Portrait de Ankit Anand

Ankit Anand

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Professeur associé
Chercheur scientifique principal, Google DeepMind
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Biographie

Ankit est actuellement chercheur principal chez Google DeepMind Montréal et membre industriel associé à Mila - Institut québécois d'intelligence artificielle. Ses intérêts de recherche se situent à l'intersection du raisonnement et de l'apprentissage par renforcement. Il s'intéresse également à l'application de méthodes d'IA contemporaines pour réaliser des avancées en mathématiques et en informatique théorique (démonstration automatisée de théorèmes et génération de contre-exemples). Récemment, il a également travaillé sur les modèles LearnLM pour développer des modèles génératifs d'IA spécialement entraînés pour la pédagogie et les applications d'enseignement et sur la façon dont l'IA pourrait avoir un impact pour une éducation équitable.

Auparavant, il a terminé son doctorat à l'IIT Delhi en travaillant avec les professeurs Mausam et Parag Singla. Au cours de son doctorat, il a travaillé sur la symétrie des algorithmes d'IA dans le contexte des modèles graphiques probabilistes et des algorithmes de recherche arborescente de Monte Carlo.

Publications

"A Retrospective Analysis of Fatal Asphyxial Deaths Based on Medico-Legal Autopsies at a Medical College in the Garhwal Region, Uttarakhand"
Pankaj Sahu
J. Krishna
Ankit Anand
Chandra Singh Garhwali
Background: Mechanical and traumatic asphyxial deaths constitute an important category of unnatural deaths encountered in forensic practice.… (voir plus) Their epidemiological profile varies according to geographical, environmental
2026-10-17
International Journal of Drug Delivery Technology (publié)
doi.org
Building capacity for cervical cancer: a mixed-method evaluation of the training program for nursing professionals
Ankit Anand
Tavleen Kaur Malik
Anisha Mathew
Bharti Singh
Anila Goswami
Sandeep Bhalla
Apoorva Karan Rai
Nurses play a critical role in patient care and health promotion. Enhancing their knowledge and capacity in cervical cancer prevention, scre… (voir plus)ening, and early detection can improve patient outcomes and community awareness. This study provides the results of the assessment of an ECHO (Extension for Community Healthcare Outcomes) tele-mentoring program designed to build the capacities of nursing professionals in these domains. A mixed-methods study was conducted. Seven hubs delivered seven virtual sessions each, totalling 49 sessions and training 447 nursing professionals. The study consisted of three components: first, pre- and post-surveys ( n = 191) assessed changes in knowledge and attitudes; second, post-session surveys ( n = 1168) gathered participant perceptions after each session. Qualitative insights were obtained through the Most Significant Change (MSC) technique, capturing in-depth stories from the hub members who delivered the training. The pre- and post-assessments and the post-session survey were conducted with the training participants and included both quantitative and open-ended questions. The Wilcoxon signed-rank test was used to analyse the statistical significance of knowledge gains, while thematic analysis was applied to the qualitative data. The Institutional Review Board of the International Institute of Health Management Research (IIHMR), Delhi, granted ethical approval for the study. Informed consent was obtained separately for each tool from the study participants. The average knowledge score improved from 6.6 to 7.5 ( p 0.001) post-intervention. A large proportion of participants reported an increased willingness to undergo cervical cancer screening (83.6%) and a commitment to promote screening and awareness among peers and communities (98%). Post-session responses found the content relevant to their clinical roles (77%) and expressed intentions to apply the learnings (78%). Feedback highlighted the need for improved session timing, internet connectivity, and inclusion of additional health topics. Qualitative stories through the Most Significant Change technique hinted that some participants had already begun fostering positive work environments and enhancing communication with patients and families. The ECHO training has strengthened the knowledge and attitudes of nursing professionals in cervical cancer prevention, detection and screening. The qualitative interviews from the stakeholders also suggest interest and motivation to continue this initiative. The findings demonstrated potential for scale-up and adaptation across other oncology-related topics, positioning nurses as catalysts for change in community health promotion.
