Buddhi Wickramasinghe

As a Machine Learning Engineer who is driven by initiative and a passion for research, I've spent over six years honing my expertise in algorithm design for Machine Learning, Deep Learning, and Speech Processing. I have the ability to quickly adapt to emerging technologies. Proficient in interdisciplinary collaboration, my research interests include Speech Processing, Deep Learning, Lifelong Learning, Explainable AI, and efficient Edge-based Machine Learning, reflecting my dedication to creating transparent, efficient, and innovative solutions. 

InfiniRel Corporation, San Jose, CA (Feb 2024 - present)

Machine Learning Consultant

Designing and developing fault prediction systems for improving reliability of renewable energy plants using generative deep learning models.

Purdue University, West Lafayette, IN (Dec 2021 - Oct 2023)

Postdoctoral Research Assistant

Research into discovering robust neuro-inspired algorithms with the goal of enabling explainable artificial intelligent systems.

University of New South Wales (UNSW), Sydney, Australia (Jul 2017 - Aug 2021)

Graduate Research Assistant

Thesis: Replay Detection in Voice Biometrics: An Investigation of Adaptive and Non-Adaptive Front-ends. 

University of Peradeniya, Kandy, Sri Lanka (Dec 2016 - Jul 2017)

Teaching Assistant

Lab demonstration, experiment design, project design, assessment, and teaching for: Image Processing, Digital Design, Programming Methodology, Embedded Systems and Computer Communication Networks.

Zone24x7 Private Limited, Colombo, Sri Lanka (Oct 2015 - Mar 2016)

Trainee Associate Electronics Engineer

Optimized encryption key management algorithms to reduce memory usage in EFTPOS systems.

University of New South Wales (UNSW), Sydney, Australia (Jul 2017 - Aug 2021)

Ph.D. in Electrical Engineering

Specialized in voice biometrics, machine learning and signal processing.

University of Peradeniya, Kandy, Sri Lanka       (Mar 2013 - Oct 2016 )

B.Sc. Eng. in Computer Engineering

GPA: 3.85/4.00 – First Class 

B. Wickramasinghe, S. Chowdhury, A. Kosta, W. Ponghiran, K. Roy, “Unlocking the Potential of Spiking Neural Networks: Understanding the What, Why, and Where,” submitted to special issue on Advancing Machine Intelligence with Neuromorphic Computing, in IEEE Transactions on Cognitive and Developmental Systems, doi: 10.1109/TCDS.2023.3329747 .

B. Wickramasinghe, G. Saha, K. Roy, “Continual Learning: A Review of Techniques, Challenges and Future Avenues,” submitted to IEEE Transactions on Artificial Intelligence (Accepted, 2023).

B. Wickramasinghe, E. Ambikairajah, V. Sethu, J. Epps, H. Li, T. Dang, "DNN controlled adaptive front-end for replay attack detection systems", Speech Communication, 154, p. 102973. doi:10.1016/j.specom.2023.102973.

B. Wickramasinghe, "Replay detection in voice biometrics: an investigation of adaptive and non-adaptive front-ends." PhD diss., UNSW Sydney, 2021. 

B. Wickramasinghe, E. Ambikairajah, J. Epps. "Biologically Inspired Adaptive-Q Filterbanks for Replay Spoofing Attack Detection." In Interspeech, pp. 2953-2957. 2019.

B. Wickramasinghe, E. Ambikairajah, J. Epps, V. Sethu, H. Li, “Auditory Inspired Spatial Differentiation for Replay Spoofing Attack Detection,” in ICASSP 2019, pp. 6011-6015, 2019.

B. Wickramasinghe, S. Irtza, E. Ambikairajah, J. Epps, "Frequency Domain Linear Prediction Features for Replay Spoofing Attack Detection," Proc. Interspeech 2018, pp. 661-665, 2018.

T. Gunendradasan, B. Wickramasinghe, P. N. Le, E. Ambikairajah, and J. Epps, "Detection of Replay-Spoofing Attacks Using Frequency Modulation Features," Proc. Interspeech 2018, pp. 636-640, 2018.

B. Wickramasinghe, P. N. Le, E. Ambikairajah, and J. Epps, “Perceptual Frequency Scale-based Filters for Replay Spoofing Attack Detection,” 2018 IEEE International Conference on Information and Automation for Sustainability (ICIAFS), Colombo, Sri Lanka, 2018.

M. Prematilake, B. Wickramasinghe, O. Vithanage, H. Gamaarachchi, R. Ragel, “Accelerating Mutual Information Analysis based Power Analysis Attacks.” 2016 IEEE International Conference on Information and Automation for Sustainability, Galle, Sri Lanka, 2016.

Programming languages

Python C C++ Verilog 

Machine Learning and Data Analytics

PyTorch TensorFlow Keras Scikit-learn 

Ample experience in machine learning and deep learning algorithms, feature extraction and feature analysis

Numeric computing

Numpy SciPy Matlab 

Tools/APIs 

CUDA Git Kaldi SpeechBrain 

Operating Systems 

Linux shell scripting kernel programming 

Non-technical

Writing, presentation, communication, adaptability, teamwork, time management, organizational skills 

Academic Conquests

• Recipient of the UNSW Tuition Fee Scholarship.

• Recipient of the ATP Research Laboratory, Data61 and CSIRO Australia Top-up scholarship.

• Ph.D. thesis got recommended for "Dean's Award for Outstanding Ph.D. Theses".

• Received the Best paper award at ESCaPe 2016 for "Accelerating Mutual Information Analysis Based Power Analysis Attacks using the GPU".

Side hustles of the mind

• Ceylon University Dramatic Society - Inter-Faculty Drama Competition.

  • Received the award for Best Supporting Actress in the drama 'The Lullaby' (2016).

  • Received an award for Special Performance in the drama 'The Noose' (2014).

AWS Cloud Practitioner Essentials

Coursera

Issued: July 2021

Credential ID: Y7HM8C3CYLNE

URL: https://www.coursera.org/account/accomplishments/certificate/Y7HM8C3CYLNE