Topic: AI Seminar: Stanley Bak
Time: Monday Nov 1, 2021 12:00 PM Eastern Time (US and Canada)

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Abstract: The field of formal verification has traditionally looked at proving properties about finite state machines or software programs. The surge in deep learning has been accompanied by a surge of progress in trying to apply mathematical and algorithmic techniques to prove things about the function being computed by a neural network.

This talk formalizes the neural network verification problem and describes technical methods for neural network verification based on reachability analysis. Improvements to analysis efficiency will be given, as well as research directions for further exploration. We also include an objective comparison performed this last summer trying to evaluate the best existing verification methods in terms of speed and network size. The competition was performed on common hardware and involved the participation of twelve international teams (the tool authors) on a common set of benchmarks. 

Biography: Stanley Bak is an assistant professor in the Department of Computer Science at Stony Brook University investigating the verification of autonomy, cyber-physical systems, and neural networks. He strives to develop practical formal methods that are both scalable and useful, which demands developing new theory, programming efficient tools and building experimental systems.
Stanley Bak received a Bachelor's degree in Computer Science from Rensselaer Polytechnic Institute (RPI) in 2007 (summa cum laude), and a Master's degree in Computer Science from the University of Illinois at Urbana-Champaign (UIUC) in 2009. He completed his PhD from the Department of Computer Science at UIUC in 2013. He received the Founders Award of Excellence for his undergraduate research at RPI in 2004, the Debra and Ira Cohen Graduate Fellowship from UIUC twice, in 2008 and 2009, and was awarded the Science, Mathematics and Research for Transformation (SMART) Scholarship from 2009 to 2013. From 2013 to 2018, Stanley was a Research Computer Scientist at the US Air Force Research Lab (AFRL), both in the Information Directorate in Rome, NY, and in the Aerospace Systems Directorate in Dayton, OH. He helped run Safe Sky Analytics, a research consulting company investigating verification and autonomous systems, and performed teaching at Georgetown University before joining Stony Brook University as an assistant professor in Fall 2020.

Fantastic Futures is an international conference series organized by AI4LAM as one of the most important global events at the intersection of AI and cultural heritage, shaping how memory institutions adapt to rapidly evolving technologies while maintaining public trust.

The conference brings together professionals, researchers, technologists, and cultural‑heritage institutions to explore how AI can support organizational workflows, users services and data preservation, access, discovery, as well as innovation in the cultural‑heritage sector:

  • Librarians, archivists, museum professionals and all other GLAM enthusiasts
  • Technologists working on cultural‑heritage applications, AI researchers and developers
  • Digital humanists, researchers of cultural heritage
  • University professors, students, and librarians
  • Policy and ethics experts, governmental employees
  • Strategic leaders and innovation managers

To register, visit AI4LAM's official website.
Making sense of Twitter @ Bloomberg presented by Daniel Preotiuc-Pietro

ABSTRACT: The Bloomberg Terminal has provided ways for investors and journalists to sift through and understand the immense volume of tweets and discover financially-relevant content ever since the SEC approved the use of Twitter for company disclosures back in 2013.

In the first part of the talk, I will showcase how tweets impact financial markets and how Bloomberg is using Natural Language Processing methods to identify financially relevant tweets that move the markets. Our processing pipeline feeds directly to clients, journalists in the newsroom and powers several news analytic products offered by the company including trending companies and consumer sentiment for publicly traded equities.

However, understanding user pragmatic intent in individual tweets would allow us to gain deeper insights and enable new applications. I will present several recent research studies focused on understanding intent including identifying complaints and the roles with which vulgarity is used in social media and how these can help improve applications such as sentiment analysis and hate speech detection.

BIO: Daniel Preotiuc-Pietro is a Senior Research Engineer and Team Lead at Bloomberg LP, where he works on analyzing and building models for real-world large scale social media and news mining and information extraction. His research interests are focused on understanding the social and temporal aspects of text, especially from social media, with applications in domains such as Social Psychology, Law, Political Science and Journalism. Several of his research studies were featured in popular press including the Washington Post, BBC, New Scientist, Scientific American or FiveThirtyEight. He is a co-organizer of the Natural Legal Language Processing workshop series. Prior to joining Bloomberg LP, Daniel was a postdoctoral researcher at the University of Pennsylvania with the interdisciplinary World Well Being Project and obtained his PhD in Natural Language Processing and Machine Learning at the University of Sheffield, UK.
Date: March 11, 2022
Time: 2:40PM EST

