April 2020

Founded and Started Soniox

Soniox Inc

Soniox mission is to accelerate the adoption of speech-based applications and spark innovation of human-machine voice interaction. We have developed the most accurate speech recognition system and made it freely available for anyone to use.

May 2017

Speech Activity Detection for Facebook Videos


Developed a novel neural network model for speech activity detection for Facebook videos that recognizes human speech segments in the audio stream. The model is fast and accurate. Integrated the model into production and achieved 40%+ reduction in transcribed audio data on Facebook videos without affecting the Word Error Rate.

April 2017

CommAI: Evaluating the first steps towards a useful general AI

Marco Baroni, Armand Joulin, Allan Jabri, Germàn Kruszewski, Angeliki Lazaridou, Klemen Simonic, Tomas Mikolov ICLR 2017 Workshop

We propose a set of concrete desiderata for general AI, together with a platform to test machines on how well they satisfy such desiderata, while keeping all further complexities to a minimum.
December 2016

Machine Intelligence Workshop at NIPS 2016

NIPS 2016

Co-organized the Machine Intelligence at NIPS 2016. The workshop aimed to stimulate theoretical and practical advances in the development of machines endowed with human-like general-purpose intelligence, focusing in particular on benchmarks to train and evaluate progress in machine intelligence.

Workshop Page

October 2016

Environment for Communicated-based AI

Facebook Research

Release of environment for Communicated-based AI, a platform for training and evaluating AI systems on communication-based tasks.

CommAI-env Page

June 2016

DeepText for Facebook Messenger


Designed and developed DeepText models for intent classification and slot extraction for Facebook Messenger. Applied DeepText models to recognize ride intents for Uber, Lyft, and Taxi in Messenger, and extract object and price in the for-sale Facebook posts.

Facebook Article

TechCrunch Article

2013 - 2015

M.S. in Computer Science

University of Utah

M.S. Thesis: Concept Aware Co-occurrence and its Applications
Studied the problem of learning concept-level representations from large amount of unstructured text data. Developed a structured prediction model for short text understanding: segmentation and disambiguation of phrases into concepts with limited syntax and context information.


2013 - 2015

Teaching Assistant

University of Utah

Teaching Assistant at the University of Utah for two classes: Software Practice and Database Systems. Organized and taught lab projects, held office hours, graded projects, assignments and exams.

Summer 2014

Internship at Google: Search Query Understanding

Google Research, Mountain View

Internship at Google Research with Haixun Wang. Developed models for search query understanding: learning segmentation and disambiguation of phrases in queries. The model improved the understanding of intents and products in the Google Shopping search queries.

2008 - 2013

B.S. in Mathematics and Computer Science

University of Ljubljana, Slovenia

Undergraduate Thesis: Network structural properties and their application to missing property prediction.
Studied the problem of predicting missing relation-types for objects in large-scale knowledge bases, DBpedia and Freebase (Google Knowledge Graph).


Summer 2012

Slib Library

Designed and developed a library for generic implementation of data structures that support complex queries on data (e.g. combination of Balanced Tree with Hash Table and Double Linked List). The library achieves high performance and low memory usage, and can be easily embedded into other projects.

Code (GoogleCode)

Summer 2011

Internship at Stanford: Missing Link Prediction with Network Motifs

Research internship at Stanford with Prof. Jure Leskovec. Worked on discovery of network motifs (statistically significant sub-graphs), and used the network motifs with SVM to predict the missing links in information networks.

Code (GitHub)

Summer 2010

Internship at Stanford: Community Detection in Social Networks

Research internship at Stanford with Prof. Jure Leskovec. Investigated and developed methods for community detection in large social networks based on Clique Percolation Method.

Code (GitHub)

2008 - 2009

A New Algorithm for Finding Frequent Items in Streams of Data

Klemen Simonic, Janez Brank, Marko Grobelnik

First year undergraduate research project: A New Algorithm for Finding Frequent Items in Streams of Data. We present a new algorithm for finding frequent items in a stream of data that requires a small fraction of resources compared to the total quantity of data.