Portrait of Negar Rostamzadeh

Negar Rostamzadeh

Core Industry Member
Adjunct professor, McGill University, School of Computer Science
Senior Research Scientist, Google Brain Ethical AI Team
Research Topics
Computer Vision
Generative Models
Multimodal Learning

Biography

Negar Rostamzadeh is a Senior Research Scientist at Google Responsible AI team and an Associate Industrial member at Mila - Quebec Artificial Intelligence Institute. Her research primarily focuses on understanding the social implications of machine learning and evaluation systems, as well as developing equitable and fair ML systems.

Negar holds a deep interest in the creative applications of computer vision and their impact on society and artists. She is the founder and program chair of the workshop series, "Computer Vision for Fashion, Art, and Design," as well as "Ethical Considerations in Creative Applications," featured at Computer Vision venues from ECCV 2018 to CVPR 2023.

Before joining Google, Negar worked as a research scientist at Element AI (Service Now), where she specialized in efficient learning from limited data in computer vision and multi-modal problems.

She completed her PhD in 2017 at the University of Trento under the supervision of Prof. Nicu Sebe, focusing on Video Understanding problems. She also spent two years at MILA (2015-2017), working on attention mechanisms in videos, generative models, and video captioning under the guidance of Prof. Aaron Courville. In 2016, she had the opportunity to intern with Google's Machine Intelligence team.

Negar actively contributes to various community engagements within the AI community. She has served as the program chair for the workshop series, "Science meets Engineering of Deep Learning," at ICLR, FAccT, and NeurIPS. Since 2020, she has been a board member of the Montreal AI Symposium, and in 2019, she held the position of Senior Program Chair. Negar is also an Area Chair for Vision Conferences such as CVPR and ICCV, and gave multiple keynotes in various workshops and conferences.

Current Students

Alireza Dehghanpour Farashah
PhD - McGill University
Principal supervisor :
Golnoosh Farnadi
Website
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… (see more)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 (published)
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 … (see 4 more)
Heather Cole-Lewis
Melanie Calvert
Alastair Denniston
Xiaoxuan Liu
2023-10-25
Nature Medicine (published)
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… (see more) 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 (preprint)
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 … (see more)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 (published)
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 (published)
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 (published)
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 … (see 8 more)
Negar Rostamzadeh
Elizabeth Sapey
Neil J. Sebire
Viknesh Sounderajah
Charlotte Summers
Darren E. Treanor
Alastair Denniston
Xiaoxuan Liu
2022-09-25
Nature Network Boston (published)
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 (published)
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 (preprint)
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… (see more)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 (published)
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 … (see more)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… (see more)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 (preprint)
arxiv.org
arxiv.org