--- title: UMD Experts Available to Discuss Artificial Intelligence date: 2023-01-01T00:00:00-05:00 author: Jordan Dunklee canonical_url: "https://umdrightnow.umd.edu/news-releases/umd-experts-available-to-discuss-artificial-intelligence" section: News Releases --- News Releases # UMD Experts Available to Discuss Artificial Intelligence ![Researcher Cornelia Fermuller sits behind a computer screen](https://omc-system.transforms.svdcdn.com/production/images/umdrn/AI_UMIACS_07252023_SC_9135.JPG?w=4000&h=2668&auto=compress%2Cformat&fit=crop&dm=1770151338&s=90facdf7956c2a09a783500e027e79b3) January 01, 2023 Are you a journalist looking for new angles on the impact of artificial intelligence? From ethics, equity and policy to climate modeling, health innovation and cybersecurity, University of Maryland experts are exploring AI's influence across industries and society, bringing depth, context and clarity to one of the most transformative technologies of our time. **(Please contact mediainfo@umd.edu for more information or help setting up an interview.)** ## Foundations & Core Technologies AI Foundations & Reliability [**Christopher Antoun**](https://ischool.umd.edu/directory/christopher-antoun/): Intersection of data science and survey methodology; use of new technology and data collection methods [**Bahar Asghari**](https://www.cs.umd.edu/~bahar/): Computer architecture and systems, specializing in domain-specific architectures and high-performance computing [**Radu Balan**](https://math.umd.edu/~rvbalan/): AI and data science for analyzing and combining complex signals, such as audio, images, or sensor data, to extract clear and useful information [**John Baras**](https://isr.umd.edu/clark/faculty/357/John-S-Baras): Systems engineering; control theory; optimization; communication networks; machine learning; artificial intelligence applications in robotics, signal processing, network security, and healthcare management systems [**Abhinav Bhatele**](https://www.cs.umd.edu/~bhatele/): Parallel computing; high-performance computing; scientific computing; machine learning; AI applications in computational science [**Jordan Boyd-Graber**](https://users.umiacs.umd.edu/~ying/): Machine learning; interpretable AI; human-AI interaction; natural language understanding [**Zikui Cai**](https://zikuicai.github.io/): Trustworthy machine learning; AI robustness; model safety; privacy; computer vision; robotics [**Giovanni Luca Ciampaglia**](https://ischool.umd.edu/directory/giovanni-luca-ciampaglia/): Algorithmic transparency; information integrity; social network dynamics; misinformation detection; computational social science [**Marine Carpuat**](https://www.cs.umd.edu/~marine/): Natural language processing (NLP); multilingual NLP; machine translation; trustworthy AI; computational linguistics [**Hal Daumé III**](https://aim.umd.edu/people/hal-daume-iii): AI for human self-efficacy; mitigating societal harms of AI; natural language processing; trustworthy AI; natural language processing; machine learning; computational linguistics [**Tudor Dumitraș**](https://ece.umd.edu/clark/faculty/384/Tudor-Dumitra%C8%99): Data-driven security; machine learning systems for detecting attacks and predicting security incidents; security of machine learning in adversarial environments [**Ramani Duraiswami**](https://www.cs.umd.edu/people/ramanid): Algorithm development; systems development and applications in spatial audio, computer vision and acoustics [**Sanghamitra Dutta**](https://ece.umd.edu/clark/faculty/1711/Sanghamitra-Dutta): Reliable machine learning; trustworthy AI; efficient algorithms; social good applications [**Soheil Feizi**](https://www.cs.umd.edu/~sfeizi/): AI reliability; trustworthy AI; robustness, generalizability & interpretability; accuracy of AI detection tools; reasoning in AI; foundational AI systems [**Tom Goldstein**](https://www.cs.umd.edu/~tomg/): Building responsible AI systems; machine learning; large-scale optimization; signal processing; computer vision; applied mathematics [**Mohammad Hajiaghayi**](https://www.cs.umd.edu/~hajiagha/): Algorithmic game theory; combinatorial optimization; network design; distributed computing; computational geometry [**Furong Huang**](https://furong-huang.com/): Trustworthy machine learning; interpretable and reliable AI; AI for sequential decision-making; high-dimensional statistics; generative models; reinforcement learning for robotics [**Mohit Iyyer**](https://www.cs.umd.edu/~miyyer/): Natural language processing (NLP); deep learning; machine learning; multimodal models; large language models (LLMs) [**Ang Li**](https://ece.umd.edu/clark/faculty/1839/Ang-Li): Wireless communications; machine learning; AI hardware acceleration; signal processing; mobile systems [**Lizhen Lin**](https://blog.umd.edu/lizhen01/): Bayesian statistics; deep learning theory; geometric deep learning; statistical theory [**Zhicheng Liu**](https://www.zcliu.org/): Human-centered AI; data visualization; visual analytics; human-computer interaction; machine learning interpretability [**Christopher Metzler**](https://www.cs.umd.edu/~metzler/): Computational imaging; signal processing; machine learning; wireless communications [**Michael Otte**](https://aero.umd.edu/clark/faculty/796/Michael-Otte): Robotic path planning; distributed multi-agent systems; artificial intelligence algorithms; distributed systems; graph theory; machine learning [**Louiqa Raschid**](https://www.rhsmith.umd.edu/directory/louiqa-raschid): Challenges of data management, data integration and performance for applications in life sciences, web data delivery, health information, financial information systems, humanitarian IT applications and grid computing [**Aravind