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NYU (New York University)
4.5
based on 3 Reviews
About NYU (New York University)
Founded in1831 (194 yrs old)
India Employee Count--
Global Employee Count10k-50k
HeadquartersNew York, United States
Office Locations
--
Websitenyu.edu
Primary Industry
Other Industries
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Higher education is at the core of this Big Apple institution. The setting and heritage of New York University (NYU) make it one of the nation's most popular educational institutions. With more than 50,000 students attending its 14 schools and colleges, NYU is among the largest private schools in the US. Its Tisch School of the Arts is well-regarded, and its law school and Leonard N. Stern School of Business are among the foremost in the country. NYU occupies five major centers in Manhattan; its Washington Square campus is in the heart of Greenwich Village. The school was founded in 1831. Notable alumni include former Federal Reserve Chairman Alan Greenspan and film producer Oliver Stone.
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NYU (New York University) Ratings
based on 3 reviews
Overall Rating
4.5/5
How AmbitionBox ratings work?
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4
1
3
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2
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Category Ratings
4.5
Company culture
4.5
Job security
4.5
Salary
4.5
Work-life balance
4.5
Skill development
4.5
Promotions
4.5
Work satisfaction
NYU (New York University) is rated 4.5 out of 5 stars on AmbitionBox, based on 3 company reviews. This rating reflects a generally positive employee experience, indicating satisfaction with the company’s work culture, benefits, and career growth opportunities. AmbitionBox gathers authentic employee reviews and ratings, making it a trusted platform for job seekers and employees in India.
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NYU (New York University) Reviews
Compare NYU (New York University) with Similar Companies
Change Company | Change Company | Change Company | ||
|---|---|---|---|---|
Overall Rating | 4.5/5 based on 3 reviews | 4.3/5 based on 862 reviews | 4.2/5 based on 1k reviews | 3.4/5 based on 3.3k reviews |
Highly Rated for | Skill development Work-life balance Salary | Skill development Work-life balance Company culture | Skill development Work satisfaction Salary | No highly rated category |
Critically Rated for | No critically rated category | Job security Promotions | No critically rated category | Work-life balance Promotions Job security |
Primary Work Policy | - | Work from office 82% employees reported | Work from office 91% employees reported | Work from office 87% employees reported |
Rating by Women Employees | - no rating available | 4.3 Good rated by 459 women | 4.1 Good rated by 333 women | 3.4 Average rated by 911 women |
Rating by Men Employees | - no rating available | 4.2 Good rated by 299 men | 4.2 Good rated by 565 men | 3.4 Average rated by 2.2k men |
Job security | 4.5 Good | 3.2 Average | 3.7 Good | 3.1 Average |
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NYU (New York University) Salaries
NYU (New York University) salaries have received with an average score of 4.5 out of 5 by 3 employees.
Teaching Assistant
(3 salaries)
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₹8 L/yr - ₹12 L/yr
Construction Management
(3 salaries)
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₹1.8 L/yr - ₹2.3 L/yr
Post Doctoral Fellow
(2 salaries)
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₹29.3 L/yr - ₹37.4 L/yr
Security Operations Center Lead
(2 salaries)
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₹90.3 L/yr - ₹99.8 L/yr
Graduate Assistant
(2 salaries)
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₹5 L/yr - ₹6.3 L/yr
Machine Learning Intern
(1 salaries)
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₹0.9 L/yr - ₹1.2 L/yr
Postdoctoral Associate
(1 salaries)
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₹63.7 L/yr - ₹70.4 L/yr
Full Stack Developer
(1 salaries)
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₹21.6 L/yr - ₹27.6 L/yr
Finance Manager
(1 salaries)
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₹4.1 L/yr - ₹5.2 L/yr
Front Office Executive
(1 salaries)
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₹3.6 L/yr - ₹4.6 L/yr
NYU (New York University) News
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Midjourney’s surprise: new research on making LLMs write more creatively
- Midjourney, known for its AI image generators, released new research on training text-based large language models (LLMs) to write more creatively.
- The collaboration with New York University introduces two new techniques, DDPO and DORPO, to expand the range of possible outputs while maintaining coherence and readability.
- The research goes beyond academic exercises and could fuel a new wave of LLM training among enterprise AI teams, product developers, and content creators.
