Resourceful Graduate Assistant bringing demonstrated success in [Skill] and in-depth knowledge of [Area of expertise]. Illustrating proven history of academic excellence and team development through contributions to [School]. Highly adept in all aspects of research design with ability to conduct research to advance goals and objectives. Instrumental in contributing to articles and reports published in peer-reviewed publications.
Ambitious Research Assistant with experience in conducting complex research into [Type] research while training undergraduate students. On track to complete [Type] in [Area of study]. Well versed in research design, data collection and analysis and laboratory operations.
• Independent study on pitch perturbation paradigms to investigate the effects of varied delays and amplifications in auditory feedback. Study setup design, acoustic data collection from subjects, and data analysis has been carried out. Manuscript on results in preparation
• Training undergraduates and research associates on calibration techniques and experimental setups for data collection across behavioral studies and computational analysis of data
• Comprehensive behavioral study on Parkinson's (PD) patients and age-sex matched adults to identify auditory responses to both auditory and somatosensory perturbations to voice and speech. The data collected will be utilized to generate modifications in a PD-DIVA model reflecting PD population-based variations in the speech motor control system.
• Individual project on modifying the speech motor control model DIVA to include vocal fundamental frequency as a controlled variable. A preliminary step towards incorporating a laryngeal model to the DIVA neural speech motor control model, which could provide insight on how neural control of speech acts in relation with mechanical control of the larynx.
Contribution in exploring the application feasibility of a simplified speech motor control model (simpleDIVA) in data fitting from various studies.
• Applied a simplified DIVA model on published Parkinson's disease patient behavioral data on sensorimotor adaptation experiments from STEPP lab.
• Developed a Graphic User interface was developed for the model as part of the collaborated tasks. (http://sites.bu.edu/guentherlab/software/simplediva-app/)
Contribution to the NSF-supported project “The effects of delayed auditory feedback on speech sequencing: acoustics, physiology, and computational modeling”
• Developed the initial modelling simulations for objective 2 of the grant: Neural modelling of Delayed Auditory Feedback based speech errors by using a cortical rhythm hypothesis in a competitive queuing network based on the GODIVA model for speech sequencing.
• Developed MATLAB based differential equation models for 1) feedback based response suppression in a competitive queuing network based on a simplified GODIVA model and 2) Phase coupled cortical rhythms and amplitude coupled production and perception signals to the cortical rhythms, to identify the percentage of perception of the delayed feedback signal and thus quantification of errors in perception.
• Application Development: Research & Algorithm Development (C, Java, Python, MATLAB) for medical wearables and IoT solutions for customer requirements. Exposure in Bio signal acquisition (EEG/ECG/PPG), signal conditioning algorithms, processing and analytics.
• Firmware Development: Algorithm implementation in firmware (embedded C, C++); Communication Protocols: Bluetooth/ BLE, UART, SPI, I2C ; Architectures: TI – RTOS ; Biosensors: IMU, SPO2; Exposure in TI chips (ADS1299 / CC2640), Invensense MPU9250.
• Projects : Algorithm, firmware and mobile app backend development ;
Motion based algorithms to track human motion, posture, and activity rate for fitness wearables.
PPG based heart rate, respiratory rate, sleep analysis (based on heart rate variability)
Gesture Recognition API using machine learning and time series pattern matching techniques to be used in consumer wearable applications.
Microphone noise suppression &cry detection algorithms for application in IoT devices.
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