Anna Choromanska

Assistant Professor

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Anna Choromanska

Professor Anna Choromanska did her Post-Doctoral studies in the Computer Science Department at Courant Institute of Mathematical Sciences in NYU and joined the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering in Spring 2017 as an Assistant Professor. She is affiliated with the NYU Center for Data Science.

Prof. Choromanska's research interests focus on machine learning both theoretical and applicable to the variety of real-life phenomena. Currently, her main research projects focus on numerical optimization, deep learning, large data analysis, and learning from data streams. Prof. Choromanska also works on machine learning for robotics and autonomous systems. She collaborates with NVIDIA (New Jersey lab) on the autonomous car driving project. 

Prof. Choromanska was a recipient of The Fu Foundation School of Engineering and Applied Science Presidential Fellowship at Columbia University in the City of New York. She co-authored several international conference papers and refereed journal publications, as well as book chapters. The results her works are used in production by Facebook (training production vision systems and entry to COCO competition) and Baidu, and in product development by NVIDIA. She is also a contributor to the open source fast out-of-core learning system Vowpal Wabbit (aka VW). Prof. Choromanska gave over 50 invited and conference talks and serves as a book editor (MIT Press volume), organizer of top machine learning events (workshops at conferences such as the  International Conference on Neural Information Processing Systems), and a reviewer and area chair for several top machine learning conferences and journals.

Prof. Anna Choromanska is also a pianist who has been playing piano since the age of six and has diplomas of two music schools. Her piano performance can be found here. She was also a bronze medalist of amateur couple dance. She was practicing standard and latin dance in the Columbia University Ballroom Dance Team. Prof. Choromanska is also an avid salsa dancer. She performed in Ache Performance Project of Frankie Martinez, the one of the most innovative and renowned Latin contemporary dancers of his generation, and practiced individually with one of the most charismatic female mambo dancers, Lori Ana Perez-Piazza. She also likes dancing hula, especially during her travels to Hawaii. Her dance performances can be found herehere and here. Finally, prof. Choromanska loves painting and fashion design techniques.

 

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Prof. Choromanska established the ECE Seminar Series on Modern Artifical Intelligence at NYU Tandon. 

Playlist of all talks is here.

Invited speakers Spring 2018:

Yann LeCun                    talk video

Yoshua Bengio               talk video

Stefano Soatto               talk video

Vladimir Vapnik              talk video      slides 

Invited speakers Fall 2018:

Anima Anandkumar       talk video

David Blei

Richard J. Roberts, 1993 Nobel Laureate in Physiology or Medicine

 

Prof. Choromanska also established the ECE Machine Learning Reading Group "Mambo with Machine Learning" at NYU Tandon. 

Invited speakers Spring 2018:

Irina Rish

​Robert Schapire

​Alina Beygelzimer

​Mariusz Bojarski

Invited speakers Fall 2017:

Krzysztof Choromanski

 

Prof. Choromanska runs the ECE NYU TANDON MACHINE LEARNING LAB. Her students are:

Maryam

 

 

 

 

 

 

 

 

Maryam Majzoubi 
maryam dot majzoubi at gmail.com
https://engineering.nyu.edu/maryam-majzoubi
PhD candidate 
School of Engineering Fellowship holder

 

Shihong

 

 

 

 

 

 

 

 

Shihong Fang
sf2584 at nyu dot edu
https://engineering.nyu.edu/shihong-fang
PhD candidate

 

Apoorva

 

 

 

 

 

 

 

 

Apoorva Nandini Saridena
ans609 at nyu dot edu
PhD candidate (and former Master's student)
Summer Intern at NVIDIA: Summer 2018 and Summer 2019

 

Yunfei

 

 

 

 

 

 

 

 

Yunfei Teng
yt1208 at nyu dot edu
PhD candidate (and former Master's student: THEODOR TAMIR AWARD FOR BEST MS THESIS IN ELECTRICAL AND COMPUTER ENGINEERING)
Morse Fellowship holder
School of Engineering Fellowship holder
Summer Intern at NVIDIA: Summer 2018

 

Devansh

 

 

 

 

 

 

 

 

 

 

 

Devansh Bisla
db3484 at nyu dot edu
https://devansh20la.github.io/
PhD candidate (and former Master's student)
School of Engineering Fellowship holder
Summer Intern at Hearst: Summer 2018 

Prof. Choromanska's former Master's students:

Shreya Kadambi
Cameron Archibald Johnson

Students that prof. Choromanska advised on selected projects are:

Benjamin Cowen
Ish Kumar Jain
Naman Patel

Research Interests: Machine learning, Numerical optimization, Deep learning large data analysis, Learning from data streams, Learning with expert advice, Supervised and unsupervised online learning, Clustering, Structured prediction, Autonomous systems, Self-driving cars, and Machine learning in biomedical applications

Warsaw University of Technology
MSc, Department of Electronics and Information Technology, 2009

Columbia University in the City of New York
M.Phil. and Ph.D., Department of Electrical Engineering, 2014


IBM T.J.Watson Research Center
Research Collaboration
From: August 2017 to present
Working on biologically plausible algorithms for training deep networks (collaborators: Irina Rish).

