This is an AAO appeal decision — a case denied once and appealed. It shows how the framework is applied; it is not the filing population.
The Petitioner, a professor and researcher with a PhD, proposed to continue conducting research with potential impact on the U.S. economy and broader implications within her field.
Field: unspecified scientific research field · Read the decision (uscis.gov)
EB-2 threshold — addressed
Director had already found EB-2 classification met as an advanced degree professional (PhD from a U.S. university); AAO confirms this on appeal.
Prong 1 — national importanceestablished
Prong 2 — well positionedestablished
Prong 3 — balance of factorsestablished
How the evidence was treated
- citations publications · credited
- media · credited
- recommendation letter · credited
- degree · credited
This record is one of thousands, each coded for the reasons it turned. A placement shows where your profile sits in that record, not what it predicts. See where your profile sits — 90 seconds →
Notable
A rare petitioner-favorable outcome: AAO sustains the appeal, finding all three Dhanasar prongs satisfied, crediting corroborated citation history, news coverage, and independent recommendation letters without discounting any evidence.
Authorities this decision leans on
From the doctrinal survivor set — 187 authority tests across 47 distinct authorities cleared the differential-lift gates. Only those appear here.
- 8 C.F.R. 204.5(k)(2)
- 8 C.F.R. 204.5(k)(3)(ii)
- Poursina v. USCIS
What this case teaches
Analyst reading of the decision text.
Petitioner's detailed statement of future research, corroborated by citation history, media coverage, and independent expert letters, satisfied all three Dhanasar prongs.
Pair a concrete forward-looking research plan with independent corroboration (citations, media, expert letters) linking past success to broader field-wide impact.
moderate
field_advancement · economic_growth_generic
mixed
Cases in adjacent profiles
- The Petitioner, a senior principal biostatistician, proposes to develop innovative statistical and machine-learning meth
- The Petitioner proposed to use advanced deep learning and machine learning models to create efficient, safe, and well-pe
- The Petitioner proposes to advance machine learning methods for the design and optimization of advanced metamaterials an
- The Petitioner, a postdoctoral chemist, proposed to continue developing high-performance materials and novel synthetic m
- The Petitioner proposes to design novel sensor data processing methods and machine learning algorithms for smart wearabl