Portrait de Negar Rostamzadeh

Negar Rostamzadeh

Membre industriel principal
Professeur associé, McGill University, École d'informatique
Chercheuse scientifique principale, Google Brain Ethical AI Team
Sujets de recherche
Apprentissage multimodal
Modèles génératifs
Vision par ordinateur

Biographie

Negar Rostamzadeh est chercheuse principale au sein de l'équipe Google Responsible AI et membre industrielle associée à Mila - Institut québécois d'intelligence artificielle. Ses recherches portent principalement sur la compréhension des implications sociales de l'apprentissage automatique et des systèmes d'évaluation, ainsi que sur le développement de systèmes d'intelligence artificielle équitables et justes.

Negar s'intéresse de près aux applications créatives de la vision par ordinateur et à leur impact sur la société et les artistes. Elle est la fondatrice et la présidente du programme de la série d'ateliers « Computer Vision for Fashion, Art, and Design », ainsi que « Ethical Considerations in Creative Applications », présentés sur les sites de Computer Vision depuis ECCV 2018 jusqu'à CVPR 2023.

Avant de rejoindre Google, Negar a travaillé comme chercheuse chez Element AI (Service Now), où elle s'est spécialisée dans l'apprentissage efficace à partir de données limitées en vision par ordinateur et dans les problèmes multimodaux.

Elle a obtenu son doctorat en 2017 à l'Université de Trente sous la supervision du professeur Nicu Sebe, en se concentrant sur les problèmes de compréhension vidéo. Elle a également passé deux ans à MILA (2015-2017), travaillant sur les mécanismes d'attention dans les vidéos, les modèles génératifs et le sous-titrage vidéo sous la direction du Prof. Aaron Courville. En 2016, elle a eu l'occasion de faire un stage au sein de l'équipe Machine Intelligence de Google.

Negar contribue activement à divers engagements communautaires au sein de la communauté de l'IA. Elle a été présidente du programme pour la série d'ateliers « Science meets Engineering of Deep Learning » à l'ICLR, FAccT et NeurIPS. Depuis 2020, elle est membre du conseil d'administration du Symposium d'IA de Montréal et, en 2019, elle a occupé le poste de présidente principale du programme. Negar est également Area Chair pour des conférences sur la vision telles que CVPR et ICCV, et a donné plusieurs keynotes dans divers ateliers et conférences.

Étudiants actuels

Alireza Dehghanpour Farashah
Doctorat - McGill
Superviseur⋅e principal⋅e :
Golnoosh Farnadi
Site web
Github

