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<!-- Intro -->
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		<h1>{{site.acronym}}</h1>
		<p>
			<!-- <strong>{{site.title}}</strong><br />November 14, 2023 &mdash; 8:30am-noon CT &mdash; Room D222<br /> -->
			<strong>{{site.title}}</strong><br />November 18<br />
			Atlanta, GA, USA
			<span style="font-size: 0.6em; top: 0; right: 1em; position: absolute; text-align: right;">
				In conjunction with<br />
				<a href="https://sc24.supercomputing.org/" target="_blank" style="border-bottom: 0">
					<img src="/assets/images/sc24_H_black_shaded.png" style="height: 5em" /></a>
				<br /><br />
				Proceedings by<br />
				<img src="/assets/images/2560px-IEEE_logo.svg.png" style="height: 3em" />
			</span>

		</p>
		<p>
			{{site.acronym}} focuses on the many facets of scientific workflow composition, management, sustainability, and
			application to domain sciences in an increasingly diverse landscape. The workshop covers a broad range of topics
			in the scientific workflow lifecycle that include: reproducible research with workflows; workflow execution in
			distributed and heterogeneous environments; application of AI/ML in workflow management; workflow
			provenance; serverless workflows; exascale computing with workflows; stream-processing, interactive, adaptive
			and data-driven workflows; workflow scheduling and resource management; workflow fault-tolerance, debugging,
			performance analysis/modeling; big data and AI workflows, etc.
		</p>
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			<li><a href="#one" class="button scrolly">Learn more</a></li>
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<!-- Workshop Program -->

