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Yi-Te (Eeder) Hsu

Master’s student in Computer Science

Johns Hopkins University

About

I am Yi-Te Hsu, currently an M.S. Computer Science student at Johns Hopkins University. Before coming to JHU, I was a research assistant at Academia Sinica conducting research on speech processing with Dr. Yu Tsao. I mainly focused on model compression and acceleration for deep neural networks.

I also collaborated with Prof. Frank Rudzicz at University of Toronto (UofT) and Vector Institute as a visiting researcher. I worked on healthcare projects for detecting pathological voice and identifying Alzheimer’s disease.

My interests include speech processing, AI and ML. I am excited about applying these techniques to solving the real-world problems.

I am looking for 2020 summer internships!

Interests

  • Speech and Voice technology
  • Bio-acoustic Signal Processing
  • Healthcare
  • Natural Language Processing
  • Machine Learning

Education

  • MS in Computer Science, 2019-

    Johns Hopkins University

  • Visiting Student Researcher, 2018

    University of Toronto

  • BSc in Electrical Engineering, 2017

    National Taiwan University

Recent Publications

Experience

 
 
 
 
 

Research Intern

Vector Institute; University of Toronto

Sep 2018 – Dec 2018 Toronto
  • Healthcare Project – Robust pathological voice detection system: Used bidirectional LSTM to develop an early detection system. Combined with an unsupervised domain adaptation method to solve the channel mismatch of different devices.
  • NLP Project – Detection of Alzheimer’s disease: Proposed a method to transfer Mandarin features to English ones with the corpus of a picture description task. Combined algorithms from different languages to achieves multi-language application.
 
 
 
 
 

Research Assistant

Academia Sinica

Feb 2018 – Jul 2019 Taipei
  • Quantization on deep neural network: Proposed a novel exponent-only floating-point quantized neural network (EOFP-QNN) to quantize the model. Achieved a 4x compression rate.
  • Acceleration on a compressed DNN: Proposed IA-Net, which can simultaneously compress the model size and accelerate the inference process by replacing the floating-point multiplier with an integer adder without performance degradation.
  • Bio-signal processing: Cooperated with the otolaryngologist and speech therapist at Far Eastern Memorial Hospital in Taiwan. Combined deep learning and machine learning techniques to develop the disease classification system by the patients’ voice. Taught electronic engineering and researched semiconductor physics.
 
 
 
 
 

Data Scientist Intern

Mobagel

Jun 2016 – Feb 2017 Taipei
  • Applied ML techniques and statistic model to extract core information from different types of IoT data. Applied unsupervised machine learning methods to detect anomalies with unlabeled data
  • Predicted the office space occupancy rate for 85% accuracy with the time-series data for 6 months from real-time sensors.
  • Product deployment: Deploy the machine learning models to real products. Cooperate with collaborates efficiently. Gained solid experience in machine learning and engineering.

Projects

Facebook Likes Estimator

Facebook Likes Estimator for Major News Publishers’ Pages.

how social media influence human emotion

An analysis of how social media influence human emotion.

MovieWatson

An intelligent movie recommendation system.

Speaker Identification

Using FFT and signal processing techniques to identify speakers.