Current instructor

Ramya Korlakai Vinayak

What students say

Original student reviews · all captured dates

No unambiguous review-profile match is available.

Personal experiences, not a representative survey. Profile matching and captured coverage are shown in the source details.

Classes with Ramya Korlakai Vinayak

Senior Honors Thesis is an individual study for seniors completing theses for honors in the Computer Sciences major, arranged with a faculty member.

Offering recorded · Spring 2027 · Sources

No recorded grade history

Individual study for seniors completing theses for honors in the Computer Sciences major, continuing COMP SCI 681.

Offering recorded · Spring 2027 · Sources

Recent recorded grades — Spring 2022: 4.00 GPA, 100.0% A/AB (n=1 letter grades).

No recorded grade history

Directed study projects for Computer Science juniors and seniors, arranged with a faculty member.

Offering recorded · Spring 2027 · Sources

historical GPA · grades

Fall 2022–Spring 2026 · limited sample

Pre-dissertator independent research supervised by a faculty member for students who have completed a master's degree but have not reached dissertator status.

Offering recorded · Spring 2027 · Sources

No recorded grade history

Independent Study course for ECE featuring directed study projects arranged with the instructor.

Offering recorded · Spring 2027 · Sources

historical GPA · grades

Fall 2022–Spring 2026 · limited sample

Advanced Independent Study for ECE involving directed study projects arranged with the instructor.

Offering recorded · Spring 2027 · Sources

Recent recorded grades — Spring 2022: 4.00 GPA, 100.0% A/AB (n=1 letter grades).

historical GPA · grades

Spring 2023–Fall 2023 · limited sample

Independent work on master's research overseen by a qualified instructor. Students demonstrate the ability to formulate, analyze, and independently solve advanced engineering problems and communicate research results orally and in writing.

Offering recorded · Spring 2027 · Sources

No recorded grade history

Theoretical foundations of large-scale machine learning covering mathematical optimization, randomized algorithms, and tradeoffs like convergence speed and scalability.

Offering recorded · Spring 2027 · Sources

historical GPA · grades

Spring 2024–Spring 2024 · limited sample

Recorded teaching history

Distinct courses recorded per term

Explore courses taught by this instructor →
Browse recorded courses
TermCourseTitle
Spring 2027COMPSCI 681Senior Honors Thesis
Spring 2027COMPSCI 682Senior Honors Thesis
Spring 2027COMPSCI 699Directed Study
Spring 2027COMPSCI 790Master's Thesis
Spring 2027COMPSCI 799Master's Research
Spring 2027COMPSCI 899Pre-dissertator Research
Spring 2027COMPSCI 990Dissertation
Spring 2027ECE 399Independent Study
Spring 2027ECE 699Advanced Independent Study
Spring 2027ECE 790Master's Research
Spring 2027ECE 826Theoretical Foundations of Large-scale Machine Learning
Spring 2027ECE 890Pre-dissertator's Research
Spring 2027ECE 990Dissertator's Research
Fall 2026COMPSCI 298Directed Study in Computer Science
Fall 2026COMPSCI 681Senior Honors Thesis
Fall 2026COMPSCI 682Senior Honors Thesis
Fall 2026COMPSCI 699Directed Study
Fall 2026COMPSCI 799Master's Research
Fall 2026COMPSCI 899Pre-dissertator Research
Fall 2026COMPSCI 990Dissertation
Fall 2026ECE 331Introduction to Random Signal Analysis and Statistics
Fall 2026ECE 399Independent Study
Fall 2026ECE 699Advanced Independent Study
Fall 2026ECE 790Master's Research
Fall 2026ECE 890Pre-dissertator's Research
Fall 2026ECE 990Dissertator's Research

Teaching history may be incomplete. Course pages contain course-specific feedback and citations.

Instructor identity & provenance
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