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Student Affinity Groups

Are you a Stanford student interested in creating meaningful interdisciplinary connections within the Stanford community? Do you have ideas on advancing AI to improve the human condition?

If so, you’re invited to apply to have HAI sponsor your affinity group. HAI Student Affinity Groups are small teams of interdisciplinary students (Stanford undergraduates, graduates, and postdocs) who have a shared interest in a topic related to the development or study of human-centered AI. Affinity Groups provide a space for students to share ideas, develop intellectually and strengthen the community of future leaders dedicated to building AI that benefits all of humanity.

The affinity groups have been selected for the 2023-2024 school year. 

View the recipients

Steps to Get Started

  1. Identify a topic of focus and gather an interdisciplinary group of students who share interest in that topic. If you have a topic idea but are looking for others to join your group, fill out this Interest Form. Responses can be found here.
  2. Identify two students from different disciplines to serve as the leads.
  3. Identify a faculty mentor; no formal time commitment is required of faculty. If you need support in reaching out to faculty, please contact HAI Research Associate Christine Raval.
  4. Submit the application form detailing the goals for your group and steps you’ll take to achieve those objectives.

Benefits of Joining HAI Student Affinity Groups

  • Funding of up to $1,000 for the academic year to spend on small quarterly or biweekly gatherings, such as: workshopping lunches, student-hosted speakers, book discussions or discussion series.
  • Space (physical and intellectual) to share knowledge across disciplines and create collaborations.
  • Access to a community of researchers, faculty, and staff committed to promoting human-centered uses of AI, and ensuring that humanity benefits from the technology and that the benefits are broadly shared.
  • Inside scoop on HAI events, research, publications, and volunteer opportunities.


  • Applications due August 25, 2023.
  • Student Affinity Groups will be announced in late September and will run during the fall, winter, and spring quarters.
  • Each group must have students from two or more disciplines.
  • Each group must have one faculty mentor; no formal time commitment is required of faculty.
  • At the end of the Spring quarter, groups must submit a report summarizing the outcomes. If members elect to continue, they must reapply.


  • How can funds be used? Possible expenses include food expenses, creating marketing materials, or to purchase other materials needed for the program (books, software, printing, etc).
  • Can people join mid-program? Yes, new members can join at any point during the academic year.
  • Is more funding available if larger project ideas are developed through the affinity groups? Promising research ideas that develop through the affinity group could make a great proposal for the HAI seed grant program.

2023-24 Academic Year

  • Oftentimes making technology accessible to people with disabilities is a game of catch-up because tools and techniques are not born accessible. This affinity group will be a space where people with disabilities at Stanford can have intentional conversations and develop strategic plans to ensure that emerging technologies, policies, and procedures around generative AI include the interests of people with disabilities. Specific sub-topics will include advocating for fair disability representation in data, articulating research directions for advancements in AI that are grounded in the experiences of people with disabilities, and how people with disabilities can make an impact on the Stanford community and AI community at large through navigating careers/advocacy efforts in AI.

    Sean Follmer Faculty SponsorSchool of EngineeringMechanical Engineering
    Gene KimStudent Co-LeadSchool of Humanities & SciencesSymbolic systems
    Aya MouallemStudent Co-Lead School of EngineeringElectrical Engineering
    Trisha KulkarniGraduate StudentSchool of EngineeringComputer Science
  • The "AI for Climate Resilience: Bridging Technology and Humanity" group seeks to harness AI to advance climate resilience. By synergizing expertise from computer science, economics, ethics, design, and policy, our initiative aims to contextualize current challenges to drive climate solutions that resonate across cultural contexts, bolster community resilience, and uphold human-centered governance. We firmly believe that collaborative cross-sector engagement and knowledge exchange are critical to innovating AI-enabled climate solutions grounded in equity, collaboration, and sustainable impact.

