Event owner lens
Use this record to benchmark whether a comparable event makes the work after the room visible. The score is 34/100, so the next move is to benchmark the weakest pillars before repeating the format.
technology ai startup agenda agenda analysis
2026 Conference - Workshops - AI&PDE: ICLR 2026 Workshop on AI and Partial Differential Equations
This technology ai startup agenda in Technology / AI / Startup shows 23 visible agenda rows from iclr.cc and scores 34/100: a thin but inspectable design signal. The clearest public signals sit in Future-of-Work Fit and Participation Architecture; the main limits are Follow Through and Network Design. Visible mechanisms include Participant work, Network design, and Learning transfer. The public record does not show follow-up or tracking, so the score should be read as design intent rather than durable impact. A practical reading: For a reader, this is a comparison record more than a model to copy: it reads as a technology ai startup agenda, with the strongest visible signal in future-of-work fit and participation architecture and the biggest open question around follow through and network design. The practical test is whether the published agenda connects the room to post-event continuation and evidence. This page is an original public-evidence analysis, not a copy of the source agenda or an endorsement of the event. The score places the visible agenda in the thin outcome architecture band. The strongest visible pillars are Future-of-Work Fit, Participation Architecture, and Evidence Maturity; the thinnest visible pillars are Follow Through, Network Design, and Learning Transfer. Visible mechanisms include Participant work, Network design, and Learning transfer. The extracted agenda preview includes 43 visible rows. The most common formats are Unknown, Presentation, and Poster Session; the most common inferred purposes are Unknown, Knowledge Transfer, and Showcase.
Eight-pillar fingerprint
Hover any pillar to see what it measures and, where it scored low, what the agenda is missing.
Participation Architecture?50
Participation Architecture - 50/100. Participant work, contribution, interaction, and alternatives to passive broadcast.
Follow Through?5
Follow Through - 5/100. Owners, dates, commitments, progress checks, and accountability after the room.Missing: Add named owners, dates, implementation checkpoints, and a visible post-event continuation path.
Problem Specificity?43
Problem Specificity - 43/100. A clear costly problem, objective, decision, or performance target.
Personalization?31
Personalization - 31/100. Role, path, goal, preparation, or connection tailoring for participants.
Network Design?16
Network Design - 16/100. Structured weak ties, bridge-building, mixers, and relationship persistence.Missing: Replace generic networking blocks with designed introductions, ask-offer exchanges, peer groups, or bridge-building rituals.
Learning Transfer?22
Learning Transfer - 22/100. Applied practice, feedback, workplace use, refreshers, and 30-90 day transfer.Missing: Build transfer into the agenda through practice, feedback, job aids, reflection, and 30-90 day use cases.
Evidence Maturity?45
Evidence Maturity - 45/100. Baseline, comparison, follow-up, isolation, and attribution confidence.
Future-of-Work Fit?52
Future-of-Work Fit - 52/100. Value against time, hybrid reality, accessibility, AI, and meeting load.
Fix the gaps
Field-tested exercises matched to this agenda's weakest pillars, from the exercise library.
Follow Through (5/100)
Exercises that strengthen it: Mental Toughness Workshop • WorkshopBank · 15% Solutions • WorkshopBank · Network Patches
Network Design (16/100)
Exercises that strengthen it: Fun Trust-Building Group Game for Team Connection - Izzat... · Exciting Group Trust Activity for Building Confidence... · PFP: September 2005
Learning Transfer (22/100)
Exercises that strengthen it: Team Challenge Activity for Group Problem-Solving: Don't... · Thrilling, Quick Elimination Game to Boost Energy - Match... · Creative Brain Teaser & Fun Geometric Team Puzzle - T-Puzzle
Agenda Preview
The actual agenda we captured. Every block is classified by format and purpose. Open any block to see how we read it; the colored edge shows whether it is participant work, broadcast, logistics, or a showcase.
