Artificial Intelligence Experts

Scientific workflows, big data applications, distributed computing, reproducible science, Kepler Scientific Workflow System, AI in tackling climate challenges like wildfire

Developing theory to better understand neural networks in particular and AI in general, Machine learning and deep learning

AI for the humanities, developing generative modeling techniques for understanding and generating structured human data–including natural language, music, historical ciphers, document images and other complex artifacts

Robotics and AI, with an emphasis on a systems view of problems, focusing on everything from self-driving vehicles to warehouse robots

Using innovative multi-modal data fusion and learning algorithms, and edge computing and networking architectures, to enable novel 5G and 6G use cases like predictive personalized health, X-Reality and smart and sustainable mobility, Using AI for precise and personalized diagnosis and treatment of patients with chronic conditions

Using principles from AI to optimize large-scale engineering systems and the scientific discovery pipeline, Using AI principles to optimize beam-tracking, end-to-end routing, multi-user scheduling and communication secrecy in next generation communication networks, AI solutions for experiment design

Using AI networks to mine data sets of neuronal ensembles to identify patterns of behavioral strategies, Reinforcement learning, Exploring links between AI networks and the brain

Personalized machine learning and large language models in applications ranging from personalized recommendations, to chatbots, health care and fashion design

Nakashole is interested in Natural Language Processing, Her work focuses on the healthcare domain with a particular interest in question answering, entity linking and making healthcare information accessible to the general public using NLP methods, She also works on low resource languages with the goal of widening the population of people that can benefit from language technology

Use of AI and machine learning to accelerate the discovery and development of new materials with superior properties for applications ranging from advanced batteries and LEDs to structural alloys for extreme environments

Using AI tools to teach programming to undergraduate students, Active learning pedagogies and using machine learning techniques to predict struggling students

Human-robot interaction and human-centered AI, Assistive/accessible technology, Embodied AI and health informatics, AI technology in acute care, neuro-rehabilitation and home health, AI and health equity, AI ethics and robot ethics

Neural networks, Computational and theoretical neuroscience, including how our understanding of neurobiology has paved the way for brain-inspired artificial intelligence advancements<

Large language models and their applications, Detecting and preventing toxic prompts cloaked in benign language in chatbots powered by large language models, Developing data-driven approaches to transform massive text data into actionable knowledge

Performance-guaranteed AI models for sustainable energy systems, AI models that optimize indoor air quality and energy consumption in smart buildings, Developing AI and control techniques to balance electricity generation and demand in microgrids powered by renewable energy sources

The intersection of neuroscience, neural computing and engineering; understanding the algorithms the biological brain uses in order to develop novel mathematical models for artificial neural networks and machine learning

Content moderation and AI, Privacy and surveillance, Auditing and regulation, Bias in AI, Equity in AI, Transparency

Geometric deep learning, graph learning, topological data analysis, machine learning for complex unstructured data, Learning enabled optimization, with applications to chip design, material science and neuroscience

High-capacity data cyberinfrastructure, high-energy particle physics

Machine learning, especially for large-scale spatiotemporal data, Deep learning, Generative AI, Spatiotemporal reasoning, The inclusion of scientific knowledge into AI systems, Learning dynamical systems in climate, health care and physical sciences

Computationally-based art, Algorithmic bias, AI and gender, AI and subjective characteristics, Bias in AI image generators

Computational systems that augment and expand human creativity, Social, cultural, political, ethical and philosophical implications of AI, Using art to create speculative simulations and data dramatizations exploring the intricacies of human-machine entanglements; perception and states of consciousness; the tensions between ecology, technology, science and spirituality

Balancing AI regulation and innovation, comparative analysis of AI, AI in critical infrastructure, Certification and regulatory measures, International collaboration on AI regulation

Ethical, psychological and policy/regulatory issues around AI and robotics across multiple sectors, including transportation, health care, privacy, defense and security, causal AI systems, particularly for biomedical and public health data, computational models of human learning and reasoning

Brain-computer interfaces, machine learning, AI and computer vision, Vision/visual psychophysics, Computational neuroscience and facial expression analysis, Human consequences of AI, as well as improving equity in education using AI and using tech to reduce economic inequality and inequity in health care

