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In the opening session of the 2025-2026 Population Analytics in an Aging Society Training Program, we will feature four notable alumni from the CAnD3 program. The primary goal of the CAnD3 Training Program is to enhance the skills of Fellows in population data science and computational population social science, empowering them to make evidence-based decisions in their respective areas of expertise. The alumni participating in this session will reflect on their experiences with CAnD3 and illustrate how they have applied the knowledge and skills gained from the program to foster data-driven decision-making in the academic, private, and public sectors.

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12:30 - 12:35 | Welcome and introductions

12:35 - 13:15 | Panel session

13:15 - 13:25 | Moderated Q&A session

13:25 - 13:30 | Closing and upcoming sessions

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This is an online webinar hosted on Zoom. To receive details to enter the event, please register.

Featured CAnD3 Alumni

Samuel Nemeroff (he/him/his/they/them/theirs) 

2024-2025 CAnD3 Alum

Samuel is an MA student in Sociology at the University of New Brunswick, Fredericton, under the supervision of Dr. David Hofmann. Samuel’s research focuses on radicalization and the manifestations of far-right extremism in North America and the Western world, with the aim of contributing quantitative methods and analysis to the growing body of research on extremism in the armed forces. His undergraduate Honours thesis, also completed at UNB under the supervision of Dr. Hofman, explored educational programs aimed at countering and preventing youth radicalization. 

Khandideh Williams (she/her/hers)

2023-2024 CAnD3 Alum

Khandideh Williams is a PhD student in the Family Medicine and Primary Care program at ÂÌñÉç. She holds a Bachelor of Science degree with a major in Microbiology and Immunology. Committed to addressing racial disparities in health outcomes and healthcare experiences, Khandideh successfully fast-tracked from her master’s program to the doctoral program in the winter 2022 semester. Her doctoral research seeks to investigate racial disparities and perceived anti-Black racial discrimination in Canadian primary health care. Her endeavours also include the development of clinical strategy and health care policy recommendations towards improving the quality of health care experiences among Black communities and other racialized groups in Canada.

Merita Limani (she/her/hers)

2022-2023 CAnD3 Alum

Merita Limani holds a dual PhD in Sociology from the University of Western Ontario and in Political Science from St. Cyril & Methodius University. She was a CAnD3 Fellow (2023 cohort). Her research focuses on gender disparities, political behaviors, and the intersections of health, aging, and policy in Canada and globally. She also brings over a decade of professional experience in project management, policy, and international development, having led initiatives with the Council of Europe, USAID, and the Swiss Development Agency in the areas of human rights, gender equality, gender-based violence, rule of law, and democratization.

Sofia Beatriz Gil-Clavel (she/her/hers)

2021-2022 CAnD3 Alum

Sofia Gil-Clavel earned her Ph.D. from the Max Planck Institute for Demographic Research (Germany) and the University of Groningen (Netherlands). Prior to her doctoral studies, she completed a degree in Actuarial Science at the National Autonomous University of Mexico (UNAM) and earned an M.Sc. in Computer Science from the Mathematical Research Center of Mexico (CIMAT). During her Ph.D., she participated in several international training programs, including the International Max Planck Research School for Population, Health and Data Science (IMPRS-PHDS) in Germany and the Consortium on Analytics for Data-Driven Decision-Making (CAnD3) in Canada. Her research leverages Big Data sources, such as SCOPUS, Facebook, and Twitter, to investigate society’s drivers of climate change adaptation, older adults’ use of communication technologies, and migrants’ cultural integration. She applies a combination of computer science techniques (including machine learning and metaheuristics) with statistical and actuarial methods (such as generalized linear models, survival analysis, and bootstrapping).