Publications

On The Local Geometry of Deep Generative Manifolds
Ahmed Imtiaz Humayun
Ibtihel Amara
Candice Schumann
Golnoosh Farnadi
Negar Rostamzadeh
Mohammad Havaei
In this paper, we study theoretically inspired local geometric descriptors of the data manifolds approximated by pre-trained generative mode… (voir plus)ls. The descriptors – local scaling (ψ), local rank (ν), and local complexity (δ) — characterize the uncertainty, dimensionality, and smoothness on the learned manifold, using only the network weights and architecture. We investigate and emphasize their critical role in understanding generative models. Our analysis reveals that the local geometry is intricately linked to the quality and diversity of generated outputs. Additionally, we see that the geometric properties are distinct for out-of-distribution (OOD) inputs as well as for prompts memorized by Stable Diffusion, showing the possible application of our proposed descriptors for downstream detection and assessment of pre-trained generative models.
2024-06-16
ICML.cc/2024/Workshop/GRaM (publié)
openreview.net
openreview.net
The value of standards for health datasets in artificial intelligence-based applications
Anmol Arora
Joseph E. Alderman
Joanne Palmer
Shaswath Ganapathi
Elinor Laws
Melissa D. McCradden
Lauren Oakden-Rayner
Stephen R. Pfohl
Marzyeh Ghassemi
Francis McKay
Darren Treanor
Negar Rostamzadeh
Bilal Mateen
Jacqui Gath
Adewole O. Adebajo
Stephanie Kuku
Rubeta Matin
Katherine Heller
Elizabeth Sapey
Neil J. Sebire … (voir 4 de plus)
Heather Cole-Lewis
Melanie Calvert
Alastair Denniston
Xiaoxuan Liu
2023-10-25
Nature Medicine (publié)
doi.org
Breaking Barriers to Creative Expression: Co-Designing and Implementing an Accessible Text-to-Image Interface
Atieh Taheri
Mohammad Izadi
Gururaj Shriram
Negar Rostamzadeh
Shaun Kane
Text-to-image generation models have grown in popularity due to their ability to produce high-quality images from a text prompt. One use for… (voir plus) this technology is to enable the creation of more accessible art creation software. In this paper, we document the development of an alternative user interface that reduces the typing effort needed to enter image prompts by providing suggestions from a large language model, developed through iterative design and testing within the project team. The results of this testing demonstrate how generative text models can support the accessibility of text-to-image models, enabling users with a range of abilities to create visual art.
2023-09-04
ArXiv (prépublication)
doi.org
arxiv.org
Beyond the ML Model: Applying Safety Engineering Frameworks to Text-to-Image Development
Shalaleh Rismani
Renee Shelby
Andrew J Smart
Renelito Delos Santos
AJung Moon
Negar Rostamzadeh
Identifying potential social and ethical risks in emerging machine learning (ML) models and their applications remains challenging. In this … (voir plus)work, we applied two well-established safety engineering frameworks (FMEA, STPA) to a case study involving text-to-image models at three stages of the ML product development pipeline: data processing, integration of a T2I model with other models, and use. Results of our analysis demonstrate the safety frameworks – both of which are not designed explicitly examine social and ethical risks – can uncover failure and hazards that pose social and ethical risks. We discovered a broad range of failures and hazards (i.e., functional, social, and ethical) by analyzing interactions (i.e., between different ML models in the product, between the ML product and user, and between development teams) and processes (i.e., preparation of training data or workflows for using an ML service/product). Our findings underscore the value and importance of examining beyond an ML model in examining social and ethical risks, especially when we have minimal information about an ML model.
2023-08-28
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (publié)
doi.org
arxiv.org
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction
Renee Shelby
Shalaleh Rismani
Kathryn Henne
AJung Moon
Negar Rostamzadeh
Paul Nicholas
N'Mah Yilla-Akbari
Jess Gallegos
Andrew J Smart
Emilio Garcia
Gurleen Virk
2023-08-28
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (publié)
doi.org
arxiv.org
From Plane Crashes to Algorithmic Harm: Applicability of Safety Engineering Frameworks for Responsible ML
Shalaleh Rismani
Renee Shelby
Andrew J Smart
Edgar Jatho
Joshua A. Kroll
AJung Moon
Negar Rostamzadeh
2023-04-18
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (publié)
doi.org
openreview.net
Tackling bias in AI health datasets through the STANDING Together initiative
Shaswath Ganapathi
Johannes Palmer
J. Alderman
Melanie Calvert
Cyrus Espinoza
Jacqui Gath
Marzyeh Ghassemi
Katherine Heller
Francis McKay
Alan Karthikesalingam
S. Kuku
Maxine E. Mackintosh
Sinduja Manohar
Bilal Mateen
Rubeta Matin
Melissa D. McCradden
Lauren Oakden-Rayner
Johan Ordish
Russell Pearson
S. Pfohl … (voir 8 de plus)
Negar Rostamzadeh
Elizabeth Sapey