Srinivasan**](https://www.cs.umd.edu/~srin/): Machine learning theory; interpretability & fairness in AI; algorithms; probabilistic & randomized methods; data science; network science; applications in health, e-commerce, cloud computing, internet advertising [**Tracy Sweet**](https://education.umd.edu/directory/tracy-sweet): Data science; bias in machine learning; social network analysis; network interference; statistical modeling [**Uzi Vishkin**](https://ece.umd.edu/clark/faculty/491/Uzi-Vishkin): Parallel computing; parallel algorithms; theoretical computer science [**Haizhao Yang**](https://haizhaoyang.github.io/): Symbolic machine learning for interpretable AI; foundations of deep learning; machine learning–based scientific problem solving [**Kaiqing Zhang**](https://kzhang66.github.io/): Systems and control; game theory; reinforcement learning; multi-agent systems [**Tianyi Zhou**](https://tianyizhou.github.io/): Machine learning; natural language processing; optimization; AI and robotics AI and Cybersecurity, Cryptography & Trustworthy Systems [**John Beieler**](https://research.umd.edu/directory/john-beieler): Applied research in AI, quantum computing, human-machine teaming and social and behavioral sciences for national security; scientific advancements in intelligence; human language technology; machine learning; vulnerabilities in AI; machine learning research; development, deployment and scaling of large machine learning systems [**Susan G. Campbell**](https://ischool.umd.edu/directory/susan-g-campbell/): Cognitive skills for cybersecurity; effective human-technology interaction; visual cognition and assessment; cognitive psychology; technology usability [**Rama Chellapa**](https://ece.umd.edu/clark/faculty/369/Rama-Chellappa): Computer vision; signal and image processing; pattern recognition; AI in biometrics; robust and secure biometrics; statistical inference; multi-dimensional stochastic processes [**Tudor Dumitraș**](https://ece.umd.edu/clark/faculty/384/Tudor-Dumitra%C8%99): Data-driven security; machine learning systems for detecting attacks and predicting security incidents; security of machine learning in adversarial environments [**Nancy Gallagher**](https://spp.umd.edu/our-community/faculty-staff/nancy-gallagher): Responsible use of AI in the military domain; international security; arms control and nonproliferation; nuclear policy; cybersecurity; space security [**Charles Harry**](https://spp.umd.edu/our-community/faculty-staff/charles-harry): Governance of emerging technologies including AI; international security; cybersecurity; data science; complexity theory; intelligence [**Jonathan Katz**](https://www.cs.umd.edu/people/jkatz2): Cryptography and computer security; foundational cryptographic methods; theoretical security protocols [**Ian Miers**](https://www.cs.umd.edu/~imiers/): Privacy-preserving cryptography; secure payments; smart contracts; blockchain security; applied security and privacy solutions [**Michelle Mazurek**](https://mmazurek.umiacs.io/): Human-centered computer security; security behavior research; end-user security adoption; end-to-end encryption usability [**James Purtilo**](https://www.cs.umd.edu/people/purtilo): Software engineering, focusing on software architecture and design; cybersecurity and robust systems [**Nirupam Roy**](https://www.cs.umd.edu/~nirupam/): Deepfake detection and prevention; spatial sensing for robots and drones; AI for situational awareness; cybersecurity; AI-enabled robotics [**Alan Zaoxing Liu**](https://zaoxing.github.io/): Computer systems; networking; cybersecurity; data analytics; approximate computing AI and Autonomous Vehicles & Robotics [**Yiannis Aloimonos**](https://www.umiacs.umd.edu/our-experts/faculty/yiannis-aloimonos): Real-time perceptual systems; vision-guided navigation and manipulation; animal-inspired robotic vision; computer vision; space-time representation [**Calin Belta**](https://ece.umd.edu/clark/faculty/1872/Calin-Belta): Robotics; formal methods; motion planning; autonomous systems; swarm robotics; human-robot collaboration; synthetic biology [**Cornelia Fermüller**](https://users.umiacs.umd.edu/~fermulcm/): Computer vision; robotics; biologically inspired active vision; human-robot collaboration; motion processing for drones and robots [**Ming Lin**](https://www.cs.umd.edu/people/lin): Robotics; computer vision; machine learning; virtual reality; augmented reality; physics-based simulation [**Dinesh Manocha**](https://www.cs.umd.edu/people/dmanocha): Computer graphics; geometric modeling; motion planning; virtual reality; robotics; machine learning [**Mohammad Nayeem Teli**](https://www.cs.umd.edu/~nayeem/): Vaccine design with protein language models; assistive technology for the visually impaired; navigating robot interactions; large language models; computer vision and perception [**Michael Otte**](https://aero.umd.edu/clark/faculty/796/Michael-Otte): Robotic path planning; distributed multi-agent systems; artificial intelligence algorithms; distributed systems; graph theory; machine learning [**Huaishu Peng**](https://www.cs.umd.edu/people/huaishu): Wearable and interactive technologies; assistive devices; Internet of Things (IoT); human-computer interaction; sensor design [**Abhinav Shrivastava**](https://www.cs.umd.edu/~abhinav/): Computer vision; machine perception; AI and robotics; machine learning; VR/AR [**Pratap Tokekar**](https://tokekar.com/): AI and robotics for environmental sensing; autonomous drones and robot teams; agricultural and civil engineering applications; robotics for accessibility [**Nirupam Roy**](https://www.cs.umd.edu/~nirupam/): Deepfake detection and prevention; spatial sensing for robots and drones; AI for situational awareness; cybersecurity; AI-enabled