- By incorporating deviation, the models learn to produce high-quality but more varied responses, ensuring AI-generated stories explore a wider range of characters, settings, and themes.
VentureBeat | 25 Mar, 2025
[R] Transformers without Normalization (FAIR Meta, New York University, MIT, Princeton University)
- Transformers without Normalization: A recent work by Jiachen Zhu, Xinlei Chen, Kaiming He, Yann LeCun, and Zhuang Liu explores the use of Transformers without normalization layers.
- They introduce a technique called Dynamic Tanh (DyT), which can be used as a drop-in replacement for normalization layers in Transformers.
- The study demonstrates that Transformers without normalization, when combined with DyT, can achieve the same or better performance compared to their normalized counterparts.
- These findings challenge the traditional belief that normalization layers are essential in modern neural networks.
Reddit | 15 Mar, 2025
![[R] Transformers without Normalization (FAIR Meta, New York University, MIT, Princeton University)](https://rdwgroup.com/wp-content/uploads/2018/10/reddit2-800x450-1.png)
AI researchers launch AnySense app for gathering visual training data
- A group of researchers at New York University has launched an iOS app called AnySense for gathering visual training data for robotics models.
- The AnySense app is a part of the Robot Utility Models (RUM) project, which aims to generalize training for robots and allow them to perform in new environments.
- The app integrates the iPhone's sensors with external multisensory inputs, interfaces with the versatile tactile sensor called AnySkin, and is fully open-source and available for the robotics community.
- The AnySense app is now available for download from the Apple App Store.
The Robot Report | 5 Mar, 2025
NYU Abu Dhabi Researchers Unveil Innovative Tool to Improve Precision in Cold-Temperature Cancer Surgery
- NYU Abu Dhabi researchers have developed a groundbreaking tool to enhance cancer surgery precision using fluorescence imaging during cryosurgery.
- The innovative tool utilizes a nanoscale material that illuminates cancer cells under freezing conditions, aiding in distinguishing malignant tissues from healthy ones.
- Published in the American Chemical Society journal, the study highlights the development of a nanoscale Covalent Organic Framework (nTG-DFP-COF) by the Trabolsi research group at NYUAD.
- The material's enhanced fluorescence properties enable real-time monitoring during cryosurgery, ensuring accurate tumor removal and preservation of healthy tissue.
- Integrating fluorescence imaging with cryosurgery merges diagnostic and therapeutic functions, potentially reducing the need for repeat surgeries and expediting patient recovery.
- The tool's ability to retain fluorescence in the presence of ice crystals offers a revolutionary approach in enhancing surgical precision for cancer removal.
- This advancement not only transforms cancer surgery but also sets the stage for broader applications of fluorescence imaging in medical procedures.
- Dr. Farah Benyettou and Professor Ali Trabolsi express optimism about the tool's impact on revolutionizing cancer surgery and improving patient outcomes.
- The innovation bridging imaging and therapy aims to enhance cancer treatment efficacy by providing surgeons with real-time visualization for precise tumor removal.
- The research represents a significant leap in enhancing patient care and reshaping the patient experience in cancer treatment, with potential for future medical innovations.
Bioengineer | 4 Mar, 2025
Meta AI Releases the Video Joint Embedding Predictive Architecture (V-JEPA) Model: A Crucial Step in Advancing Machine Intelligence
- Researchers from FAIR at Meta, Inria, École normale supérieure, CNRS, PSL Research University, Univ. Gustave Eiffel, Courant Institute, and New York University introduced V-JEPA, a vision model trained exclusively on feature prediction for unsupervised video learning.
- V-JEPA outperforms other methods on Something-Something-v2 and remains competitive on Kinetics-400, demonstrating that feature prediction alone can produce efficient and adaptable visual representations with shorter training durations.
- V-JEPA shows superior performance across video and image tasks in frozen evaluation, except for ImageNet classification, and outperforms ViT-L/16-based models with fine-tuning while requiring fewer training samples.
- V-JEPA excels in motion understanding and video tasks, training more efficiently, and demonstrates strong label efficiency, maintaining accuracy with fewer labeled examples.