    Name RoleSchoolDepartment
    Mykel J. Kochenderfer  Faculty SponsorSchool of EngineeringAeronautics and Astronautics (Computer Science, by courtesy)
    Serena Lee    Student Co-Lead School of Humanities & SciencesData Science and Social Systems
    Bhu Kongtaveelert    Student Co-Lead School of EngineeringComputer Science & Art Practice
    Gabrielle TanGraduate StudentSchool of SustainabilitySustainability Science and Practice MS 
    Derick GuanUndegraduate StudentSchool of Humanities & SciencesMathematical and Computational Science
    Griffin ClarkUndergraduate StudentSchool of EngineeringChemical Engineering
  • At all levels, the US healthcare system is both complex and opaque; it is a web of intertwined and conflicting incentives, outdated technology, and lack of transparency. AI has the potential to improve healthcare accessibility and equity while reducing cost and improving outcomes, however has been demonstrably difficult to implement in the healthcare system. This affinity group unravels the landscape of healthcare challenges and ideates novel ways to use AI to address key healthcare challenges. How can AI augment the capabilities of clinicians to deliver care efficiently? Can models and algorithms help patients navigate the convoluted landscape of health insurance, providers, and employers? We frame our sessions around expert-led discussion sessions, design sprints, and case studies, each of which will focus on a specific area of interest within healthcare.

    Sophia WangFaculty SponsorSchool of MedicineOphthalmology
    Akash ChaurasiaStudent Co-Lead School of EngineeringComputer Science
    Priyanka ShresthaStudent Co-Lead School of EngineeringComputer Science
    Isaac BernsteinGraduate StudentSchool of MedicineMedicine
    Ank AgarwalGraduate StudentSchool of MedicineMedicine
    Mahdi Honarmand Graduate StudentSchool of EngineeringMechanical Engineering
    Aditya NarayanGraduate StudentSchool of MedicineMedicine
    Shobha DasariGraduate StudentSchool of EngineeringComputer Science
  • We’re interested in interfaces, agents, and tools for audiovisual performance. Recent advances in foundation models have garnered popular interest in applications of AI in artistic domains, however, with this progress comes crucial ambiguities in how humans can and should relate to AI-augmented creative practices. Our group invites students working in and adjacent to music, theater, audio signal processing, computer graphics, virtual reality, generative models, and HCI to study how new forms of computation can shape their work. The organizing goals of our group are (1) to further an understanding of how we as humans ought to relate to machine collaborators, and, in turn, how models ought to be designed to learn from behavior and adapt to users’ needs, (2) to promote artists pushing the boundaries of generative tools and shaping the frontiers of human-computer interaction, (3) to explore how new computational tools give new perspective to the nature of intention, identity, and authenticity in artistic practice, and (4) to connect scholars and artists across disciplines to work together on new creative projects.

    Julius Orion SmithFaculty SponsorSchool of Humanities and SciencesMusic
    Nic BeckerStudent Co-Lead School of EngineeringComputer Science
    Alex HanStudent Co-LeadSchool of Humanities and SciencesMusic
    Miranda Li Student Co-LeadSchool of EngineeringComputer Science
    Eito MurakamiStudent Co-Lead School of Humanities and SciencesMusic
  • We are interested in understanding how AI can be a gamechanger for employee productivity. Employees globally are plagued by “information overload” at the workplace, in part caused by the SaaS revolution of the 2000s and proliferation of tools. Evidence shows: (i) An average employee spends 2.5 – 5 hours daily on just different communication platforms; (ii) 1 out of 2 workers feel navigating across platforms is more annoying than losing weight! and (iii) 68% feel they don’t have uninterrupted focus time. Research conducted by one of the Co-leads points to extreme employee quotes such as “digital communication fatigue is the biggest bane of my life and is making me an ineffective leader.” As a result, employees suffer longer working hours, mental exhaustion, and a loss of personal time. This is also a silent killer of business output with 2 out of 3 business leaders already seeing a slowdown in strategic thinking and innovation among teams. This issue has become more pressing after the shift to hybrid / remote working. AI promises a new frontier for human productivity. We want to explore how AI can be leveraged to empower employees, cut through the noise and busy work, and “maximize output per unit of human effort”.

    Dan Iancu   Faculty SponsorGraduate School of BusinessOperations, Information and Technology
    Saloni GoelStudent Co-Lead Graduate School of BusinessGraduate School of Business
    Siya Goel Student Co-Lead School of EngineeringComputer Science
    Sanjit NeelamGraduate StudentSchool of EngineeringComputational and Mathematical Engineering
    Teddy GaneaGraduate StudentSchool of EngineeringMath and Symbolic Systems
    Roger LiangGraduate StudentGraduate School of BusinessGraduate School of Business
    Thai TongGraduate StudentGraduate School of BusinessGraduate School of Business
  • In this affinity group, we will investigate the human-centered governance of AI. Governance is crucial in shaping the direction of AI research, the manifestation of its beneficial impacts, and mitigation of its harms. While discussions on what ethical and responsible AI entails have become increasingly popular, there is also a pressing need for deliberation on how governance itself should be structured and implemented in order to be effective, proactive, and inclusive.

