Ramana M Pidaparti Professor, School of Environmental, Civil, Agricultural, and Mechanical Engineering Dr. Pidaparti’s research interests are in the broad areas of multidisciplinary design innovation and informatics, AI and Machine Learning applications, Biomedical Engineering and Bio-inspired Design and Intelligence. He develops and applies AI/ML models to a variety of engineering and computing applications. Currently, he is studying bio-inspired intelligence in self-assembly systems, AI for Middle School Teachers and STEM education. He is a member of professional societies including Fellow of American Association for the Advancement of Science; Fellow of Royal Aeronautical Society; Fellow of American Society of Mechanical Engineers; Associate Fellow of American Institute of Aeronautics & Astronautics; and member of American Society of Engineering Education. His research topics include: Design engineering and innovation Computational Informatics and Engineering Bio-inspired Intelligence and Computing STEM education Education: Ph.D., Aeronautics & Astronautics, Purdue University, 1989 M.S., Aerospace Engineering, University of Maryland, 1985 M.S., Aeronautical Engineering, Indian Institute of Science, 1982 B. S., Civil Engineering, Andhra University, 1980 Read more about Ramana M Pidaparti Personal Website: Design Informatics and Computational Engineering Lab
Fei Dou Assistant Professor, School of Computing Education: Ph.D. in Computer Science and Engineering, University of Connecticut Read more about Fei Dou Personal Website: https://fayedou.github.io/
Mekala Sundaram Assistant Professor, Department of Infectious Diseases Dr. Sundaram is a quantitative ecologist who uses computational tools and big data approaches to study where viruses occur and which hosts carry pathogens. She uses machine learning methods to piece together a complete picture of virus replication and outbreak occurrence. The mechanisms underlying these questions usually involve a plethora of factors, such as competition, predation, biogeography, host cell receptor sequences, nutrition, immune defenses, host ecology, human socioeconomic conditions and anthropogenic pressures arising from changes in land-use. The Sundaram lab quantitatively integrates these myriad factors using data from different disciplines and scales to explore where viruses occur and where to expect pandemics. Research in the lab is currently focused on forecasting future epidemics and pandemics. To further this work, active areas of research include: Developing informed machine learning algorithms to forecast infectious diseases Generating predictive socioeconomic GIS layers Quantifying zoonotic potential of different groups of animals. Interested students are encouraged to reach out to Dr. Mekala Sundaram. To learn more about the lab, our webpage is mekalasundaram.com. Education: PhD in Quantitative Ecology MS in Conservation Biology BSc in Life Sciences and Biochemistry Read more about Mekala Sundaram Personal Website: https://www.mekalasundaram.com/
Youjin Kong Assistant Professor, Department of Philosophy Youjin Kong is an Assistant Professor of Philosophy at the University of Georgia. Located at the nexus of Social-Political Philosophy, Feminist Philosophy, and Ethics of Artificial Intelligence (AI), her research aims to analyze and challenge social injustice through philosophical frameworks. This research manifests in two key areas: Bias and Fairness in AI: Dr. Kong analyzes how AI can reproduce and exacerbate gender and racial injustice, and develops philosophical frameworks for improving justice in socio-technical systems. Her work in this area examines the dominant interpretations of fairness and intersectionality in the AI fairness literature (ACM FAccT 2022), and proposes a paradigm shift towards a more robust notion of AI fairness, which she calls "strong" fairness (APA Blog 2022). Social Identity and Power: Dr. Kong examines the nature and meaning of social identity (such as race, ethnicity, and gender), particularly how it is shaped by and shapes societal power dynamics. Her article in ERGO (2023) addresses the relationship between social identity and power by engaging in a case study of Asian American experiences during COVID-19. Currently, She is working on building a non-idealizing, decolonial ontology of social identity that could serve as a bridge between AI fairness research and women of color feminism. She has won a Best Paper Prize from the Society for the Advancement of American Philosophy. To learn more about Dr. Kong’s work, please visit her website: www.youjinkong.com. Read more about Youjin Kong
