Anna Choromanska
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Assistant Professor
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Alfred. P. Sloan Fellow
Anna Choromanska is an an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) at the NYU Tandon School of Engineering. She is also affiliated with the NYU Center for Data Science (CDS), NYU Center for Urban science and Progress (CUSP), NYU Center for Advanced Technology in Communications (CATT), and Connected Cities with Smart Transportation (C2SMART) Center. Prior to joining ECE, she was conducting Post-Doctoral studies in the Computer Science Department at the Courant Institute of Mathematical Sciences in NYU under the guidance of Turing Award winner, Prof. Yann LeCun. Prof. Choromanska is a recipient of multiple awards, including the NSF CAREER Award, Alfred. P. Sloan Fellowship, and two IBM Global University Program Academic Awards.
Research: Prof. Choromanska's main research focus is deep learning (DL). This form of AI is useful for automatically finding high-quality representations of complex data that are suited for particular learning tasks. As data sets grow inexorably in size and complexity, it becomes ever more difficult to pull useful features from them using hand-crafted feature extractors; thus DL frameworks are becoming increasingly popular. The “Holy Grail” of DL, and one of the toughest challenges in all of modern ML, is to develop a fundamental understanding of DL optimization and generalization. Such an understanding is considered essential for designing efficient (fast-converging), accurate (well-generalizing), and scalable (applicable to large data sets and models and heavily parallelizable) DL optimization strategies. Better algorithmic tools for DL optimization and generalization should have strong impacts on a wide range of large data applications, with substantial savings of time and resources (today the cost of training a single state-of-the-art DL model can reach hundreds of thousands of dollars). Prof. Choromanska's research seeks to address these DL optimization/generalization challenges. In her laboratory, Prof. Choromanska studies how deep neural networks (DNNs) learn, and how to condition the DNN learning process to converge efficiently to high-quality solutions by properly designing the training and/or the DL system architecture. Her research is highly multi-disciplinary and involves DL sub-disciplines including optimization, continual learning, distributed
optimization, sparse coding, and conditional computations. This multi-disciplinary aspect of her research encourages the combination of experimental and theoretical work. Autonomous driving and extremely large dataset analysis are my principal applications of interest.
Service and industrial impact: 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.
Other interests: 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 here, here and here. Finally, prof. Choromanska loves painting and fashion design techniques.
Prof. Choromanska is the director of the Learning Systems Laboratory (LSL).
Education
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
Pre-Professional Experience
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
Summer Internship 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).
Awards
HONORS, AWARDS, AND ACHIEVEMENTS
Scientific:
NSF CAREER Award, 2021
IBM Global University Program Academic Award, 2021
Alfred P. Sloan Research Fellowship in Computer Science, 2020
IBM Faculty Award, 2020
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 own implementations: Majority of codes connected with published papers are publicly released (website and/or GitHub).
Open source systems: Vowpal Wabbit (aka VW) open source fast out-of-core learning system library and program.
Industry:
Robotic platform based on subscale car from [S. Fang, A. Choromanska, Reconfigurable Network for Efficient Inferencing in Autonomous Vehicles, 2018] deployed by NVIDIA Automotive HMI team for testing autonomous driving systems NVIDIA
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
Publications
Conferences:
H. He, J. Wang, A. Choromanska, Adjacent Leader Decentralized Stochastic Gradient Descent, in the European Conference on Artificial Intelligence (ECAI), 2024. Acceptance Rate [23\%]. pdf
T. Dimlioglu, A. Choromanska, GRAWA: Gradient-based Weighted Averaging for Distributed Training of Deep Learning Models, in the International Conference on Artificial Intelligence and Statistics (AISTATS), 2024 pdf
