Victor Dibia

None

Monthly visits:35026

Description:

Victor Dibia is an expert in Applied Machine Learning and HCI. He is currently a Principal Research Software Engineer at the Human-AI eXperiences (HAX) team, Microsoft Research where he focuses on Generative AI. His research interests are at the intersection of human-computer interaction (HCI), computational social science, and applied machine learning. His research has been published at conferences such as ACL, EMNLP, AAAI, and CHI and has received multiple best paper awards. His work has also been featured in outlets such as the Wall Street Journal and VentureBeat. He is an IEEE Senior member, a Google Certified Professional (Data Engineer, Cloud Architect), and currently a Google Developer Expert in Machine Learning. He holds a PhD in Information Systems from City University of Hong Kong (recipient of the HKPFS scholar award by the Hong Kong Research Grants Council). His dissertation studied developer contribution behavior in software crowdsourcing contests - factors influencing participation, the impact of incentives on participation behavior, and the problem-solving process within crowdsourcing contests. Prior to City University, he studied at the Information Networking Institute at Carnegie Mellon University where he earned a Master's degree in Information Networking. He previously worked as a Principal Research Engineer at Cloudera Fast Forward Labs, Research Staff Member at IBM Research, Technical Lead for MIT Global Startup Labs, Researcher at the Innovation Management Lab, Athens Information Technology Athens Greece, and founder/lead developer for a small startup focused on West African markets.

For Tasks:

generate visualizations verify signatures explore representations create interactive experiences experiment with multimodal models

For Jobs:

data scientist machine learning engineer hci researcher software engineer product manager

Features

Automatic Generation of Grammar-Agnostic Visualizations and Infographics
UI interface for experimenting with multimodal (text, image) models (stable diffusion)
Deep Learning Library for Automatic Offline Signature Verification Tasks
Visual Exploration of Representations Learned by Convolutional Neural Networks
Interactive Experiences with Tensorflow.js

Advantages

Can automatically generate grammar-agnostic visualizations and infographics from text.
Provides a user-friendly interface for experimenting with multimodal models.
Can be used to automatically verify signatures offline.
Can be used to visualize the representations learned by convolutional neural networks.
Can be used to create interactive experiences in the browser.

Disadvantages

May not be able to generate visualizations and infographics that are visually appealing.
May not be able to handle complex multimodal models.
May not be able to verify all types of signatures.
May not be able to visualize all types of representations learned by convolutional neural networks.
May not be able to create all types of interactive experiences in the browser.

Frequently Asked Questions

Q:What is Victor Dibia's expertise?

A:Victor Dibia is an expert in Applied Machine Learning and HCI.
Q:Where does Victor Dibia currently work?

A:Victor Dibia currently works at Microsoft Research as a Principal Research Software Engineer.
Q:What are Victor Dibia's research interests?

A:Victor Dibia's research interests are at the intersection of human-computer interaction (HCI), computational social science, and applied machine learning.
Q:What awards has Victor Dibia received?

A:Victor Dibia has received multiple best paper awards at conferences such as ACL, EMNLP, AAAI, and CHI.
Q:What is Victor Dibia's highest level of education?

A:Victor Dibia holds a PhD in Information Systems from City University of Hong Kong.