    Specifically, we will study and engage with the different stakeholders involved in AI governance (e.g. international governing leaders, tech entrepreneurs, engineers, ethics nonprofits, users, domain specialists, and educators). We will also seek to understand the parts of a governance toolkit (e.g. private and public regulations, funding, policies, laws, human rights doctrines, economic incentives, technical risk assessment measures, and enterprise software for governance).

    Through discussions, speaker events, and outreach, we will merge disciplines such as computer science, management, international relations, and social science. We will understand the technical challenges AI poses for governance, as well as compare and evaluate existing governance frameworks. Valuing diverse perspectives, we aim to conduct panels with speakers across institutions, geographical regions worldwide, and applications of AI. Lastly, we hope to create opportunities for Stanford students and Bay Area residents to explore the intersection of novel innovations in AI governance with their career aspirations and the public sector.

    Taylor Madigan Faculty SponsorSchool of Humanities and SciencesPhilosophy
    Priti RangnekarStudent Co-Lead School of EngineeringComputer Science
    John LeeStudent Co-Lead Graduate School of BusinessGraduate School of Business
    Javokhir ArifovUndergraduate StudentSchool of EngineeringComputer Science
    Larissa Lauer Undergraduate StudentSchool of Humanities and SciencesData Science & Social Systems
    Ayush AgarwalUndergraduate StudentSchool of Humanities and SciencesSymbolic Systems
    Emily TianshiUndergraduate StudentSchool of Humanities and Social ScienceInternational Relations and Data Science
    Kenneth BuiUndergraduate StudentSchool of EngineeringComputer Science
  • The computational journalism HAI affinity group focuses on cultivating diverse perspectives in understanding how machine learning and artificial intelligence can be used responsibly to produce stories that serve the public. From algorithmic accountability journalism that aims to inspect and hold code itself accountable, to emerging research on computational tools and software produced by journalists to tell better stories with data, we want to use this space to convene conversations on how AI is and can be used in newsrooms across journalists, technologists in media, and other practitioners. Computational journalism spans many fields at Stanford and we hope that this affinity group can cultivate a more diverse space to discuss these issues, spanning technical and non-technical researchers, as all are impacted by the news and should have a say in its production.

    Maneesh Agrawala Faculty SponsorSchool of EngineeringComputer Science
    Dana ChiuehStudent Co-Lead School of EngineeringComputer Science
    Tianyu FangStudent Co-Lead School of Humanities & SciencesPolitical Science
    Elias AcevesGraduate StudentSchool of Humanities & SciencesLatin American Studies
    Michelle Cai Undergraduate StudentSchool of Humanities & SciencesHistory
    Chih-Yi ChenGraduate StudentSchool of EngineeringMaterials Science & Engineering
  • The Ethical and Effective Applications of AI in Education affinity group explores the central question: “Who are we prioritizing in human-centered AI, and which ‘humans’ are included in the loop?” with a deep focus on education. Our group includes student perspectives from computer science, education, law, psychology, and more. Together, we’ll facilitate discussions with guest speakers, Stanford affiliates (current students, faculty, alumni) who are grappling with current challenges related to education and AI and can share case studies from their experience. These discussions will be recorded to share with the broader Stanford-HAI community. Group members will engage with readings and materials recommended by each guest speaker, before entering these cross-disciplinary conversations. Through our sessions, we will be:

    • Deepening Awareness: Investigate the systemic inequalities present in education systems, questioning who benefits and who might be left behind as AI systems are rapidly integrated.
    • Learning through Collaboration: Make space for robust peer-to-peer exchanges to elevate diverse perspectives and synthesize interdisciplinary insights.
    • Engaging in Critical Dialogue: Engage both intellectually and personally, as we bring each of our unique holistic human experiences into the dialogue.
    • Taking Action: Develop clear objectives and possible next steps that each of us can take in our respective disciplines to address issues of inequality in education.
    • Meeting Current Challenges: Engage with and evaluate contemporary case studies, readings and research.
    • Bridging Academic-Industry Gaps: Build bridges between academic research on AI and education, and industry-wide implications for EdTech product development.
    Dora DemszkyFaculty SponsorGraduate School of EducationLearning Sciences and Technology Design
    Samin KhanStudent Co-Lead Graduate School of EducationEducation Data Science