2026-10-13
BMC Nursing (publié)
doi.org
Equilibrando Reforma e Realidade: Promessas e Armadilhas do Projeto de Lei de Alteração da Lei de Arbitragem e Conciliação de 2024 – Um Estudo do Marco Arbitral Indiano
Ankit Anand
Rajneesh Kumar
M. Shamima Parveen
Gurminder Kaur
Hrishikesh Manu
The draft Arbitration and Conciliation (Amendment) Bill, 2024 (hereinafter “Draft Bill”) is a significant step in India’s ongoing camp… (voir plus)aign to enact legislation on modern lines and in harmony with contemporary international practices. The Draft Bill is considered a major legislative reform aimed at expediting procedures, minimising delays, and curbing undue judicial interference, thereby complementing India’s efforts to become an arbitration-friendly destination. Despite these objectives, significant doctrinal, procedural and institutional uncertainties arise from the proposed amendments, raising questions about whether the reforms will achieve their stated goals. This article seeks to map the changes proposed by the Draft Bill and place them in the broader context of India’s arbitration law reform. It analyses the transformative potential of the proposed changes for India’s dispute-resolution landscape vis-à-vis international benchmarks. The study adopts a doctrinal and analytical methodology, drawing on statutory analysis, judicial precedents and comparative international arbitration practices. The analysis finds that the Draft Bill strengthens efficiency and institutionalisation in several respects. However, specific proposals risk generating fresh ambiguities and procedural fragmentation if not carefully implemented. It also attempts to present focused suggestions for refining the implementation of the rescue provisions, maintaining procedural autonomy, popularising institutional arbitration, and aligning arbitration practice with related dispute-resolution mechanisms. It demonstrates that the success of the reforms will hinge upon the effective execution of policy and the translation of legal intent into actual arbitral practice. In this manner, the reforms may help India achieve its ambition to become a global arbitration hub.
2026-07-08
Frontiers in Signal Processing (publié)
openalex.org
Perturbative study of Supercritical Crossover in Noncommutative-corrected Spacetime
Ankit Anand
Shoucheng Wang
We analytically study the Widom line and supercritical crossover of noncommutative charged AdS black holes. Treating the noncommutative para… (voir plus)meter
2026-06-07
arXiv (prépublication)
doi.org
arxiv.org
Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning
Anthony GX-Chen
Ankit Anand
Gheorghe Comanici
Zaheer Abbas
Eser Aygün
David Smalling
Shibl Mourad
Doina Precup
Andre Barreto
Mark Rowland
Classical reinforcement learning (RL) typically seeks a deterministic policy that maximizes the expected sum of a scalar reward. Yet, modern… (voir plus) applications such as language model fine-tuning or scientific discovery demand diversity. Existing remedies such as entropy regularization or diversity bonuses often require fragile trade-offs that sacrifice performance for stochasticity or rely on heuristic metrics that can misalign policy rankings. We argue that diversity is more naturally understood as the rational response to uncertainty in the reward. When the reward function is not perfectly known--as is the case with ambiguous preferences or imperfect reward models--committing to a single action can be sub-optimal. Building on this, we propose a fundamental reformulation of the RL objective by replacing the scalar reward with a distribution over reward functions, and applying a non-linear objective over sets of actions. The result is a framework in which calibrated behavioural diversity emerges naturally, remains controllable through the reward function distribution, and is obtained without sacrificing expected reward. Focusing on the contextual bandit setting, we derive a principled gradient estimator for this objective and prove that our formulation naturally generalizes both vanilla policy gradient and more recently developed action-set approaches. Our empirical results demonstrate that this framework offers a robust and theoretically grounded alternative for complex RL tasks where the traditional formulation of the problem fails to induce the desired breadth of agent behaviour.
