Agni Orfanoudaki is an Associate Professor of Operations Management at the Saïd Business School of Oxford University. Alongside her role, Agni is a Management Studies Fellow at Exeter College and a visiting scholar at the Harvard Kennedy School as a Harvard Data Science Initiative Fellow. Prior to joining Oxford, Agni received a Ph.D. in Operations Research from the Massachusetts Institute of Technology. Her primary research interests lie at the intersection of optimization and machine learning, with applications to healthcare and insurance. Specifically, she has worked on problems related to missing data imputation, survival analysis, clustering, personalized risk prediction, and medical therapy prescription. She has collaborated with numerous institutions, including a major medical society, an international reinsurance company, and more than eight hospitals from the US and Europe.

Talk Title: Pricing the Cost of Algorithmic Risk: A Case Study for Medical Malpractice

Talk Abstract: As machine learning algorithms start to get integrated into the decision-making process of healthcare organizations, insurance products will be developed to protect their owners from risk. We introduce a quantitative framework for insurance companies, machine learning modelers, and healthcare practitioners in order to price the risk of these products. Using properties of the model, such as discrimination performance, interpretability, generalizability, and robustness, we provide mathematical formulations for its financial evaluation. We present a case study of medical malpractice in the context of breast cancer detection where we estimate the risk exposure of a binary classifier.

Paper Reference: Bertsimas, D. and Orfanoudaki, A., 2021. Pricing Algorithmic Insurance. arXiv preprint arXiv:2106.00839.

Asieh Ahani is the head of data Analytics at MassMutual. She has a Bachelor’s degree in Electrical and Electronics Engineering from Ferdowsi University of Mashhad, a Master’s degree in Bioengineering and Biomedical Engineering from K.N. Toosi University of Technology and a Ph.D. in Bioengineering and Biomedical Engineering from Northeastern University. Upon joining MassMutual in 2017, she brought extensive experience leveraging data science in the marketing domain, working with Visual IQ and Northeastern University.

In MassMutual She lead one of data science domains called customer journey on data-driven research, problem solving and algorithm development through the systematic application of mathematics, statistics and computer science, as well as cutting-edge data technologies. Her work revolved around studying fundamental and high impact MassMutual marketing, servicing, retention and claim efforts to drive efficiency, lower costs, and connect people to the products most suitable to protect them and their families that directly impact the direction of the company as well as the industry at large.

She is currently leading MassMutual’s data analytics team. The team uses analytics technology to deliver data insights, reports and metrics, derived from a common set of data, to groups of users via a set of portals, in compliance with a governed playbook. This strategy enables scalability through the re-use of technology and data, which leads to lower costs as well as faster production of higher quality information.

Wen Liu is an Optimization Engineer at Meta. Her work contributes to predicting future workloads on the Content Delivery Network, building system tools to transform those predictions into plans that are optimized for reliability/performance/cost constraints, and improving user quality of experience, partnering with product teams to optimize the interaction between the application and the network.

Prior to joining Meta, Wen worked as a Data Scientist at Walmart Labs that majorly contributed to building the state-of-the-art deep learning models, implementing and evaluating model performances on core NLP applications.

Wen obtained her Ph.D. from Data Science Program at Worcester Polytechnical Institute in 2019. Her doctoral dissertation was conducted under the guidance of Prof. Andrew C. Trapp and Prof. Soussan Djamasbi. Its major subject focused on the application of mathematical optimization techniques and algorithms to solve the problem of identifying fixations in eye tracking data. This problem applied in the business analytics research area for user experience and behavioral analytics, whereas it could also be generalized as formulating optimal clusters in time series in data mining.

Talk Tilte: A Brief Introduction of Content Delivery Network and Network Data Scientist

Talk Abstract: Content delivery network (CDN) is the transparent backbone of the internet in charge of content delivery. Whether we are aware of it or not, everyone interacts with CDNs on a daily basis for perusing social media feeds, shopping online stores, and watching YouTube videos. This talk will briefly introduce why to use CDN and how does CDN work. Also, there are so many aspects that data scientist can help with improving the network performance. This talk will introduce some pieces of work for a network infrastructure data scientist, and will provide a general guideline for how to pursue this type of roles in industry.

Shikha Agarwal is a Data & Applied Scientist at Microsoft. She contributes towards identifying problem areas and providing solutions to partner teams to optimize and improve Microsoft products in the Windows and Azure space. Presently, she is excited about working on Windows Experimentation Platform. Shikha graduated with her Ph.D. in Econometrics and Quantitative Economics from University of Washington in 2020 and has been working with Microsoft since then.

Dr. Samira Pouyanfar is a data scientist at Microsoft. She received her Ph.D. in Computer Science from School of Computing and Information Sciences, Florida International University and her master’s degree in Artificial Intelligence from Sharif University of Technology, Iran. Her focus areas include data science, machine learning, deep learning, Responsible AI, computer vision, and NLP. She has published over 30 research papers in international journals and conference proceedings. At Microsoft, she applies data science and machine learning techniques to solve business problems. Her work on Responsible AI includes bias and fairness analysis, differential privacy, error analysis, and model interpretability. She also serves as an external reviewer of top AI conferences including ICML, NeurIPS, ICLR, CVPR, and several IEEE Transactions journals.

Ferdane Bekmezci is a Senior Data Scientist at Microsoft. For the past 14 years, she worked with big data to make data-driven decisions for feature development in Windows and Azure. Since 2020, she is one of the Responsible AI Champs leading Windows & Devices organization to build and deploy AI systems responsibly. She holds a B.S degree in International Business from the University of Maastricht, from the Netherlands.

Elaine O. Nsoesie is an Assistant Professor at Boston University School of Public Health and an Assistant Director of Research at Boston University Center for Antiracist Research. She is also on IPA at the National Institutes of Health where she co-leads the AIM-AHEAD program. She is a founding member of the Faculty of Computing & Data Sciences, and a Data Science Faculty Fellow at Boston University. She has a PhD in Computational Epidemiology, MS in Statistics and BS in Mathematics. Her research is focused on the use of data and technology to address racial and health inequity. She is the founder of Rethé (rethe.org) – an initiative focused on providing scientific writing tools and resources to student communities in Africa to increase representation in scientific publications. She has written for NPR, The Conversation, Public Health Post, and Think Global Health. Elaine was born and raised in Cameroon.

Dr. Yang is a member of the Data systems Research for Exploration, Analytics, and Modeling (DREAM) lab and of the Center for Data Science.

Dr. Yang obtained her Ph.D. from the Tandon School of Engineering, New York University, under the supervision of Prof. Julia Stoyanovich. Dr. Yang’s research work during the Ph.D. study can be found in details at DataResponsibly.

Dr. Yang’s primary area of research centers around data management, especially about ethical concerns including fairness, transparency, explainability, and the social impact of the algorithms in data-driven systems.

I am interested in designing algorithms for mitigating undesirable outcomes from automatic decision-making processes and developing tools to apply these algorithms in different applications.