About Me

I received my Ph.D. and M.S. in Computational Mechanics at University at Buffalo and B.S. in Computer Science and Mechanical Engineering at Politechnica University, Bucharest. During my Ph.D., I have pioneered a framework for using high fidelity simulations and innovative statistical analysis for large datasets and distributed systems. Much of this work has been published in premier scientific journals and presented at numerous international conferences. Here is a list of my publications on Google Scholar.

My industry experience is in probabilistic robotics research and development, advanced system analysis and control with applications to self-driving cars. I am also pursuing a graduate certificate at Stanford in Artificial Intelligence.

Contact Details

Ramona Stefanescu


Stanford University

Graduate Certificate in Artificial Intelligence Sept. 2017 - Dec. 2018

Relevant Coursework: Artificial Intelligence Convolutional Neural Networks for Visual Recognition Computer Vision, From 3D Reconstruction to Recognition Introduction to Logic

Buffalo University

Ph.D. Mechanical Engineering Computational Mechanics Aug. 2008 - Sept. 2014

M.S. Mechanical Engineering Computational Mechanics Aug. 2007 - Feb. 2011

Politechnica University

B.S. Mechanical Engineering Mechatronics Sept. 2002 - July 2007

Hyperion University

B.S. Computer Science Systems with Microprocessors Sept. 1999 - July 2004



Team Lead Localization May 2017 - Present

Implemented a pipeline for Simultaneous Localization and Mapping (SLAM) using visual odometry, inertial measurements and map information. Worked on a complete computer vision system for mono and stereo camera, which included the determination of both intrinsic and extrinsic parameters (camera calibration), feature detection and tracking, outliner detection and non-linear estimation.

Developed a Bayesian Filter to combine different sensor information to obtain the optimal pose estimate of a vehicle.


Self-Driving Car Nanodegree Program Mentor Dec. 2016 - Oct. 2017

Mentored students in the Udacity's Classroom. Provide guidance in the area of computer vision and deep learning, with emphasize on Convolution Neural Networks, Behavior Cloning using TensorFlow and Keras.

Future Mobility USA Corp.

Autonomous Driving Localization Systems Lead Aug. 2016 - May 2017

Formulated and implement localization and mapping algorithms to enable Level 4 autonomous driving. Responsible for building highly efficient, large-scale, distributed data processing pipeline for mapping and localization. Determine platform software requirements and architecture, and decide on the functional components and commendable features for an autonomous driving system.

Identified new technologies that were brought into the team to provide new innovations.

Mercedes Benz Research & Development North America

Software Engineer, Autonomous Driving Aug. 2015 - Aug. 2016

Responsible for advanced research topics in the area of localization for self-driving cars. Developed efficient and robust algorithms for localization, combining novel tracking techniques with stochastic filtering and graph optimization methods. Designed algorithms such as visual inertial odometry, dead reckoning, map matching and data association for mapping and localization. Evaluated different loosely and tightly coupled GPS/IMU systems and integrated an Interacting Multiple Model (IMM) for a more accurate state estimation.

Represented the company at various conferences and meetings.

Mentored junior engineers on best practices and the current state of the art in the field.

Buffalo University

Postdoctoral Fellow Aug. 2016 - May 2017

Addressed the problem of fast emulator construction by developing novel strategies for Big Data from computationally expensive simulations. Used a combination of efficient sparse representations of simulation “data” with graph theory, low-rank approximation and multilevel-multiscale methodologies. Improved the Gaussian Process Regression limitation regarding memory requirements and computational demands.

Used Bayesian Model Averaging (BMA) to predict the probability density function (PDF) of the quantity of interest to be predicted and forecasted.

Served as a Co-Principal Investigator in a series of proposal submission including Partnerships for Innovation: Accelerating Innovation Research - Technology Translation (PFI: AIR-TT).


NSF i-CORPS Award, Apr. 2014

Finalist (top 5 out of 75) of Panasci Technology Entrepreneurship Competition, Apr. 2014

e-Lab Entrepreneurship course fellowship award, Jan. 2014

Student award MAE Graduate Student Poster Competition, Mar. 2013

The Buffalo chapter of ASME recognition, Apr. 2013

Travel award Summer School in “Low-Dimensional Structure in High-Dimensional Systems”, SAMSI, Raleigh, NC, 2013

Travel award Gene Golub SIAM Summer School on “Simulation and Supercomputing in the Geosciences”, Monterey, CA, 2012

Ph.D./M.S Fellowship Award, University at Buffalo

Class Valedictorian, Politechnica University

3rd place at The Inter-University Mathematics Competition for undergraduates students

Erasmus Scholarship at Galway-Mayo Institute of Technology (GMIT), Ireland – Digital & Software System Engineering, Sep. 2004 - June 2005


Languages: C/C++, Python, ROS, bash scripting.

Libraries: Tensor Flow, Ceres, g2o.