prof. Serpa has developed a suite of tools designed to help these entities harness the capabilities of big data and artificial intelligence for their causes, through a Sustainability Data hub: The Data Mangrove (datamangrove.com).

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