Funders › NY › Association for Computing Machinery Inc › 2023
Grants paid by Association for Computing Machinery Inc, tax year 2023
EIN 13-1921358 · New York, NY · Form 990, Schedule I · NTEE U030
In tax year 2023, Association for Computing Machinery Inc (EIN 13-1921358) reported 6 grants paid totaling $1,438,635. Dataset version 2026.09.0, built 2026-09-03.
Every grant, 2023
| Tax year | Recipient | Match | Amount | Type | Purpose | Source filing |
|---|---|---|---|---|---|---|
| 2023 | Putationalcom LLC Austin, TX | A | $1,000,000 | paid | 2022 A.M. TURING AWARD RECIPIENT | 202441359349305114 |
| 2023 | Computer Science Teachers Association Chicago, IL | A | $403,135 | paid | GENERAL SUPPORT | 202441359349305114 |
| 2023 | Duke University Durham, NC | A | $10,000 | paid | COMMUNITY RESEARCH GRANT | 202441359349305114 |
| 2023 | Dartmouth College Hanover, NH | A | $10,000 | paid | CRAWDAD PROJECT SUPPORT | 202441359349305114 |
| 2023 | University of Pittsburgh Pittsburgh, PA | A | $10,000 | paid | SIGMOBILE STUDENT COMMUNITY GRANT | 202441359349305114 |
| 2023 | Association for Information System Atlanta, GA | A | $5,500 | paid | ICIS 2022 CONFERENCE SUPPORT | 202441359349305114 |
Match tier: A Reported EIN · B Exact name and place · C Strong name match · D Probable name match · U Unresolved. Tiers C and D are inferred, not reported; see how matching works.
Recipient matching for this dataset version has not yet completed its independent precision check. Tier A rows carry the EIN the filer reported; tiers B–D are the matcher's inference and should be read as leads until the check is published on the methodology page.
Derived from IRS Form 990 e-file XML. Dataset version 2026.09.0, built 2026-09-03. All years for this funder.
The same organization elsewhere in the program: exempt status and filing health · federal awards · grant guidance · open opportunities.
This is informational only, derived from public data on the dates shown. It is not an eligibility determination, and not legal, tax, or accounting advice. Verify against the official source before relying on it.