I'm a Rice University sophomore focused on Computer Science and Economics, eager to enhance my skills and competitiveness in tech.
Explore my projects, where I apply my skills in Python, SQL, and more to solve real-world problems and innovate in tech.
Feb 2026
Implemented an nth-order Markov chain model in Python to predict daily stock price movements for GOOG, FSLR, and DJIA, achieving the lowest MSE of 0.80 on DJIA across 500 trials using two years of historical data Designed a modular prediction pipeline with separate modeling, weighted random choice,
Mar 2026
Implemented Reed-Solomon error correction for QR codes in Python using GF(256) finite field arithmetic, building an immutable Polynomial class with addition, subtraction, multiplication, and polynomial long division operations to generate error correction bytes from encoded message data
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Hydroficient · ✅ Verified by Extern · ⏱️ In progress
Build, hack, and defend real IoT infrastructure, then hand your security playbook to an actual company. You’ll launch realistic cyberattacks, from spoofing and replay attacks to unauthorized shutoffs, and implement defenses like TLS and mTLS.
I'm a Rice University sophomore focused on Computer Science and Economics, eager to enhance my skills and competitiveness in tech.
I am Dara Odukoya, a sophomore at Rice University pursuing a Bachelor's in Computer Science and Economics. I have a passion for technology and data analysis, and I am eager to strengthen my skills and gain experience in the industry.
Externships
Hydroficient IoT Cyber Defense Externship
Hydroficient
Experience
Instructor & Canvas Lead
Rice Center for Engineering Leadership(RCEL) · June 2026 - July 2026
Technology Job Simulation Participant
Deloitte (via Forage) · Jun 2026
Education
Rice University
B.A. Computer Science, Economics · Class of 2029
Skills
Feb 2026
Implemented an nth-order Markov chain model in Python to predict daily stock price movements for GOOG, FSLR, and DJIA, achieving the lowest MSE of 0.80 on DJIA across 500 trials using two years of historical data Designed a modular prediction pipeline with separate modeling, weighted random choice,
Implemented an nth-order Markov chain model in Python to predict daily stock price movements for GOOG, FSLR, and DJIA, achieving the lowest MSE of 0.80 on DJIA across 500 trials using two years of historical data Designed a modular prediction pipeline with separate modeling, weighted random choice,
Mar 2026
Implemented Reed-Solomon error correction for QR codes in Python using GF(256) finite field arithmetic, building an immutable Polynomial class with addition, subtraction, multiplication, and polynomial long division operations to generate error correction bytes from encoded message data
Implemented Reed-Solomon error correction for QR codes in Python using GF(256) finite field arithmetic, building an immutable Polynomial class with addition, subtraction, multiplication, and polynomial long division operations to generate error correction bytes from encoded message data
✅ Verified by Extern · ⏱️ In progress
Build, hack, and defend real IoT infrastructure, then hand your security playbook to an actual company. You’ll launch realistic cyberattacks, from spoofing and replay attacks to unauthorized shutoffs, and implement defenses like TLS and mTLS.
During my externship with Hydroficient, I engaged in building and defending IoT infrastructure while developing a comprehensive security playbook. I executed realistic cyberattacks to identify vulnerabilities, implemented robust defenses, and collaborated with industry professionals to enhance IoT security measures. This hands-on experience equipped me with practical skills in cybersecurity for
Throughout the project, I completed a series of tasks that deepened my understanding of IoT security. I explored the technology landscape, identified vulnerabilities, and worked on implementing defensive strategies. Below are the key activities I undertook during this externship.
On a simulated hotel water system, I documented system architecture, inventoried assets with CIA priorities, and applied STRIDE to identify spoofing, tampering, DoS, and replay risks. The deliverable included three prioritized critical concerns and mitigation recommendations.