Title: Towards Scalable and Efficient Machine Learning as a Service (MLaaS)

Abstract:
Driven by the explosive growth of big data, the sustained advances of
Machine Learning (ML), and the fast evolving of computer system
techniques, the past few years have witnessed a surging demand for
Machine Learning as a Service (MLaaS). MLaaS is an emerging computing
paradigm that facilitates ML model design, training, inference serving
and provides optimized executions of ML tasks in an automated,
scalable, and efficient manner. In this talk, I will demonstrate how
to integrate ML algorithm research and system research in synergy to
address the pressing challenges in MLaaS. I will first share a story
about how our system experience led to a novel large batching
algorithm design that revolutionizes large-scale training. Then I will
tell another story about how our gradient compression algorithm
research helped us to discover overlooked critical features of modern
ML systems and thereby build a compression-aware distributed ML
system. I will also briefly discuss a promising future of harnessing
serverless computing for MLaaS model inference serving. I will
conclude my talk with a discussion of interdisciplinary research and
future plans.

Bio:
Dr. Feng Yan is an Assistant Professor of Computer Science and
Engineering at University of Nevada, Reno (UNR) and director of the
Intelligent Data and Systems Lab (IDS Lab). Dr. Yan received M.S. and
Ph.D. degrees in Computer Science from the College of William and Mary
and worked at Microsoft Research and HP Labs. Dr. Yan's research
bridges the fields of big data, machine learning, and systems. The
focus of his research is on developing methodologies and building
systems that are automated, high-performing, efficient, robust, and
user-centric. Some of his recent research topics include large-scale
distributed deep learning, machine learning as a service (MLaaS),
federated learning, AutoML, serverless computing, and broad topics in
cloud and HPC. Dr. Yan is also dedicated to interdisciplinary research
and has established fruitful collaborations with domain experts in
areas such as health, physics, geography, material science, mechanical
engineering, civil engineering, and innovated big data and AI-driven
approaches for these domains. Dr. Yan and his team are actively
publishing at the most prestigious venues in computer system area
(such as SOSP, SC, HPDC, USENIX ATC, EuroSys, FAST, VLDB, etc.) and
machine learning area (such as NIPS/NeurIPS, KDD, AAAI, etc.). Dr. Yan
and his students are the recipients of the Best Student Paper Award of
IEEE CLOUD 2018, the Best Paper Award of CLOUD 2019, and the Best
Student Paper Award of ITNG 2021. Dr. Yan is the recipient of the NSF
CAREER Award, the NSF CRII Award, the CSE Best Researcher Award, and
has been nominated for the Regents' Rising Researcher Award. Dr. Yan
serves as Social Media Chair of ACM SIGMETRICS. To learn more
information, please visit Dr. Yan's homepage:
https://www.cse.unr.edu/~fyan.
Abstract: Retrieval-augmented generation (RAG) systems empower large language models (LLMs) to access external knowledge during inference. Recent advances have enabled LLMs to act as search agents via reinforcement learning (RL), improving information acquisition through multi-turn interactions with retrieval engines. However, existing approaches either optimize retrieval using search-only metrics (e.g., NDCG) that ignore downstream utility or fine-tune the entire LLM to jointly reason and retrieve--entangling retrieval with generation and limiting the real search utility and compatibility with frozen or proprietary models. In this work, we propose s3, a lightweight, model-agnostic framework that decouples the searcher from the generator and trains the searcher using a Gain Beyond RAG reward: the improvement in generation accuracy over naïve RAG. s3 requires only 2.4k training samples to outperform baselines trained on over 70 × more data, consistently delivering stronger downstream performance across six general QA and five medical QA benchmarks.

Speaker: Peter Zeng

Location: CS2311

Abstract: My presentation will be focused on introducing the use of Screenomics, a passive sensing approach that directly collects time-intensive data from participants' smartphones, to observe and analyze adolescents' digital behaviors across multiple timescales. I will present our completed and ongoing efforts using Screenomics to (1) evaluate the biases of self-reports of screen time and app use, (2) describe how adolescents use their smartphones during school hours and overnight, (3) examine longitudinal associations between adolescents' social media use and mental health, and (4) capture adolescents' communication pattern with parents. I will also introduce the theoretical framework and study plan for a new NIH-funded project that aims to identify adolescents' social media management strategies (SMMS) and how SMMS are related to adolescents' actual social media use and mental health. I will conclude with a discussion of future directions for interventions to promote healthy digital practices among adolescents.

