A deep learning approach for mapping music genres
Sharaj Panwar, Arun Das, Mehdi Roopaei, Paul Rad, "A deep learning approach for mapping music genres." In the proceedings of 2017 12th Syst. Syst. Eng. Conf. SoSE 2017, 2017.
CV
Education
- B.Tech. in Electrical and Electronics Engineering, Cochin University of Science and Technology, 2013
- M.S. in Computer Engineering, University of Texas at San Antonio, 2016
- Ph.D in Electrical Engineering, University of Texas at San Antonio, 2021 (expected)
Work experience
- September 2015: Research Fellow
- The Open Cloud Institute & Secure AI and Autonomy Lab
- Duties includes, but not limited to:
- Developing prototype AI algorithms, coding them, validating them, and publishing results.
- Leads the computer vision team at Secure AI and Autonomy Lab.
- Mentoring undergrad and graduate students to help formulate their thesis, projects, and guidance towards research papers.
- Maintaing Secure AI and Autonomy Lab’s Github Organization and Repositories.
- Writing proposals, grants, and patents, to promote research at UTSA.
- Managing cloud computing clusters for Dr. Paul Rad’s students.
- Creating and maintaining cloud computing images for machine learning research (Jupyter Lab with Tensorflow, PyTorch - CPU and GPU support).
- Finding new venues of research, devices for data collection, potential collaborations.
- Supervisor: Dr. Paul Rad
- May 2019 - August 2019: AI Graduate Intern
- Intel Corporation (Intel Artificial Intelligence Product Group (AIPG))
- Duties included, but not limited to:
- Implemented recurrent neural network (RNN) architectures.
- Listed required ops to be enabled in Intel hardware for RNN integrations.
- Conducted bi-weekly discussions to promote research on LSTM integrations involving ops and other requirements.
- Wrote code for enabling neural network quantization of RNNs in Intel hardware.
- Explored the ops requirements in Google’s NMT, Transformers, BERT architectures and evaluated the implentations for use in Intel hardware.
- September 2016 - December 2016: OpenStack Developer Intern
- The Open Cloud Institute and Intel Corporation
- Duties included, but not limited to:
- Acquired expertise in various OpenStack services and researched on Machine Learning system design for the cloud.
- Developed deep-learning ready cloud appliances for one-click deployment on accelerated GPU and CPU clouds.
Publications
Patient facial emotion recognition and sentiment analysis using secure cloud with hardware acceleration
Alex Torres, Hao Yan, Armin Aboutalebi, Arun Das, Lide Duan, Paul Rad, "Patient facial emotion recognition and sentiment analysis using secure cloud with hardware acceleration." Comput. Intell. Multimed. Big Data Cloud with Eng. Appl., 2018.
A Distributed Secure Machine-Learning Cloud Architecture for Semantic Analysis
Arun Das, Wei-Ming Lin, Paul Rad, "A Distributed Secure Machine-Learning Cloud Architecture for Semantic Analysis." Appl. Cloud Deep Semant. Recognit., 2018.
Image Graph Production by Dense Captioning
Amin Sahba, Arun Das, Paul Rad, Mo Jamshidi, "Image Graph Production by Dense Captioning." In the proceedings of World Autom. Congr. Proc., 2018.
Distributed machine learning cloud teleophthalmology IoT for predicting AMD disease progression
Arun Das, Paul Rad, Kim Choo, Babak Nouhi, Jonathan Lish, James Martel, "Distributed machine learning cloud teleophthalmology IoT for predicting AMD disease progression." Futur. Gener. Comput. Syst., 2019.
Systems and methods for evaluating individual, group, and crowd emotion engagement and attention
Najafirad, Peyman, and Arun Das. "Systems and methods for evaluating individual, group, and crowd emotion engagement and attention." U.S. Patent Application No. 16/251,768.
Deep Learning Optimization for Edge Devices: Analysis of Training Quantization Parameters
Alicja Kwasniewska, Maciej Szankin, Mateusz Ozga, Jason Wolfe, Arun Das, Adam Zajac, Jacek Ruminski, Paul Rad, "Deep Learning Optimization for Edge Devices: Analysis of Training Quantization Parameters." IECON Proc. (Industrial Electron. Conf., 2019.
Talks
Accelerating Machine Learning on Chameleon Cloud
Talk at Argonne National Lab, Lemont, Illinois
Opportunities and Challenges in Artificial Intelligence
Talk at Cochin University of Science and Technology - Webinar Series, Youtube Webinar