BioNLP Workshop - Revision history

Dina at 18:42, 24 June 2026

2026-06-24T18:42:47Z

← Older revision		Revision as of 18:42, 24 June 2026
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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>

			<h3>Schedule</h3>
			* [https://docs.google.com/document/d/1aGaDRmcGNPdeGZbO3dDglFA7zf9XYxqcli0POcCXVFk/edit?usp=sharing July 3 ]
			* [https://docs.google.com/document/d/1GHnmXhsumseG6Kxm6Vt8iCWSv9uxsEDZ/edit?usp=sharing&ouid=114874328739866572425&rtpof=true&sd=true July 4 ]

	===Invited Talk===		===Invited Talk===
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	<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>		<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>

Dina at 09:30, 1 June 2026

2026-06-01T09:30:30Z

← Older revision		Revision as of 09:30, 1 June 2026
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	<h4>Program Committee</h4>		<h3>Program Committee</h3>
	<ul>		<ul>
	<li> Abdulrahman AAl Abdulsalam, Sultan Qaboos University, Oman </li>		<li> Abdulrahman AAl Abdulsalam, Sultan Qaboos University, Oman </li>

Dina at 09:29, 1 June 2026

2026-06-01T09:29:29Z

Show changes

Dina: /* IMPORTANT DATES */

2026-05-25T03:19:29Z

IMPORTANT DATES

← Older revision		Revision as of 03:19, 25 May 2026
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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>

	~~[[July 3 schedule]]~~

	===Invited Talk===		===Invited Talk===

Dina: /* IMPORTANT DATES */

2026-05-25T02:57:07Z

IMPORTANT DATES

← Older revision		Revision as of 02:57, 25 May 2026
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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>

			[[July 3 schedule]]

	===Invited Talk===		===Invited Talk===

Dina: /* SUBMISSION INSTRUCTIONS */

2026-05-18T05:04:05Z

SUBMISSION INSTRUCTIONS

← Older revision		Revision as of 05:04, 18 May 2026
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	Two types of submissions are invited: full papers and short papers.		Two types of submissions are invited: full papers and short papers.

	Full papers should not exceed eight (8) pages of text, plus unlimited references. These are intended to be reports of original research. BioNLP aims to be the forum for interesting, innovative, and promising work involving biomedicine and language technology, whether or not yielding high performance at the moment. This by no means precludes our interest in and preference for mature results, strong performance, and thorough evaluation. Both types of research and combinations thereof are encouraged.		Full papers should not exceed eight (8) pages of text, plus unlimited references. THE FINAL VERSIONS FOLLOW ACL RULES on +1 page. These are intended to be reports of original research. BioNLP aims to be the forum for interesting, innovative, and promising work involving biomedicine and language technology, whether or not yielding high performance at the moment. This by no means precludes our interest in and preference for mature results, strong performance, and thorough evaluation. Both types of research and combinations thereof are encouraged.

	Short papers may consist of up to four (4) pages of content, plus unlimited references. Appropriate short paper topics include preliminary results, application notes, descriptions of work in progress, etc.		Short papers may consist of up to four (4) pages of content, plus unlimited references. THE FINAL VERSIONS FOLLOW ACL RULES on +1 page. Appropriate short paper topics include preliminary results, application notes, descriptions of work in progress, etc.

	Electronic Submission		Electronic Submission

Dina at 22:24, 9 May 2026

2026-05-09T22:24:11Z

← 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===

Dina: /* IMPORTANT DATES */

2026-05-01T10:31:20Z

IMPORTANT DATES

← Older revision		Revision as of 10:31, 1 May 2026
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	* Paper submission deadline: <s>April 17 (Friday)</s> April 20, Monday, 2026		* Paper submission deadline: <s>April 17 (Friday)</s> April 20, Monday, 2026
	* Notification of acceptance: May 6 (~~Wednesday~~), 2026		* Notification of acceptance: May 8 (Friday), 2026
	* Camera-ready paper due: May 12 (Tuesday), 2026		* Camera-ready paper due: May 19 (Tuesday), 2026
	* Pre-recorded video due (hard deadline): June 4, 2026		* Pre-recorded video due (hard deadline): June 4, 2026
	* 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

Dina: /* IMPORTANT DATES */

2026-04-29T13:43:33Z

IMPORTANT DATES

← Older revision		Revision as of 13:43, 29 April 2026
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	* Paper submission deadline: <s>April 17 (Friday)</s> April 20, Monday, 2026		* Paper submission deadline: <s>April 17 (Friday)</s> April 20, Monday, 2026
	* Notification of acceptance: May 4 (~~Monday~~), 2026		* Notification of acceptance: May 6 (Wednesday), 2026
	* Camera-ready paper due: May 12 (Tuesday), 2026		* Camera-ready paper due: May 12 (Tuesday), 2026
	* Pre-recorded video due (hard deadline): June 4, 2026		* Pre-recorded video due (hard deadline): June 4, 2026

Dina: /* IMPORTANT DATES */

2026-04-20T21:45:58Z

IMPORTANT DATES

← Older revision		Revision as of 21:45, 20 April 2026
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	* Notification of acceptance: May 4 (Monday), 2026		* Notification of acceptance: May 4 (Monday), 2026
	* Camera-ready paper due: May 12 (Tuesday), 2026		* Camera-ready paper due: May 12 (Tuesday), 2026
			* Pre-recorded video due (hard deadline): June 4, 2026
	* 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>