v₀ · Chattogram, Bangladesh · 22.35°N, 91.78°E
Computer Science & Engineering graduate of IIUC and a machine-learning researcher in the making. Constellations, after all, are just graphs — and she's learning to read them.
v₁ · origin node
It started small: one line of code that actually worked, and the little thrill of making a computer do something. That grew into a B.Sc. in Computer Science & Engineering at the International Islamic University Chittagong — four years of late nights, stubborn bugs, and slowly falling for the quieter corners of CS: data, theory, and how things fit together. She's still learning. Honestly, that's her favorite part.
v₂ · the toolkit
A researcher's stack, built patiently: languages for thinking precisely, databases for keeping the world in order, and machine learning for finding what hides between the rows.
From pointer arithmetic to list comprehensions — comfortable across paradigms, from bare-metal to high-level.
Database management systems — schema design, normalization, and queries that respect both the data and whoever reads it next.
Her home ground: representation learning, graph neural networks, and models that spot what humans miss.
v₃ · the anomaly
Online platforms are graphs of people. Most nodes behave like stars — steady, predictable. But spammers move in coordinated packs, at suspicious speeds. Her undergraduate dissertation taught a machine to see them.
“Spammer Group Identification Using Heterogeneous Variational Graph Autoencoder with Velocity-Adaptive Temporal Modeling”
Real networks aren't one kind of thing — users, posts, reviews, timestamps — all different node types, all in one graph.
A variational graph autoencoder compresses the network into its essence, then tries to rebuild it. What can't be rebuilt cleanly is suspicious.
Spam has a tempo. The model adapts to how fast behavior changes over time, catching bursts of coordinated activity.
Not just single spammers — whole spammer groups, identified by how they cluster, move, and act together.
v₄ · the build log
Coursework, experiments, and things built at 2 a.m. because the idea wouldn't wait — all of it lives on GitHub, in the open.
Graph autoencoder pipelines, temporal modeling, and spammer-group detection benchmarks.
view on GitHub → ML NOTEBOOKSModels, training runs, and the notebooks where ideas get tested against real data.
view on GitHub → COURSEWORK & MOREC, C#, and Python projects from four years of CSE — database systems included.
browse all repos →v₅ · off the clock
A well-trained researcher knows when to stop training. Off the clock, she's fiercely committed to two disciplines of her own.
Some people optimize hyperparameters. She also optimizes REM cycles. The best debugging happens after eight hours — this is science, allegedly.
Travel is graph traversal in the physical world: new nodes, new edges, new data. The itinerary list is long, and the visited-set keeps growing.
v₆ · the next edge
Degree in hand, and hoping to keep growing as a researcher — good problems, good mentors, and work worth losing a little sleep over (only a little — she guards her sleep). Always happy to talk about research, ideas, or anything graph-shaped.
eftehabinte380@gmail.com