Hi! My name is Ayaan.

I research and build practical AI applications.

As a M.S. in engineering student at Columbia, I pride myself in my problem solving abilities. These days I research data generation in both digital twin and neuroscience applications.

Check out my outstanding student feature, or my DEF CON 33 and 34 presentation.

I love to read, watch cinema, go on long walks, and occasionally blog.

Feel free to connect me on LinkedIn, or view my Google Scholar and GitHub sites. Please email me at: ayaan.qayyum@columbia.edu

Education

Columbia University

M.S. in Electrical Engineering | Sep 2025 — Present

Specialization in Data-Driven Analysis and Computation.

GPA: 3.76/4.0.

Tesla Scholar

Rutgers University

B.S. in Electrical Engineering | May 2025 | Summa Cum Laude

GPA: 3.92/4.0.

Dean's List for all semesters

Professional Experience

Designed the first PNA-based GNN that beats four baseline architectures by a wide margin (spearman rho=0.88 versus baseline of 0.59)
Derived a first-order Jacobian surrogate for neuron-ablation data labels, a ~1000x reduction at N=1000, with empirical validation
Built a 49k parameter model that beats larger models with generalization across network width (N=10 to N=1000)

Designed physics-informed synthetic data pipelines for mmWave radar, improving 3D localization accuracy by up to 3.7 AP points
Built the paired real-simulated radar dataset of 14 objects, enabling 95.3% 14-way object recognition, co-authored two papers

Improved click PR-AUC by 5% for display and sponsored search ads ranking models with a 42% parameter reduction over baseline
Invented a SHAP parameter-cost-per-feature metric where PCA failed, flagging 3 defective features, cutting 20% excess model size
Diagnosed training instability via per-layer gradient-norm analysis, added residual connections; cutting training epochs by 50%
Redesigned the published multi-task hierarchy into a bifocal structure, achieving a 23% param reduction

REALTIME team at WINLAB, Rutgers University
Built distributed learning algorithms across a 32-node testbed, validating convergence rates in real-world constraints
Led a team to conduct large-scale network experiments with Python (Socket, MPI) to evaluate sync/async communication
Varied topology, communication frequency and latency to discover breakdown conditions (>14 iterations without update)

On Campus Experience

Mentored teams to develop two search-and-rescue drones within 3 ft of localization, two papers at MIT URTC & provisional patent

Significantly improved the learning experience in Engineering Economics by introducing innovative teaching methods, such as developing an online interactive learning tool and designing worksheets, used by over 100 students per month
Contributed to a 20% increase in the class average scores through effective pedagogical techniques
Developed and utilized diverse communication, advising, and teaching skills to enhance student understanding
Actively participated in academic success of students, reinforcing key concepts, providing personalized assistance
Taught and advised students on Engineering Economics course material, wrote 1000+ textbook questions

Taught and engaged 1,000+ students by designing and delivering professional development lectures in class
Designed four professional development workshops for classroom use, with lesson plans and teacher training
Worked closely with stakeholders to optimize the delivery of intended materials for maximum student impact

Webmaster from Aug 2023 to May 2024.
Organized and facilitated community-building events and educational programs for residents
Provided crisis intervention and support, serving as a resource for student concerns and conflicts
Collaborated with residential life staff on policy enforcement and student conduct issues