S. Fang, H. Zhu, D. Bisla, A. Choromanska, S. Ravindran, D. Ren, R. Wu, ERASE-Net: Efficient Segmentation Networks for Automotive Radar Signals, in the IEEE International Conference on Robotics and Automation (ICRA), 2023 pdf
D. Bisla, J. Wang, A. Choromanska, Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape, in the International Conference on Artificial Intelligence and Statistics (AISTATS), 2022. Acceptance Rate [29\%]. pdf
Y. Teng, A. Choromanska, M. Campbell, S. Lu, P. Ram, L. Horesh, Overcoming Catastrophic Forgetting via Direction-Constrained Optimization, in the European Conference on Machine Learning and Data Mining (ECML-PKDD), 2022. Acceptance Rate [26\%]. pdf
S. Fang, A. Choromanska, Backdoor attacks on the DNN Interpretation System, in the AAAI Conference on Artificial Intelligence (AAAI), 2022. Acceptance Rate [15%]. (extension of NeurIPS 2020 paper) pdf
A. N. Saridena, A. Choromanska, Efficient patching of DNNs for Autonomous Vehicles, in the IEEE International Conference on Robotics and Automation (ICRA), 2022
D. Bisla, A. N. Saridena, A. Choromanska, A Theoretical‐Empirical Approach to Estimating Sample Complexity of DNNs, in the IEEE Conference on Computer Vision and Patern Recognition (CVPR) Second Workshop on Fair, Data-Efficient, and Trusted Computer Vision (TCV), 2021 pdf
C. Lema, A. Choromanska, Approximating Ground State Energies and Wave Functions of Physical Systems with Neural Networks, in the Neural Information Processing Systems Conference (NeurIPS) Workshop on Machine Learning and the Physical Sciences, 2020 pdf
S. Fang, A. Choromanska, Backdoor attacks on the DNN Interpretation System, in the Neural Information Processing Systems Conference (NeurIPS) Workshop on Dataset Curation and Security, 2020 pdf
J. Wang, A. Choromanska, SGB: Stochastic Gradient Bound Method for Optimizing Partition Functions, in the Neural Information Processing Systems Conference Workshop on Optimization for Machine Learning (NeurIPS OPT), 2020 pdf
Y. Teng, A. Choromanska, M. Campbell, Continual learning with direction-constrained optimization, in the Neural Information Processing Systems Conference (NeurIPS) Workshop on Meta-Learning, 2020 pdf
A. Pacchiano, J. Parker-Holder, Y. Tang, A. Choromanska, K. Choromanski, M. I. Jordan, Learning to score behaviors for guided policy optimization, in the International Conference on Machine Learning (ICML), 2020 pdf
S. Fang, A. Choromanska, Multi-modal Experts Network for Autonomous Driving, in the IEEE International Conference on Robotics and Automation (ICRA), 2020 pdf
M. Majzoubi, A. Choromanska, LdSM: Logarithm-depth Streaming Multi-label Decision Trees, in the International Conference on Artificial Intelligence and Statistics (AISTATS), 2020 talk pdf
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 (see also pdf for a gentle extension of this work and the link for the PyTorch-based comprehensive distributed training library for deep networks that contains codes for LSGD, as well as for several other methods)
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%]. talk 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%]. (extension of ICLR 2015 paper) talk 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%] (Student Best Paper Award, First Place, on the 7th Annual Machine Learning Symposium, New York Academy of Science, 2012) talk 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%] (Student Paper Award, Third Place, on the 6th Annual Machine Learning Symposium, New York Academy of Science, 2011) talk 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:
T. Dimlioglu, J. Wang, D. Bisla, A. Choromanska, S. Odie, L. Bukhman, A. Olomola, J. D. Wong, Automatic Document Classification via Transformers for Regulations Compliance Management in Large Utility Companies, in the Neural Computing and Applications, 2023 pdf
A. N. Saridena, A. Choromanska, DNN Patching: Progressive Fixing and Augmenting the Functionalities of DNNs for Autonomous Vehicles, in the IEEE Robotics and Automation Letters (RA-L), 2022 pdf
N. Patel, A. N. Saridena, A. Choromanska, P. Krishnamurthy, F. Khorrami, Learning-Based Real-Time Process-Aware Anomaly Monitoring for Assured Autonomy, in the IEEE Transactions on Intelligent Vehicles, 2020 pdf
B. Cowen, A. Nandini Saridena, A. Choromanska, LSALSA: Accelerated Source Separation via Learned Sparse Coding, in the Machine Learning, 2019 (the paper was also accepted for presentation in the ECML-PKDD conference) 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 Journal of Statistical Mechanics: Theory and Experiment, 2019 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
Codes are on Github or available upon request.
