The names below and on the ballot appear in the alphabetical order based on last names.
Anna Rumshisky is an Associate Professor of Computer Science at the University of Massachusetts Lowell, where she leads the Text Machine Lab for NLP. Her research centers on large language models, with a focus on efficient large-model training, model analysis, and interpretability. Over the past five years, she has also held a joint role in industry as an Amazon Academic with the Amazon AGIF (Artificial General Intelligence Foundations) organization, working on foundational model development at scale. She has served as Program Chair for NAACL 2021 and as an elected member of the NAACL Executive Board (2022–2023), and has held senior area chair roles across major conferences including ACL, EMNLP, NeurIPS, ICLR, and ICML. She has organized numerous workshops and tutorials, including the Clinical NLP and Insights from Negative Results in NLP series, and founded the annual New England NLP Meeting series. Drawing on her experience bridging academic and industry research environments, she brings a broad view of how to sustain and evolve the NLP community as it grows in scale and scope.
I am honored to run for Chair of the NAACL Executive Committee. Having served as NAACL 2021 Program Chair and as an elected board member (2022-2023), I have witnessed firsthand both the remarkable growth of our community and the challenges that come with scaling while preserving what makes NAACL special. My dual perspective, gained from spending half my time in academia at UMass Lowell and half in industry, has given me insight into how we can strengthen our community across different research environments.
If elected as Chair, I would focus on the following three areas:
Sustaining Community at Scale. As our field experiences explosive growth with the rise of large language models, NAACL must evolve its structures while maintaining the collaborative spirit that has defined our community. I will work to ensure that increased submission volumes do not come at the cost of review quality or meaningful participation. This includes exploring innovative approaches to peer review within the ARR system, drawing on successful practices from large ML conferences such as NeurIPS and ICLR. Examples include AI-assisted review quality tracking to support area chairs, calibration periods for AC and reviewer discussions to improve consistency, and Best Reviewer awards and registration perks for top reviewers and area chairs to sustain engagement and reward high-quality work.
Strengthening Academia-Industry Partnerships. The boundary between academic and industry NLP research has become increasingly fluid. As model scaling (and the science of model scaling) becomes more and more dominant as the way to improve abilities of AI models, frontier research is shifting to industry labs that can afford the experiments needed to make advances in training models at scale. At the same time, the cost of experiments at scale is influencing how research priorities are set in frontier labs. This setup discourages the exploration of unproven directions since it’s very costly. Consequently, much industry research follows a very narrow trajectory, potentially risking convergence to a local optimum. The role of academic research has always been exactly this kind of exploration. But at the same time, a lot of academic work becomes “proof of concept” until scaled. Understanding this interplay and ensuring that it is understood and encouraged by both sides is crucial. Having worked in both environments for the past five years, I’ve seen how crucial and how challenging it is to maintain productive collaboration between these communities. I will advocate for initiatives that facilitate knowledge transfer and collaboration and work to develop programs that help academic researchers navigate industry partnerships while maintaining open science principles.
Expanding Regional Engagement Across the Americas. NAACL’s evolution to represent the Nations of the Americas is more than a name change - it’s a commitment to genuinely serve researchers across both continents. My experience founding the New England NLP Meeting series has shown me the value of regional community building. I will work to support and expand similar initiatives throughout the Americas, ensuring that researchers in underserved regions have pathways to participate fully in NAACL. This includes advocating for conference locations beyond major US cities and expanding mentorship and financial support programs for researchers from underrepresented institutions.
Muhao Chen is an Assistant Professor at the Department of Computer Science, UC Davis, where he leads the Language Understanding and Knowledge Acquisition (LUKA) Group. He received his Ph.D. from the Department of Computer Science at UCLA, and B.S. in Computer Science from Fudan University. His research focuses on robust and accountable ML, particularly on accountability and security issues of large language models and agentic AI. He is a co-founder and the secretary of ACL Special Interest Group in NLP Security (SIGSEC). His work has been recognized with EMNLP Outstanding Paper Awards (2023, 2024), an ACM SIGBio Best Student Paper Award (2021), faculty research awards from Amazon (2022, 2023) and Cisco, and funding support from multiple NSF, DARPA, IARPA, and industry grants.
As NAACL expands to serve the entire Americas and as our field increasingly shapes society, I will focus on the following priorities.
Supporting early career development. I will support students and early-stage researchers in academia and industry through structured mentoring sessions at conferences and online sessions. I will help build online social groups and channels that provide shared resources and peer support for career growth.
Bridging academic and industry research. I will enable stronger two way exchange through practitioner panels, deployment focused tutorials, and shared tasks grounded in real constraints. I will encourage the contribution of datasets, evaluation artifacts, and safety findings with clear openness and governance. I will support collaboration models that respect publication needs while enabling practical impact.