    Regina Ta


    Student Co-Lead School of EngineeringComputer Science

    Radhika Kapoor 


    Graduate StudentGraduate School of EducationDevelopmental and Psychological Sciences
    Khaulat AbdulhakeemGraduate StudentGraduate School of EducationEducation Data Science
    Carl Shen Graduate StudentSchool of EngineeringComputer Science
  • Our methodology centers around “Bias Limitation and Cultural Knowledge”. We aim to forge pathways for transformative solutions to facilitate the creation of inclusive and equitable AI — systems vigilant against bias, informed by best practices, sensitive to cultural nuances, and dedicated to fair representation and treatment. We explore technical, social, and political considerations to illuminate the necessity of incorporating diverse cultural perspectives, experiences, norms, and knowledge into the algorithm design, development, deployment, and analysis processes. We are committed to amplifying marginalized voices, especially within our Black & African Diaspora communities. We welcome anyone seeking to foster a more inclusive and equitable technological landscape to join us in critical dialogue, discourse, and discovery.

    Mehran SahamiFaculty SponsorSchool of EngineeringComputer Science
    Asha JohnsonStudent Co-Lead School of EngineeringManagement Science & Engineering (Master's), Computer Science (Undergraduate)
    Saron Samuel Student Co-Lead School of EngineeringComputer Science
    Justin HallUndergraduate StudentSchool of EngineeringComputer Science
    Gabrielle PoliteUndergraduate StudentSchool of Humanities and SciencesSymbolic Systems
    Andrew BempongUndergraduate StudentSchool of EngineeringComputer Science
    Saba WeatherspoonUndergraduate StudentGlobal StudiesInternational Relations
    Abel Dagne Undergraduate StudentSchool of EngineeringComputer Science
    Eban EbssaUndergraduate StudentSchool of Humanities and SciencesSymbolic Systems
  • The Social NLP affinity group will focus on topics at the intersection of social sciences and AI, with an emphasis on foundation models and NLP. We will tackle (1) Significant innovations in AI methods, models, or design paradigms applied to social problems, and (2) New theories and concepts from social sciences and new ways to study them using AI. Concrete examples of such topics include: simulating human behaviors with foundation models, AI-driven persuasion, or human information seeking in the foundation models era. Our group is inherently interdisciplinary, including students from Computer Science, Psychology, and Linguistics departments who are well-positioned to address these complex issues.

    Diyi YangFaculty SponsorSchool of EngineeringComputer Science
    Kristina Gligoric Student Co-Lead School of EngineeringComputer Science
    Maggie PerryStudent Co-Lead School of Humanities and SciencesPsychology
    Weiyan ShiPostdoctoral ScholarSchool of EngineeringComputer Science
    Omar ShaikhGraduate StudentSchool of EngineeringComputer Science
    Cinoo LeePostdoctoral ScholarSchool of Humanities and SciencesPsychology
    Myra ChengGraduate StudentSchool of EngineeringComputer Science
    Yiwei LuoGraduate StudentSchool of Humanities and SciencesLinguistics
    Tiziano PiccardiPostdoctoral ScholarSchool of EngineeringComputer Science
  • In a world where automation is becoming increasingly dominant, it is vital to discuss the future of human-machine interaction once AI becomes software and data beyond the screen. We will focus on bringing together a community of students from all schools with a common interest in responsibly furthering the human condition with AI-enabled hardware systems. We will host guest speakers from a variety of backgrounds, from robotics researchers to lawyers, government and industry. After each event, we will explore key questions including the technical, ethical, legal, and moral questions of AI-enabled machines, especially as they enter our workplaces and homes. At the end, we hope to publish an artifact of our research into the future of AI-enabled machines, and recommend avenues for researchers and practitioners.

