Programs matched
to your profile.

Stanford University

BS Computer Science — AI/ML track

Your compiler research + YCRG bio ML pipeline maps directly onto Stanford's CS+AI emphasis. CS with AI track keeps you closer to your technical core and opens more research doors than Symbolic Systems.

Yale University

Statistics & Data Science (B.S.)

Yale's S&DS prizes applied statistical rigor over ML hype. Your data verification narrative fits perfectly. DataViz Games + EEG/NYAS work will stand out in a pool that skews toward pure theory.

Princeton University

Operations Research & Financial Engineering (ORFE)

Your Monte Carlo engine is a direct ORFE signal — most applicants have no quant project at all. ORFE bridges stats, optimization, and real-world systems, aligning tightly with your "decision-ready insights" narrative.

University of Pennsylvania

Statistics & Data Science — CAS (B.A./B.S.)

Penn's Stats dept is strong and less insanely competitive than Wharton. Your profile reads as a strong CAS applicant; the major lets you cross-register into Wharton quant courses anyway.

Carnegie Mellon University

Statistics & Machine Learning (SML) — ED

SML is CMU's best-kept secret: smaller than CS, deeply technical, and perfectly matched to your cancer imaging ML + EEG work. ED here is strategically smart — CMU rewards demonstrated commitment and your fit is unusually tight. This is your strongest ED play.

Columbia University

Applied Mathematics — SEAS (B.S.)

ApplMath in SEAS is uncommon among applicants — most default to CS or CS+Econ, so you face less internal competition. Your "noisy signals → decision-ready insights" narrative maps cleanly onto applied math's identity, and the major lets you pull freely from Stats, IEOR, and CS. Hook your Why Columbia essay to the Data Science Institute and Zuckerman Institute — your EEG/NYAS prosthetics work creates a direct bridge.

Tsinghua / Peking University

Data Science (Peking) or CS—AI (Tsinghua Yao Class)

Apply to Peking's School of Mathematical Sciences or Tsinghua's Yao Class. Both are hyper-competitive but your Carnegie Hall + research profile is unusual for international applicants from the US.

Duke University

Statistical Science (B.S.)

Duke's StatSci is rigorous and research-focused. Their Bass Connections program lets you plug into real data science research projects — directly aligned with your existing lab experience at YCRG and NYAS.

Cornell University

ORIE — Operations Research & Information Engineering

ORIE over Stats because your Monte Carlo engine and data systems framing is optimization-flavored, not just analysis. ORIE grads go into quant finance, ML engineering, and applied research — exactly your trajectory.

UC Berkeley

Data Science (L&S) — declare Stats upper div

Apply Data Science L&S (more accessible than EECS/CS), then layer in upper-division Stats and ML courses. Berkeley's DSP and URAP programs give research access. Your Kaggle top-4% record strengthens this significantly.

University of Michigan

Statistics & Data Science — LSA (B.S.)

Michigan's Stats program is top-10 nationally. The LSA pathway keeps flexibility; you can add a CS minor freely. Strong sports analytics and computational bio research — both squarely in your wheelhouse.

UIUC

Statistics + CS (dual degree)

UIUC's Stat+CS is one of the strongest technical programs in the country at this selectivity tier. Apply to the dual degree directly — your USACO Silver and LeetCode record matters here, as CS admissions weighs demonstrated coding ability heavily.

Georgia Tech

Computational Data Analysis — MACS track

GT's MACS pathway into CDA is underrated. Strong co-op culture means real industry DS experience by graduation. Your systems/research narrative fits their applied engineering ethos well.

University of Maryland

CS + Statistics double major — CMNS

UMD's CS+Stats combo is strong and undervalued. Proximity to NIH and federal research agencies means exceptional computational bio research access — directly relevant to your YCRG cancer imaging work.

UW–Madison

Statistics (B.S.)

One of the best public Stats departments in the US, period. Small cohort, strong faculty, great for applied work. Often overlooked in favor of bigger brand names — but the program quality punches well above its ranking.

UNC Chapel Hill

Statistics & Analytics (B.S.)

UNC's Stats program has strong faculty and a collaborative culture. Good fit if you want research mentorship in a mid-sized program. Their Data Science initiative is growing fast.

UT Austin

Statistics — Turing Scholars if eligible

Apply through the Elements of Computing / Statistics track. If your stats qualify for Turing Scholars (CNS honors), pursue it — that cohort gets priority research access and is significantly more selective and rewarding.

Rutgers University–New Brunswick

Statistics (B.S.) — SAS

In-state flagship with a solid Stats department. Merit aid very likely given your profile. Research access at RUCDR and proximity to NYC pharma/finance industry for internships.

Purdue University

Data Science (B.S.)

Purdue's DS program is well-structured and industry-connected. Strong STEM culture. Good fallback with real career outcomes in ML/data engineering roles at top companies.

Penn State

Statistics (B.S.) — Schreyer Honors College

Apply to Schreyer Honors College specifically — it transforms Penn State academically and includes a thesis requirement that suits your research background perfectly. Don't apply without targeting Schreyer.

Stevens Institute of Technology

Data Science (B.S.)

Strong merit scholarships very likely given your profile. Small program, good faculty, NYC metro location for internships. Their Quantitative Finance track is worth exploring alongside DS.

NJIT

Data Science (B.S.) — Albert Dorman Honors College

Apply through the Honors College specifically. You'd almost certainly be near the top of their incoming class — research funding, priority course access, and strong merit aid all follow from that position.