Broadening participation across the Americas. I will increase participation from Central and South America through NAACL and *ACL conferences hosted in these regions, regional workshops, and satellite and hybrid language inclusive activities that reflect the Nations of the Americas Chapter mission and build lasting local communities.
Responsible research and utility of AI. I will advocate practices that make our research safe, reproducible, and useful. This includes open evaluation protocols, transparent reporting of risks and limitations, and community resources for safety testing. I will promote benchmarks and tutorials that connect fundamental research to real world utility while upholding ethical and societal safeguards.
Sunipa Dev is a Senior Research Scientist in the Future of AI and Society organization at Google Research. Her work focuses on the intersection of language, AI, and society, with the goal of developing safe, inclusive, and globally useful AI systems. Currently, she is concentrating on grounding generative models in socio-cultural contexts to ensure their responses cater to diverse user experiences and needs worldwide.
Sunipa is deeply involved in the *CL and affiliated conference communities. She has organized several workshops, notably co-founding the Cross Cultural Considerations in NLP workshop, which has been instrumental in uniting researchers focused on multi-cultural NLP. She also previously served as an organizer for the Widening NLP (WiNLP) workshop, dedicated to supporting early-career and underrepresented researchers. Her extensive service includes roles such as Ethics Chair for EMNLP 2024, Workshop Chair for EMNLP 2025, and upcoming Program Chair for EMNLP 2026, in addition to her ongoing involvement as a Senior Area Chair (SAC) for ARR and CL conferences.
Broadening NAACL’s Reach and Inclusivity The renaming of our chapter to “Nations of the Americas” signifies a profound commitment that demands tangible action. It is imperative that we actively champion research and researchers from across the Global Majority. I will advocate for an increase in dedicated research tracks, workshops, and resources for underrepresented languages and cultures, ensuring that our conferences extend beyond a sole focus on high-resource language technologies. Furthermore, a crucial aspect of this mission involves proactively identifying conference venues and implementing policy structures that reduce barriers to participation. This includes working towards tiered registration fees, increasing need based fee waivers, and formalizing robust support systems to accommodate diverse needs. It also touches upon enhancing the hybrid experience in order to make it more engaging and inclusive of those who are not able to attend in person.
Enhancing the Review Process As the volume of submissions continues to rise, it is critical that we implement structural changes to uphold the rigor and developmental value of our review process. I will explore and advocate for mechanisms that directly link the quality of reviewing service at all levels to current and future submission privileges. This approach will cultivate a greater sense of community accountability and help mitigate the submission of low-effort or late reviews and meta reviews. Furthermore, I will work towards dedicating workshop space at the conference for reviewer training, facilitating the integration of newer researchers into the process and educating them on best practices.
I’m Francisco (Paco) Guzmán, Head of AI Research at Handshake. I lead research on data quality and evaluation; creating data and systems that expand the knowledge and reliability of frontier AI through expert data. Previously at Meta, I led efforts like NLLB-200 (expanding translation to 200 languages and billions of speakers) and SeamlessM4T (recognized by TIME as one of 2023’s best inventions), pushing multilingual and open-science work that expanded language technology access worldwide. I also led post-training teams for Llama 3.1, 3.2, and 3.3; advancing open-weights models that empower researchers globally.
I created the AI Learning Alliance (AILA) with Georgia Tech, partnering with HBCUs, HSIs to democratize AI education; co-created the TICO-19 initiative to make COVID-19 information accessible in 35+ languages; organized the first WMT shared task on African languages; and have served as Co-Research Director at AMTA (2020-2022), Ethics Co-Chair at EMNLP 2023, and Area Chair at conferences including NeurIPS, ACL, and EMNLP. I’m deeply committed to mentorship and to building research ecosystems in industry and academia that scale inclusion and provide opportunities for everyone.
NAACL is where rigor, openness, and inclusivity converge. I’m running for the Board to strengthen those pillars by investing in three areas:
Compute access fund: Access to compute has become the major inequalizer in research. Smaller universities across the Americas, particularly in Latin America, cannot compete when frontier research requires resources only industry can afford. I will establish a NAACL Compute Fund in partnership with cloud providers (e.g., AWS, Google Cloud, Azure), making grants available to researchers at institutions without significant compute budgets. This fund will lower the entrance bar so more researchers in the Americas can conduct cutting-edge research and remain competitive in the AI era. Success will be measured by: number of institutions served, papers published using the grants, and geographic distribution across the hemisphere.
Evaluation standards and reproducibility: With training data contamination threatening benchmark validity, establish lightweight, NAACL-endorsed rubrics for dataset quality, reproducibility, and contamination checks. This raises the floor for fairness and transparency in our review and benchmark culture, protecting research integrity while making good science easier to verify.