    Mark CutkoskyFaculty SponsorSchool of EngineeringMechanical Engineering
    Julia Di Student Co-Lead School of EngineeringMechanical Engineering
    Jeremy ToppStudent Co-Lead Graduate School of BusinessGraduate School of Business
    Jorge Andres QuirogaGraduate StudentGraduate School of BusinessGraduate School of Business
    Ali Kight Graduate StudentSchool of EngineeringBioengineering
    Hojung ChoiGraduate StudentSchool of EngineeringMechanical Engineering
    Rachel ThomassonGraduate StudentSchool of EngineeringMechanical Engineering
    Nikil RaviGraduate StudentSchool of EngineeringComputer Science
    Cem GokmenGraduate StudentSchool of EngineeringComputer Science
  • WellLabeled is an affinity group aimed at addressing the ethical challenges related to data annotation in AI development, particularly focusing on toxic and harmful content. The group's goal is to investigate welfare-maximizing annotation approaches by synthesizing ideas from human-centered design, economics, and machine learning. To achieve this, WellLabeled aims to host discussions and speaker seminars. In particular we aim to focus our attention on how to regulate annotators' exposure to distressing content, establish fair compensation mechanisms based on measured harm, and investigate validation methods through human-subject studies.

    Sanmi Koyejo  Faculty SponsorSchool of EngineeringComputer Science
    Mohammadmahdi Honarmand Student Co-Lead School of EngineeringMechanical Engineering
    Zachary RobertsonStudent Co-Lead School of EngineeringComputer Science
    Jon QianGraduate StudentGraduate School of BusinessGraduate School of Business
    Nava HaghighiGraduate StudentSchool of EngineeringComputer Science

2022-23 Academic Year

  • Our affinity group is focused on employing AI for solving problems linked to climate and environmental issues. Climate change is one of the biggest challenges faced in the 21st century and is a complex issue that requires diverse perspectives. Discussions will cover the science behind greenhouse gas, disastrous effects of climate change (wildfire, flooding, etc.), humanity’s role in mitigating this issue, and human-centered AI developments that can solve climate-related issues. Discussion topics will be moderated by affinity group leaders Wai Tong Chung, a PhD student and HAI Grad Fellow, and Greyson Assa, a Master's student at the Doerr School of Sustainability.

    Wai Tong ChungGraduate Student Co-LeadSchool of EngineeringMechanical Engineering
    Greyson AssaGraduate Student Co-LeadSchool of SustainabilitySustainability Science and Practice
    David WuGraduate StudentSchool of EngineeringAeronautics and Astronautics
    James HansenGraduate StudentSchool of EngineeringAeronautics and Astronautics
    Khaled YounesGraduate StudentSchool of EngineeringMechanical Engineering
    Seth LiyanageGraduate StudentSchool of EngineeringMechanical Engineering
    Allison CongGraduate StudentSchool of EngineeringMechanical Engineering
    Matthias IhmeFaculty SponsorSchool of EngineeringMechanical Engineering/SLAC
  • We are interested in the conditions under which human AI collaboration leads to better decision-making. Algorithms are increasingly being used in high-stake settings, such as in medical diagnosis and refugee settlement. However, algorithmic recommendation in the setting of human AI collaboration can lead to perverse effects. For example, doctors may not put in as much effort when recommendations from algorithms are readily available. Similarly, the introduction of algorithmic recommendation can cause moral hazard, leading to worse decision-making. Our affinity group would like to explore the conditions and incentives affecting human AI collaboration, integrating theories from political science, communication, and HCI.

    Eddie YangGraduate Student Co-LeadSchool of Humanities & SciencesCenter on Democracy, Development and the Rule of Law
    Yingdan LuGraduate Student Co-LeadSchool of Humanities & SciencesCommunication
    Matt DeButtsGraduate StudentSchool of Humanities & SciencesCommunication
    Yiqin FuGraduate StudentSchool of Humanities & SciencesPolitical Science
    Yiqing XuFaculty SponsorSchool of Humanities & SciencesPolitical Science
  • Recent developments in foundation models like Stable Diffusion and GPT-3 have enabled AI to create in ways that were previously only possible by humans—marking an evolution of AI from a problem-solving machine to a generative machine. Simultaneously, we are seeing these models moving from research to  industry. The productization of AI for creative purposes (writing, image generation, etc.) is just beginning to emerge, but will accelerate in the coming years, impacting the media industry and creatives of all kinds (filmmakers, photographers, writers, professional artists, etc.).