NVIDIA (New Jersey lab)
Research Collaboration
From: May 2016 to present
Working on machine learning platforms for self-driving cars (collaborators: Urs Muller and Larry Jackel).

New York University, Courant Institute of Mathematical Sciences, Computer Science Department
Post-Doctoral Associate
From: April 2014 to December 2016 
Working on deep learning (advisor: Prof. Yann LeCun).

Microsoft Research, New York
Research Collaboration and Reserch Collaboration
From: June 2012 to September 2013 and September 2013 to June 2014
Working on logarithmic time extreme multiclass classi cation (advisor: Dr John Langford).

IBM T.J.Watson Research Center
Research Collaboration
From: May 2012 to June 2013
Recipient of a grant from the Speech and Language Algorithms Department at IBM T. J. Watson Research Center (for one semester). Working on optimization for large scale learning problems involving conditional random fields, log-linear models, and deep belief networks (advisor: Dr Dimitri Kanevsky, since 04.2013 joint work also with Prof. Aleksandr Aravkin).

ATT Research Laboratories
Summer Internship
From: July 2012 to September 2012
Working on iPLAN project: data analysis and modeling, and data matching (advisor: Dr Alice Chen, manager: Dr Phyllis Weiss).

University of Hawaii at Manoa, Deptartment of Electrical Engineering
Visiting Summer Scholar
From: November 2008 to November 2008
Working on Empirical Mode Decomposition (advisor: Prof. David Y. Y. Yun).

University of Pennsylvania, Smell and Taste Center, Department of Otorhinolaryngology, Head and Neck Surgery
Visiting Summer Scholar
From: September 2008 to September 2008 (with several week cooperation before)
Working on improving software and hardware for electrogustometric medical trials (advisor: Prof. Richard Doty).

University of North Texas Health Science Center, Center for Commercialization of Fluorescence Technologies
Visiting Summer Scholar
From: September 2008 to September 2008 
Working on fast algorithms for visualization and analysis of lung epithelial cells imagined using fluorescence technology (advisor: Prof. Ignacy Gryczynski and Prof. Zygmunt Gryczynski).

Centre de Recherche du Centre Hospitalier Universitaire de Montreal, in cooperation with the Center for Commercialization of Fluorescence
Technologies, University of North Texas Health Science Center, Forth Worth, Texas

Summer Internship
From: July 2008 to September 2008
Working on fast algorithms for visualization and analysis of lung epithelial cells imagined using fluorescence technology (advisor: Prof. Ryszard Grygorczyk). The project was supported by the Canadian Institutes of Health Research (CIHR) and Natural Sciences and Engineering Research Council of Canada (NSERC).

 


Conferences:

M. Bojarski, A. Choromanska, K. Choromanski, B. Firner, L. Jackel, U. Muller, P. Yeres, K. Zieba, VisualBackProp: efficient visualization of CNNs for autonomous driving, in the International Conference on Robotics and Automation (ICRA), 2018​ pdf

N. Patel, A. N. Saridena, A. Choromanska, P. Krishnamurthy, F. Khorrami, Adversarial Learning Based On-Line Anomaly Monitoring for Assured Autonomy, in the International Conference on Intelligent Robots and Systems (IROS), 2018

S. Minaee, Y. Wang, A. Choromanska, S. Chung, X. Wang, E. Fieremans, S. Flanagan, J. Rath, Y. W. Lui, A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI, in the International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018 pdf

N. Patel, A. Choromanska, P. Krishnamurthy, F. Khorrami, Sensor Modality Fusion with CNNs for UGV Autonomous Driving in Indoor Environments, in the International Conference on Intelligent Robots and Systems (IROS), 2017 pdf

Y. Jernite, A. Choromanska, D. Sontag, Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation, in the International Conference on Machine Learning (ICML), 2017 pdf

P. Chaudhari, A. Choromanska, S. Soatto, Y. LeCun, C. Baldassi, C. Borgs, J. Chayes, L. Sagun, R. Zecchina, Entropy-SGD: Biasing Gradient Descent Into Wide Valleys, in the International Conference on Learning Representations (ICLR), 2017. Acceptance Rate [36%]. pdf