robotics [**Mumu Xu**](https://user.eng.umd.edu/~mumu/): Multi-agent autonomous systems; machine learning-informed planning tactics for robotics and autonomy; aerospace engineering [**Yaser Yacoob**](https://www.umiacs.umd.edu/our-experts/faculty/yaser-yacoob): Facial expression detection and tracking; lip reading; human activity recognition; augmented reality; interactive robots; motion and image segmentation [**Miao Yu**](https://me.umd.edu/clark/faculty/607/Miao-Yu): AI and sensing, robotics and computer vision; developing intelligent systems for challenging environments; autonomous underwater and under-ice vehicles; deep learning for environmental modeling and aquaculture [**Tianyi Zhou**](https://tianyizhou.github.io/): Machine learning; natural language processing; optimization; AI and robotics AI and Computer Vision & Perception [**Yiannis Aloimonos**](https://www.umiacs.umd.edu/our-experts/faculty/yiannis-aloimonos): Real-time perceptual systems; vision-guided navigation and manipulation; animal-inspired robotic vision; computer vision; space-time representation [**Ramani Duraiswami**](http://users.umiacs.umd.edu/~ramanid/): Algorithm development; systems development and applications in spatial audio, computer vision and acoustics [**Roger Eastman**](https://www.cs.umd.edu/~reastman/): Computer vision; image registration; pattern recognition [**Cornelia Fermüller**](http://users.umiacs.umd.edu/~fermulcm/): Computer vision; robotics; biologically inspired active vision; human-robot collaboration; motion processing for drones and robots [**Ruohan Gao**](https://ruohangao.github.io/): Computer vision; machine perception; multisensory AI; robotics; machine learning; data science [**Jia-Bin Huang**](https://jbhuang0604.github.io/): Computer vision; computer graphics; machine learning [**David Jacobs**](https://www.cs.umd.edu/~djacobs/): Computer vision; machine learning; object recognition; image clustering; structure-from-motion; human-computer interaction [**Dinesh Manocha**](https://www.cs.umd.edu/people/dmanocha): Computer graphics; geometric modeling; motion planning; virtual reality; robotics; machine learning [**Christopher Metzler**](https://www.cs.umd.edu/people/metzler): Computational imaging; signal processing; machine learning; wireless communications [**David Mount**](https://www.cs.umd.edu/users/mount/): Computational geometry; applications in image processing, pattern recognition, information retrieval and computer graphics [**Mohammad Nayeem Teli**](https://www.cs.umd.edu/~nayeem/): Vaccine design with protein language models; assistive technology for the visually impaired; navigating robot interactions; large language models; computer vision and perception [**Miao Yu**](https://me.umd.edu/clark/faculty/607/Miao-Yu): AI and sensing, robotics and computer vision; developing intelligent systems for challenging environments; autonomous underwater and under-ice vehicles; deep learning for environmental modeling and aquaculture ## Society, Ethics & Governance AI and Society, Ethics & Policy [**Neda Atanasoski**](https://aim.umd.edu/people/neda-atanasoski): Feminism and AI; feminist and critical race approaches to science & technology; AI and the future of work & human rights [**Holly Brewer**](https://history.umd.edu/directory/holly-brewer): Why AI needs humans and humanists; early American history; history of modern justice and slavery [**Ilaria Canavotto**](https://sites.google.com/view/ilariacanavotto/): Intersection between AI, law and machine ethics [**Fabrizio Cariani**](https://cariani.org/): Philosophy of language; epistemology; logic; AI ethics; formal social epistemology [**Giovanni Luca Ciampaglia**](https://ischool.umd.edu/directory/giovanni-luca-ciampaglia/): Algorithmic transparency; information integrity; social network dynamics; misinformation detection; computational social science [**Hal Daumé III**](https://aim.umd.edu/people/hal-daume-iii): AI for human self-efficacy; mitigating societal harms of AI; natural language processing; trustworthy AI; natural language processing; machine learning; computational linguistics [**Sanghamitra Dutta**](https://ece.umd.edu/clark/faculty/1711/Sanghamitra-Dutta): Reliable machine learning; trustworthy AI; efficient algorithms; social good applications [**John Dickerson**](https://jpdickerson.com/): Ethical AI systems; fairness in AI; practical AI applications in societal contexts; machine learning and economics; market design; optimization [**Sheena Erete**](https://aim.umd.edu/people/sheena-erete): Community-based technology design; equity in AI and machine learning tools; technology for civic engagement, health, education and violence [**Jen Golbeck**](https://ischool.umd.edu/directory/jennifer-golbeck/): AI, social media, privacy and trust on the web; algorithmic transparency and ethics; social computing; data privacy; data journalism [**Vaishnav Kameswaran**](https://www.trails.umd.edu/kameswaran): AI & accessibility; human-computer interaction; algorithmic fairness; accessibility in algorithmic decision-making [**Jessica Lee Mathiason**](https://wgss.umd.edu/directory/jessica-mathiason): Cultural studies; disability studies; feminist science studies; history of medicine & technology; LGBTQ studies; media studies; medical humanities; social justice [**Minoo Modaresnezhad**](https://www.rhsmith.umd.edu/directory/minoo-modaresnezhad): Human-AI interaction (HAI); collaborative learning in HAI teams; participatory AI; ethical dimensions of AI; individual behavior and cognition within information systems; crowdsourcing-based systems; social networking systems; individual security and privacy [**Hawra Rabaan**](https://ischool.umd.edu/directory/hawra-rabaan/): Equity-centered design; human-computer