Room vs wrapper
14 percent of the 43 classified blocks put participants to work; the rest broadcast, show, or handle logistics. That mix is what drives the participation score.
Participant workBroadcastShowcaseLogistics
all eventBlog Track PostersPoster sessionShowcase+
Format · ShowcasePoster sessionPresenters display work; attendees browse and ask questions. Some interaction, not structured work.
Evidence basisOutcome inferred from formatInferred from format
all eventJournal Track PostersPoster sessionShowcase+
Format · ShowcasePoster sessionPresenters display work; attendees browse and ask questions. Some interaction, not structured work.
Evidence basisOutcome inferred from formatInferred from format
all eventWorkshopsWorkshopParticipant work+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisParticipant work is implied by the formatInferred from format
5:00 AM - 1:00 PMWorkshop Sun, Apr 26, 2026 • PDT 201 A/BWorkshopParticipant work+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisParticipant work is implied by the formatInferred from format
all eventAI&PDE: ICLR 2026 Workshop on AI and Partial Differential EquationsWorkshopParticipant work+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisParticipant work is implied by the formatInferred from format
5:00 AM( ) Opening Remarks Renato Cerqueira VideoOpeningOrientation+
Format · BroadcastOpeningFormat not classified from the source; treated as a broadcast block by default.
Evidence basisNo participant output visible from this rowRead from source, no work signal
5:01 AM( ) Clécio R. Bom (CBPF) Clecio De Bom VideoPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
5:02 AM( ) Build AI to push scientific discovery - Neural operators and function-space generative models Jiachen Yao VideoPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
5:45 AM( ) Poster Session I LinkPoster sessionShowcase+
Format · ShowcasePoster sessionPresenters display work; attendees browse and ask questions. Some interaction, not structured work.
Evidence basisOutcome inferred from formatInferred from format
6:35 AM( ) The Future of AI and PDEs: An Industrial View Lucas Nissenbaum ⋅ Pablo Blanco ⋅ Alvaro Coutinho ⋅ John Smith ⋅ Ana Paula Muller VideoPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
7:35 AM( ) Maximilian Herde (ETH Zürich) VideoPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
9:00 AM( ) Jingmin Sun (Johns Hopkins/CMU) VideoPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
9:25 AM( ) Unlocking the Performance of Neural Surrogates in Real-World Applications A. Cristiano I. Malossi VideoPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
10:10 AM( ) Rose Yu (UC San Diego & Amazon Scholar) Rose Yu VideoPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
10:55 AM( ) Poster Session II LinkPoster sessionShowcase+
Format · ShowcasePoster sessionPresenters display work; attendees browse and ask questions. Some interaction, not structured work.
Evidence basisOutcome inferred from formatInferred from format
11:45 AM( ) EqGINO: Equivariant Geometry-Informed Fourier Neural Operators for 3D PDEs Sungwon Kim ⋅ Juho Song ⋅ Seungmin Shin ⋅ Guimok Cho ⋅ Sangkook Kim ⋅ Chanyoung Park Video LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
12:00 PM( ) A Multigrid-inspired Neural Iterative Solver for Poisson Equations on Large Voxel Grids Kangbo Lyu ⋅ Ruihong Cen ⋅ Yushen Wu ⋅ Tao Du Video LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
12:15 PM( ) Constructing Machine-Precision Neural Networks with Quasi-Interpolants Catherine Deng ⋅ Junmiao Hu ⋅ Milan Rohatgi ⋅ Jerry Liu ⋅ Christopher Re Video LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
12:30 PM( )Generalization Analysis and Improved Shape Representation with Neural Signed Distance Functions Meenakshi Krishnan ⋅ Ramani Duraiswami Video LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
12:45 PM( ) Out-of-distribution generalization of deep-learning surrogates for 2D PDE-generated dynamics in the small-data regime Binh Duong Nguyen ⋅ Stefan Sandfeld Video LinkPresentationKnowledge transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisNo participant output visible from this rowRead from source, no work signal
all eventMain ConferenceUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
all eventBlog Track PostersPoster SessionShowcase+
Format · ShowcasePoster SessionPresenters display work; attendees browse and ask questions. Some interaction, not structured work.
