ACL Wiki - Recent changes [en]

User talk:Saad

Chunliang Lyu — Mon, 11 May 2026 16:04:09 GMT

Welcome!

New page

'''Welcome to ''ACL Wiki''!'''
We hope you will contribute much and well.
You will probably want to read the [https://www.mediawiki.org/wiki/Special:MyLanguage/Help:Contents help pages].
Again, welcome and have fun! [[User:Chunliang Lyu|Chunliang Lyu]] ([[User talk:Chunliang Lyu|talk]]) 16:04, 11 May 2026 (UTC)

User:Saad

Chunliang Lyu — Mon, 11 May 2026 16:04:09 GMT

Creating user page for new user.

New page

I am an AI Lead with expertise in Natural Language Generation working for Shopware remotely in Düsseldorf, Germany Germany. I lead a team of data scientists to build meaningful applied solutions, whilst actively participating in research. Online, I can be found on Bluesky or on LinkedIn.

Since January, 2025 I have been elected to serve on the board for the ACL Special Interest Group for Natural Language Generation (SIGGEN). I also try and help to maintain the open source SimpleNLG realiser project on GitHub.

User:Saad

Chunliang Lyu — Mon, 11 May 2026 16:04:07 GMT

User account Saad was created by Chunliang Lyu and password was sent by email

BioNLP Workshop

Dina — Sat, 09 May 2026 22:24:11 GMT

← Older revision		Revision as of 22:24, 9 May 2026
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	===IMPORTANT DATES ===		===IMPORTANT DATES ===

	~~<b>Tentative</b>~~
	<br>		<br>
	(All submission deadlines are 11:59 p.m. UTC-12:00 “anywhere on Earth”)		(All submission deadlines are 11:59 p.m. UTC-12:00 “anywhere on Earth”)
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	* Workshop: July 3 - 4, 2026, Collocated with ACL 2026 in San Diego, CA		* Workshop: July 3 - 4, 2026, Collocated with ACL 2026 in San Diego, CA
	<br>		<br>
	~~Please watch~~ for the ~~updates!~~
			===Invited Talk===

			<h4>Annika Marie Schoene, PhD, Assistant Professor, Bouvé College of Health Sciences </h4>

			<h4>AI Safety in Healthcare : Ethical and Technical Considerations</h4>

			<p>Abstract: The rapid integration of artificial intelligence into healthcare, spanning ambient documentation, virtual nursing, and medical imaging, has outpaced the development of robust oversight mechanisms. While the global AI-in-healthcare market is projected to exceed $868 billion by 2030, incidents of algorithmic bias and unequal care delivery have exposed significant gaps between responsible AI principles and clinical implementation. This talk examines AI safety as a foundational pillar of responsible AI in health contexts, arguing that fairness, transparency, accountability, and human autonomy cannot remain aspirational without corresponding technical and governance infrastructure. I survey existing frameworks and identify a critical missing layer of concrete, operationalizable evaluation methods that bridge ethical principles and technical practice. To address this gap, I present work toward an actionable framework for responsible AI integration in clinical settings, grounded in the AI Ethics Box, a structured taxonomy derived from biomedical ethics. The framework maps ethical domains to specific technical tests, supporting both pre- and post-deployment evaluation. I also describe an ongoing co-design process with clinical and community stakeholders to ensure real-world feasibility. I conclude that AI in healthcare demands genuinely interdisciplinary research to build tools that improve health outcomes without compromising patient safety.</p>

			<p>Biography: Annika Marie Schoene, PhD is a computer scientist and researcher in AI safety, working on the evaluation, robustness, and security of large-scale AI systems, including large language models. She develops technical methods and evaluation frameworks to identify and mitigate high-risk behaviors such as jailbreaks, harmful outputs, and unsafe system behavior, with the goal of enabling the safe and trustworthy deployment of AI in public health, health systems, and healthcare settings. She is currently an Assistant Professor in the Department of Public Health and Health Sciences and Technical Lead for the Responsible AI Practice at Northeastern University. Beyond her core research, she works across academic, industry, public-sector and not-for-profit settings to generate evidence that supports non-technical stakeholders and policymakers in making informed decisions that reduce algorithmic harm and inform organizational decision-making. She is also a Visiting Scientist at MaineHealth and the University of Southampton (UK), and a Faculty Fellow at the Institute for Social Justice and Healthy Equity, and serve as a Scientific Expert Advisor at Meta on AI safety. She holds a PhD in Computer Science from the University of Hull (UK). During her doctoral training, she conducted research on machine and deep learning methods for analyzing complex real-world text and interned at IBM Research (UK), continuing this collaboration throughout her PhD and postdoctoral work. She completed her postdoctoral training at the University of Manchester’s National Centre for Text Mining (NaCTeM), where she worked on natural language processing in health-related contexts. Prior to her current role, she was a Research Scientist at the Institute for Experiential AI (EAI), working primarily on research involving the development and evaluation of AI methods in applied health contexts.</p>


	===SUBMISSION INSTRUCTIONS===		===SUBMISSION INSTRUCTIONS===

Employment opportunities, postdoctoral positions, summer jobs

Prthompson — Fri, 08 May 2026 09:14:20 GMT

Added Research Assistant/Associate Opening in Agentic Biomedical AI at the University of Manchester

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 <!-- USE "SHOW PREVIEW" TO VERIFY YOUR EDITS -->
 <!-- INSERT YOUR JOB AD IMMEDIATELY BELOW THIS HEADER -->
 == PhD Position on Procedural Generation of Verifiable Reasoning Benchmarks from Advanced and Non-Standard Logics (Inria Lille, France) ==