Marktechpost | 23 Feb, 2025
Revolutionary AI System Unveils Urban Green Space Disparities, Highlighting Environmental Inequities
- A research team led by Rumi Chunara at NYU has developed an AI system to track urban green spaces, aiding in urban sustainability amidst environmental challenges.
- The AI model was tested in Karachi, Pakistan, showing improved accuracy in mapping diverse urban greenery crucial for urban planning and public health.
- The AI system overcomes limitations of traditional satellite analysis, detecting 37% more urban vegetation, crucial for mitigating climate impacts and promoting well-being.
- The system utilized DeepLabV3+ and 'green augmentation' techniques, enhancing accuracy by 13.4% compared to existing methods.
- Achieving 89.4% accuracy and 90.6% reliability, this AI system outperformed traditional approaches, aiding in identifying disparities in green space distribution.
- Economic indicators were linked to greenery presence, showing that areas with more paved roads had higher vegetation cover and lower surface temperatures.
- Comparing with Singapore, the benefits of intentional urban greening initiatives were evident, emphasizing the importance of equitable access to green spaces.
- The research methodology is available for other cities, highlighting the significance of integrating nature into urban design for sustainable, healthy communities.
- Chunara's work underscores the call for action in leveraging AI technologies to shape cities prioritizing residents' health and well-being worldwide.
- The study serves as a roadmap for cities striving to rebalance their ecosystems, ensuring equitable access to the benefits of green spaces amid urban growth.
Bioengineer | 21 Feb, 2025
Cooperative Behavior of Cancer Cells Enables Efficient Nutrient Scavenging
- Cancer cells exhibit cooperative behavior under nutrient-scarce conditions, shifting the understanding of tumor biology and potential therapeutic strategies.
- A study by New York University reveals that tumor cells engage in cooperative interactions when faced with amino acid scarcity.
- The researchers used advanced tools to track cell behavior and found that tumor cells collaborate to scavenge and share limited resources.
- The study highlights the importance of understanding intercellular interactions and offers new insights for targeted cancer therapies.
Bioengineer | 19 Feb, 2025
NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
- A team from NYU developed an open-source benchmark dataset and automated framework to evaluate the performance of Large Language Models (LLMs) in solving Capture the Flag (CTF) challenges in cybersecurity.
- The benchmark database includes metadata for LLM testing and adaptive learning, compiling various CTF challenges from popular competitions.
- The automated framework utilizes the advanced function calling capabilities of LLMs, allowing for efficient evaluation of five LLMs, including both black-box and open-source models.
- The project aims to improve the efficiency of LLMs in interactive cybersecurity tasks and automated task planning, providing insights into the potential of AI-driven solutions in real-world threat management.
Arxiv | 19 Feb, 2025
NYU Researchers Introduce WILDCHAT-50M: A Large-Scale Synthetic Dataset for Efficient LLM Post-Training
- NYU researchers have introduced WILDCHAT-50M, a large-scale synthetic dataset for efficient LLM post-training.
- The lack of standardized datasets hinders the evaluation and optimization of post-training techniques for LLMs.
- WILDCHAT-50M is the largest and most diverse public dataset of chat transcripts, comprising responses from over 50 open-weight models.
- The dataset demonstrates enhanced model alignment, response coherence, and instruction-following capabilities, while improving runtime efficiency.
Marktechpost | 5 Feb, 2025
Google DeepMind Unveils Inference Time Scaling for Diffusion Models
- Google DeepMind, in collaboration with MIT and NYU, introduces inference time scaling for diffusion models.
- Increasing inference time compute leads to substantial improvements in generated samples' quality.
- The research explores the effectiveness of inference-time search methods across different models.
- These techniques can potentially be applied to video generation as well.
Analyticsindiamag | 17 Jan, 2025
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NYU (New York University) FAQs
When was NYU (New York University) founded?
NYU (New York University) was founded in 1831. The company has been operating for 194 years primarily in the Education & Training sector.
Where is the NYU (New York University) headquarters located?
NYU (New York University) is headquartered in New York.
What are the pros of working in NYU (New York University)?
Working at NYU (New York University) offers several advantages that make it an appealing place for employees. The company is highly rated for company culture, job security and promotions / appraisal, based on 3 employee reviews on AmbitionBox.
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