Neil J. Sebire
Viknesh Sounderajah
Charlotte Summers
Darren E. Treanor
Alastair Denniston
Xiaoxuan Liu
2022-09-25
Nature Network Boston (publié)
doi.org
Healthsheet: Development of a Transparency Artifact for Health Datasets
Negar Rostamzadeh
Diana Mincu
Subhrajit Roy
Andrew J Smart
Lauren Wilcox
Mahima Pushkarna
Jessica Schrouff
Razvan Amironesei
Nyalleng Moorosi
Katherine Heller
2022-06-19
2022 ACM Conference on Fairness, Accountability, and Transparency (publié)
doi.org
arxiv.org
Sociotechnical Harms: Scoping a Taxonomy for Harm Reduction
Renee Shelby
Shalaleh Rismani
Kathryn Henne
AJung Moon
Negar Rostamzadeh
Paul Nicholas
N'mah Fodiatu Yilla
Jess Gallegos
Andrew J Smart
Emilio Garcia
Gurleen Virk
2021-12-31
arXiv.org (prépublication)
doi.org
Reinforced active learning for image segmentation
Arantxa Casanova
Pedro O. Pinheiro
Negar Rostamzadeh
Christopher Pal
Learning-based approaches for semantic segmentation have two inherent challenges. First, acquiring pixel-wise labels is expensive and time-c… (voir plus)onsuming. Second, realistic segmentation datasets are highly unbalanced: some categories are much more abundant than others, biasing the performance to the most represented ones. In this paper, we are interested in focusing human labelling effort on a small subset of a larger pool of data, minimizing this effort while maximizing performance of a segmentation model on a hold-out set. We present a new active learning strategy for semantic segmentation based on deep reinforcement learning (RL). An agent learns a policy to select a subset of small informative image regions -- opposed to entire images -- to be labeled, from a pool of unlabeled data. The region selection decision is made based on predictions and uncertainties of the segmentation model being trained. Our method proposes a new modification of the deep Q-network (DQN) formulation for active learning, adapting it to the large-scale nature of semantic segmentation problems. We test the proof of concept in CamVid and provide results in the large-scale dataset Cityscapes. On Cityscapes, our deep RL region-based DQN approach requires roughly 30% less additional labeled data than our most competitive baseline to reach the same performance. Moreover, we find that our method asks for more labels of under-represented categories compared to the baselines, improving their performance and helping to mitigate class imbalance.
2019-12-31
ICLR (publié)
openreview.net
openreview.net
Retrieving Signals with Deep Complex Extractors
Chiheb Trabelsi
Olexa Bilaniuk
Ousmane Dia
Ying Zhang
Mirco Ravanaelli
Jonathan Binas
Negar Rostamzadeh
Christopher Pal
Recent advances have made it possible to create deep complex-valued neural networks. Despite this progress, many challenging learning tasks … (voir plus)have yet to leverage the power of complex representations. Building on recent advances, we propose a new deep complex-valued method for signal retrieval and extraction in the frequency domain. As a case study, we perform audio source separation in the Fourier domain. Our new method takes advantage of the convolution theorem which states that the Fourier transform of two convolved signals is the elementwise product of their Fourier transforms. Our novel method is based on a complex-valued version of Feature-Wise Linear Modulation (FiLM) and serves as the keystone of our proposed signal extraction method. We also introduce a new and explicit amplitude and phase-aware loss, which is scale and time invariant, taking into account the complex-valued components of the spectrogram. Using the Wall Street Journal Dataset, we compared our phase-aware loss to several others that operate both in the time and frequency domains and demonstrate the effectiveness of our proposed signal extraction method and proposed loss.
2019-10-20
NeurIPS.cc/2019/Workshop/Deep_Inverse (poster)
openreview.net
openreview.net
Reinforced Imitation in Heterogeneous Action Space
Konrad Żołna
Negar Rostamzadeh
Yoshua Bengio
Sungjin Ahn
Pedro O. Pinheiro
Imitation learning is an effective alternative approach to learn a policy when the reward function is sparse. In this paper, we consider a c… (voir plus)hallenging setting where an agent and an expert use different actions from each other. We assume that the agent has access to a sparse reward function and state-only expert observations. We propose a method which gradually balances between the imitation learning cost and the reinforcement learning objective. In addition, this method adapts the agent's policy based on either mimicking expert behavior or maximizing sparse reward. We show, through navigation scenarios, that (i) an agent is able to efficiently leverage sparse rewards to outperform standard state-only imitation learning, (ii) it can learn a policy even when its actions are different from the expert, and (iii) the performance of the agent is not bounded by that of the expert, due to the optimized usage of sparse rewards.
2019-04-05
ArXiv (prépublication)
arxiv.org
arxiv.org