Evidence basisMediumRead from source
all eventJournal Track PostersPoster SessionShowcase+
Format · ShowcasePoster SessionPresenters display work; attendees browse and ask questions. Some interaction, not structured work.
Evidence basisMediumRead from source
all eventWorkshopsWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventCommunityUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
5:00 AM - 1:00 PMWorkshop Sun, Apr 26, 2026 • PDT 201 A/BWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventAI&PDE: ICLR 2026 Workshop on AI and Partial Differential EquationsWorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventTimezone: America/LosAngelesUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisLowRead from source
5:00 AM( ) Opening Remarks Renato Cerqueira VideoOpening RemarksOrientation+
Format · BroadcastOpening RemarksFraming or welcome from the stage. Orients the room, not participatory.
Evidence basisMediumRead from source
5:01 AM( ) Clécio R. Bom (CBPF) Clecio De Bom VideoUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
5:02 AM( ) Build AI to push scientific discovery - Neural operators and function-space generative models Jiachen Yao VideoUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
5:45 AM( ) Poster Session I LinkPoster SessionShowcase+
Format · ShowcasePoster SessionPresenters display work; attendees browse and ask questions. Some interaction, not structured work.
Evidence basisMediumRead from source
6:35 AM( ) The Future of AI and PDEs: An Industrial View Lucas Nissenbaum ⋅ Pablo Blanco ⋅ Alvaro Coutinho ⋅ John Smith ⋅ Ana Paula Muller VideoUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
7:35 AM( ) Maximilian Herde (ETH Zürich) VideoUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
9:00 AM( ) Jingmin Sun (Johns Hopkins/CMU) VideoUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
9:25 AM( ) Unlocking the Performance of Neural Surrogates in Real-World Applications A. Cristiano I. Malossi VideoUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
10:10 AM( ) Rose Yu (UC San Diego & Amazon Scholar) Rose Yu VideoUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
10:55 AM( ) Poster Session II LinkPoster SessionShowcase+
Format · ShowcasePoster SessionPresenters display work; attendees browse and ask questions. Some interaction, not structured work.
Evidence basisMediumRead from source
11:45 AM( ) EqGINO: Equivariant Geometry-Informed Fourier Neural Operators for 3D PDEs Sungwon Kim ⋅ Juho Song ⋅ Seungmin Shin ⋅ Guimok Cho ⋅ Sangkook Kim ⋅ Chanyoung Park Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
12:00 PM( ) A Multigrid-inspired Neural Iterative Solver for Poisson Equations on Large Voxel Grids Kangbo Lyu ⋅ Ruihong Cen ⋅ Yushen Wu ⋅ Tao Du Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
12:15 PM( ) Constructing Machine-Precision Neural Networks with Quasi-Interpolants Catherine Deng ⋅ Junmiao Hu ⋅ Milan Rohatgi ⋅ Jerry Liu ⋅ Christopher Re Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
12:30 PM( )Generalization Analysis and Improved Shape Representation with Neural Signed Distance Functions Meenakshi Krishnan ⋅ Ramani Duraiswami Video LinkPresentationKnowledge Transfer+
Format · BroadcastPresentationSpeakers present, the audience receives. Awareness only unless paired with practice or follow-up.
Evidence basisMediumRead from source
12:45 PM( ) Out-of-distribution generalization of deep-learning surrogates for 2D PDE-generated dynamics in the small-data regime Binh Duong Nguyen ⋅ Stefan Sandfeld Video LinkUnknownUnknown+
Format · BroadcastUnknownFormat not classified from the source; treated as a broadcast block by default.