interaction (HCI); technology for asocial good; cultural and gender-based inequities [**Katie Shilton**](https://ischool.umd.edu/directory/katie-shilton/): Trustworthy AI; human-in-the-loop content moderation; experiential data ethics education; big data research ethics [**Jessica Vitak**](https://ischool.umd.edu/directory/jessica-vitak/): Privacy and ethical implications of technology; informed decision-making tools for technology use and sensitive data sharing [**Fumeng Yang**](https://www.fmyang.com/): Data and AI literacy; human-AI interaction; information visualization; election forecast visualizations; applications in politics, public policy and K-12 education AI and Government, Democracy & Policymaking [**Jennifer Bachner**](https://sph.umd.edu/people/jennifer-bachner): Data analytics for evidence-based policymaking; government responsiveness; public opinion modeling [**David Backer**](https://spp.umd.edu/our-community/faculty-staff/david-backer): How AI forecasting can enhance prevention and response to food security and nutrition crises; evolution of global humanitarian response architecture [**Gustavo Flores-Macías**](https://provost.umd.edu/news/announcing-our-new-school-of-public-policy-dean): AI in relation to crime, policing and militarization of public safety [**Nancy Gallagher**](https://spp.umd.edu/our-community/faculty-staff/nancy-gallagher): Responsible use of AI in the military domain; international security; arms control and nonproliferation; nuclear policy; cybersecurity; space security [**Eaman Jahani**](https://www.rhsmith.umd.edu/directory/eaman-jahani): How social networks and online platforms affect outcomes and lead to unequal distribution of resources; digital twins to assist policymakers in designing interventions for community challenges [**Zubin Jelveh**](https://ischool.umd.edu/directory/zubin-jelveh/): Data science for public policy; prediction models for domestic and gun violence; record linkage for criminal justice administrative data; data-driven reduction in violence and harms associated with the criminal justice system; the science of science; connecting techniques from machine learning to problems in the social sciences [**Doug Lombardi**](https://education.umd.edu/directory/doug-lombardi): AI-driven tools to deepen scientific and civic reasoning in K-12 education; scientific thinking, reasoning and testing in classroom contexts; critical thinking and collective agency in response to science denial and disinformation [**Fumeng Yang**](https://www.fmyang.com/): Data and AI literacy; human-AI interaction; information visualization; election forecast visualizations; applications in politics, public policy and K-12 education AI and Media, Social Media & Misinformation [**Cody Buntain**](https://ischool.umd.edu/directory/cody-buntain/): How people use online information systems during moments of uncertainty, crisis, conflict and political or social unrest; climate misinformation and disinformation; how AI alters the information environment; AI-driven tools for emergency response [**Giovanni Luca Ciampaglia**](https://ischool.umd.edu/directory/giovanni-luca-ciampaglia/): Algorithmic transparency; information integrity; social network dynamics; misinformation detection; computational social science [**Jen Golbeck**](https://ischool.umd.edu/directory/jennifer-golbeck/): AI, social media, privacy, and trust on the web; algorithmic transparency and ethics; social computing; data privacy; data journalism [**Naeemul Hassan**](https://ischool.umd.edu/directory/naeemul-hassan/): Computational journalism; data mining; social media analysis; natural language processing (NLP); machine learning [**Doug Lombardi**](https://education.umd.edu/directory/doug-lombardi): AI-driven tools to deepen scientific and civic reasoning in K-12 education; scientific thinking, reasoning and testing in classroom contexts; critical thinking and collective agency in response to science denial and disinformation [**Julia Mendelsohn**](https://ischool.umd.edu/directory/julia-mendelsohn/): Computational social science; political behavior; language and politics; data science [**Sean Mussenden**](https://merrill.umd.edu/directory/sean-mussenden): Innovative, AI-powered software tools to help reporters find and make sense of information; data-driven investigative reporting that drives change [**Jui Ramaprasad**](https://www.rhsmith.umd.edu/directory/jui-ramaprasad): Interactions on online platforms and their impact; impact of platform features and social influence on users; interplay between online incivility and engagement [**Nirupam Roy**](https://www.cs.umd.edu/~nirupam/): Deepfake detection and prevention; spatial sensing for robots and drones; AI for situational awareness; cybersecurity; AI-enabled robotics [**Daniel Trielli**](https://merrill.umd.edu/directory/daniel-trielli): Algorithmic curation impact on journalism; misinformation and disinformation; computational journalism; media literacy and algorithmic accountability [**Derek Willis**](https://merrill.umd.edu/directory/derek-willis): Data journalism education; innovative storytelling with data; journalism training; investigative reporting ## Human Interaction & Communication AI and Human-Computer Interaction & Accessibility [**Evan Golub**](https://www.cs.umd.edu/~egolub/professional.shtml): Human-computer interaction; user experience design; digital media; creative coding; curriculum development [**Hernisa Kacorri**](https://ischool.umd.edu/directory/hernisa-kacorri/): Human-centered AI; accessibility technology; teachable machines; empowering users as active directors of AI; assistive technology; AI interaction design [**Vaishnav Kameswaran**](https://www.trails.umd.edu/kameswaran): AI & accessibility; human-computer interaction; algorithmic fairness; accessibility in algorithmic decision-making [**Huaishu Peng**](https://www.cs.umd.edu/people/huaishu): Wearable and interactive technologies; assistive devices; Internet of Things (IoT); human-computer interaction; sensor design [**Galina Reitz**](https://ischool.umd.edu/directory/galina-madjaroff-reitz/): AI-driven agents to support people with Parkinson’s disease; accessible interface design for individuals with disabilities; voice-based technologies; accessible health technologies; patient-centered design; inclusive methods [**Mohammad Nayeem Teli**](https://www.cs.umd.edu/~nayeem/): Vaccine design with protein language models; assistive technology for the visually impaired; navigating robot interactions; large language models; computer vision and perception AI and Language & Communication [**Marine Carpuat**](https://www.cs.umd.edu/~marine/): Natural language processing (NLP); multilingual NLP; machine translation; trustworthy AI; computational linguistics [**Hal Daumé III**](https://aim.umd.edu/people/hal-daume-iii): AI for human self-efficacy; mitigating societal harms of AI; natural language processing; trustworthy AI; natural language processing; machine learning; computational linguistics [**Naomi Feldman**](https://users.umiacs.umd.edu/~nhf/): Machine learning to understand language acquisition and structure; strategies for language learning and speech-language pathology; computational linguistics; education technology [**Philip Resnik**](https://psresnik.github.io/): Computational linguistics; computational modeling [**Julie Yelle**](https://languagescience.umd.edu/directory/julie-yelle): Computational linguistics; natural language processing; language variation AI and Humanities [**Neda Atanasoski**](https://aim.umd.edu/people/neda-atanasoski): Feminism and AI; feminist and critical race approaches to science & technology; AI and the future of work & human rights [**Holly Brewer**](https://history.umd.edu/directory/holly-brewer): Why AI needs humans and humanists; early American history; history of modern justice and slavery [**Matthew Kirschenbaum**](https://english.umd.edu/directory/matthew-kirschenbaum): Digital humanities; AI in communication; media history; writing technologies; digital literary heritage [**Jessica Lee Mathiason**](https://wgss.umd.edu/directory/jessica-mathiason): Cultural studies; disability studies; feminist science studies; history of medicine & technology; LGBTQ studies; media studies; medical humanities; social justice [**Jeffrey Moro**](https://mith.umd.edu/people/jeffrey-moro/): Environmental and atmospheric data; media theory and history; vintage and obsolete computing; software preservation; experimental modes of writing and storytelling AI and Art [**Lillian-Yvonne Bertram**](https://english.umd.edu/directory/lillian-yvonne-bertram): Computational poetry; AI and digital technologies in creative writing; intersections of race, gender and identity with technology; experimental literary forms combining data and AI-generated content [**Lins Derry**](https://www.linsderry.com/): Interaction design; performance art; experimental arts; technology and design integration [**Cy Keener**](https://art.umd.edu/directory/cy-keener): Environmental art; data visualization; climate change; interdisciplinary collaboration; sensor-based installations ## Industry & Infrastructure AI and Health [**Ashok Agrawala**](https://www.cs.umd.edu/~agrawala/): Smart environments and sensors; real-time systems design; wearables; health informatics; location determination [**Elizabeth Aparicio**](https://sph.umd.edu/people/elizabeth-aparicio): Chatbots to provide health information; health bots for new parents [**David Backer**](https://spp.umd.edu/our-community/faculty-staff/david-backer): How AI forecasting can enhance prevention and response to food security and nutrition crises; evolution of global humanitarian response architecture [**Margrét Bjarnadóttir**](https://www.rhsmith.umd.edu/directory/margret-bjarnadottir): Large-scale data decision-making; algorithmic analysis of workplace gender disparity; AI applications in healthcare, finance, and sports analytics [**Fardina Alam**](https://www.cs.umd.edu/people/fardina): AI for protein structure prediction; machine learning in drug discovery; responsible AI; AI applications in education [**Behtash Babadi**](https://ece.umd.edu/clark/faculty/356/Behtash-Babadi): Machine learning for neural data analysis, biosensing and health data; computational neuroscience; neuromorphic adaptive systems; neural signal processing [**Balakumar Balachandran**](https://me.umd.edu/clark/faculty/508/Balakumar-Balachandran): Nonlinear dynamics and control; system identification; mathematical modeling of disease dynamics; space-based climate solutions; vibrations and structural health [**Hassanatu Blake**](https://sph.umd.edu/people/hassanatu-blake): Health equity; digital health innovation; global health education; social determinants of health; health systems and program management [**Daniel Butts**](https://biology.umd.edu/people/daniel-butts): AI modeling in neuroscience; computational neurobiology; sensory processing; neural coding; brain function modeling [**Jie Chen**](https://sph.umd.edu/people/jie-chen): Health policy; aging research; public health management; AI applications in health systems [**Can Firtina**](https://canfirtina.com/): Bioinformatics; computational genomics; real-time genome analysis; AI applications in genome editing and disease modeling [**Rodolphe Gentili**](https://sph.umd.edu/people/rodolphe-gentili): Cognitive-motor neuroscience; brain-computer interfaces; human-machine interaction; rehabilitation robotics; computational modeling [**Reza