From cell segmentation and visual tracking to intracranial EEG and in-silico speech motor control: six years of research on how biological systems learn.
I'm a Ph.D. student in Biomedical Engineering (Neural Engineering) at Boston University and a Graduate Fellow in Dr. Frank Guenther's Speech Neuroscience Lab. I have six years of research experience across machine learning, computer vision and neural signal processing.
I build biophysically grounded models of neural dynamics and analyze electrophysiological and behavioral time series. The aim is to find the principles behind learning and adaptability in biological systems and turn them into neuromimetic artificial systems that help design treatments for neurological disorders. My path runs from few-shot cell segmentation and efficient visual tracking, through predictive-coding robotics and online learning, to intracranial EEG and in-silico models of speech motor control.
Away from the lab I cook, hike, camp, read, write poems and science fiction, and listen to podcasts about where human civilization is heading.
PI · Dr. Frank Guenther
Computational modeling of neural dynamics in speech motor control. Work in progress; details will follow publication.
Stangl Lab · Intracranial EEG
End-to-end pipeline for iEEG from chronically implanted NeuroPace RNS depth electrodes in the medial temporal lobe. It covers artifact and epileptiform-transient rejection, theta (4–12 Hz) amplitude and phase measured against a fitted 1/f background, and circular–linear statistics linking theta to spatial navigation.
Sen Lab · Auditory scene analysis
Co-designed a scalable cortical network with top-down attentional modulation of inhibitory interneurons. It takes ITD/ILD binaural cues as input and is evaluated by spike-timing-based decoding as competing talkers are added.
Forecasting and anomaly detection on multivariate telemetry from vaccine filling lines, using VAE-based LSTMs trained on nominal behavior. The ONNX models run live in the Seeq/Sanofi cloud for real-time streaming inference.
PI · Dr. Gianfranco Doretto
Predictive-coding online learning for biomimetic robots. Online stochastic optimization for temporally dependent streams (ECCVW '24). Test-time adaptive tracking on edge devices (WACV '25). Few-shot non-convex cell segmentation (ISBI '24).
Lab contract with IstoVisio (syGlass)
Through the lab's contract with IstoVisio, developed AutoTrack, adaptive 3D single-particle tracking with spatiotemporal attention fusion, and syFind, self-adapting 3D cell counting and segmentation. syFind was demoed at the SfN 2022 Expo inside the syGlass VR microscopy platform.
CellTranspose few-shot convex cell segmentation (WACV '23, BRAIN Initiative Trainee Highlight Award). Fine-grained classification of 1,000 plant species (CVPRW '21). An IoT pipeline for Fitbit physiological time series (NSF AI & Health 2020).
CS 320 Analysis of Algorithms (Spring '24, '25) · CS 678 Computer Vision (graduate, Fall '24) · CS 472 Computer Graphics (Fall '23).
9th Annual BRAIN Initiative Meeting · top 60
Top 1% of ~4,000 graduates
Highest distinction among ~600 nominees
NRT Graduate Trainee
Graduate School Research Fellowship
BU Neurophotonics Center
Memberships: IEEE (2022–) · Upsilon Pi Epsilon (2021–) · ACM (2019–)