Gaurav R. Sinha Assistant Professor, School of Social Work Gaurav Sinha's research agenda lies at the intersections of poverty, social justice, and mental health, aiming to advance financial and mental health equity. He combines data science and advanced statistical skills with qualitative methods and strength-based approaches to analyze and compare patterns in naturalistic interactions. His specific research emphasis lies in understanding the role of mental health, socioeconomic stability, and financial well-being among vulnerable populations. Sinha's scholarly contributions have received extensive coverage in leading national and international media outlets, including NPR, Scientific American, Atlanta Journal Constitution, the Chicago Tribune, CNBC, MarketWatch, YahooFinance, Business Standard, and Xinhua. As a social worker, he is dedicated to enhancing the transparency, ethics, and practicality of algorithms and other emerging technologies. His long-term research vision is to establish and replicate user-friendly, technology-based interventions aimed at improving the economic wellbeing and mental health of vulnerable young populations and their families. Education: Ph.D., Social Work, University of Illinois at Urbana Champaign MSW, University of Delhi Read more about Gaurav R. Sinha Personal Website: https://grsinha.weebly.com/
AI and Data Science Across Disciplines Symposium Thursday, November 30, 2023 - 10:00am Georgia Center for Continuing Education - Masters Hall Please Join us November 30th, 2023 from 10:00AM to 2:00PM for an interdisciplinary symposium on AI and Data Science, sponsored by the Institute for Artificial Intelligence and the Office of the Provost. Keynote Address (10AM) Ian Bogost, Barbara and David Thomas Distinguished Professor, Washington University in St. Louis. Bio: Dr. Ian Bogost is the Barbara and David Thomas Distinguished Professor at Washington University in St. Louis, where he is also Professor and Director of Film & Media Studies and Professor of Computer Science and Engineering. He has written 10 books, including Alien Phenomenology or What It's Like to be a Thing, Play Anything: The Pleasure of Limits, the Uses of Boredom, and the Secret of Games, and (with Nick Montfort) Racing the Beam The Atari Video Computer System. He is co-director of Washington University’s Program in Public Scholarship and a contributing editor at The Atlantic, where he covers technology, design, culture, and education. He is also an award-winning game designer and a founding partner at the independent game studio Persuasive Games LLC. Title "Process Optimization Ate the University" Abstract: Generative artificial intelligence and its associated methods have been around for a long time, but it wasn’t until a year ago that everyone discovered them, thanks to the public launch of ChatGPT last November. Almost immediately, speculation, excitement, and dread about the promise and peril of genAI overflowed. By some accounts, it marked the end of writers, translators, programmers, essays, college tests, and more. But a year hence, the truth of AI’s practical uses proves weirder and more complicated. As an example of wrestling with that complexity, this talk will cover the real and imagined uses of AI in universities, along with what those uses have revealed about a different but related problem: Our collective obsession with process optimization at the cost of imagination. Lightning Talks (11:15AM-12:05, 12:20-1PM) There will be two sessions of lightning talks, with a short break in-between. (Abstracts) Ari Schlesinger (School of Computing): “Working Towards Human-Centered AI” He Li (School of Chemical, Materials and Biomedical Engineering): “Physics-informed machine learning for infectious disease forecasting” Tianming Liu (School of Computing): “When Brain-Inspired AI Meets Artificial General Intelligence (AGI)” Soheyla Amirian (School of Computing): “AI-Powered Healthcare: A Computational Journey with the UGA AMIIE Lab” Hongyue Sun (Environmental, Civil, Agricultural and Mechanical Engineering): “Data Science Enabled Decision-making in Advanced Manufacturing and Personalized Safety” Gerald Kane (Terry College of Business): "Avoiding an Oppressive Future of Machine Learning: A Design Theory for Emancipatory Assistants" Neal Outland (Department of Psychology): "Harmonizing AI Integration in the US Economy: Bridging Diverse Stakeholder Perspectives for Seamless Transition" Guoyu Lu (School of Electrical and Computer Engineering) : "3D Structure Modeling and Assessment for Crops and Beyond" Lakshmish Ramaswamy (School of Computing): "Robust Environmental AI for Urban and Coastal Sustainability" Interdisciplinary Panel Discussion (1-2 PM) The hour-long panel will consist of our guest speaker and faculty from the UGA AI community. Topic: "AI: Keeping Pace with Advances"Moderator: Jeanette Taylor (Vice Provost for Academic Affairs)Panelists: Ian Bogost (Guest Speaker) Tianming Liu (Professor, School of Computing) Youjin Kong (Assistant Professor, Department of Philosophy) Megan Mittelstadt (Director, Center for Teaching and Learning) Venue Location Georgia Center for Continuing Education - Masters Hall 1197 South Lumpkin StreetAthens, GA 30602-3603(map) Read more about AI and Data Science Across Disciplines Symposium