Mentorship and regional collaboration: Scale mentorship programs connecting students with experienced researchers from academia and industry. Establish a NAACL Research Apprenticeship Program that provides grants for paid internships at leading labs across the Americas; targeting not only NLP students but also researchers from other disciplines (Biology, Physics, Chemistry, Social Sciences) to foster interdisciplinary AI research addressing real-world problems across language barriers. Support regional workshops and shared tasks that strengthen our continental identity; ensuring researchers have platforms to showcase work on problems unique to the Americas.
NAACL’s strength lies in its people and its standards. My goal is to help the organization set both higher and fairer; so good science is easier to do and verify across the Americas. I commit to substantial time for board work and championing transparency in our publication guidelines.
Lifu Huang is an Assistant Professor of Computer Science at the University of California, Davis, where he leads research at the intersection of natural language processing, multimodal learning, and AI for science. His work has been honored with an Outstanding Paper Award at ACL 2023, a Best Paper Honorable Mention at SIGIR 2023, and the Best Paper Award at the AI4Research workshop of AAAI 2025. His research has been supported by the NSF (including an NSF CAREER Award), DARPA, and industry partners. Huang has a sustained record of service and leadership in the NLP and AI communities, serving as Co-Chair of the ACL 2021 Diversity & Inclusion Committee, Publication Co-Chair for ACL 2023 and NAACL 2024, and Senior/Area Chair across multiple *CL venues, where he received the Outstanding Area Chair Award at EMNLP 2023. He has organized numerous international workshops (ICLR, AAAI, IJCAI, WWW, AKBC, AAAI Spring Symposium) on emerging topics such as agentic AI for scientific discovery, and served as a Program Chair for SouthNLP 2024, the first regional NLP initiative in the southern United States.
Our field continues to grow at an unprecedented pace, fueled by large foundation models and their transformative applications across science and society. This rapid evolution brings both opportunities and challenges. If elected, I will work to ensure that NAACL remains an intellectual leader, supporting rigorous science, inclusive participation, and impactful collaboration.
Reimagining the Review Ecosystem: The rapid growth in submissions has placed unprecedented pressure on our review system, raising concerns about fairness, quality, and reviewer workload. Addressing these challenges requires both structural changes and innovations such as human–AI collaboration. I will advocate for initiatives such as adopting a two-stage review process, as in AAAI, to ensure reviewers focus their efforts on papers with real potential; setting up clearer guidelines for review discussions to avoid unnecessary ad hoc experimental requests; leveraging AI assistants for triaging low-quality papers, monitoring reviewer performance, synthesizing discussions for area chairs, etc., to make the process more efficient, fair, and sustainable for our growing community.
Building Sustainable Academia–Industry Synergy: The divide between academia and industry is widening, with industry advancing massive-scale pretraining while academia pursues fundamental innovations under resource constraints. NAACL can serve as a vital convening ground for sustainable collaboration by introducing industry–academia tracks, establishing resource-sharing partnerships, and expanding communication channels to promote transparency on practical challenges, emerging research directions, and ethical considerations, to ensure both communities can benefit from and contribute to the field’s progress.
Fostering Open, Interdisciplinary, and Reproducible Research: With much of today’s work centered on large langauge models (LLMs) and their applications, NAACL must adapt to this shift while ensuring the field’s long-term vitality. As LLMs increasingly permeate domains such as healthcare, social science, materials science, education, etc., it is essential to create new channels, such as interdisciplinary tracks and workshops, that invite researchers from diverse fields to contribute and foster genuine cross-disciplinary collaboration. Equally important is a stronger commitment to openness—releasing models, datasets, benchmarks, weights, and full experimental details to drive transparency, reproducibility, and long-term progress.
Broadening Access Across the Research Community: Expanding participation and visibility for researchers from under-resourced regions and institutions is essential, particularly for those unable to attend conferences due to high travel costs or visa restrictions. Overcoming these barriers requires deliberate action when selecting conference locations, structuring travel grant programs, and shaping participation policies, ensuring that all researchers can contribute meaningfully and engage fully in our community regardless of location or circumstance.
Ruihong Huang is an associate professor of Computer Science at Texas A&M University. She has actively published in event-centric NLP, discourse analysis and dialogue understanding, and more recently on LLM safety and ethics. Her work has been recognized by a 2020 NSF CAREER award. She has regularly served as an area chair and senior area chair at NLP and AI conferences, she was a demo chair for EMNLP 2019 and ACL 2023, she was a Diversity & Inclusion chair for AACL 2022.
As a board member, I would like to work on the following challenges (and opportunities) within our community:
Open research and reproducibility: I strongly believe in the importance of open research—specifically, the release of code and data, as well as the transparent discussion of limitations, ethical considerations, and potential flaws in our work. In an era where proprietary models often dominate the landscape, our community must prioritize accessibility and collaboration. I’m excited to work together to foster an environment where transparency and collaboration thrive!