M. Bojarski, A. Choromanska, K. Choromanski, F. Fagan, C. Gouy-Pailler, A. Morvan, N. Sakr, T. Sarlos, J. Atif, Structured adaptive and random spinners for fast machine learning computations, in the International Conference on Artificial Intelligence and Statistics (AISTATS), 2017. Acceptance Rate [31.70%]. pdf

A. Choromanska, K. Choromanski, M. Bojarski, T. Jebara, S. Kumar, Y. LeCun, Binary embeddings with structured hashed projections, in the International Conference on Machine Learning (ICML), 2016. Oral presentation: Acceptance Rate [24.27%]. pdf

A. Choromanska, J. Langford, Logarithmic Time Online Multiclass prediction, in the Neural Information Processing Systems Conference (NIPS), 2015. Spotlight talk: Acceptance Rate [3.65%]. You can find my talk under the following link: https://www.microsoft.com/en-us/research/video/nips-poster-spotlight-session-9-conference-closing/?from=http%3A%2F%2Fresearch.microsoft.com%2Fapps%2Fvideo%2F%3Fid%3D259660 pdf

S. Zhang, A. Choromanska, Y. LeCun, Deep learning with Elastic Averaging SGD, in the Neural Information Processing Systems Conference (NIPS), 2015. Spotlight talk: Acceptance Rate [3.65%]. You can find the talk under the following link: https://www.microsoft.com/en-us/research/video/nips-poster-spotlight-session-3/?from=http%3A%2F%2Fresearch.microsoft.com%2Fapps%2Fvideo%2F%3Fid%3D259601 pdf

A. Choromanska, Y. LeCun, G. Ben Arous, Open Problem: The landscape of the loss surfaces of multilayer networks, in the Conference on Learning Theory (COLT), Open Problems, 2015 pdf

A. Choromanska, M. B. Henaff, M. Mathieu, G. Ben Arous, Y. LeCun, The Loss Surfaces of Multilayer Networks, in the International Conference on Artificial Intelligence and Statistics (AISTATS), 2015 pdf

A. Choromanska, T. Jebara, H. Kim, M. Mohan, C. Monteleoni, Fast spectral clustering via the Nystrom method, in the International Conference on Algorithmic Learning Theory (ALT), 2013 pdf

A. Choromanska, K. Choromanski, G. Jagannathan, C. Monteleoni, Differentially-Private Learning of Low Dimensional Manifolds, in the International Conference on Algorithmic Learning Theory (ALT), 2013 pdf

T. Jebara, A. Choromanska, Majorization for CRFs and Latent Likelihoods, in the Neural Information Processing Systems Conference (NIPS), 2012. Spotlight talk: Acceptance Rate [3.58%]. You can find my talk under the following link: http://videolectures.net/machine_choromanska_majorization/ (Student Best Paper Award, First Place, on the 7th Annual Machine Learning Symposium, New York Academy of Science, 2012) pdf

A. Choromanska, C. Monteleoni, Online clustering with experts, in the International Conference on Artificial Intelligence and Statistics (AISTATS), 2012. Oral presentation: Acceptance Rate [5.97%]. You can find my talk under the following link: https://www.youtube.com/watch?v=dPFhwrHd7ak (Student Paper Award, Third Place, on the 6th Annual Machine Learning Symposium, New York Academy of Science, 2011) pdf and supplement

Journals and book chapters:

A. Choromanska, K. Choromanski, G. Jagannathan, C. Monteleoni, Differentially-Private Learning of Low Dimensional Manifolds, Theoretical Computer Science, 2015 pdf

A. Choromanska, S-F. Chang, R. Yuste, Automatic Reconstruction of 3D neural morphologies using multi-scale graph-based tracking, Frontiers in Neural Circuits, 6:25, 2012 pdf

A. Y. Aravkin, A. Choromanska, T. Jebara, D. Kanevsky, Chapter: Semistochastic quadratic bound methods, in Log-Linear Models, Extensions and Applications, MIT Press, 2018 

Phd Thesis:

A. Choromanska, Selected machine learning reductions, PhD Thesis, 2014 pdf

Workshops:

S. Zhang, A. Choromanska, Y. LeCun, Deep learning with Elastic Averaging SGD (initial results), in the International Conference on Learning Representations (ICLR) Workshop, CoRR, abs/1412.6651v5, 2015

A. Y. Aravkin, A. Choromanska, T. Jebara, D. Kanevsky, Semistochastic quadratic bound methods (initial results), in the International Conference on Learning Representations (ICLR) Workshop, CoRR, abs/1309.1369, 2014 pdf

A. Choromanska, A. Agarwal, J. Langford, Extreme Multi Class Classification, in the Neural Information Processing Systems Conference (NIPS) Workshop: eXtreme Classification, 2013 

A. Choromanska, D. Kanevsky, T. Jebara, Majorization for Deep Belief Networks, in the Neural Information Processing Systems Conference (NIPS) Workshop: Log-linear models, 2012

