Emory University’s AI.Humanity Initiative continues its upward trajectory in 2023, welcoming impressive new faculty members across the university. So far this year, the initiative supported 19 new faculty hires (for a total of around 40 since launch), and plans to support the hire of up to 60. Together with other new recruits and long-term faculty with expertise in AI, they are part of Emory's robust and growing community of AI.Humanity faculty scholars.
Aligned with its mission to shape the AI revolution to better human health, generate economic impact, and promote social justice, these new faculty appointments inject AI-related research and teaching into a diverse range of disciplines including liberal arts, health care, business, law and ethics.
“This extraordinary group represents some of the nation’s leading AI scholars,” says Ravi V. Bellamkonda, provost and executive vice president for academic affairs. “They join an existing community of faculty who are harnessing Emory’s multidisciplinary strengths to ensure ethical, effective use of technology to solve societal challenges. Together, they lead the way in understanding and applying AI to serve humanity and advance our world.”
Looking ahead to 2024, Emory’s AI.Humanity recruitment activities will concentrate on a targeted “cluster hire” in the areas of AI ethics, policy and regulation. The Office of the Provost will contribute support toward the appointment of five full-time faculty members and encourages joint hiring and collaboration across schools.
Specific areas of focus encompass, among others, algorithmic bias, data governance and privacy, the environmental implications of AI, ethical and legal obligations of AI developers and users, and the exploration of AI models and intellectual property.
For more information, contact Senior Vice Provost for Academic Affairs Lanny S. Liebeskind.
AI.Humanity New Faculty
The following faculty were hired in 2023 with support from the AI.Humanity Initiative, joining 19 colleagues hired through the initiative in 2022.
Research focus: Applying AI and natural language processing to real-world health data analytics; clinical decision support; data-centric AI; bias mitigation strategies and algorithmic fairness; AI governance and ethics.
Joining August 2024
Research focus: Computation and learning in brains and machines. Research involves bringing techniques and formalism from theoretical physics and mathematics to tackle problems in machine learning, cognitive science, and neuroscience.
Research focus: Theoretical machine learning, deep learning theory, optimization; integrable systems, random matrix theory, partial differential equations.
Faculty Website
Research focus: Improving AI approaches to understanding our past. History of property rights, origins of eminent domain, and the story of rent control.
Research focus: Clarifying when and why learning and decision-making goes awry in depression and anxiety disorders and developing potent and scalable translational interventions.
Research focus: Computational geography, human mobility and network sciences, disaster mitigation and resilience, human-environment interaction, urban informatics, and geo-artificial intelligence (GeoAI).
Research focus: Improving firms' core business strategies by using machine learning, econometrics, and mathematical modeling. Product returns management, customer search, and personalized promotions.
Research focus: Design of data/AI-driven systems that target health equity for marginalized communities and care workers. Gender and wellbeing, participatory and community perspectives, future of care work, human-computer interaction for development (HCI4D), global health.
Research focus: Deep learning on graphs, data-centric AI, data mining with applications in single-cell analysis, transportation, social science.
Research focus: Neurological disorders, mental health, human activity recognition, computer vision, machine learning, edge computing, wearables, ubiquitous computing.
Research focus: Agent-based simulation and modeling, steering behaviors, AI integration in simulation models, user-simulation model interaction, Wineinformatics.
Research focus: Algorithmic hiring, algorithmic bias and fairness, and human-AI interaction.
Research focus: Empirical legal studies, judicial behavior, law and politics, election law.
Research Focus: Develop, better understand, and improve causal inference methods by leveraging advances in econometrics, machine learning, and AI.
Research focus: Natural language processing, computational social science, cultural analytics.
Research focus: Single cell genomics and genetics; gene network inference.
Research focus: Philosophical foundations of machine learning, causal inference and uncertainty quantification in machine learning, algorithmic fairness and game theory.
Joining January 2024
Research focus: Application of deep learning techniques to pathology data with a focus on multimodal approaches integrating histology, genomics, and natural language.
Research focus: End-to-end data science pipelines for wearable device and biomedical imaging data, and statistical methods related to functional data analysis, spatial point processes, and statistical computing.