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		<h2 style="color: black;">Workshop Program (Monday 18, 9am to 5:30pm, Room B302)</h2>
		<table>
			<thead>
				<th style="color: black;">Time</th>
				<th style="color: black;">Event</th>
			</thead>
			<tr>
				<td style="font-size: 0.8em">9:00am-9:14am</td>
				<td><strong>Welcome</strong>
					<br /><i>Silvina Caino-Lores, Anirban Mandal</i></td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">9:14am-9:37am</td>
				<td>
					Paper: 
					<strong>
						A software Ecosystem for Multi-Level Provenance Management in Large-Scale Scientific Workflows for AI Applications
					</strong>
					<br />
					<i>
						Padovani, Anantharaj, Sacco, Kurihana, Bunino, Tsolaki, Girone, Antonio, Sopranzetti, Fronza, Fiore
					</i>
				</td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">9:37am-10:00am</td>
				<td>
					Paper: 
					<strong>
						Performance Characterization and Provenance of Distributed Task-based Workflows on HPC Platforms
					</strong>
					<br />
					<i>
						Gueroudji, Phelps, Islam, Carns, Snyder, Dorier, Ross, Pouchard					
					</i>
				</td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">10:00am-10:30am</td>
				<td>
					<strong>
						Break
					</strong>
				</td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">10:30am-11:21am</td>
				<td>Invited Talk: <strong>Assessing and Advancing the Potential of Quantum Computing: A NASA Case Study </strong>
					<br /><i>Eleanor Rieffel</i></td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">11:21am-11:44am</td>
				<td>
					Paper: 
					<strong>
						Parsl+CWL: Experiences Combining the Python and CWL Ecosystems
					</strong>
					<br />
					<i>
						Karle, Clifford, Babuji, Chard, Katz, Chard
					</i>
				</td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">11:44am-12:07pm</td>
				<td>
					Paper: 
					<strong>
						Towards Generating Contracts for Scientific Data Analysis Workflows
					</strong>
					<br />
					<i>
						Vu, Kehrer
					</i>
				</td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">12:07pm-12:30pm</td>
				<td>
					Paper: 
					<strong>
						GNU Parallel: Enabling Low-Overhead HT-HPC Workflows at Extreme Scale
					</strong>
					<br />
					<i>
						Maheshwari, Arndt, Karimi, Yin, Suter, Johnson, Ferreira da Silva
					</i>
				</td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">12:30pm-2:00pm</td>
				<td>
					<strong>
						Lunch
					</strong>
				</td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">2:00pm-2:05pm</td>
				<td>
					Panel Lightning Talk: 
					<strong>
						Towards a Cohesive Ecosystem of Workflows, Data, Artificial Intelligence, and Humans
					</strong>
					<br />
					<i>
						Paine, Deshmukh, Gunter, O'Donnell, Poon, Ramakrishnan
					</i>
				</td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">2:05pm-2:10pm</td>
				<td>
					Panel Lightning Talk: 
					<strong>
						Accelerating Operation of Complex Workflows through Standard Data Interfaces
					</strong>
					<br />
					<i>
						Paul, Regli
					</i>
				</td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">2:10pm-2:15pm</td>
				<td>
					Panel Lightning Talk: 
					<strong>
						Trust and Verification of AI-Based Decision Making for Future Scientific Workflows: Challenges and Solutions
					</strong>
					<br />
					<i>
						Giannakou, Amusat, Ramakrishnan
					</i>
				</td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">2:15pm-2:20pm</td>
				<td>
					Panel Lightning Talk: 
					<strong>
						Integrating Evolutionary Algorithms with Distributed Deep Learning for Optimizing Hyperparameters on HPC Systems
					</strong>
					<br />
					<i>
						Coletti, Santos Souza, Skluzacek, Suter, Ferreira da Silva
					</i>
				</td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">2:20pm-2:30pm</td>
				<td>
					Invited Panel Lightning Talk: 
					<strong>
						Workflows on LUMI: Europe's most powerful supercomputer
					</strong>
					<br />
					<i>
						Tomasz Malkiewicz
					</i>
				</td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">2:30pm-3:00pm</td>
				<td>
					Panel Discussion: 
					<strong>
						Future of Scientific Workflows
					</strong>
					<br />
					<i>
						Coletti, Giannakou, Malkiewicz, Paine, Paul, Santos Souza, Caino-Lores, Mandal
					</i>
				</td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">3:00pm-3:30pm</td>
				<td>
					<strong>
						Break
					</strong>
				</td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">3:30pm-3:53pm</td>
				<td>
					Paper: 
					<strong>
						Managing Workflow Malleabillity in Urgent Computing for Earthquake Alerts
					</strong>
					<br />
					<i>
						Ejarque, Monterrubio-Velasco, Bhihe, Pienkowska, de la Puente, Badia					
					</i>
				</td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">3:53pm-4:16pm</td>
				<td>
					Paper: 
					<strong>
						A Microservices Architecture Toolkit for Interconnected Science Ecosystems
					</strong>
					<br />
					<i>
						Brim, Drane, McDonnell, Engelmann, Malviya Thakur					
					</i>
				</td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">4:16pm-4:39pm</td>
				<td>
					Paper: 
					<strong>
						Shepherd: Seamless Integration of Service Workflows into Task-Based Workflows through Log Monitoring
					</strong>
					<br />
					<i>
						Islam, Thain					
					</i>
				</td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">4:39pm-5:02pm</td>
				<td>
					Paper: 
					<strong>
						Laminar 2.0: Serverless Stream Processing with Enhanced Code Search and Recommendations
					</strong>
					<br />
					<i>
						Rotchford, Evans, Filgueira				
					</i>
				</td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">5:02pm-5:25pm</td>
				<td>
					Paper: 
					<strong>
						Serverless Computing for Dynamic HPC Workflows
					</strong>
					<br />
					<i>
						Thurimella, Raith, Hong Enriquez, Da Silva, Rattihalli, Gavrilovska, Milojicic				
					</i>
				</td>
			</tr>
			<tr>
				<td style="font-size: 0.8em">5:25pm-5:30pm</td>
				<td><strong>Closure</strong>
					<br /><i>Silvina Caino-Lores, Anirban Mandal</i></td>
			</tr>
		</table>
	</div>
</section>


