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 optimization (deep learning landscape, deep learning optimization, and general machine learning optimization), large data analysis (extreme multi-class and multi-label classification and density estimation), and machine learning for robotics and autonomy (autonomous driving systems, self-driving cars, AI-based robotics). Prof. Choromanska 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. 

The series aims to bring together faculty, students, and researchers to discuss the most important trends in the world of AI, and the talks are live streamed and viewed around the globe, helping to spread the word about the amazing work going on in the AI community. The invited speakers are the world-renowned experts whose research is making an immense impact on the development of new machine learning techniques and technologies. 

Playlist of all talks is here. Website of the seminar is here

List of past invited speakers: Leon Bottou, Francis Bach, Raia Hadsell, Martial Hebert, Tony Jebara, Manuela Veloso, Eric Kandel, Anima Anandkumar, David Blei, Richard J. Roberts, Yann LeCun, Yoshua Bengio, Stefano Soatto, Vladimir Vapnik 

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

List of past invited speakers: Hal Daume III, Augustin Chaintreau, Shipra Agrawal, Brian Kingsbury, Suman Jana, Jennifer Wortman Vaughan, Narges Razavian, Larry Jackel, Irina Rish, Robert Schapire, Alina Beygelzimer, ​Mariusz Bojarski, 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; thesis advising)
Summer Intern at NVIDIA: Summer 2018 and Summer 2019

 

Yunfei

 

 

 

 

 

 

 

 

Yunfei Teng
yt1208 at nyu dot edu
PhD candidate (and former Master's student; thesis advising: 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; thesis advising)
School of Engineering Fellowship holder
Summer Intern at Hearst: Summer 2018 

Prof. Choromanska's former Master's students:

Suchetha Siddagangappa
Shreya Kadambi (thesis advising)
Cameron Archibald Johnson (thesis advising)

Graduate students that prof. Choromanska advised on selected projects:

Arihant Jain (Masters)
Benjamin Cowen (PhD; advising on the project that became a part of his PhD thesis)
Ish Kumar Jain (Masters)
Naman Patel (PhD)

Undergraduate students that prof. Choromanska advised on selected projects:

Munib Mesinovic (Undergraduate Summer Research Program)

 

Broader Impacts:

Prof. Choromanska and members of her ECE NYU TANDON MACHINE LEARNING LAB support the participation of women and under-represented minorities in STEM fields and promote the participation of undergraduate and high-school students in STEM fields. Prof. Choromanska is an active member of Women at Tandon and works in the committee for creating the Women's Center at NYU Tandon. Her students participate in the peer-to-peer mentoring program for women in STEM. In July 2018 prof. Choromanska, together with her PhD student S. Fang and undergraduate student L. Nertomb, organized K12 ARISE Summer High School Program for 12 high school students titled "AI4AV: autonomous driving with deep learning models" offering 3-week training in the area of machine learning and autonomous driving. Selected photos from the program are shown below. Prof. Choromanska will participate in K12 ARISE Summer High School Program also in the Summer (July-August) 2019 offering similar research training for high-school students.

 

ARISE1

 

ARISE2

Research Interests: Optimization (Deep Learning Landscape, Deep Learning Optimization, and General Machine Learning Optimization), Large Data Analysis (Extreme Multi-class and Multi-label Classification and Density Estimation), Machine Learning for Robotics and Autonomy (Autonomous Driving Systems, Self-driving Cars, AI-based Robotics)

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:

Y. Teng, W. Gao, F. Chalus, A. Choromanska, D. Goldfarb, A. Weller, Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models, in the Neural Information Processing Systems Conference (NeurIPS), 2019. Acceptance Rate [21%]. pdf

A. Choromanska, B. Cowen, S. Kumaravel, R. Luss, M. Rigotti, I. Rish, B. Kingsbury, P. DiAchille, V. Gurev, R. Tejwani, D. Bouneouf, Beyond Backprop: Online Alternating Minimization with Auxiliary Variables, in the International Conference on Machine Learning (ICML), 2019. Acceptance
Rate [23%]. pdf

D. Bisla, A. Choromanska, R. Berman, D. Polsky, J. Stein, Towards Automated Melanoma Detection with Deep Learning: Data Purication and Augmentation, in the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ISIC Skin Image Analysis Workshop, 2019 pdf

S. Fang, A. Choromanska, Reconfigurable Network for Efficient Inferencing in Autonomous Vehicles, in the International Conference on Robotics and Automation (ICRA), 2019 pdf

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 pdf

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

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. 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. 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, 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

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

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

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

Journals and book chapters:

B. Cowen, A. Nandini Saridena, A. Choromanska, LSALSA: Accelerated Source Separation via Learned Sparse Coding, Machine Learning, 2019 (the paper was also accepted for presentation in the ECML-PKDD conference) pdf

Y. Teng, A. Choromanska, Invertible Autoencoder for domain adaptation, in the MDPI Computation, 2019 pdf

A. Choromanska, I. K. Jain, Extreme Multiclass Classification Criteria, in the MDPI Computation, 2019 pdf

N. Patel, A. Choromanska, P. Krishnamurthy, F. Khorrami, A Deep Learning Gated Architecture for UGV Navigation Robust to Sensor Failures, in the Journal of Robotics and Autonomous Systems, 2019 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 pdf

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

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

Phd Thesis:

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

Technical reports:

D. Bisla, A. Choromanska, VisualBackProp for learning using privileged information with CNNs, 2019 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:

 

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 

 

 

 

 


Together with Prof. Katepalli Sreenivasan we are looking for PhD candidates interested in pursuing cross-disciplinary research in machine learning and physics. Specifically, we are interested in using AI methods in turbulent flows for both applied and theoretical purposes. The questions of interest could range from turbulence modeling (of interest in aerospace and mechanical engineering industry) to the identification of patterns and determination of their importance (of potential significance in astrophysical and geophysical flows) to their use in the discovery of fundamental processes of energy transfer across scales---this problem being of fundamental importance in all strongly nonlinear systems. Interested candidates should apply to the ECE and MAE Departments and indicate that they are interested in this joint position.