The intersection of music and AI, Computer music and machine improvisation, Using generative AI for music and human-AI co-creativity

AI in K-12 education, AI literacy, developing national educational guidelines for AI, Integration of AI tools/AI literacy education in classrooms and teacher education to prepare the future workforce, Educational robotics, Human-computer interaction, human-robot interaction

Disinformation generated by AI, social responsibility of AI, the “AI Revolution,” Using AI innovatively in the college classroom, AI as part of the writing process, Ethnographer of computation, Human-computer interaction

Learning to code with the help of AI, Teaching programming in the age of ChatGPT, AI for scientific programming

AI and education, Large language models, Human consequences of AI, Academic integrity

AI labor, AI ethics, how technologies and tech companies affect people, their work and neighborhoods

Using machine learning to study ancient languages and societies

Generative AI in business, including for process improvement, cost reduction and changing scope of work, AI in curriculum at business schools, AI in the workplace

Critical data and AI studies, human-centered design, the politics of computing, How knowledge production and technical practices are culturally constructed and conditioned by the politics of race, gender, class and coloniality in the United States, Ghana and Nigeria

Politics of information and misinformation, automated content analysis, and the politics of censorship and propaganda of authoritarian states, How censorship can influence AI, using AI and machine learning to investigate social media and AI threats to democracy

Speech recognition, text-to-speech, computational analysis of speech, large language models, medical natural language processing, machine learning and AI in pedagogy and teaching, free and open software and culture

AI in acoustic modeling and sound spatialization

AI approaches and experimental techniques to gain insight into how cancers develop and grow

Using computational techniques to investigate the structure, function and dynamics of complex biological systems, Using AI-enhanced simulations as tools to develop new medicines and vaccines

Using AI to predict health outcomes and identify disease, specifically acute kidney injury before it occurs in the hospital

Use and regulation of AI in health care, Health care-related internet searches

AI in ophthalmology

Using AI for deep-learning genomics, to better understand how DNA is organized and activated and deactivated in cells

AI workflows in surgical and perioperative care

Using AI to link gene expression with disease states in order to create better models of disease and discover new treatment targets

Use of AI and radiology imaging

Developing and using AI tools to predict treatment resistance in tumors and to develop new cancer drugs

Using AI in drug discovery, specifically to target deadly and drug resistant enzymes in tuberculosis (TB) cases

AI implementation in health systems, Impact of AI in health care, AI in health innovation

Using AI tools to investigate bacteriophage (phage), viruses that are considered promising therapeutic sources in the growing antibiotic resistance crisis, AI research includes which mutations lead to infection, detecting genes involved in infection and resistance and analyzing how phage evolve

Use of multi-scale engineering approaches to understand, diagnose and treat heart disease and discover the biological principles underlying human athletic performance

AI workflows in radiology

AI risk assessment, Ethical and social implications of digital and AI strategies used in research, Informed consent, Bias in AI, Data management, AI in healthcare

Using machine learning to reduce physician workload

Use of AI in health care

AI in health care, Ethical use of AI in health

Using AI to detect sepsis in patients receiving care in the hospital

Computing in health care, Using AI to predict health outcomes and identify disease, specifically acute kidney injury before it occurs in the hospital

Using AI to improve weather predictions and improve the prediction of atmospheric rivers

Using AI tools to prepare for, respond to and recover from wildfires and other natural hazards

The intersection between oceanography and machine learning: using machine learning to extract actionable insights from large underwater acoustic datasets, with a deep understanding of physical, biological and anthropogenic sound sources, Using AI to identify dolphin species, Using machine learning to study the impact of environmental disasters, like the Deep Horizon oil spill, on marine mammals

Use of AI in seismology, combining physics-based computer simulations with data-driven techniques and theoretical analysis to uncover the physical mechanisms relevant to understanding earthquakes, Using AI-enhanced tools to evaluate earthquake scenarios on demand

Using AI to study coral reefs and accelerate the process by which the researchers can observe how corals are growing, shrinking, changing, dying and recruiting

The application of machine learning in climate science, Machine learning tools and techniques to better understand the complex interactions between aerosols and clouds, and their representation within global climate models