<!-- Invited Speaker -->
<!--
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		<h2>Invited Speakers</h2>
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				<span class="icon"><img src="/assets/images/organization/montella.jpg" width="80px"
						style="border-radius: 10%"></span>
				<h3 style="margin: 0">Raffaele Montella</h3>
				<p style="margin: 0">University of Naples “Parthenope”, Italy</p>
				<br /><br />
				<h3>Workflow Building Blocks: The Success Story of Environmental Modeling, HPC, and AI for Predicting Farmed Seafood Bacteria Contamination</h3>
				<p>
					Scientific workflows processing enormous amounts of data using distributed HPC systems or on-demand computational resources are a solid and reliable paradigm in data science. The orchestration of environmental models to produce simulations or forecasts is a more widespread routine production workflow application. This presentation concerns our vision of workflows as building blocks for environmental applications, combining numerical and artificial intelligence models to produce augmented environmental forecasts and predictions. <a href="https://github.com/dagonstar/" target="_blank">DagOnStar</a> is the workflow engine developed at the HPSC SmartLab of the University of Naples "Parthenope" for orchestrating environmental models used by the Center for Monitoring and Modeling Marine and Atmosphere (<a href="https://meteo.uniparthenope.it" target="_blank">CMMMA</a>) applications to orchestrate the weather and marine forecast production. The Center runs a routinary workflow application to predict the contamination by Escherichia Coli (E. Coli) in farmed mussels, augmenting the forecasted pollutant transport and diffusion (<a href="https://github.com/ccmmma/wacommplusplus" target="_blank">WaComM++ model</a>) with an artificial intelligence model  (<a href="https://github.com/ccmmma/aiquam" target="_blank">AIQUAM model</a>) trained with microbiological measurements. The first assessment and evaluation of the system demonstrate that the workflow application can predict E. Coli presence with an accuracy of more than 90%.  
				</p>
				<p style="font-size: 0.9em; font-style: italic">
					Raffaele Montella is an Associate Professor with tenure in Computer Science at the Department of Science and Technologies (DiST), University of Naples “Parthenope" (UNP), Italy. He got his degree in (Marine) Environmental Science at the University of Naples “Parthenope" in 1998. He defended his Ph.D. thesis on "Environmental modeling and Grid Computing techniques" earning a Ph.D. in Marine Science and Engineering at the University of Naples "Federico II". He leads the High-Performance Scientific Computing (HPSC) Laboratory and the IT infrastructure of the UNP Center for Marine and Atmosphere Monitoring and Modeling (CMMMA). His main research topics and scientific production are focused on: tools for high-performance computing, cloud computing, and GPUs with applications in the field of computational environmental science (multi-dimensional geo-referenced big data, distributed computing for modeling, and scientific workflows and science gateways) leveraging on his previous (and still ongoing) experiences in embedded, mobile, wearable, pervasive computing, and Internet of Things.					 
				</p>
			</section>
		</div>

		<div class="features">
			<section style="padding: 1em; width: 100%;">
				<span class="icon"><img src="/assets/images/organization/christine.jpg" width="80px"
						style="border-radius: 10%"></span>
				<h3 style="margin: 0">Christine Kirkpatrick</h3>
				<p style="margin: 0">San Diego Supercomputing Center, USA</p>
				<br /><br />
				<h3>FAIRIST of them all: Meeting researchers where they are with just-in-time, FAIR implementation advice</h3>
				<p>
					Intellectual freedom, curiosity, and creativity are qualities of the academic landscape that appeal to many researchers. But a blank page in the wrong context can halt creativity, such as creating a data management and sharing plan. A goal for research support staff, as well as for researchers themselves, is to lower time spent on the mechanics of research to make more time for open-ended discovery. For data-driven science, this includes collecting and processing data so that one can find and combine research objects later. It also means preparing research objects, such as data, software, and workflows, for later reuse by others. The FAIR principles provide a conceptual framework for comprehensively ensuring research assets are accessible for reuse. Currently researchers apply the FAIR practices as best they can, based on community practices, lessons learned on the job, and other mentorship they may have received. This talk will explore how FAIR implementation practices – or any other practice that aids data management and sharing, can be provided to researchers and customized to their specific research tasks. Research workflows can be improved through new ways of sharing hard won knowledge, and through processes that allow for peer assessment. These data sources can be repurposed in existing tools or through new interfaces, such as the FAIR+ Implementation Survey Tool (FAIRIST).
				</p>
				<p style="font-size: 0.9em; font-style: italic">
					Christine Kirkpatrick leads the San Diego Supercomputer Center’s (SDSC) Research Data Services division, which manages large-scale infrastructure, networking, and services for research projects of regional and national scope. Her duties also include a leadership role on the Schmidt Futures Foundation and NSF-funded Open Storage Network and as leader of the Data Core for the NIH-funded Metabolomics Workbench, a national data repository for metabolomics studies. Her research in computer science has centered on improving machine learning processing through research data management techniques. In addition to being PI of the EarthCube Office (ECO), Kirkpatrick founded the US GO FAIR Office, is PI of the West Big Data Innovation Hub, and Co-PI on an NSF Accelnet: Designing a Water, Data, and Systems Science Network of Networks to Catalyze Transboundary Groundwater Resiliency Research. She serves as the Secretary General of the International Science Council's Committee on Data (CODATA), co-Chairs the FAIR Digital Object Forum, is on the external Advisory Board for the European Open Science Cloud (EOSC) Nordic, and the National Academies of Sciences’ U.S. National Committee for the Committee on Data.
				</p>
			</section>
		</div>
	</div>
</section>
-->