Research Labs and Groups Centers, Institutes, laboratories, and research groups that IAI Faculty Fellows lead or participate in. This list should not be considered exhaustive. AI for STEM Education Center (AI4STEM) AI4STEM Education Center is a hub for faculty members from the Mary Frances Early College of Education, Franklin College of Arts and Sciences, and College of Engineering, aiming to foster interdisciplinary collaboration on using AI to advance STEM education Applied Machine Intelligence Initiative and Education (AMIIE) Laboratory The main goal and purpose of the AMIIE Laboratory led by Soheyla Amirian is to conduct and promote fundamental and applied Artificial Intelligence (AI) research and education in solving real-world problems, ranging from health informatics to surveillance systems and security. Computational Nano/Bio-Mechanics Lab The ultimate goal of our research is to understand the fundamental principles that control mechanical properties and behavior of materials in both engineering and biology by virtue of theoretical analyses, computational modelings, and experimental investigations. Our current research mainly focuses on mechanics of 2D nanomaterials and nanostructures; mechanical principles of cortical folding; mechanics of hierarchical structures in biological materials such as bone, shell and nacre; bio-inorganic interfaces; mechanics of cell and nanoparticle interactions; instability of soft matters etc. Developmental Dynamics Lab The lab focuses on how behaviors and social interactions impact developmental trajectories throughout infancy and into toddlerhood. Evolutionary Computation & Machine Learning (ECML) Lab Research in the Evolutionary Computation & Machine Learning (ECML) Lab is centered around Genetic and Evolutionary Algorithms, Machine Learning and the intersection/ cross-fertilization of the two fields. We conduct research in genetic algorithm methodologies and applications in science and engineering with emphasis on using machine learning approaches to enhance evolutionary optimization. We also develop, apply and analyze machine learning approaches for numerous Bioinformatics and computational biology domains. Forest Management, Planning, GIS, GNSS A research group within the Warnell School of Forestry & Natural Resources, led by Dr. Pete Bettinger. Generative AI Research Cluster Harnessing the power of generative AI to address complex challenges and inspire transformative solutions across domains. Georgia Gambling and Decision Lab The Georgia Gambling and Decision Lab is based in the Psychology Department of the University of Georgia. The lab is collaborative in nature, and has been directed by Dr. Adam Goodie since 1998. Our research is dedicated to the multidisciplinary domains of gambling studies and judgment and decision making under uncertainty. The primary areas of current research interest are: 1) understanding gaming, gambling, and other risk-based behaviors; 2) approaches to the origin, maintenance, prevention and treatment of problem gambling; 3) personality effects and individual differences in decision making and gambling; and 4) cross-cultural differences in gambling and risk attitude. Georgia Informatics Institutes for Research and Education (GII) The explosion of digital information has created new opportunities in so many fields-from the sciences to engineering and the humanities. The goal of the Georgia Informatics Institutes (GII) is to help faculty use informatics as a tool to help answer research questions while making it easier for them to incorporate informatics into their instruction. Heterogeneous Robotics (HeRo) Research Lab HeRo lab conducts experimental and application-oriented research in heterogeneous robotics systems of varying functionalities and mobility capabilities. Specifically, the current research focus are on multi-robot systems, wireless networks, intelligent & intuitive teleoperation, human-robot interfaces, robotics applied in nuclear, radioactive, rescue, disaster, and challenging environments, and machine learning applications