Ghodssi**](https://ece.umd.edu/clark/faculty/396/Reza-Ghodssi): AI-enabled biodevices; chemical and biological sensing; energy harvesting for healthcare applications [**Mika Hamer**](https://sph.umd.edu/people/mika-hamer): Healthcare research; healthcare policy and public health ethics; effects of healthcare coverage, benefit design and reimbursement on healthcare access and utilization [**Xiaoming (Shawn) He**](https://bioe.umd.edu/clark/faculty/799/Xiaoming-Shawn-He): Innovative solutions for early disease detection and treatment; AI-enhanced multiscale biomaterials and devices; engineering and delivery of small molecules, genes, proteins, cells, tissues and organs; 3D biomimetic systems with bioinspired complexity [**Heng Huang**](https://www.cs.umd.edu/~heng/): Machine learning; data mining; big data computing; natural language processing; bioinformatics; neuroinformatics; precision medicine; health informatics; computer vision; medical image analysis [**Joseph JaJa**](https://ece.umd.edu/clark/faculty/415/Joseph-JaJa): High-performance computing; machine learning with neuroscience applications; data science; bias and inaccuracy in AI models for breast cancer detection [**Jay Lee**](https://me.umd.edu/clark/faculty/1772/Jay-Lee): Industrial AI; smart manufacturing; prognostics and health management; digital twins; AI-augmented ICU and medical analytics [**Menglu Liang**](https://sph.umd.edu/people/menglu-liang): Biostatistics; cardiovascular disease detection; Bayesian network meta-analysis; dynamic prediction in survival analysis [**Huang Lin**](https://sph.umd.edu/people/huang-lin): Biostatistics; machine learning; deep learning; microbiome studies; metabolomics; causal inference [**Tianzhou (Charles) Ma**](https://sph.umd.edu/people/tianzhou-ma): Biostatistics; machine learning; Bayesian analysis; high-dimensional data integration; genetics and bioinformatics [**Erin Molloy**](https://ekmolloy.github.io/): Evolutionary genomics; AI-driven data analysis for medicine, public health, and agriculture; evolutionary biology; genomic data interpretation [**John Moult**](https://www.ibbr.umd.edu/profiles/john-moult): Computational biology; protein folding; artificial intelligence in biology [**Mohammad Nayeem Teli**](https://ece.umd.edu/clark/faculty/986/Mohammad-Nayeem-Teli): Vaccine design with protein language models; assistive technology for the visually impaired; navigating robot interactions; large language models; computer vision and perception [**Rob Patro**](https://combine-lab.github.io/): AI-driven algorithms for genomic data analysis; bioinformatics for drug discovery and disease diagnosis [**Mihai Pop**](https://pop-lab.org/): Machine learning for genome assembly and analysis; metagenomics; understanding human pathogens; computational biology; microbial genomics [**Adam Porter**](https://www.cs.umd.edu/people/aporter): Software engineering, AI systems, human–AI collaboration, health computing, software reliability [**Jeremy Rubin**](https://sph.umd.edu/people/jeremy-rubin): Machine learning techniques to predict kidney function outcomes from renal biopsy scans [**S. Cenk Sahinalp**](https://www.cs.umd.edu/people/sahinalp): Biomolecular sequence analysis; AI approaches to cancer genomics; computational biology [**Tobias Stillman**](https://spp.umd.edu/our-community/faculty-staff/tobias-stillman): Health, nutrition, food security and food systems in fragile and humanitarian settings [**Paula Strassle**](https://sph.umd.edu/people/paula-strassle): Epidemiology; health disparities; data linkage; high-dimensional health data; racial and ethnic minority populations; rural populations [**Yang Tao**](https://bioe.umd.edu/clark/faculty/222/Yang-Tao): Bioimaging; machine vision; AI-driven robotics; bioengineering; food engineering; biomedical and biopharmaceutical fields; smart manufacturing [**Mei-Ling Ting Lee**](https://sph.umd.edu/people/mei-ling-ting-lee): Biostatistics; time-to-event modeling; machine learning neural networks; machine learning applications in biostatistics [**Praytush Tiwary**](https://chem.umd.edu/people/pratyush-tiwary): Computational modeling of complex systems; AI for pharmaceutical and chemical sciences; machine learning for materials science [**Yiqun Xie**](https://geog.umd.edu/facultyprofile/xie/yiqun): Geospatial information science; artificial intelligence, machine learning and deep learning for spatial data; GeoAI; data mining and data science; applications in smart cities, transportation, agriculture and public health [**Yiwei Xu**](https://ischool.umd.edu/directory/yiwei-xu/): Human-centered AI; health informatics; social data science; information behaviors; health equity AI and Infrastructure, Architecture & Urban Design [**Ashok Agrawala**](https://www.cs.umd.edu/~agrawala/): Smart environments and sensors; real-time systems design; wearables; health informatics; location determination [**Bilal Ayyub**](https://cee.umd.edu/clark/faculty/243/Bilal-M-Ayyub): Autonomous systems and control; big data analytics; infrastructure risk and reliability; climate adaptation; global catastrophic risk; civilian and defense infrastructure resilience; offset technology prediction [**Vanessa Frías Martínez**](https://ischool.umd.edu/directory/vanessa-frias-martinez/): Urban computing; data mining; modeling human mobility and the built environment; how machine learning models can aid decision makers in poverty, natural disasters or smart cities [**Chester Harvey**](https://arch.umd.edu/people/chester-harvey): Urban data science; use of innovative technologies to provide early warnings of risks to community development and resilience; transportation systems; pedestrian behavior; urban design; land use analysis [**Andressa