Bio: Xiaoran Sun, Ph.D., is an assistant professor in the Department of Family Social Science, College of Education and Human Development at University of Minnesota (UMN). She is the director of the UMN Technology, Teens, and Families Lab and a core faculty of the Learning Informatics Lab. She is also affiliated with the UMN Data Science Initiative and the Minnesota Population Center. Her research is mainly focused on using innovative approaches, such as passive sensing and machine learning, to examine children's and parents' use of technology and the implications for their wellbeing. Her work is being funded by the U.S. National Institute of Mental Health and the Spencer Foundation.

Abstract: AI has achieved remarkable advancements in image recognition and natural language processing. However, its applications in Earth and environmental sciences are still emerging. Unprecedented data from satellites, sensors, and in-situ measurements oIers new opportunities to improve physics-based models and forecasts of environmental systems with AI and to gain deeper insights into these phenomena. Extreme systems, such as weather and climate events, pose distinct challenges for AI, such as limited sampling of rare events, non-trivial data augmentation, errors-in-variables, and complexities of transfer learning across diverse tasks. In this talk, we will explore some of these challenges and showcase AI architectures designed to address them. We will use specific examples of forecasting dust storms, precipitation extremes, flash floods, and drought events in the Middle East. Finally, we will discuss a different AI approach for studying sinkhole formation in the Dead Sea.

Speaker: Prof. Yinon Rudich, Department of Earth and Planetary Sciences, Weizmann Institute, Israel


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Visual Analytics and Machine Learning for Biomedical Imaging Diagnosis

 

Arie Kaufman

 

We present an integrated approach using visual analytics and machine learning (ML) to diagnose abnormalities in 3D radiological imaging and biological microscopes. The primary example will involve 3D virtual pancreatography (VP), a novel visualization-ML procedure and application for non-invasive diagnosis and classification of pancreatic lesions, the precursors of pancreatic cancer. Currently, non-invasive screening of patients is performed through visual inspection of 2D axis-aligned CT images, though the relevant features are often not clearly visible nor automatically detected. VP is an end-to-end visual diagnosis system that includes an ML-based automatic segmentation of the pancreatic gland and the lesions, a semi-automatic approach to extract the primary pancreatic duct, an ML-based automatic classification of lesions into four prominent types, and specialized 3D and 2D exploratory visualizations of the pancreas, lesions and surrounding anatomy. We combine volume rendering with pancreas- and lesion-centric visualizations and measurements for effective diagnosis. We designed VP through close collaboration and feedback from expert radiologists, and evaluated it on multiple real-world CT datasets with various pancreatic lesions and case studies examined by the expert radiologists. Other applications include virtual colonoscopy, COVID-19, pathology, brain neurites, etc.


Biography: Arie Kaufman is Distinguished Professor and formerChair of the Department of Computer Science at Stony Brook University, where he is also Director of the Center for Visual Computing (CVC), and Chief Scientist at the Center of Excellence in Wireless and Information Technology (CEWIT). 

He received his PhD in Computer Science at Ben-Gurion University of the Negev in 1977.   He is known for his work in visualization, graphics, virtual reality, user interfaces, multimedia, and their applications, especially in bio-medicine. He is especially well known for his work on the 3-dimensional virtual colonoscopy, a revolutionary low-risk technique for colon cancer screening, and for pioneering the use of Graphics Processing Units (GPUs) and GPU-clusters. In 2012, he presided over the development and opening of the Reality Deck, the largest virtual reality display in the world, at Stony Brook University.

Kaufman was the founding Editor in Chief of IEEE Transactions on Visualization and Computer Graphics (TVCG), co-founded the IEEE Visualization Conference and Volume Graphics series, and is currently the director of IEEE Computer Society Technical Committee on Visualization and Graphics. He is an IEEE Fellow, ACM Fellow, winner of many awards, including the IEEE Visualization Career Award, and member of the European Academy of Sciences.



Steven Skiena is inviting you to a scheduled Zoom meeting.

Topic: AI Seminar: Arie Kaufman
Time: Apr 21, 2021 10:00 AM Eastern Time (US and Canada)

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