CV
Basics
| Name | Pehuén Moure |
| Label | Machine Learning and Neuroscience Researcher |
| pehuen@ini.ethz.ch | |
| Summary | Researcher at the intersection of machine learning and neuroscience, specializing in developing robust AI systems that generalize across domains: from neuroprosthetic control to multi-agent robotics. Passionate on translating theoretical advances into practical solutions, demonstrated through implementations at Amazon Robotics and interdisciplinary work in visual prostheses at ETH Zurich. |
Work
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2022.10 - Present PhD Candidate in Information Technology and Electrical Engineering
ETH Zurich - Institute of Neuroinformatics
- Developing novel deep learning approaches for controlling neural activity in visual prostheses
- Created end-to-end optimization framework for prosthetic vision using spiking neural network
- Built method for improving generalization in RL through regulation of Bayesian neural networks
- Leading development on automated speech recognition tool for children with speech impairments
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2018.06 - 2021.10 Data Scientist & Software Engineer
Amazon Robotics
- Utilized reinforcement learning policy techniques to improve sortation center throughput
- Developed adaptive sampling evolutionary algorithm for optimizing fulfillment center maps
- Designed and developed data compute engine and data lake using Elastic Map Reduce cluster
- Simulated new robotic solutions and calculated impact at fulfillment centers
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2017.06 - 2017.08 Strategy and Modeling Summer Analyst
Morgan Stanley Quantitative Finance
- Developed chat-bot assistant to facilitate traders' data processing using common language (NLP)
- Implemented hierarchical clustering algorithm to automate trade recommendations
- Conducted technical research for trade flow prediction based on client correlation algorithm
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2017.02 - 2018.05 Chief Technology Officer
Suna Breakfast
- Built startup providing affordable breakfast delivery options to Cornell students
- Directed a 20-person team in building React Native applications and backend in AWS
Education
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2022.10 - Present -
2020.10 - 2022.05 -
2016.02 - 2018.08
Awards
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Meinig Family Cornell National Scholar
Cornell University
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Morgan Stanley Robert B. Fisher Scholar
Morgan Stanley
Publications
2025
- Deep Learning Based Control of Electrically Evoked Activity in Human Visual Cortex2025In preparation
- EMBCTemporally-Varying Stimulations for Cortical Visual Neuroprosthetic using Spiking Neural NetworksIn International Conference of Engineering in Medicine and Biology Society, 2025
- EMBCA Behavioral Study of Event-based Depth-Filtered Prosthetic Vision in Simulated Dynamic EnvironmentsIn International Conference of Engineering in Medicine and Biology Society, 2025
- L4DCIn Learning for Dynamics and Control Conference, 2025
- DISSAdapting Foundation Speech Recognition Models to Impaired Speech: A Semantic Re-chaining Approach for Personalization of German SpeechIn Disfluency in Spontaneous Speech Workshop: Interspeech, 2025
2024
- CVPRIn Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024
- NeurIPSIn Proceedings of UniReps: the First Workshop on Unifying Representations in Neural Models, 2024
- NeurIPSIn Neural Information Processing Systems, 2024
2021
- ICRAIn International Conference on Robotics and Automation, 2021
2018
- DISCan Interactive Systems Be Designed for Conviviality? A Case StudyIn Proceedings of the 2018 ACM Conference Companion Publication on Designing Interactive Systems, 2018
Volunteer
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2019.09 - 2020.08 Pediatric Oncology Research Assistant
Dana Farber Cancer Institute
- Developed pipeline for automating analysis of chip-seq and RNA-seq data
- Interacted with team of scientists to build tools that speed up production of results
Skills
| Machine Learning | |
| Reinforcement Learning | |
| Deep Neural Networks | |
| Bayesian Neural Networks | |
| Computer Vision |
| Software Engineering | |
| Python | |
| AWS | |
| Agile Development | |
| ROS |
Languages
| English | |
| Fluent |
| Spanish | |
| Native |
Projects
- 2024.01 - 2024.06
Speech Recognition for Children with Speech Impairments
Developed tool for improving speech recognition accuracy in children with speech impairments
- Implemented Whisper model with bayesian mechanism to improve accuracy on small datasets
- Coordinated team of 5 students to develop tool for real-time speech recognition
- 2016.07 - 2018.08
Autonomous Bicycle Project
Co-founded and led software team for autonomous bicycle development
- Built object avoidance system using SLAM algorithm in ROS with TX-1 and ZED stereo camera
- Managed team of 12 students working to create centralized code structure across 4 sub-teams