<!-- Important Dates -->
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		<h2>Important Dates</h2>
		<ul class="contact">
			<li>
				<!-- <h3><strike>August 11</strike> <span style="color: red">August 16, 2023</span> (final extension)</h3> -->				
				<span style="color: #ffffff">Papers and Abstracts Submission: <strike style="color: rgb(160, 160, 160)">August 4</strike> August 8, 2024 (Final extension)</span>
			</li>
			<li>
				<span style="color: #ffffff">Paper and Abstract Acceptance Notifications: September 6, 2024</span>
			</li>
			<li>
				<span style="color: #ffffff">Camera-ready Submissions: September 27, 2024</span>
			</li>
			<li>
				<span style="color: #ffffff">Workshop: November 18, 2024</span>
			</li>
		</ul>
		<p style="color: #ffffff">
			All deadlines are <a href="https://www.timeanddate.com/time/zones/aoe" target="_blank">Anywhere on Earth
				(AoE)</a>.
		</p>
	</div>
</section>

<section id="cfp" class="wrapper style2 fade-up" style="background-color: #c7c7c7;">
	<div class="inner">
		<h1>Call for Papers</h1>

		<p>
			Scientific workflows have underpinned some of the most significant discoveries of the past several decades. Workflow management systems (WMSs) provide abstraction and automation that enable researchers to easily define sophisticated computational processes, and to then execute them efficiently on parallel and distributed computing systems. As workflows have been adopted by multiple scientific communities, they are becoming more complex and require more sophisticated workflow management capabilities. A workflow can now analyze terabyte-scale data sets, execute millions of individual tasks; coordinate heterogeneous resources and tasks from edge to core; and process near real-time data streams, files, and data placed in different types of storage systems. The computations can be single core workloads, loosely-coupled tasks, or tightly-coupled computations, and can run in heterogeneous distributed computing platforms all within a single anirban@
		<strong>Steering Committee</strong>
		<div class="features">
			<section style="padding: 1em; background-color: rgba(255, 255, 255, 0.3);">
				<span class="icon"><img src="/assets/images/organization/abramson.jpg" width="50px"
						style="border-radius: 10%"></span>
				<h3 style="margin: 0">David Abramson</h3>
				<p style="margin: 0">University of Queensland, Australia</p>
			</section>
			<section style="padding: 1em; background-color: rgba(255, 255, 255, 0.3);">
				<span class="icon"><img src="/assets/images/organization/atkinson.jpg" width="50px"
						style="border-radius: 10%" /></span>
				<h3 style="margin: 0">Malcolm Atkinson</h3>
				<p style="margin: 0">University of Edinburgh, UK</p>
			</section>
			<section style="padding: 1em; background-color: rgba(255, 255, 255, 0.3);">
				<span class="icon"><img src="/assets/images/organization/deelman.png" width="50px"
						style="border-radius: 10%"></span>
				<h3 style="margin: 0">Ewa Deelman</h3>
				<p style="margin: 0">University of Southern California, USA</p>
			</section>
			<section style="padding: 1em; background-color: rgba(255, 255, 255, 0.3);">
				<span class="icon"><img src="/assets/images/organization/taufer.jpg" width="50px"
						style="border-radius: 10%" /></span>
				<h3 style="margin: 0">Michela Taufer</h3>
				<p style="margin: 0">University of Tennessee, USA</p>
			</section>
		</div>
		

		<strong>Program Committee</strong>
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			{% for pc in site.data.committee %}
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				<h3 style="margin: 0">{{pc.name}}</h3>
				<p style="margin: 0">
					<i class="flag-icon flag-icon-{{pc.country}}"
						style="border-radius: 100%; width: 1em; height: 1em"></i>
					{{pc.institution}}
				</p>
			</section>
			{% endfor %}
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	</div>
</section>

<!-- Contact -->
<section id="contact" class="wrapper style2 fade-up">
	<div class="inner">
		<h2>Contact</h2>
		<p style="margin-bottom: 0" >
			<!--For information please direct your inquiries to <a href = "mailto: sc-ws-works@info.supercomputing.org"><strong>sc-ws-works@info.supercomputing.org</strong></a>, or contact the workshop chairs:-->
			For information please direct your inquiries to the workshop chairs:
		</p>
		<ul style="margin-left: 1em">
			<li>Silvina Caino-Lores, <a href = "mailto:silvina.caino-lores@inria.fr"><strong>silvina.caino-lores@inria.fr</strong></a></li>
			<li>Anirban Mandal, <a href = "mailto:anirban@renci.org"><strong>anirban@renci.org</strong></a></li>
		</ul>
	</div>
</section>