Diversity and inclusion: As the NLP community is growing rapidly in recent years, ensuring diversity and inclusion is more crucial than ever. It’s not just about representing different demographics; it’s equally important to engage participants at various stages of their careers, particularly early-career researchers. Building on the great work of past board members, I envision initiatives that actively support underrepresented communities in our conferences. This could include backing workshops like WiNLP, which elevate diverse voices in NLP, and supporting mentoring programs, which promote our field to younger generations, such as junior graduate students and even undergraduate students, to entice researchers from varied backgrounds. By fostering a welcoming and collaborative environment, we can ensure that everyone has a seat at the table.
Interdisciplinary research: The applications of large language models (LLMs) are expanding quickly, as we deploy more NLP systems, it’s crucial to address their social impacts, safety, and ethical implications. Tackling these challenges calls for a strong emphasis on interdisciplinary research. I am committed to promoting collaboration across various fields—be it psychology, law, healthcare, or business—because I believe that insights from these areas can drive innovative advancements in NLP. As a board member, I will advocate for initiatives that create dedicated tracks or workshops which encourage interdisciplinary efforts and ensure NLP technologies contribute positively and responsibly to society.
Xiaolei Huang is the UMRF Ventures Research Endowed Assistant Professor of Computer Science and serves as Co-Director of the Center for Emerging Technologies in AI (CERTAIN) at the University of Memphis. His research focuses on trustworthy NLP and health informatics, with applications to motivational interviewing, pediatric oncology, and public health. He is the recipient of an NSF CAREER Award (2025), a Best Paper Award at IEEE ICHI (2024), and the Ralph E. Powe Junior Faculty Enhancement Award (2022). He has led multiple NSF-supported projects, including an NSF MRI project that promotes AI and HPC workforce development in the US Mid-South. Dr. Huang also has served as program co-chair (ICHI 2025–26). He received his PhD in Information Science from the University of Colorado Boulder in 2020.
It is an honor to be nominated as a NAACL board member. If elected, I will focus on the following 3 principles:
Student and Early-Career Support. I will promote student research workshops and create mechanisms, including awards (e.g., rising star), to recognize rising faculty members and early-career researchers who make meaningful contributions to the community. I will work to strengthen mentorship and networking between 1) researchers and students and 2) senior and junior researchers.
Student Recruitment and Inclusive Workforce Development. I will advocate for programs that broaden participation and reduce barriers for students from all groups and low-resourced institutions. I aim to develop new platforms that augment and ease student and intern recruitments for our faculty and industrial members so that we can attract talents from worldwide to institutions in the Americas.
Interdisciplinary and Cross-region Bridge. I will promote interdisciplinary themes between NLP and other disciplines, such as health informatics and STEM education across regions in the Americas (e.g., United States and Latin America).
Dr. Shubhra Kanti Karmaker (“Santu”) is an Assistant Professor of Computer Science at the University of Central Florida, affiliated with the Institute of Artificial Intelligence (IAI). His research lies at the intersection of Natural Language Processing (NLP) and Information Retrieval (IR), driven by the central question: “How can we create new technologies to bridge the current gaps between humans and AI tools to democratize AI beyond the tech-savvy communities?” He has published extensively in premier NLP/AI venues such as ACL, EMNLP, NAACL, SIGIR, WWW, COLING, and ACM TIST, and has secured over $1.4 million in research funding as Lead PI from NSF, AFOSR, ARO, and USDA. Dr. Karmaker currently serves as Action Editor and Communications Chair for the ACL Rolling Review (ARR) and was Tutorial Chair for CIKM 2022.
Dr. Karmaker completed his Ph.D. in Computer Science at the University of Illinois Urbana-Champaign (UIUC) and later worked as a Postdoctoral Research Associate in the Laboratory for Information and Decision Systems (LIDS) at the Massachusetts Institute of Technology (MIT). During his Ph.D., he gained industry research experience through summer internships at Microsoft Research, Yahoo Research, and WalmartLabs.
If elected to the NAACL Board, I will focus on strengthening the integrity, fairness, and accountability of our NLP research ecosystem through the following priorities:
Integrity of Language Model Evaluation: The NLP community faces an urgent challenge in maintaining evaluation integrity, especially as data leakage becomes increasingly difficult to avoid. Too often, models are evaluated on data overlapping with their training sources—an issue frequently underreported in publications. I will advocate for requiring a dedicated “Evaluation Integrity” section within ethics statements to ensure greater transparency, disclosure, and reproducibility in language model research.
Fair Treatment of Resubmitted Papers: Authors who revise and resubmit to ARR frequently face inconsistent reviewing because prior reviewers are often unavailable, resulting in papers being treated as entirely new submissions. Having personally experienced this frustration, I will work with ARR Editors, in my role as Communications Chair, to introduce distinct review forms for new and resubmitted papers—-promoting continuity, fairness, and proper contextualization of prior feedback.
Reviewer Accountability in ARR: High-quality reviewing is essential to our field’s credibility. I will advocate for improved reviewer accountability through structured feedback mechanisms, recognition for exemplary reviewers, and clear policies to address unresponsive or superficial reviews.