Martinez**](https://arch.umd.edu/people/andressa-martinez): Architecture; urbanism; digital fabrication; design computation; architectural education [**Austin Raimond**](https://arch.umd.edu/people/austin-raimond): AI in the architectural design profession [**Pratap Tokekar**](https://tokekar.com/): AI and robotics for environmental sensing; autonomous drones and robot teams; agricultural and civil engineering applications; robotics for accessibility [**Yiqun Xie**](https://geog.umd.edu/facultyprofile/xie/yiqun): Geospatial information science; artificial intelligence, machine learning and deep learning for spatial data; GeoAI; data mining and data science; applications in smart cities, transportation, agriculture and public health AI and Business, Entrepreneurship & Supply Chain Management [**Ashok Agrawala**](https://www.cs.umd.edu/~agrawala/): Smart environments and sensors; real-time systems design; wearables; health informatics; location determination [**Bilal Ayyub**](https://cee.umd.edu/clark/faculty/243/Bilal-M-Ayyub): Autonomous systems and control; big data analytics; infrastructure risk and reliability; climate adaptation; global catastrophic risk; civilian and defense infrastructure resilience; offset technology prediction [**Vanessa Frías Martínez**](https://ischool.umd.edu/directory/vanessa-frias-martinez/): Urban computing; data mining; modeling human mobility and the built environment; how machine learning models can aid decision makers in poverty, natural disasters or smart cities [**Chester Harvey**](https://arch.umd.edu/people/chester-harvey): Urban data science; use of innovative technologies to provide early warnings of risks to community development and resilience; transportation systems; pedestrian behavior; urban design; land use analysis [**Andressa Martinez**](https://arch.umd.edu/people/andressa-martinez): Architecture; urbanism; digital fabrication; design computation; architectural education [**Austin Raimond**](https://arch.umd.edu/people/austin-raimond): AI in the architectural design profession [**Pratap Tokekar**](https://tokekar.com/): AI and robotics for environmental sensing; autonomous drones and robot teams; agricultural and civil engineering applications; robotics for accessibility [**Yiqun Xie**](https://geog.umd.edu/facultyprofile/xie/yiqun): Geospatial information science; artificial intelligence, machine learning and deep learning for spatial data; GeoAI; data mining and data science; applications in smart cities, transportation, agriculture and public health AI and Education [**Wei Ai**](https://aiwei.me/): AI to identify and promote effective K-12 teaching practices; role of social identity and non-verbal cues in virtual collaboration [**Naomi Feldman**](https://users.umiacs.umd.edu/~nhf/): Machine learning to understand language acquisition and structure; strategies for language learning and speech-language pathology; computational linguistics; education technology [**Hong Jiao**](https://education.umd.edu/directory/hong-jiao): Psychometrics; machine learning in educational assessment; item response theory; computer-based testing; large-scale testing analysis [**Fengfeng Ke**](https://education.umd.edu/directory/fengfeng-ke): Human-AI collaboration; personalized learning systems; mixed-reality learning environments; neurodiversity education; adaptive learning technologies [**Jing Liu**](https://education.umd.edu/directory/jing-liu): Computational social science; AI applications for improving K-12 teaching and learning; education research; learning analytics [**Doug Lombardi**](https://education.umd.edu/directory/doug-lombardi): AI-driven tools to deepen scientific and civic reasoning in K-12 education; scientific thinking, reasoning and testing in classroom contexts; critical thinking and collective agency in response to science denial and disinformation [**Irina Muresanu**](https://music.umd.edu/directory/irina-muresanu): AI and virtual reality in music education; digital humanities; violin and strings performance; arts and technology integration [**Ji Seung Yang**](https://education.umd.edu/directory/ji-seung-yang): Designing and improving tests and surveys; creating tools to measure knowledge, skills, and attitudes; developing methods to ensure results are accurate, reliable, and work across different groups. [**David Weintrop**](https://ischool.umd.edu/directory/david-weintrop/): Design and evaluation of equitable computational learning experiences; computational education research ## Science & Engineering AI and Climate, Environment & Agriculture [**Malarvizhi Arulaj**](https://cisess.umd.edu/meet-our-scientists/malarvizhi-arulraj/): Applied machine learning and statistical methods to weather and climate-related problems; precipitation remote sensing; satellite meteorology; hydrology [**Bilal Ayyub**](https://cee.umd.edu/clark/faculty/243/Bilal-M-Ayyub): Autonomous systems and control; big data analytics; infrastructure risk and reliability; climate adaptation; global catastrophic risk; civilian and defense infrastructure resilience; offset technology prediction [**Maria Cameron**](https://www.math.umd.edu/~mariakc/): Algorithms for natural-world problems; quantification of rare events; applications in geophysics, chemical physics and materials science; mathematical modeling; complex systems analysis [**Kamal Chowdhury**](https://essic.umd.edu/people/faculty-bio/?faculty=kamal-chowdhury): Water and energy systems modeling; climate change impact assessment; sustainable infrastructure planning; AI in environmental systems [**Evan Economo**](https://entomology.umd.edu/economoevan.html): Biodiversity informatics; evolutionary ecology; computational biology; insect biodiversity [**Andrés Hernández-Serna**](https://geog.umd.edu/facultyprofile/hernandez-serna/andres): Remote