A. Choromanska and C. Monteleoni, Online Clustering with Experts (initial results), in the International Conference on Machine Learning (ICML) Workshop: Online Trading of Exploration and Exploitation 2, Journal of Machine Learning Research (JMLR) Workshop and Conference Proceedings, 2011 pdf

Technical reports:

Y. Teng, A. Choromanska, M. Bojarski, Invertible Autoencoder for domain adaptation, 2018 pdf

M. Bojarski, P. Yeres, A. Choromanska, K. Choromanski, B. Firner, L. Jackel, U. Muller, Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car, CoRR, abs/1704.07911, 2017 pdf

A. Choromanska, K. Choromanski, M. Bojarski, On the boosting ability of top-down decision tree learning algorithm for multiclass classification, CoRR, abs/1605.05223, 2016 pdf

M. Bojarski, A. Choromanska, K. Choromanski, Y. LeCun, Differentially- and non-differentially-private random decision trees, CoRR, abs/1410.6973, 2015 pdf

K. Choromanski, A. Choromanska, M. Bojarski, Deep Neural Networks reconstruct graphons, 2015

A. Agarwal, A. Choromanska, K. Choromanski, Notes on Using Determinantal Point Processes for Clustering with Applications to Text Clustering, CoRR, abs/1410.6975, 2014 pdf

A. Choromanska, T. Jebara, Stochastic Bound Majorization, CoRR, abs/1309.5605, 2013 pdf

Preprints:

A. Choromanska, S. Kumaravel, R. Luss, I. Rish, B. Kingsbury, R. Tejwani, D. Bouneffouf, Beyond Backprop: Alternating Minimization with co-Activation Memory, 2018 (submitted) pdf

S. Fang, A. Choromanska, Reconfigurable Network for Efficient Inferencing in Autonomous Vehicles, 2018 (submitted)​

D. Bisla, A. Choromanska, VisualBackProp for learning using privileged information with CNNs, 2018 (submitted) pdf

B. Cowen, A. Nandini Saridena, A. Choromanska, LSALSA: efficient sparse coding in single and multiple dictionary settings, 2018 (submitted) pdf

D. Bisla, A. Choromanska, R. Berman, D. Polsky, J. Stein, Beating Melanoma with Deep Learning: letting the data speak, 2018 (submitted)

A. Choromanska, I. K. Jain, Extreme Multiclass Classification Criteria, 2018 (submitted) pdf

N. Patel, A. Choromanska, P. Krishnamurthy, F. Khorrami, A Deep Learning Gated Architecture for UGV Navigation Robust to Sensor Failures, 2018 (submitted) 

Codes are on Github or available upon request.


HONORS, AWARDS, AND ACHIEVEMENTS

Scientic:

Theodor Tamir Award for best Ms Thesis in Electrical and Computer Engineering awarded to my student, Yunfei Teng, for his Master's thesis conducted under my advisorship

Student Best Paper Award, First Place, for the work T. Jebara, A. Choromanska, Majorization for CRFs and Latent Likelihoods, 7th Annual Machine Learning Symposium, New York Academy of Science, 2012

Student Best Paper Award, Third Place, for the work A. Choromanska, C. Monteleoni, Online clustering with experts, 6th Annual Machine Learning Symposium, New York Academy of Science, 2011

The Fu Foundation School of Engineering and Applied Science Presidential Fellowship holder, Columbia University in the City of New York, 2009-2012

Departmental Scholarship holder for the Achievements in Science, Warsaw University of Technology, Department of Electronics and Information Technology, 2005-2009

Winner (first place) of the National Mathematics Competition held by Warsaw University of Technology, 2004

Laureate of the National Physics Competition held by Warsaw University of Technology, 2004

Other:

Diploma of the Warsaw School of Art \Labirynt" (painting), 2007

Bronze medalist of amateur couple dance, 2006

Diploma of the Summer School of Italian Language in Rome, 2006

CONTRIBUTOR

Open source systems: Vowpal Wabbit (aka VW) open source fast out-of-core learning system library and program.

Open source own implementations: Majority of codes connected with published papers are publicly released (website and/or GitHub).

Industry:

EASGD algorithm from [S. Zhang, A. Choromanska, Y. LeCun, Deep learning with Elastic Averaging SGD, in the Neural Information Processing Systems Conference (NIPS), 2015] is used in production by Facebook (training production vision systems and entry to COCO competition) and Baidu

Robotic platform based on subscale car from [S. Fang, A. Choromanska, Recongurable Network for Efficient Inferencing in Autonomous Vehicles, 2018] deployed by NVIDIA Automotive HMI team for testing autonomous driving systems NVIDIA