Together, these initiatives aim to make our review and evaluation processes more transparent, fair, and trustworthy - strengthening the foundation of NAACL’s research community.
Parisa Kordjamshidi is an Associate Professor of Computer Science and Engineering at Michigan State University. Her research focuses on reasoning in language and vision models, looking into this problem from various aspects of reasoning including spatial reasoning, compositional generalization, dealing with uncertainty while leveraging integration of symbolic and subsymbolic representations. She received her Ph.D. from KU Leuven and did a postdoctoral research at the University of Illinois Urbana-Champaign. She is a recipient of the NSF CAREER Award, Amazon Faculty Research Award, and Fulbright Scholar Award, and her research team received the NAACL 2025 Outstanding Research Paper Award. She serves as an Associate Editor of JAIR, Co-Editor-in-Chief of ARR, and Action Editor for TACL, and has served on the organizing committee, area chair, and senior program committee of major conferences including AAAI, ACL, NAACL, EACL, EMNLP, ECML and more. During her sabbatical, she was an IVADO Visiting Scholar at Mila–Quebec AI Institute, a Visiting Associate Professor at UCLA in Fall 2025, and will be a Visiting Faculty Member at Bloomberg in Spring 2026.
I would like to highlight the pressing issues in four main areas where major conferences such as NAACL can have significant and continued impact:
I) Shaping Research Progress: Major conferences such as NAACL can strengthen the scientific research by promoting rigorous evaluation, validation, and reproducibility as central principles of research progress. Conference structures and peer-review processes should be designed to encourage diversity of topics, transparency, and open-source contributions, rather than benchmark-driven performance alone. This is the distinctive academic mission of advancing shared scientific understanding compared to industry-led developments.
II) Real-world Application: Encouraging application-oriented research is vital for translating technical innovation into societal benefit. Such research needs to consider real-domain data and human evaluation, safety assurance, and ethical accountability to ensure responsible deployment in real-world contexts. Major conference such as NAACL can play a pivotal role in fostering interdisciplinary collaboration that identifies what works in practice, what does not, and why, thereby advancing trustworthy, and ethically grounded language comprehension and generation systems.
III) NLP Democratization: Ensuring equitable access to natural language research work, data, and resources is fundamental to inclusive innovation and global progress. NAACL can champion open and participatory frameworks that lower barriers to entry and empower diverse communities around the world to contribute to language intelligence development. It can also encourage investments that ensure NLP research benefits are broadly distributed rather than concentrated within a limited set of institutions or regions.
IV) NLP Education: Expanding NLP literacy across the public, policymakers, and domain experts—who are not necessarily experts in this area—is essential for a realistic understanding of the functionality of the intelligent models and an informed use across all sectors. Specially, democratizing NLP without providing adequate education can introduce societal risks. NAACL can contribute to leading developing educational initiatives that foster a well-informed society capable of engaging critically and constructively with language intelligent models and computational linguisitcs. This will empower participation in shaping future of NLP rather than leaving uneducated voices to become barriers to progress.
Fei Liu is an Associate Professor in the Computer Science Department at Emory University, specializing in natural language generation, large language model inference, reasoning, deep learning, and generative AI. Her research focuses on pushing the boundaries of natural language generation and understanding, developing innovative model architectures, training methodologies, and robust evaluation metrics. With the rapid growth of information from diverse sources, Dr. Liu develops techniques that enable systems to effectively distill, interpret, and generate meaningful insights from massive datasets.
Dr. Liu held a postdoctoral fellowship at Carnegie Mellon University, where she was a member of Noah’s ARK. Prior to that, she worked as a senior scientist at Bosch Research in Palo Alto, California. Bosch is one of Germany’s largest companies and a leading provider of intelligent car systems and home appliances. Dr. Liu received her Ph.D. in computer science from the University of Texas at Dallas, supported by the Erik Jonsson Distinguished Research Fellowship. Her academic journey began at Fudan University, where she completed her bachelor’s and master’s degrees in computer science.
Dr. Liu has published over 90 peer-reviewed papers in top-tier conferences and journals, and she regularly serves on program committees for major international conferences. In 2015, she was selected for the “MIT Rising Stars in EECS” program. Her research has been recognized with multiple awards, including a Best Paper Award Finalist at WWW 2016, an Area Chair Favorite Paper at COLING 2018, an Amazon AWS Machine Learning Research Award in 2020, the NSF CAREER Award in 2022, and a SAC Highlights Award at ACL 2025 (awarded to the top 1% of over 8,000 paper submissions).
I’m honored to be considered for the NAACL Board and would be excited to contribute to the community in three key areas: improving the review process in the LLM era, fostering interdisciplinary research, e.g., Agentic AI + X, and supporting open, reproducible science.