2026-06-01
arXiv (prépublication)
doi.org
arxiv.org
EXPRESS: Climate Communications in IPOs: Unpacking the Influence of Climate Disclosure Volume, Sender, and Message Characteristics
Ankit Anand
Alok R. Saboo
Ritesh Adhyapak
Climate disclosures have emerged as a prominent communication tool for firms facing growing pressure to address climate challenges, yet thei… (voir plus)r impact on firm performance remains unclear. This study proposes a nonlinear (U-shaped) relationship between climate disclosure volume and IPO firm performance, grounded in a damage-limitation logic. At low to moderate levels, disclosures amplify risk salience and proprietary costs, damaging valuations. At higher levels, offsetting benefits related to information, stewardship, and climate-friendly reputation outweigh these costs. Using multi-sourced data from 1,586 IPO firms, a BERT-based large language model to identify climate-related text in prospectuses, and econometric methods that address endogeneity, the authors find support for the proposed U-shaped relationship. The research further demonstrates that sender characteristics (underwriter reputation, customer concentration, and market orientation) and message characteristics (discretionary disclosure and message clarity) moderate the nonlinear relationship. Post-hoc analyses decomposing disclosure content reveal that climate risk disclosures damage valuations. In contrast, climate risk-management disclosures (governance, strategy, and metrics/targets) generate positive effects, suggesting that disclosure effectiveness depends on both volume and content composition. These effects persist in the long-term performance of firms. The findings provide actionable insights for firms developing disclosure strategies and policymakers encouraging climate-related communication.
2026-04-12
Journal of Marketing (publié)
doi.org
Panorama of Soft Tissue Tumours at a Tertiary Care Centre in Bihar: A Retrospective Observational Study
Ankit Anand
Vibhuti Kumar
Objective and Aim: Soft tissue tumors (STTs) represent a heterogeneous group of neoplasms with diverse histogenesis, biological behavior, an… (voir plus)d clinical outcomes. The present study aims to evaluate the spectrum, frequency, demographic distribution, anatomical location, and histopathological patterns of soft tissue tumors diagnosed at a tertiary care center in Bihar, India, with special emphasis on benign–malignant correlation and clinicopathological characteristics. Materials and Methods: This retrospective observational study was conducted in the Department of Pathology at a tertiary care teaching hospital in Bihar over a period of five years (January 2019–December 2023). All histopathologically confirmed cases of soft tissue tumors were included. Tumors were classified according to the WHO Classification of Soft Tissue and Bone Tumors (2020). Statistical analysis was performed using SPSS version 26.0. Descriptive statistics, chi-square test, and logistic regression analysis were applied. Results: A total of 312 cases of soft tissue tumors were analyzed. Benign tumors constituted 76.9%, intermediate tumors 7.4%, and malignant tumors 15.7%. The most common benign tumor was lipoma (38.1%), while undifferentiated pleomorphic sarcoma (21.4%) was the most frequent malignant tumor. Malignant tumors were significantly associated with age >40 years (p 0.001) and deep-seated location (p = 0.002). Conclusion: Soft tissue tumors in Bihar show a predominance of benign lesions with lipoma bei
2026-02-21
International Journal of Current Pharmaceutical Review and Research (publié)
doi.org
Discovering Symbolic Cognitive Models from Human and Animal Behavior
Pablo Samuel Castro
Nenad Tomasev
Ankit Anand
Navodita Sharma
Rishika Mohanta
Aparna Dev
Kuba Perlin
Siddhant Jain
Kyle Levin
Noemi Elteto
Will Dabney
Alexander Novikov
Glenn C Turner
Maria K Eckstein
Nathaniel D. Daw
Kevin J Miller
Kim Stachenfeld
Symbolic models play a key role in cognitive science, expressing computationally precise hypotheses about how the brain implements a cogniti… (voir plus)ve process. Identifying an appropriate model typically requires a great deal of effort and ingenuity on the part of a human scientist. Here, we adapt FunSearch (Romera-Paredes et al. 2024), a recently developed tool that uses Large Language Models (LLMs) in an evolutionary algorithm, to automatically discover symbolic cognitive models that accurately capture human and animal behavior. We consider datasets from three species performing a classic reward-learning task that has been the focus of substantial modeling effort, and find that the discovered programs outperform state-of-the-art cognitive models for each. The discovered programs can readily be interpreted as hypotheses about human and animal cognition, instantiating interpretable symbolic learning and decision-making algorithms. Broadly, these results demonstrate the viability of using LLM-powered program synthesis to propose novel scientific hypotheses regarding mechanisms of human and animal cognition.
