Join the Department of Computer Science as we welcome Lyle Ungar, University of Pennsylvania, who will be delivering a lecture on 'Measuring Cultural Variation using Natural Language Processing.' When: 11/08/24 @ 2:30 PM Where: New Computer Science Building, Room 120. Reception to follow. Abstract: Cultures vary widely in how they view the world, for example being more individualist or collectivist. Such cultural differences are, of course, reflected in the words that people use. We first show a variety of ways in which multilingual language models are not multicultural; they speak Hindi or Mandarin, but still think like Americans. In contrast, we then present a scalable method that uses embedding-derived lexica to successfully measure regional variation in culture. Bio: Lyle Ungar is a Professor of Computer and Information Science at the University of Pennsylvania, where he also holds secondary appointments in Psychology, Bioengineering, Genomics and Computational Biology, and Operations, Information and Decisions. His group uses natural language processing and explainable AI for psychological research, including analyzing social media and cell phone sensor data to better understand the drivers of physical and mental well-being. They are currently building socio-emotionally sensitive GPT-based tutors and coaches.



Abstract: The current approach to materials design, driven by strategic experimentation and supported by physics-based simulation across relevant scales, has been the standard for decades. While the theoretical component in this workflow provides valuable understanding of material behavior, it often fails to deliver actionable guidance for implementation. Advances in artificial intelligence and machine learning (AI/ML), together with high-performance computing (HPC), now offer a viable pathway to close this gap and accelerate both discovery and process optimization. This presentation will outline practical approaches for integrating AI/ML with HPC-enabled, high-throughput computation to explore high-dimensional search spaces. Examples will include the development of engineering alloys for extreme environments, the use of neural networks to rapidly improve computational thermodynamic models, and vapor processing optimization for the manufacturing of ultra-high-temperature ceramics. I will highlight how scientific insight and domain expertise remain essential for translating surrogate model predictions into impactful outcomes. Finally, I will conclude with current challenges and future opportunities for AI/HPC-driven materials research.

Speaker: Dongwon Shin
This seminar will be held in person and online

Join Zoom Meeting: https://stonybrook.zoom.us/j/93730374357?pwd=YDLJ7ELqOQnTZEQhlN8Pa4TuhaiFK8.1


The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning.



ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics.

ICLR is one of the fastest growing artificial intelligence conferences in the world. Participants at ICLR span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

The rapidly developing field of deep learning is concerned with questions surrounding how we can best learn meaningful and useful representations of data. ICLR takes a broad view of the field and includes topics such as feature learning, metric learning, compositional modeling, structured prediction, reinforcement learning, and issues regarding large-scale learning and non-convex optimization.

A non-exhaustive list of relevant topics explored at the conference include:



  • Unsupervised, Semi-supervised, and Supervised Representation Learning
  • Representation Learning for Planning and Reinforcement Learning
  • Metric Learning and Kernel Learning
  • Sparse Coding and Dimensionality Expansion
  • Hierarchical Models

  • Optimization for Representation Learning
  • Learning Representations of Outputs or States
  • Implementation Issues, Parallelization, Software Platforms, Hardware
  • Applications in Vision, Audio, Speech, Natural Language Processing, Robotics, Neuroscience, or Any Other Field


For more information or registration, please visit the official website.

Chat with Sociology faculty as they share their paths to StonyBrook-what inspired their careers, what led them to teaching,and the experiences that shaped their academic journey.

Dr. Yongjun Zhang

Assistant Professor of Sociology, Departments of Sociology and AAAS

Join this opportunity to talk to Yongjun Zhang about his new interest in the following responsible usage of AI in addressing climate and health issues. Lunch will be served.

Location: SBS Level 4- Sociology Reading Room

View more event information

The 39th Annual AAAI Conference on Artificial Intelligence will be held from February 25 to March 4, 2025 in Philadelphia, Pennsylvania, USA. More information can be found here.

The infrastructure decisions being made around AI today will shape competitive advantage, cost, control, and institutional trust for years to come.

Organizations will invest in AI either way. The strategic question is whether they remain dependent on systems they cannot fully inspect or influence, or help shape shared infrastructure that they can adapt, govern, and build upon.

We would like to invite you to a live executive briefing from the AI Alliance. The discussion will focus on what leaders should understand now:

  • The state of the movement: how 205 organizations across 29 countries are collaborating on more than 30 open projects.
  • Project Tapestry: our global initiative to build frontier-capable, open-sourced AI through shared development, while allowing participating institutions to retain control over their data, resources, and strategic priorities.
  • The business and policy landscape: how cost, sovereignty, trust, and access are reshaping AI strategy.
  • Paths to participation: how organizations can contribute through the AI Alliance Open R&D Community and the Open Source AI Innovation Association.

Register here.
Abstract: Sub-grid turbulence is challenging to resolve in climate models; therefore, it is parameterized. Traditionally, turbulent parameterizations have relied on physics-based and equation-based approaches. However, ad hoc and uncertain components in these parameterizations introduce uncertainty in future climate predictions. Recently, data-driven techniques have emerged as an alternative for modeling sub-grid fluxes. I will demonstrate the use of machine learning to model vertical turbulent fluxes in the ocean surface boundary layer and its impact on reducing biases in NOAA's Geophysical Fluid Dynamics Laboratory ocean climate model.

I will show how neural networks, trained to predict the eddy diffusivity profile from high-fidelity yet computationally expensive turbulence schemes, enhance the vertical mixing scheme in the climate model. These networks replace ad hoc components while maintaining the conservation principles of the standard ocean model equations. The enhanced scheme outperforms its predecessor by reducing biases in the mixed-layer depth and modestly improving tropical upper-ocean stratification in ocean-only global simulations. Furthermore, simplified equations that can replace the neural networks show similar improvements but with lower computational cost and better interpretability. They point to structural deficiencies in the baseline parameterization. This work is one of the first successful applications of machine learning to improve a sub-grid parameterization of turbulent mixing in ocean climate models.

IACS Seminar Speaker: Aakash Sane, Princeton University

Location: IACS Seminar Room or Zoom

Join Zoom Meeting: https://stonybrook.zoom.us/j/97764942108?pwd=MzCWupCe3L9mKdrgfO2bJg3GBbvXuf.1
Meeting ID: 977 6494 2108
Passcode: 519324

Join Klaus Mueller, professor of computer science and interim chair of the Department of Technology and Society, as he hosts Sucheta Lahiri.

Lahiri leads the AI Ethics and Risk Management function at Oxy, where she is responsible for ensuring that the company's AI solutions are developed and deployed in a manner that is ethical, efficient, trustworthy, safe, sustainable, and human-centered. She holds a doctorate from Syracuse University, along with two master's degrees in Applied Statistics and Information Science earned in India.

Zoom: https://stonybrook.zoom.us/j/7851507944?omn=98268154363#success

The Center of Excellence in Wireless and Information Technology (CEWIT) will host the 16th International Conference on Emerging Technologies for a Smarter World (CEWIT2020) virtually on November 5, 2020. The conference will center on the four major fields which are penetrating our business and personal lives: Machine Learning, Artificial Intelligence, Blockchain and Computational Medicine. For more info visit: https://www.cewit.org/.