    While hype around these new tools for creativity is exploding in the media, we have yet to find a student community at Stanford dedicated to exploring the future of creative generative AI. We are interested in understanding the technical capabilities of generative AI models, current product innovations in industry, the impact of generative AI on the future of art creation, and the social and cultural implications of new creative tools. As our team comes from a range of backgrounds (Computer Science, Symbolic Systems, Political Science, and English), our breadth of expertise will enable us to engage in cross-disciplinary conversations.

    Isabelle LeventUndergraduate Student Co-LeadSchool of Humanities & SciencesSymbolic Systems
    Lila ShroffUndergraduate Student Co-LeadSchool of Humanities & SciencesEnglish
    Regina TaGraduate StudentSchool of Humanities & SciencesSymbolic Systems
    Millie LinGraduate StudentSchool of EngineeringComputer Science
    Sandra LuksicResearch AssistantSchool of Humanities & SciencesResearch Assistant, Ethics in Society
    Mina LeeGraduate StudentSchool of EngineeringComputer Science
    Michelle BaoGraduate StudentSchool of EngineeringComputer Science
    Rob ReichFaculty SponsorSchool of Humanities & SciencesPolitical Science
  • HAI graduate fellows are planning to host a panel with three AI experts from Academic, Government, and Industry moderated by a comedian as an effort to lower the barrier of entry into the AI conversation. This event will join HAI’s effort to raise awareness and inform the general public regarding AI limitations and how AI can empower human capabilities. Our goals are to solidify the HAI graduate fellow community, connect HAI graduate fellows with the general public and Stanford community, start a fun and entertaining conversation about AI limitations, and engage with AI experts in academia, government and industry in an informal setting.

    Alberto TonoGraduate Student Co-LeadSchool of EngineeringCivil and Environmental Engineering
    Martino BanchioGraduate Student Co-LeadGraduate School of BusinessGraduate School of Business
    Yingdan LuGraduate StudentSchool of Humanities & SciencesCommunication
    Surin AhnGraduate StudentSchool of EngineeringElectrical Engineering
    Betty XiongGraduate StudentSchool of MedicineBiomedical Informatics
    Martin FischerFaculty SponsorSchool of EngineeringCivil and Environmental Engineering
  • Our group will develop tools that will improve the next-generation of foundation models. We plan on doing research at different stages of foundation model development. Our research specifically focuses on training foundation models and large language models on newer modalities such as structural biology and joint image-text pairings, and evaluating methods with meta-learning downstream task performance-in-the-loop. Most foundation model research has been in text or image data, and we plan on focusing our work in new multimodal and biological data types as well as evaluating downstream fine-tuned task performance using in-the-loop meta-learning techniques.

    Rohan KoodliGraduate Student Co-LeadSchool of MedicineBiomedical Data Science
    Gautam MittalGraduate Student Co-LeadSchool of EngineeringComputer Science
    Rajan VivekGraduate StudentSchool of EngineeringComputer Science
    Douwe KielaFaculty SponsorSchool of Humanities & SciencesSymbolic Systems
  • We propose to develop an affinity group with the topic and purpose of advancing theoretical understandings of human interaction and trust with AI-based systems and technologies. For AI to augment human intelligence while having humans in charge, we must understand how humans interact with AI technologies and build up trust with such systems. Understanding trust in human-AI interaction is critical to develop AI systems that are ethical, safe, authentic, and trustworthy. Despite the importance of this relationship, little attention in the literature has been devoted to advancing theoretical and practical knowledge of human-computer interaction with AI systems. Particularly, there is a gap on this issue that can be addressed by a multidisciplinary approach, such as leveraging knowledge across cognitive psychology, computer science, and user design fields. We also believe that such understanding with an ultimate theoretical framework developed by the end of our affinity group meetings and discussions will be beneficial for researchers and practitioners across disciplines to advance applications of AI systems in real-world problems of human lives.

    Alaa YoussefPostdoc Co-LeadSchool of MedicineRadiology
    Xi Jia ZhouGraduate Student Co-LeadGraduate School of EducationGraduate School of Education
    Michael BernsteinFaculty SponsorSchool of EngineeringComputer Science

Contact Us

For more information, contact HAI Research Associate Christine Raval.