Computer Engineering Student
I engineer robust web architectures and applied machine learning systems, grounded in clean software design and empirical research. Currently open to engineering roles, applied research, and technical collaborations.
Undergraduate computer engineer interested in computational models, full-stack systems, and software that makes complex ideas simple to work with.
I grew up in Bhakundebesi, a quiet corner of Namobuddha, constantly taking apart whatever electronics I could get my hands on. Half the time, the devices never quite went back together with the same number of screws, but the habit stuck. That early curiosity about how things work eventually led me to computer engineering, where at least compiler errors don't leave spare metal parts on the floor.
Most of my technical work sits at the intersection of web architecture and applied machine learning. I like taking mathematical or probabilistic concepts and turning them into responsive, dependable software, plus whatever emerging technology happens to catch my attention that week. I see code less as raw syntax and more as structured reasoning, a way of organizing logic so that difficult problems become transparent and maintainable rather than accidental puzzles. It's closer to architecture for thought, a method for taking a messy idea and turning it into something people can actually use without needing a manual.
When I step away from the terminal, I'm usually hiking, reading about astrophysics and geopolitics, or watching documentaries that have zero bearing on my coursework. I contain multitudes, or at least a lot of unrelated hobbies. Turns out walking uphill in the hills around Kathmandu does wonders for debugging memory leaks and recalibrating perspective.
Systems programming, algorithmic thinking, and building reliable backends across compiled and interpreted languages.
Python, JavaScript, Java, C++
Responsive interfaces, thoughtful state management, and modern web applications that feel fast and intuitive.
HTML/CSS, React, Node.js, Next.js
Domain-specific modeling, retrieval systems, and machine learning pipelines grounded in verified context.
Qiskit, Blockchain, Computer Vision, Reinforcement Learning, RAG, LLMs, Prompt Engineering
Schema design, relational and document databases, vector search indexing, and resilient API layers.
MongoDB, PostgreSQL, RESTful APIs, GraphQL, Vector DBs, Supabase
“Build with discipline. Question assumptions. Seek clarity over complexity.”
Engineering roles, applied AI development, and independent software initiatives.
Solvrays
AI that works where insurance actually gets done. Solvrays unifies data, documents, and systems so operational work moves seamlessly across the technology you already have. No rip and replace.
TuringtonLab
An independent space dedicated to building websites, personal and experimental projects, technical documentation, software development, AI and technology experiments, and exploring and building new ideas.
Peer-reviewed and analytical publications in machine learning, risk prediction, and applied AI systems.
Author: Bijay Bartaula
A comparative study of Logistic Regression, Random Forest, and XGBoost for diabetes risk prediction using 100,000 patient records. XGBoost achieved the best performance with an ROC-AUC of 0.9782 and accuracy of 97.14%.
Author: Bijay Bartaula
An exploration of how AI could transform matchmaking and romantic relationships, covering personalization, affective computing, behavioral analytics, and the ethical implications of AI-mediated intimacy.
Author: Bijay Bartaula
A retrieval-augmented generation system designed for academic advising. Combines vector search over institutional curricula with LLM reasoning to map prerequisite dependencies and degree trajectories.
Author: Bijay Bartaula
Unsupervised K-means clustering applied to quantum circuit representations on the IBMQ Athens hardware dataset to identify optimal qubit allocation patterns and reduce gate error overhead.
Author: Bijay Bartaula
Multi-model ensemble combining CatBoost and TabNet architectures on clinical and cognitive datasets to evaluate predictive accuracy for early neurodegenerative detection.
Author: Bijay Bartaula
An exploratory research system translating unstructured scientific observations and tabular measurements into structured, testable hypotheses using automated LLM validation pipelines.
A curated portfolio of full-stack platforms, AI/ML systems, and interactive tools.
AI/ML
AI/ML
AI/MLA domain-engineered spiritual analysis platform combining astronomical calculations and high-precision numerology. Synthesizes astrological algorithms, horoscope telemetry, and explainable Gemini AI insights into personalized reports.
Web
Web
Web
AI/ML
AI/ML
Web
AI/ML
Web
AI/ML
Web
Web
Web
Game
Web
AI/ML
AI/ML
Web
Web
Web
Web
Web
Web
Web
Web
Web
Web
Game
Game
AI/ML
AI/MLA computer vision automation utility using OpenCV and MediaPipe to trigger system power states via hand landmarks. Explores touchless peripheral control for desktop operating systems.
AI/MLUnsupervised K-means clustering applied to quantum circuit DAG topologies from the IBMQ Athens dataset. Groups circuit execution patterns to guide hardware-efficient qubit mapping.
AI/MLAn intelligent computer vision system leveraging YOLOv8 for passenger density monitoring and anomaly detection. Enhances vertical transit safety through automated visual stream analysis.
AI/MLA healthcare machine learning pipeline comparing ensemble classifiers across 100,000 clinical records. Identifies high-risk indicators to support early diagnostic screening in preventative medicine.
AI/MLAn ensemble prediction framework combining CatBoost and TabNet architectures on clinical biomarkers. Evaluates neurodegenerative risk patterns with high interpretability for diagnostic support.
AI/MLA statistical classification pipeline analyzing physiological signals, lifestyle metrics, and sleep architecture. Identifies early indicators of sleep apnea and insomnia from tabular health data.
Academic foundation in computer engineering, mathematics, and computational systems.
NCIT, Pokhara University
Specializing in software development, computer networks, and artificial intelligence.
Banepa NIST
Completed science stream with focus on Physics, Chemistry, Mathematics, and Computer Science.
SJSS
Foundation education with distinction in Mathematics and Science subjects.
Cornfield Chase
Hans Zimmer
Essays on software architecture, the mechanics of intelligence, and the evolution of engineering.
“"You are what your deep, driving desire is. As your desire is, so is your will. As your will is, so is your deed. As your deed is, so is your destiny." - Upanishads”
An essay on information abundance, epistemic access, and how the bottleneck to knowledge shifted from discovery to asking the right question.
An architectural examination of multimodal foundation models, frontier cognitive scaling, and what upcoming system paradigms reveal about the trajectory of artificial intelligence.
The evolutionary trajectory of AI-assisted software engineering, transitioning from expressive conversational vibes to precise vector contexts and modular cognition.
What specialized skill registries and modular agent ecosystems reveal about how expertise is systematized, packaged, and automated in the age of intelligence.
An architectural examination of how modern software foundations are shifting from deterministic MVC controllers to context-engineered cognition loops.
A meditation on code, competition, and cultural evolution in Nepalese universities.
How deliberate question-craft transforms artificial intelligence from computational tools into collaborative thought partners.
Open to research collaborations, software engineering opportunities, and technical discussions.
Feel free to reach out via email for inquiries, collaborations, or discussions.
Send Emailbijay.algo@gmail.com