Encouraging Interdisciplinary Collaboration We have a real opportunity to strengthen NAACL as a space where interdisciplinary research is welcomed and well-supported. I would advocate for clear pathways for these submissions, through special tracks, diverse review expertise, and community workshops, so we can better reflect the broad impact of language technologies.
Improving the Peer Review Process The peer review process faces new challenges in the era of LLMs. I believe we can adapt constructively. I would support efforts such as making review comments public for all accepted papers, light auditing of review quality, and recognition for thoughtful reviewing. These steps would help preserve the integrity of peer review while acknowledging current practices.
Promoting Open and Reproducible Research I care deeply about research that is transparent and accessible. I’d like to explore stronger support for reproducibility, such as incentives for sharing code and data, and space for replication studies, especially those focused on underrepresented languages and domains. These practices are essential for scientific progress and inclusivity.
I look forward to contributing to a NAACL that continues to grow as an open, thoughtful community!
Ana Marasović is an Assistant Professor in the Kahlert School of Computing at the University of Utah. Her research interests broadly fall into NLP, human-centered AI, and interpretability. Previously, she was a Young Investigator at the Allen Institute for AI with a courtesy appointment at the University of Washington, and completed her PhD at Heidelberg University. She is a co-recipient of the Best Paper Award at ACL 2023, the Best Paper Honorable Mention at ACL 2020, and the Best Paper Award at SoCal 2022 NLP Symposium. Her work was selected as a CoLM 2025 Spotlight. She is also the University of Utah One-U Responsible AI Initiative Faculty Fellow and UC Berkeley EECS 2020 Rising Star.
If elected to the NAACL board, I would work to:
Simplify research submission and presentation processes:
Re-engage the broader LLM research community with NAACL:
Improve the reviewing process:
Kun Qian is a Senior Machine Learning Manager at Adobe. He received his PhD in Computer Science from the University of California, Santa Cruz, where he worked on approximation algorithms for data integration problems. His work now centers on applied machine learning, natural language interfaces to databases, human-in-the-loop AI, and building agentic solutions for enterprises. Before joining Adobe, Kun held research and engineering roles at IBM Research, Amazon, and Apple, contributing to projects in open-domain knowledge graphs, conversational AI, and enterprise-scale AI systems. He has published more than 30 papers at venues such as ACL, EMNLP, COLING, AACL, AAAI, VLDB, KDD, ICDE, PODS, EDBT, and IUI, and has received best paper awards at ISWC and IAAI. He is also an inventor on multiple patents in applied AI and knowledge-driven NLP systems.
I am honored by the nomination to serve on the NAACL board. NAACL has always felt to me like a place where research ideas meet practice, and where our community grows stronger through that exchange. I would be grateful for the chance to give back and help guide its future. If elected, I would focus on these priorities:
Bridging academia and industry. Many recent advances in NLP have come from industry labs, while academia continues to shape the fundamental questions and directions of our field. Having worked in both settings, I’ve seen how valuable it is when these communities engage with each other. I want to help NAACL be a space where those conversations happen more naturally—whether through joint workshops, shared resources, or open challenges.
Language AI for social good. Beyond technical progress, I believe our community has a responsibility to consider how NLP can improve lives. I am particularly interested in how language technologies can support people with social disabilities, such as autism, by helping with communication and interaction. More broadly, I want NAACL to encourage research that addresses accessibility, equity, and other societal needs where language AI can make a meaningful difference.
User-centric evaluation and real-world feedback loops. Too often, our systems are evaluated only on static benchmarks, while real-world use reveals very different strengths and weaknesses. I would like NAACL to encourage research that includes human evaluation, iterative feedback, and deployment studies, so that our models are measured not just by accuracy, but by how well they actually serve people. Building these feedback loops into our research will make our work more robust, trustworthy, and impactful outside the lab.
Jingbo Shang is an Associate Professor at the University of California, San Diego, jointly appointed in the Computer Science and Engineering Department and the Halıcıoğlu Data Science Institute. His research focuses on weak supervision, information extraction & discovery, and large language models, with a wide range of applications to various tasks and domains, including biomedical, business, IoT, and more. He has published extensively in ACL, EMNLP, NAACL, ICML, NeurIPS, ICLR, and other leading venues like Nature, Science, and SenSys. Beyond research, he has served the community as an Action Editor for Transactions of the ACL (TACL), Area Chairs for *ACL and ARRs, and an organizer of the 2024 SoCal NLP Symposium and 2024 HDSI–TILOS “LLM Meets Theory” workshop. His research has been supported by prestigious funding sources, including the NSF CAREER Award, NIH grants, and multiple industrial research awards.
It is an honor to be nominated for the NAACL Board. If elected, my goal is to strengthen NAACL as a community that is simultaneously rigorous, inclusive, and forward-looking. I will focus on the following priorities:
Supporting diverse research directions in the LLM era. While large language models dominate today’s NLP landscape, I believe NAACL must continue to nurture high-risk, high-reward ideas, spanning new architectures, efficient training, human-centered evaluation, and theory-driven approaches. I will advocate for review practices and conference programming that encourage intellectual diversity.