Students and Alumni
Prof. Choromanska's current PhD students:
Yunfei Teng
yt1208 at nyu dot edu
PhD candidate (and former Master's student)
School of Engineering Fellowship holder (PhD program)
Morse Fellowship holder (Master's program)
Theodor Tamir Awardee for the best MS thesis in Electrical and Computer Engineering
Summer Intern at Facebook Research New York: Summer 2022
Summer Intern at ByteDance (the parent company of TikTok), Summer 2021
Summer Intern at Facebook Research New York: Summer 2020
Summer Intern at IBM T. J. Watson Research Center: Summer 2019
Summer Intern at NVIDIA: Summer 2018
Jing Wang
jw5665@nyu.edu
PhD candidate
Dean's Fellowship holder
Summer Intern at Recurrent AI (SaleTech company in China), Summer 2022
Summer Intern at Haihua Institute for Frontier Information Theory (research center jointly hosted by Institute for Interdisciplinary Information Sciences at Tsinghua University and Beijing Haidian District government), Summer 2021
Tolga Dimlioglu
tolga.dimlioglu@ug.bikent.edu.tr
PhD candidate
School of Engineering Fellowship holder
Summer Intern at Siemens, Summer 2023
Haoran Zhu
hz1922@nyu.edu
PhD candidate
Kristi Topollai
kristitsope@gmail.com
PhD candidate
School of Engineering Fellowship holder
Prof. Choromanska's former PhD students:
Shihong Fang
Employer after graduation: Nuro
Summer Intern at NVIDIA: Summer 2020
Maryam Majzoubi
Employer after graduation: Google (Google Lens team)
School of Engineering Fellowship holder
Summer Intern at Google Research New York: Summer 2020
Summer Intern at Microsoft Research New York: Summer 2019
Apoorva Nandini Saridena
Employer after graduation: NVIDIA (Holmdel, New Jersey location)
Summer Intern at NVIDIA: Spring 2021, Spring 2020, Summer 2019, Summer 2018
Devansh Bisla
Employer after graduation: NVIDIA (Holmdel, New Jersey location)
School of Engineering Fellowship holder
Summer Intern at Microsoft: Summer 2021
Summer Intern at NVIDIA: Summer 2020
Summer Intern at Hearst: Summer 2018
Prof. Choromanska's former Master's students:
Haoran Zhu (thesis advising)
Yunfei Teng (thesis advising)
Devansh Bisla (thesis advising)
Apoorva Nandini Saridena (thesis advising)
Shreya Kadambi (thesis advising)
Sachit Nagpal (thesis advising)
Jatin Palchuri (thesis advising)
Cameron Archibald Johnson (thesis advising)
Suchetha Siddagangappa
Karnik Panchal
Graph Thongwat
Twishikana Bhattacharjee
Yifan Yang
Yilu Peng
Rishabh Bahuguna
Arihant Jain
Graduate students that prof. Choromanska advised on selected projects:
Benjamin Cowen (PhD; advising on projects that became parts of his PhD thesis)
Naman Patel (PhD)
Jing Wang (Master's)
Ish Kumar Jain (Master's)
Arihant Jain (Master's)
Undergraduate students that prof. Choromanska advised on selected projects:
Cesar Lema (Undergraduate Senior Project)
Munib Mesinovic (Undergraduate Summer Research Program)
ECE Seminar Series on Modern Artifical Intelligence
Prof. Choromanska founded 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.
The seminar became the flagship venue of the NYU Tandon School of Engineering attracting broad audience from the industry (major tech companies as well as start-ups) and academia (universities from New York and New Jersey areas), and even high schools (Prof. Choromanska collaborates with Brooklyn Technical High School and all-girls Hewitt School, students from these schools attend the seminar). The talks are live-streamed and viewed around the entire world.
Playlist of all seminar talks is here.
Website of the seminar is here.
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
Broader Impacts
Prof. Choromanska and the members of her laboratory support the participation of women and underrepresented and minority groups in STEM fields and promote the participation of undergraduate and high-school students in STEM fields. Prof Choromanska is engaged in building a racially and ethnically diverse workforce 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. The photo below shows Dean J. Kovacevic, prof. M. Veloso (the speaker in the ECE Seminar Series on Modern Artificial Intelligence at NYU Tandon), prof. Choromanska, and prof. I. Rish promoting 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, deep learning optimization, and autonomous driving. Prof. Choromanska participated in K12 ARISE Summer High School Program also in the Summer 2019, Summer 2020, and Summer 2021, each time offering two-month research training for high-school students. Her PhD students, Tolga Dimlioglu, Devansh Bisla, Apoorva Nandini Saridena, and Haoran Zhu were assisting her in organizing Summer 2019, 2020, and 2021 programs. Prof. Choromanska's programs are organized under a motto: "it takes a spark to ignite a fire'' and their goal is to motivate high school students to choose a career path in STEM. Selected photos from her programs are shown below.
LSL Sponsors
Prof. Choromanska and the LSL members are grateful to their research sponsors:
- NSF
- DARPA
- Alfred P. Sloan Foundation
- NVIDIA
- NXP
- Con Edison
- BAE
Information for candidates
I am always looking for strong candidates for the PhD program in the areas of Deep Learning (with the emphasis on Optimization and Training Methods for Deep Learning Systems), Robotics (with the emphasis on Autonomous Driving), and general Machine Learning.
Research Centers, Labs, and Groups
Center for Advanced Technology in Telecommunications (CATT)
Creating economic impact through research, technology transfer, and faculty entrepreneurship.
