BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Venue Radar//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:CHIL 2026 Calendar
BEGIN:VEVENT
UID:chil-2026-deadline-full-paper@scientific-conference-calendar
DTSTAMP:19700101T000000Z
DTSTART:20260205T115900Z
DTEND:20260205T125900Z
SUMMARY:[Healthcare & Clinical AI][Probabilistic\, Causal & Uncertainty ML]
  CHIL 2026 - Submissions due
DESCRIPTION:Conference title: Conference on Health\, Inference\, and Learni
 ng\nWebsite: https://chil.ahli.cc/\nSource URL: https://chil.ahli.cc/subm
 it/call-for-papers/\nLocation: Seattle\, Washington\, USA\nTopics: Health
 care & Clinical AI\, Probabilistic\, Causal & Uncertainty ML\, Fairness &
  Responsible AI\, Machine Learning\nSize: M\nSubmission type: Paper-based
 \nConfidence: confirmed\nLast checked date: 2026-08-20\nNotes: Official C
 HIL pages list conference dates\, location\, and submission timeline.\nSc
 ope: Machine learning and inference for clinical\, public\, and populatio
 n health. Distinguishing areas include causal inference\, survival analys
 is\, multimodal health data\, generalization across populations\, interpr
 etability\, and clinical evaluation and deployment. Research may emphasiz
 e methods\, practical applications\, or societal impact. Health-focused i
 nference and evaluation remain the common thread across these tracks.\nAd
 ditional topics: Explainability & Interpretability\, Human-AI Interaction
  & Deployment\, Robustness & Safety\, Time Series & Sequential Data\nScop
 e sources (2026): https://chil.ahli.cc/submit/call-for-papers/\nScope las
 t checked: 2026-10-07
URL:https://chil.ahli.cc/submit/call-for-papers/
END:VEVENT
BEGIN:VEVENT
UID:chil-2026-deadline-doctoral-symposium@scientific-conference-calendar
DTSTAMP:19700101T000000Z
DTSTART:20260314T115900Z
DTEND:20260314T125900Z
SUMMARY:[Healthcare & Clinical AI][Probabilistic\, Causal & Uncertainty ML]
  CHIL 2026 - Doctoral symposium application
DESCRIPTION:Conference title: Conference on Health\, Inference\, and Learni
 ng\nWebsite: https://chil.ahli.cc/\nSource URL: https://chil.ahli.cc/\nLo
 cation: Seattle\, Washington\, USA\nTopics: Healthcare & Clinical AI\, Pr
 obabilistic\, Causal & Uncertainty ML\, Fairness & Responsible AI\, Machi
 ne Learning\nSize: M\nSubmission type: Paper-based\nConfidence: confirmed
 \nLast checked date: 2026-08-20\nNotes: Official CHIL pages list conferen
 ce dates\, location\, and submission timeline.\nScope: Machine learning a
 nd inference for clinical\, public\, and population health. Distinguishin
 g areas include causal inference\, survival analysis\, multimodal health 
 data\, generalization across populations\, interpretability\, and clinica
 l evaluation and deployment. Research may emphasize methods\, practical a
 pplications\, or societal impact. Health-focused inference and evaluation
  remain the common thread across these tracks.\nAdditional topics: Explai
 nability & Interpretability\, Human-AI Interaction & Deployment\, Robustn
 ess & Safety\, Time Series & Sequential Data\nScope sources (2026): https
 ://chil.ahli.cc/submit/call-for-papers/\nScope last checked: 2026-10-07
URL:https://chil.ahli.cc/
END:VEVENT
BEGIN:VEVENT
UID:chil-2026-conference@scientific-conference-calendar
DTSTAMP:19700101T000000Z
DTSTART;VALUE=DATE:20260628
DTEND;VALUE=DATE:20260701
SUMMARY:[Healthcare & Clinical AI][Probabilistic\, Causal & Uncertainty ML]
  CHIL 2026 - Conference
DESCRIPTION:Conference title: Conference on Health\, Inference\, and Learni
 ng\nWebsite: https://chil.ahli.cc/\nSource URL: https://chil.ahli.cc/\nLo
 cation: Seattle\, Washington\, USA\nTopics: Healthcare & Clinical AI\, Pr
 obabilistic\, Causal & Uncertainty ML\, Fairness & Responsible AI\, Machi
 ne Learning\nSize: M\nSubmission type: Paper-based\nConfidence: confirmed
 \nLast checked date: 2026-08-20\nNotes: Official CHIL pages list conferen
 ce dates\, location\, and submission timeline.\nScope: Machine learning a
 nd inference for clinical\, public\, and population health. Distinguishin
 g areas include causal inference\, survival analysis\, multimodal health 
 data\, generalization across populations\, interpretability\, and clinica
 l evaluation and deployment. Research may emphasize methods\, practical a
 pplications\, or societal impact. Health-focused inference and evaluation
  remain the common thread across these tracks.\nAdditional topics: Explai
 nability & Interpretability\, Human-AI Interaction & Deployment\, Robustn
 ess & Safety\, Time Series & Sequential Data\nScope sources (2026): https
 ://chil.ahli.cc/submit/call-for-papers/\nScope last checked: 2026-10-07
URL:https://chil.ahli.cc/
LOCATION:Seattle\, Washington\, USA
END:VEVENT
END:VCALENDAR