# Prasannakumaran Dhanasekaran

> Software @Commure | UCSD | Applied ML

Location: Mountain View, California, United States
Profile: https://flows.cv/prasannakumaran

Hi. I'm a CSE grad student at UCSD, enthusiastic about fields including Computational Social Science, Natural Language Processing, Graph Learning and Software Development. I aspire to build innovative solutions for societal problems to empower the global community. I am a believer and dedicated to building a harmonious world through software and AI. I am actively looking for collaborations with laboratories and companies that have similar goals and interests to mine and work towards a better world for every individual. 

Link to Porfolio: https://prasannakumaran.github.io/

## Work Experience
### Software Engineer, Scribe @ Commure
Jan 2024 – Present | Mountain View, California, United States

### Graduate Student Researcher @ UC San Diego
Jan 2024 – Jan 2024 | San Diego, California, United States
de Sa lab - Department of Cognitive Science (Prof. Virginia de Sa)
• Designed novel pretraining framework for a Brain Computer Interface (BCI) on intracortical data.
• Built an LSTM based model to decode characters and predict transitions within temporal data from single-stroked handwriting data.
• Working on fine-tuning the end-to-end pipeline using BCI signals.

### Graduate Research Assistant @ UC San Diego
Jan 2024 – Jan 2024 | San Diego, California, United States
Kiyonaga lab - Department of Cognitive Science (Prof. Anastasia Kiyonaga)
• Developed a web-application using jsPsych and javascript to obtain user responses on word pair similarity.
• Investigated the role of distributional cues and embodied mechanisms in language comprehension for individuals with varying levels of visual and verbal imagery.
• Utilized LLM and GloVe embeddings and constructed nested regression models to analyze the contribution of sensorimotor distance and vivid visual imagery scores in predicting similarity between word pairs.
• Employed statistical techniques such as likelihood ratio tests (LRT) and Akaike Information Criterion (AIC) to quantify model performance and assess significance.

### Graduate Teaching Assistant @ UC San Diego
Jan 2023 – Jan 2024 | San Diego, California, United States
• [Summer Session II - 2023] CSE - 21: Mathematics for Algorithms and Systems Analysis (Prof. Oliver Braun). 30+ students
• [Fall 2023] CSE - 21: Mathematics for Algorithms and Systems Analysis (Prof. Russell Impagliazzo). 350+ students
• [Winter 2024] COGS 9 - Introduction to Data Science (Prof. Meenakshi Khosla) 300+ students
• [Spring 2024] COGS 18 - Introduction to Python (Prof. Shannon E. Ellis) 600+ students

My duties as a TA includes:
1) holding in-person office hours
2) conducting weekly discussion sessions
3) leading the instructional team
4) designing questions and solutions for the midterm and final examinations, 
5) grading homework and exams
6) conducting on demand 1-on-1 meetings with students
7) leading programming lab

### Graduate Student Researcher @ UC San Diego
Jan 2023 – Jan 2024 | San Diego, California, United States
Shtrahman lab - Department of Neuroscience (Prof. Matthew Shtrahman)
• Working on developing computational tools for understanding neuronal circuit activity and the function of other biological networks.
• Investigating the hyper excitability, synchrony of neuronal activity in an epilepsy mouse model using calcium imaging data in the Dentate Gyrus and CA1 region of the brain.
• Conducting statistical analyses to distinguish neuron firing patterns associated with normal and epileptic brain activities, on both sparse and dense neural coded data. Employing statistical tests to discern correlations in neuron firing behavior.

### Teaching Assistant @ Solarillion Foundation
Jan 2020 – Jan 2022 | Chennai, Tamil Nadu, India
• Mentored 10+ students in research and assignments in Python & ML during the orientation and project phase. Reviewed project and transferred knowledge in report and content writing.
• Organized and hosted multiple open houses and orientation sessions for research aspirants

### Research Assistant @ Solarillion Foundation
Jan 2019 – Jan 2022 | Chennai, Tamil Nadu, India
• Proposed a Machine Learning solution for malware detection on a massive real-world mobile dataset which exhibits less than 9% inaccuracy in detecting malware and classifies the type of data stolen with 83% certainty. Formulated problem statement, designed solution, wrote and maintained codebase. Co-authored and presented this work at ICCS'2021. 
• Led a diverse group of 5 individuals and worked on a Graph Machine Learning solution for early detection of fake health news. Achieved a 17.1% accuracy increase over the benchmark, capable of predicting fake news with 79% certainty within 8 hours of broadcast. Formulated problem statement, developed architectural framework, formulated workflow, maintained and reviewed code. Published at AusDM'21.


## Education
### Master's degree in Computer Science
UC San Diego

### Bachelor of Engineering - BE in Computer Engineering
SSN College of Engineering

### High School in Science with Computer Science (CBSE ), CGPA 10 ( 10th Grade), Computer Science
Chinmaya Vidyalaya


## Contact & Social
- LinkedIn: https://linkedin.com/in/prasannakumaran
- Portfolio: https://prasannakumaran.github.io/

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Source: https://flows.cv/prasannakumaran
JSON Resume: https://flows.cv/prasannakumaran/resume.json
Last updated: 2026-04-11