to multi-agent systems. Our vision is to capacitate autonomous heterogeneous multi-human multi-robot systems with intelligent, resilient, and robust methods for cooperation, communication, and interactions. Hoarfrost Lab We are an interdisciplinary team developing cutting-edge tools to explore some of the most mysterious and urgent questions about our ocean, our Earth, and beyond. In the Hoarfrost lab, we develop deep learning and other computational approaches to capture the complexity of biological systems. We design high-throughput experimental approaches to test hypotheses and build better understanding of environmental systems. And we demonstrate the real-world impact and performance of our technologies in the field. Hopkinson Lab We study the ecology and physiology of photosynthetic organisms in marine ecosystems. Our lab focuses on phytoplankton, the unicellular algae that are responsible for nearly all photosynthesis in the ocean, salt marsh plants, and corals. Recent research has included studying the effects of climate change on phytoplankton and the metabolites they produce, investigating the taxa responsible for primary production on coral reefs, and developing methods to map marine ecosystems such as salt marshes and coral reefs. Institute for Cybersecurity and Privacy The mission of the Institute for Cybersecurity and Privacy (ICSP) is to contribute to meeting the nation's cybersecurity defense research and education needs. The goal of ICSP is to become a state hub for cybersecurity research and education, including multidisciplinary educational programs and research opportunities, outreach activities, and industry partnerships. The National Security Agency and Department of Homeland Security named the UGA Institute for Cybersecurity and Privacy a National Center of Academic Excellence in Cybersecurity Research (CAE-R), an honor that recognizes the strength of the institute's cybersecurity and privacy research program, faculty and students. Intelligent Vision and Sensing Lab A research lab within the School of Electrical and Computer Engineering, led by Dr. Guoyu Lu. Linguistic Atlas Project The Linguistic Atlas Project (LAP) consists of a set of survey research projects about the words and pronunciation of everyday American English, the largest project of its kind in the country. Ma Lab Our research aims to develop new statistical theory and methods in analyzing vast and complex data to solve scientific and engineering problems with broad societal impacts. Interdisciplinary research led by our lab is making key contributions to resolving grand challenges of big data analytics. Manufacturing Optimization, Design, and Engineering-Education Lab (MODEL) We are a diverse group of scientists working on design, manufacturing, and engineering education research Physical Activity & Community Environment (PACE) Lab Rethinking Existing Spaces for Physical Activity Quinn Research Group We are an interdisciplinary group, primarily focused on how data science can be a tool for knowledge discovery in public health applications. Spatially Explicit Artificial Intelligence (SEAI) Lab The Spatially Explicit Artificial Intelligence Lab focuses on designing better machine learning and artificial intelligence models by leveraging spatial knowledge and biases. Sustainable Bioproducts and Biofuels Research Laboratory Our research group is focused on developing circular and sustainable technologies for maximum utilization of organic materials into high-value chemicals, fuels, and bioproducts, while modeling and assessing the sustainability of agricultural, food and forestry systems. THINC Lab Students and faculty affiliated with the THINC lab conduct cutting edge research in AI and Robotics. We rigorously investigate various problems of interest in contexts such as multiagent systems, reinforcement learning, mobile robots, and the semantic web. We have published more than a hundred papers in top tier research conferences such as AAAI, UCAI, AAMAS, IROS, ICRA, and journals such as JAIR and JAAMAS. Our research is often multi-disciplinary and we collaborate with renowned researchers including psychologists, education specialists and biologists in the US and beyond. Our research has been funded by grants from NSF, multiple DoD agencies, NIH, and the industry. Torrance Center for Creativity and Talent Development The Torrance Center for Creativity and Talent Development is a service, instructional, and research center based in UGA’s Mary Frances Early College of Education. The overarching goals of the Torrance Center are to investigate, evaluate, and implement techniques for enhancing creativity across domains of