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (publié)
proceedings.mlr.press
proceedings.mlr.press
GRAIL: Graph Edit Distance and Node Alignment using LLM-Generated Code
Samidha Verma
Arushi Goyal
Ananya Mathur
Ankit Anand
Sayan Ranu
Graph Edit Distance (GED) is a widely used metric for measuring similarity between two graphs. Computing the optimal GED is NP-hard, leading… (voir plus) to the development of various neural and non-neural heuristics. While neural methods have achieved improved approximation quality compared to non-neural approaches, they face significant challenges: (1) They require large amounts of ground truth data, which is itself NP-hard to compute. (2) They operate as black boxes, offering limited interpretability. (3) They lack cross-domain generalization, necessitating expensive retraining for each new dataset. We address these limitations with GRAIL, introducing a paradigm shift in this domain. Instead of training a neural model to predict GED, GRAIL employs a novel combination of large language models (LLMs) and automated prompt tuning to generate a program that is used to compute GED. This shift from predicting GED to generating programs imparts various advantages, including end-to-end interpretability and an autonomous self-evolutionary learning mechanism without ground-truth supervision. Extensive experiments on seven datasets confirm that GRAIL not only surpasses state-of-the-art GED approximation methods in prediction quality but also achieves robust cross-domain generalization across diverse graph distributions.
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (publié)
proceedings.mlr.press
proceedings.mlr.press
Cracking the Code of Action: A Generative Approach to Affordances for Reinforcement Learning
Lynn Cherif
Flemming Kondrup
David Venuto
Ankit Anand
Doina Precup
Khimya Khetarpal
Agents that can autonomously navigate the web through a graphical user interface (GUI) using a unified action space (e.g., mouse and keyboar… (voir plus)d actions) can require very large amounts of domain-specific expert demonstrations to achieve good performance. Low sample efficiency is often exacerbated in sparse-reward and large-action-space environments, such as a web GUI, where only a few actions are relevant in any given situation. In this work, we consider the low-data regime, with limited or no access to expert behavior. To enable sample-efficient learning, we explore the effect of constraining the action space through
2025-03-04
ICLR.cc/2025/Workshop/DL4C (publié)
doi.org
openreview.net
Code as Reward: Empowering Reinforcement Learning with VLMs
David Venuto
Sami Nur Islam
Martin Klissarov
Doina Precup
Sherry Yang
Ankit Anand
Pre-trained Vision-Language Models (VLMs) are able to understand visual concepts, describe and decompose complex tasks into sub-tasks, and p… (voir plus)rovide feedback on task completion. In this paper, we aim to leverage these capabilities to support the training of reinforcement learning (RL) agents. In principle, VLMs are well suited for this purpose, as they can naturally analyze image-based observations and provide feedback (reward) on learning progress. However, inference in VLMs is computationally expensive, so querying them frequently to compute rewards would significantly slowdown the training of an RL agent. To address this challenge, we propose a framework named Code as Reward (VLM-CaR). VLM-CaR produces dense reward functions from VLMs through code generation, thereby significantly reducing the computational burden of querying the VLM directly. We show that the dense rewards generated through our approach are very accurate across a diverse set of discrete and continuous environments, and can be more effective in training RL policies than the original sparse environment rewards.
2024-07-22
International Conference on Machine Learning (Accept (Spotlight))
doi.org
proceedings.mlr.press
Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search
Abbas Mehrabian
Ankit Anand
Hyunjik Kim
Nicolas Sonnerat
Matej Balog
Gheorghe Comanici
Tudor Berariu
Andrew Lee
Anian Ruoss
Anna Bulanova
Daniel Toyama
Sam Blackwell
Bernardino Romera Paredes
Petar Veličković
Laurent Orseau
Joonkyung Lee
Anurag Murty Naredla
Doina Precup
Adam Zsolt Wagner
2023-10-26
NeurIPS.cc/2023/Workshop/MATH-AI (poster)
doi.org
openreview.net