Curriculum Vitae

SNSF professorship in Statistics and Data Science, Geneva School of Economics and Management & Faculty of Science (School of Pharmaceutical Sciences), University of Geneva, Switzerland

• Expander team in Google Research , Mountain View, CA, USA
• Research in semi-supervised learning combining deep learning and graphs.

Software Engineering Intern at Google X

• Self-Driving Car team in Google X , Mountain View, CA, USA
• Undisclosed Machine Learning projects for the Google self-driving car.

Software Developer Intern at Microsoft

• Cortana team at Microsoft Corporation, Redmond, WA, USA
• Undisclosed Machine Learning project for Cortana, Windows Phone’s digital personal assistant.

Machine Learning for understanding how the brain produces speech

• Carnegie Mellon University, Pittsburgh, PA, USA
• Advised by Prof. Tom M. Mitchell and Dr. Barnabàs Pòczos
• Developing Machine Learning models that analyze neural activity signals (ECoG and Local Field Potentials) in order to understand how different properties of speech are represented in the brain, which can have major consequences for the treatment of many neurological disorders, such as Parkinson’s disease.

Monte Carlo methods for question answering

• Carnegie Mellon University, Pittsburgh, PA, USA
• Advised by Dr. Barnabàs Pòczos
• Using Monte Carlo Tree Search to answer natural language questions, using background knowledge represented as a graph.

Machine Learning for understanding meaning representation in the brain

• Carnegie Mellon University, Pittsburgh, PA, USA
• Advised by Dr. Barnabàs Pòczos and Prof. Tom M. Mitchell
• Developed Machine Learning models that combine neural activity time series of different modalities. The goal was to use brain activity recordings, such as fMRI, EEG and MEG, to understand how the brain processes language. I focused on Deep Learning models, such as recurrent neural networks.

Computer Vision for unsupervised object discovery in video

• Carnegie Mellon University, Pittsburgh, PA, USA
• Advised by Dr. Marius Leordeanu
• Unsupervised object discovery in video based on multiple frames matching. We also proposed a fast method for detecting the main object of interest in a video, titled VideoPCA.

Research Internship in Machine Learning at EPFL

• École Polytechnique Fédérale de Lausanne, Laboratory for Probabilistic Machine Learning
• Advised by Dr. Matthias Seeger
• Used topic models to explore the correlation between social media messages from Twitter and the location of the users, with applications to user profiling, topic tracking and content recommendation. I was responsible with applying various machine learning models and parallelizing the code in order to scale well.

Research for Undergraduates Program

• Politehnica University of Timisoara, Romania
• Advised by Prof. Emilia Petrisor
• Implemented algorithms for spectral clustering of nodes in a graph, based on minimum graph cut, with applications to data mining and statistics, such as clustering information from documents on the web and medical images segmentation.

• Center for Machine Learning and Health (CMLH) Fellowship in Digital Health (2018)

• Gates Cambridge Scholarship (2014)
• Google Anita Borg Memorial Scholarship (2013)
• GE (General Electric) Foundation Scholar Leaders Program (2012)

• Best poster award at the Eastern European Machine Learning Summer School in Bucharest, Romania (2019)
• Machine Learning Department Teaching Assistant Award (2018)
• Carnegie Mellon University Neurohackathon: 2^nd place (2017)
• KTH University Programming Challenge, Sweden: Top 10 contestants (2013)
• ACM International Collegiate Programming Contest (ACM-ICPC): Honorable Mention in Southeastern European Regional (2013, 2012, 2011)
• Microsoft Imagine Cup: Top 20 in the World Finals (2012), 1^st team in the Romanian National Finals (2012)
• Romanian National Olympiad in Informatics: Gold Medal (2008), Bronze Medal (2010), 1^st Place (2004), 2^nd Place (2005), Honorable Mention (2010, 2008, 2007, 2003)
• Kangaroo International Mathematical Competition: 2^nd prize in Romanian National Finals (2009, 2010)

Teaching Assistant for Graduate Machine Learning

• Graduate level introduction to machine learning class 10-701 Graduate Machine Learning at Carnegie Mellon University
• Taught by Prof. Pradeep Ravikumar and Prof. Manuela Veloso
• I was awarded a Machine Learning Department Teaching Assistant Award

Teaching Assistant for Topics in Deep Learning

• Graduate level deep learning class 10-707 Topics in Deep Learning at Carnegie Mellon University
• Taught by Prof. Ruslan Salakhutdinov

Teaching algorithms for competitive programming

• Co-organized a competitive programming seminar at Politehnica University of Timisoara for university and high-school students interested to train for algorithmic competitions (e.g. ACM-ICPC, informatics olympiad).
• Taught algorithms and data structures used in competitive programming, designed and solved practice problems and internal competitions.

LiveX Learning Platform

• Tutoring system for kindergarten and school children based on a software platform that runs in the cloud, Windows Phone 7 devices and a set of electronic learning cubes called “IQubes” (our hardware invention).
• Project proposed by our team, called IQube, that competed in the world finals of the Microsoft Imagine Cup competition.

Face and Hand Gesture Recognition for Human - Computer Interaction

• Framework for C++ developers to extend their graphical user interfaces with more natural means of communication.
• Works in real-time using a computer web camera.

Public Transport Route Recommendation

• Python application for the Timisoara city public transport system using real-time information from GPS devices installed on public transport vehicles.
• Overlays optimal routes suggestions on Google Maps (before they supported such a feature).

Handwritten digits recognition

• C library implementing various linear algebra methods.

• Organizing a workshop on Adaptive & Multitask Learning at ICML 2019.

• Founding member of the AI+ Club at Carnegie Mellon University (CMU).

• Program Committees: ICML (2019), AISTATS (2019, 2020), ICLR (2018, 2020), ICLR-LLD (2019), PLOS ONE (2019).

• Treasurer of the Romanian Students Association at CMU.

• Member of the Doctoral Review Committee of the Machine Learning Department at CMU.

• Member of the Education Review Committee of the Machine Learning Department at CMU, which aims to improve the PhD program.

• President of the Romanian Students Association at CMU.

• Student representative in the faculty leadership board at Politehnica University of Timisoara.

• Volunteer for AIESEC, international youth organization.

• Volunteer for Liga AC, student organization at Politehnica University.