Expanding inclusivity across the Americas. Building on NAACL’s transition to the “Nations of the Americas Chapter,” I will work to lower barriers for underrepresented researchers, including travel and registration support, regional workshops, and mentorship programs that connect students and junior scholars across North, Central, and South America.
Strengthening bridges between academia, industry, and education. Having worked closely with both academic and industrial communities, I see great opportunity for NAACL to facilitate deeper exchange, through industrial tracks, interdisciplinary panels, and collaborations that connect cutting-edge research with real-world impact. I am also committed to promoting outreach to high school and undergraduate students, inspiring the next generation of computational linguists.
Enhancing transparency and sustainability in community practices. From conference organization to peer review, our processes must be clear, fair, and scalable as the community grows. I will support innovations such as reciprocal review, reviewer mentorship, and hybrid/virtual participation models that broaden access while reducing burdens.
NAACL has long been a home for rigorous science and a vibrant community. I would be honored to serve on the board and help shape its next chapter.
Avi Sil is a Senior Director of Applied Science at Oracle leading several teams on AI Agents for products. Previously, he was a people manager at IBM Research, leading the strategy for Agentic workflows and their integration into products like code assistants and IT incident management. With over a decade of experience, Avi is internationally recognized for his contributions to Retrieval-Augmented Generation (RAG) and Agentic Workflows involving large language models. His research also covers inference-scaling algorithms, uncertainty in LLMs, reinforcement learning-based verifiers, and natural language to code. He has been actively involved also in academia via various collaborations on NLP with Stanford, CMU, UIUC, UMass, Stony Brook and MIT among many others.
He actively contributes to the NLP community, serving in senior roles at major conferences like ACL and NAACL, and holds over 20 U.S. patents in AI technologies. He has held several positions as track chairs, senior area chair and editor for ACL conferences.
I’m running for a board position to strengthen NAACL’s research integrity, community wellbeing, and operational sustainability—–three areas that underpin long-term excellence but are often underserved.
Research integrity and responsible data/AI practices. I will champion clear, field-specific guidance for the responsible use of generative tools in authoring and reviewing; robust conflict-of-interest and authorship transparency; and community-vetted data governance (consent, provenance, licensing) with practical checklists. I’ll work to expand artifact badging beyond code/data release to include data documentation, safety evaluations, and compute reporting, helping our results remain trustworthy and reproducible at scale.
Accessibility, safety, and equitable participation. I will propose a standard NAACL “access package” for every event:
Sustainable, scalable operations. I will push for a re-usable “conference playbook” and shared tooling (templates, budgets, sponsor ethics policy, COI automation) to reduce organizer burnout. I will also advocate for greener events (supplier standards, experimentation with green options, low-waste policies, and carbon reporting with opt-in offsets) and for recognizing service with formal citation/credit so invisible labor is visible in hiring and promotion.
By investing in integrity, accessibility, and sustainable operations, NAACL can safeguard scientific trust, broaden who can participate, and make it easier for volunteers to deliver outstanding conferences year after year. I’d be honored to help implement these concrete, durable improvements.
Lichao Sun is an Assistant Professor in the Department of Computer Science and Engineering at Lehigh University. He received his Ph.D. in Computer Science from the University of Illinois at Chicago in 2020. His research is centered on building trustworthy AI, with a particular focus on developing robust, fair, and transparent large language models (LLMs) and generative AI. He leads several influential open-source projects, including TrustLLM, a comprehensive benchmark for evaluating the trustworthiness of LLMs, and BiomedGPT, a generalist foundation model for diverse biomedical tasks. A dedicated member of the ACL community, Lichao has served as an Area Chair for ACL and is a regular Program Committee member and Area Chair for NAACL, EMNLP, and other top-tier conferences such as NeurIPS and ICML. His work has been recognized with an NSF CRII Award, a Microsoft Accelerate Foundation Models Research (AFMR) Award, and several best paper awards and honorable mentions at venues including NeurIPS, SIGIR, and IEEE TPDS. His research is supported by the National Science Foundation (NSF), Microsoft, and OpenAI.
It is a great honor to be nominated for a position on the NAACL Board. As an active member of the NLP community for many years, I am deeply committed to the continued success of our vibrant field and am eager for the opportunity to serve our community in this new capacity. As an active member of the NLP community for many years, I am deeply committed to the continued success of our vibrant field and am eager for the opportunity to serve our community in this new capacity.
My Commitment to the Community: My dedication to our community is rooted in service. I have been a consistent Program Committee Member and Area Chair for our top conferences, including NAACL, ACL, and EMNLP, as well as for NeurIPS and ICML. This experience has given me a profound appreciation for the hard work that underpins our scientific progress and for the critical importance of maintaining a fair, rigorous, and supportive academic publishing process.