Evidence basisMediumRead from source
The Full Reading
Why It Ranks This Way +
Calibrated from GES design 32/100 and verified 33/100 with no fourth-loop cap.
Reader Takeaway. For a reader, this is a comparison record more than a model to copy: it reads as a technology ai startup agenda, with the strongest visible signal in future-of-work fit and participation architecture and the biggest open question around follow through and network design. The practical test is whether the published agenda connects the room to post-event continuation and evidence.
Strongest signals: Future-of-Work Fit, Participation Architecture, and Evidence Maturity. Weakest signals: Follow Through, Network Design, and Learning Transfer.
How This Agenda Could Improve +
- Add named owners, dates, implementation checkpoints, and a visible post-event continuation path.
- Replace generic networking blocks with designed introductions, ask-offer exchanges, peer groups, or bridge-building rituals.
- Build transfer into the agenda through practice, feedback, job aids, reflection, and 30-90 day use cases.
Fastest next move: Add named owners, dated next steps, and a visible continuation path before treating the event as outcome-ready.
Role-Specific Reading +
Sponsor lens
Look beyond exposure. Strong sponsor value would show qualified interaction, problem work, buyer learning, customer evidence, or follow-up. The practical sponsor move is to look for structured introductions, buyer-seller fit, and relationship persistence.
Designer lens
The agenda is useful as a pattern sample from iclr.cc. Redesign attention should go first to the lowest-scoring pillars; in practice, turn the thinnest agenda blocks into participant work.
Executive lens
Treat the visible agenda as an operating plan. The executive move is to require owners, dates, and evidence before treating the event as strategic. If owners, proof, and follow-through are not visible, the public record does not yet prove strategic movement.
Aggregator lens
Treat the source URL as evidence, not decoration. The data-product move is to label the source boundary clearly before ranking the record before ranking or syndicating the record.
What GES Means Here +
The Gathering Effectiveness Score is a strict 0-100 public-evidence reading of the agenda across eight pillars. It rewards visible participant work, follow-through, transfer, network design, and proof mechanisms more than polish, speaker fame, attendance, or satisfaction.
Visible mechanisms: Participant work, Network design, Learning transfer.
Evidence boundary: Scores reflect visible agenda/source evidence and should not be read as proof of causal event impact.
Limitations, Score Caps, and Review Flags +
Limitations
- No visible follow-up, progress monitoring, or longitudinal tracking.
- No baseline measurement is visible.
Score caps
- No fourth-loop score cap applied.
Review flags
- No source-backed follow-up, validation, baseline, tracking, or impact evidence.
- No tracking, validation, feedback, or impact measurement found in the visible source text.
Is this proof the event worked? +
No. This is a strict public-evidence reading of the agenda. Proof would require baseline, comparison, follow-up, attribution, and impact evidence beyond the listing.
What should a reader inspect first? +
Start with the source URL, then compare the eight pillar scores against the agenda rows. The biggest opportunities usually sit in follow-through, evidence maturity, and participant work.
Why publish weak records? +
Weak records are part of the map. They show where public agendas still describe sessions and speakers more often than outcomes, commitments, transfer, or proof.
How should I use the rows? +
Read the agenda rows as the visible design trace: formats, purposes, and evidence labels show what the public source made inspectable, not everything that happened in the room. This is a source-grounded interpretation of the public agenda record, not a copy of the source, and not an endorsement of the event.
Embed the verified badge +
This record is in the hand-verified gold set. Copy the snippet below to embed the verified badge on your own site.
<a href="https://unitedwetransform.com/events/evt_2026_2026_conference_workshops_ai_pde_iclr_2026_workshop_on_ai_and_partial_diffe"><img src="https://unitedwetransform.com/badge/ges-verified.svg" alt="GES verified by United We Transform" height="40"></a>Where To Go Next
Compare this agenda against other Technology / AI / Startup events scored on the same eight pillars.