sensing; deep learning; ecological monitoring; biodiversity assessment; land cover change analysis [**Cy Keener**](https://art.umd.edu/directory/cy-keener): Environmental art; data visualization; climate change; interdisciplinary collaboration; sensor-based installations [**Hemendra Kumar**](https://agnr.umd.edu/about/directory/hemendra-kumar/): Precision agriculture; AI in agriculture; climate-smart water management; agro-hydrological modeling; data-driven methodologies [**Maria Molina**](https://aosc.umd.edu/people/maria-molina): Use of machine learning to answer pressing questions about climate and weather extremes; extending Earth system prediction; understanding extremes and multi-scale patterns in the climate system [**Jeffrey Moro**](https://mith.umd.edu/people/jeffrey-moro/): Environmental and atmospheric data; media theory and history; vintage and obsolete computing; software preservation; experimental modes of writing and storytelling [**Catherine Nakalembe**](https://geog.umd.edu/facultyprofile/nakalembe/catherine): Machine learning and satellite remote sensing for agriculture and food security; land use mapping; humanitarian mapping; climate change applications [**Pratap Tokekar**](https://tokekar.com/): AI and robotics for environmental sensing; autonomous drones and robot teams; agricultural and civil engineering applications; robotics for accessibility [**Yiqun Xie**](https://geog.umd.edu/facultyprofile/xie/yiqun): Geospatial information science; artificial intelligence, machine learning and deep learning for spatial data; GeoAI; data mining and data science; applications in smart cities, transportation, agriculture and public health [**Miao Yu**](https://me.umd.edu/clark/faculty/607/Miao-Yu): AI and sensing, robotics and computer vision; developing intelligent systems for challenging environments; autonomous underwater and under-ice vehicles; deep learning for environmental modeling and aquaculture AI and Materials Science, Manufacturing & Engineering [**Vikrant Aut**e](https://me.umd.edu/clark/faculty/505/Vikrant-C-Aute): Simulation, design, and optimization of thermal systems; life cycle impact assessment; HVAC&R applications [**Yifei Mo**](https://mse.umd.edu/clark/faculty/691/Yifei-Mo): Computational engineering; AI-driven materials discovery; design of energy storage and conversion materials; materials science; sustainable technology [**Po-Yen Chen**](https://chbe.umd.edu/clark/faculty/1396/PoYen-Chen): AI in materials science; soft robotics; nanomaterials assembly; interpretable machine learning; autonomous systems [**John Martin**](https://aero.umd.edu/clark/faculty/1856/John-Martin): Astrodynamics; scientific machine learning; reinforcement learning; dynamical systems [**Michael Pecht**](https://me.umd.edu/clark/faculty/574/Michael-Pecht): Electronics reliability; safety and sustainability engineering; prognostics and health management (PHM) using machine learning; AI applications in engineering systems health and maintenance [**Yang Tao**](https://bioe.umd.edu/clark/faculty/222/Yang-Tao): Bioimaging; machine vision; AI-driven robotics; bioengineering; food engineering; biomedical and biopharmaceutical fields; smart manufacturing [**Mumu Xu**](https://user.eng.umd.edu/~mumu/): Multi-agent autonomous systems; machine learning-informed planning tactics for robotics and autonomy; aerospace engineering [**Hong-Zhou Ye**](https://chem.umd.edu/people/hong-zhou-ye): Machine learning-enhanced quantum chemistry tools; computational chemistry; materials discovery AI and Quantum Science [**Victor Albert**](https://www.quics.umd.edu/people/victor-albert): Theoretical quantum mechanics; AI applications in scientific problem-solving; large language models assisting scientific workflows including quantum computing [**Andrew Childs**](https://www.cs.umd.edu/~amchilds/): Quantum information processing; quantum algorithms; quantum simulation; quantum query complexity; quantum walks [**Xiaodi Wu**](https://www.cs.umd.edu/~xwu/): Practical and deployable quantum applications; efficient and reliable systems to operate near- and long-term quantum devices; feedback control on quantum devices; quantum system engineering [**Hong-Zhou Ye**](https://chem.umd.edu/people/hong-zhou-ye): Machine learning-enhanced quantum chemistry tools; computational chemistry; materials discovery [**Justyna Zwolak**](https://www.quics.umd.edu/people/justyna-zwolak): Machine learning algorithms and artificial intelligence in quantum computing; identification of stable electron spin configurations; modeling quantum dot devices; training recognition networks; auto-tuning experimental setups ## About the University of Maryland The University of Maryland (UMD) is the state's flagship university driven by a community of more than 50,000 fearless Terrapins. As a leading research university and top 20 public institution, UMD is proud to be part of the Association of American Universities. Dedicated to excellence and impact for the public good, the university is propelled by a $1.4 billion joint research enterprise. UMD is the nation’s first Do Good campus, and is consistently ranked for its innovation, research and top-tier academic programs. Located in the National Capital Region, the university offers an unparalleled student experience with federal internship opportunities, hundreds of academic programs and study abroad options, and top-ranked living-learning programs. Spurred by a culture of innovation and creativity, UMD’s faculty are global leaders in their fields and include Nobel laureates, Pulitzer Prize winners and members of the national academies. For more information about the University of Maryland, College Park, visit [umd.edu](http://umd.edu/)