Stanford University
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Igor Daniel de Araujo Evangelista
Postdoctoral Scholar, Photon Science, SLAC
BioDr. Evangelista's primary research focus lies in computational modeling and theoretical analysis of semiconductor materials using advanced quantum mechanical methods, including Density Functional Theory, Quantum Monte Carlo, and ab-initio Molecular Dynamics. Evangelista investigates the electronic, structural, and mechanical properties of materials, collaborating closely with experimental groups to bridge theoretical predictions with empirical results. He is also interested in the development of empirical potentials and enhancing materials modeling through the application of machine learning techniques.
Evangelista entered the Department of Materials Science and Engineering at the University of Delaware as a Ph.D. candidate in 2018, after completing an master degree in Physics 2016-2018 at Federal Fluminense University (Brazil). Recent work includes collaborations with experimental groups to bridge theoretical predictions with empirical results, as well as applying machine learning to creating of empirical potentials to accelerate materials modeling. Evangelista has also contributed to understanding electron mobility in metal-oxide semiconductors and strain effects in two-dimensional materials. These studies showcase his expertise in electronic structure and materials design for next-generation semiconductor technologies. -
Luis Fabiano de Assis, Ph.D.
Affiliate, Center for Human Rights and International Justice
BioDr. Luis Fabiano de Assis is a Brazilian Federal Prosecutor, Data Scientist, and Professor at the National School for Public Prosecutors in Brazil. As an internationally recognized expert on issues of new data technologies and their use to develop evidence-based decent work programs and policies, he has advised the United Nations (United Nations University, New York), the International Labour Organization (Brazil, Latin America, and Geneva), and the World Bank (Washington/DC).
Luis is a member of the Alliance 8.7 Knowledge Platform Reference Group (United Nations University), where he contributes to advancing the scientific knowledge base and facilitate uptake and development of evidence-based policy initiatives with the aim of eradicating modern slavery, forced labor, child labor, and human trafficking to achieve SDG target 8.7.
As a Chief Research & Data Officer at the Brazilian Federal Labor Prosecution Office, he has led the development of the SmartLab Initiative (http://smartlabbr.org/), an innovative multi-stakeholder knowledge-management strategy to promote human rights at work. In collaboration with the ILO and a variety of partners, the initiative has given rise to a knowledge base that combines myriad primary and secondary open data sources, providing public and private stakeholders with readily available information to improve policy-making at the national, regional, and local level. By creating open source and open data observatories, the initiative uses data and knowledge to fight human trafficking, child labor, inequality at work, and poor occupational safety and health practices.
At the National School of Prosecutors in Brazil, he teaches in the areas of law and public policies, and his courses encompass issues such as methods to move law-enforcement towards real-time interventions using big data and new technologies; behavioral sciences principles applied to policymaking and accountability systems; design of evidence-based projects, programs and policies to strengthen the rule of law and protect human rights; techniques to develop data-driven investigations and collective (class actions) lawsuits; and regulations on data protection.
His current research encompasses issues such as targeting and coverage of government cash transfers and social assistance programs towards human trafficking victims; health standards, mortality rates, and life expectancy of human trafficking survivors; value-chain studies to support private stakeholders in relation to compliance, supplier qualification processes, monitoring, due diligence, and risk assessment; studies supported by machine-learning concerning the prediction of risk, vulnerabilities and resource allocation; mapping of national and international human trafficking routes and flows, focusing on refugees from Bolivia, Venezuela, Paraguay, Haiti, and Peru; and mapping of governmental data gaps to improve human rights data collection systems.
The objectives include developing research on how the international community can benefit from existing data sources and new technologies to develop evidence-based counter-trafficking public policies, strengthen the rule of law, improve accountability systems, and protect and promote human rights broadly. Also, Luis works in Brazil to disseminate good practices to improve human trafficking data collection and usage based on the guidelines “Getting to Good Human Trafficking Data: Everyday Guidelines for Frontline Practitioners.”
LLB (Bachelor of Law, 2002), University of São Paulo
LLM (Master of Law, 2008), University of São Paulo
Ph.D. in Law (2011), University of São Paulo
Visiting Research Fellow, Stanford University (2018)
Visiting Scholar, Stanford University (2019-20) -
Aglaia Kaissa de Boer
Clinical Assistant Professor, Medicine - Pulmonary, Allergy & Critical Care Medicine
BioKaïssa de Boer, MD is a board certified pulmonologist who specializes in the care of patients with interstitial lung disease. She completed her Internal Medicine and Pulmonary training at the University of Ottawa, Ottawa, Canada where her initial interest in caring for patients with fibrotic lung disease first developed. Subsequently she completed a fellowship in Interstitial Lung Disease at the University of California, San Francisco under the direction of Dr. Harold Collard. Dr. de Boer has a special interest in patients with connective tissue disease associated lung disease and those with drug induced pneumonitis. In addition she is actively involved in the ILD training and program development of Stanford's Pulmonary Allergy and Critical Care Fellows.