My Vision for NAACL: A Responsible, Resilient, and Inclusive Future: We are at a pivotal moment. The impact of our work has never been greater, and with this influence comes profound responsibility. My vision for NAACL is to empower our community to not only lead the world in technical innovation but also to set the standard for developing trustworthy, secure, and ethical AI. If elected, I will dedicate my energy to three key pillars:
Championing Trustworthy and Resilient NLP: Our greatest challenge is shifting from “capability” to “reliability.” I will work to establish NAACL as a leading venue for research in AI safety and security. I propose creating new forums, such as dedicated special tracks or workshop series, to advance research in adversarial robustness, data privacy, and model transparency. Drawing on my work on the TrustLLM benchmark and vulnerability analysis, we can foster a proactive culture of identifying and mitigating risks, ensuring that LLM innovations are built on a foundation of trust.
Empowering the Next Generation for a New AI Era: The rapid pace of technology can be unsettling, especially for junior researchers. I will be a strong advocate for initiatives that support our students and early-career members. Leveraging my dual experience in both academia and industrial research labs, I will work to expand mentorship programs that offer clear guidance on navigating research and diverse career paths in the age of LLMs. We must ensure the next generation of NLP researchers is equipped not only with cutting-edge technical skills but also with a strong ethical compass.
Fostering a Collaborative and Interdisciplinary Ecosystem: The most impactful solutions will come from collaboration. I am committed to strengthening the ties between academia, industry, and policy stakeholders. I will champion programs that lower the barrier to interdisciplinary research, particularly where NLP is applied in high-stakes domains like medicine and security. By creating more opportunities for meaningful exchange, we can accelerate the translation of foundational research into real-world applications that are both innovative and socially responsible.
I am excited by the prospect of working with the board and all of you to build an innovative, inclusive, and responsible future for NAACL. Thank you for your consideration.
Wenpeng Yin is an Assistant Professor in the Department of Computer Science and Engineering at Penn State University. Prior to Penn State, he was an Assistant Professor at Temple University, Senior Research Scientist at Salesforce Research, and Postdoc at UPenn. Dr. Yin’s research focuses on three major thrusts: (i) developing AI, especially language techniques, to assist and accelerate scientific discovery while safeguarding academic integrity, (ii) making LLMs more controllable and deployable at the edge, and (iii) enabling robust AI reasoning in dynamic, real-world settings. He was among the early researchers advancing deep representation learning for NLP, with representative work including attention mechanisms for convolutional NNs, NLI-driven universal NLP, and his recent AI4Research series. He has co-chaired a series of workshops (e.g., Wise-Supervision 2022, AI4Research workshop series 2024–2025, SciSoc-LLM Workshop at KDD 2025, Mid-Atlantic Student Colloquium on AI, Language, and Learning 2025), served as a Senior Area Chair for leading NLP venues (e.g., ACL Rolling Review, NAACL, EMNLP, etc.), and received a couple of paper awards. NSF and Sanofi have supported his research.
I believe we are living in a remarkable era for NAACL (and broader NLP community): research advances in NLP/AI, combined with national priorities around AI innovation, are bringing unprecedented scientific progress. At the same time, we face critical challenges: (i) Bridging Academia and Industry: Many academic research directions struggle to attract industry attention or deliver real-world impact, which can dissuade top Ph.D. students from pursuing academic careers. (ii) Over-Concentration of Focus: The community risks narrowing too much around LLM-related topics, potentially overlooking long-term, diverse research directions. (iii) Barriers to Participation: International researchers, under-resourced colleges, and even high-school students often face financial and logistical obstacles that prevent them from engaging with NAACL events. (iv) Research Integrity and Quality: Widespread use of generative AI, less controlled reviewer qualifications, and limited mechanisms to ensure post-review improvements are creating new challenges for maintaining academic rigor and trustworthiness.
If elected to the NAACL Board, I aim to address these issues through the following initiatives: (i) Strengthening Industry–Academia Collaboration: I will work to organize a higher volume of industry-led talks, panels, and product-focused sessions to bring fresh ideas and practical challenges back into the academic conversation. (ii) Setting Ambitious Research Directions: I will advocate for yearly dynamic, “moonshot” themes that encourage the community to explore visionary, underexplored problems. (iii) Broadening Access to NLP Research: I plan to expand outreach efforts to engage colleges and even high-school students, aligning with the U.S. national priority of growing AI talent pipelines. (iv) Safeguarding Research Integrity: We will collaborate with platforms like OpenReview to experiment with mechanisms such as reviewer training, structured revision validation, and (where necessary) sanctions for repeated integrity violations.
My research program (AI4ResearchIntegrity) and my rich service experience have well prepared me to contribute meaningfully to NAACL’s future. I am eager to help shape a community that remains vibrant, inclusive, and globally influential.