human enterprise and to increase creative literacy in the local, state, national, and international communities. UGA Center for Brain-inspired Artificial Intelligence The University of Georgia Center for Brain-inspired Artificial Intelligence strives to discover fundamental principles in both brain science and artificial intelligence (AI), and to create new biological, physical and computational models that bridge the significant gaps between brain science and AI. UGA Small Satellite Research Laboratory (SSRL) The Small Satellite Research Laboratory (SSRL) is developing and launching new and innovative technologies into space by utilizing the CubeSat platform, a small-scale satellite that is designed for rapid iteration and development (~3 years). Currently ~50 undergraduates from all over campus (computer science, engineering, physics, mathematics, marine science, graphic design, etc.) lead and operate the lab while being mentored by faculty from five departments around campus. Virtual Experiences Laboratory The VEL serves as the center of virtual reality research and education at UGA. The mission of the VEL is to enable researchers and students to develop and study the next generation of virtual worlds, advanced user interfaces, and virtual reality applications. Visual and Parallel Computing Lab (VPCL) The goals of the VPCL are to undertake projects that advance the state of the art in the theory and applications of Visual Computing and Parallel Computing. Read more about Research Labs and Groups
Ari Schlesinger Assistant Professor, School of Computing Dr. Ari Schlesinger investigates the social and technical aspects of computing so that we can reduce discrimination and advance humanity in the tech community. Across the AI domain, there is an increasing need for socially engaged, anti-discriminatory computation. Dr. Schlesinger's research focuses on socially engaged computation to effect equitable social change and technological advancement in artificial intelligence, human-computer interaction, and computing broadly. Using novel combinations of interdisciplinary methodologies, Dr. Schlesinger's research centers on the goal of making harm-reduction strategies accessible to the general public, the research community, and the tech industry. Education: PhD, Georgia Institute of Technology Read more about Ari Schlesinger Personal Website: https://arischlesinger.com/
He Li Assistant Professor, School of Chemical, Materials and Biomedical Engineering Dr. He Li developed multiscale computational models based on physics laws using various numerical methods, such as molecular dynamics, dissipative particle dynamics and spectral element method, to simulate biological systems that span multiple spatial scales, including molecular level, protein level, cellular level, multi-cell systems, vasculature and organ systems. His work has demonstrated that computational modeling can bridge the gap between the microscopic and macroscopic physiological processes and provide innovative approaches to study key problems in biology, medicine and biomedical engineering, such as building mechanistic models to investigate the pathogenesis of human diseases and developing predictive models to examine the existing hypotheses and derive new hypotheses to steer experimental and computational studies. Dr. He Li's current research interest is to employ AI techniques to develop advanced multiscale models and build predictive AI models that can assimilate data from different sources (e.g., biophysical, biochemical, genomics, proteomics data), to improve digital health technologies. Education: Ph.D., Mechanical Engineering, University of Connecticut, 2015 M.Sc., Mechanical Engineering, University of Saskatchewan, 2008 Read more about He Li
Geng Yuan Assistant Professor, School of Computing Dr. Geng Yuan is currently an assistant professor at the School of Computing. Geng received his PhD degree in Computer Engineering at Northeastern University in 2023, supervised by Dr. Yanzhi Wang. Geng’s research interests lie in general AI systems, including energy-efficient deep learning with embedded systems, efficient training, model compression, hardware-software co-design for DNN architectures, and emerging deep learning systems. His research work has been published broadly in top conference venues, such as NeurIPS, CVPR, ICML, ISCA, DAC, etc, ranging from machine learning algorithm conferences, architecture and computer system conferences, and EDA, solid-state circuit and system conferences. Education: Ph.D. in Electrical and Computer Engineering, Northeastern University, 2023 Read more about Geng Yuan Personal Website: Personal Homepage
