AI Innovation Institute

Over the past decade, Artificial Intelligence (AI) has made stunning advances, from mastering language to solving the structure of proteins. These breakthroughs arise from more than forty years of work in neural networks, where ideas from neuroscience have inspired solutions in AI. In this lecture, Anthony Zador, MD, PhD, will explore how reverse engineering the brain's computations has driven progress in both fields, and how this back-and-forth between neuroscience and AI is set to grow even stronger -- with brain-inspired designs driving new AI advances while AI tools transform our understanding of how the brain works.

Speaker:
Dr. Zador works at the intersection of neuroscience and artificial intelligence. He is the Alle Davis Harris Professor of Biology at Cold Spring Harbor Laboratory, where he served as Chair of Neuroscience. He was named one of Foreign Policy's 100 Leading Global Thinkers and is a recipient of the Brain Research Foundation Fellowship, the Gill Symposium Transformative Investigator Award, and the Allen Distinguished Investigator Award.

Watch online at stonybrook.edu/live

https://stonybrook.zoom.us/j/99820812332?pwd=c05BSTVLNmw3L04yZjdEcG5pem1OZz09 Speaker: Alexei Koulakov of Cold Spring Harbor Laboratory Brain evolution as a machine learning problem We have entered a golden age of artificial intelligence research, driven mainly by the advances in ANNs over the last decade or so. Applications of these techniques--to machine vision, speech recognition, autonomous vehicles, machine translation and many other domains--are coming so quickly that many observers predict that the long-elusive goal of Artificial General Intelligence (AGI) is within our grasp. However, we still cannot build a machine capable of building a nest, stalking prey, or loading a dishwasher. I will describe several projects, ranging from theories of evolution of neural development to the perception of smells, in which we are attempting to understand the algorithms that the nervous system is using to solve some of these challenging problems.

The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the PhD program or (2) receive permission from the instructors.

Each seminar will consist of multiple short talks (around 10 minutes) by multiple people. Students can register for 1 credit for CSE 656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.

Abstract: Artificial Intelligence for Science (AI4Sci) has become a transformative approach in modeling and understanding complex physical systems, encompassing different scales such as atomistic systems and continuum systems. In atomistic systems, AI has shown potential in accelerating simulations, optimizing molecular dynamics, and predicting material and molecular properties through data-driven approaches, enhancing computational efficiency while preserving accuracy. For continuum systems, AI provides powerful tools for solving partial differential equations (PDEs) and learning physical patterns from data, capturing intricate dynamics that govern physical and engineering processes. This work explores AI methods--particularly equivariance for neural networks and neural operators--bridging atomistic and continuum representations. We analyze the implications of incorporating symmetries to improve model robustness and learning efficiency, providing a cohesive AI- driven framework for advancing scientific discovery. The findings aim to underscore the role of AI in enhancing accuracy, applicability, scalability, interopretability, and generalization across scales, from molecular simulations to physical modeling, opening pathways for next- generation applications in computational science. Biography: Wenhan Gao is a third-year Ph.D. student in Applied Mathematics at Stony Brook University, where he works under the supervision of Professor Yi Liu. Wenhan's research focuses on equivariant neural networks, graph neural networks, and AI for partial differential equations. Wenhan's work seeks to leverage the power of symmetries to aid AI models, particularly in fields such as computer vision (image and video generation), physical simulation (modeling climate change), and computational chemistry (drug discovery). He has published papers on the aforementioned topics in leading venues like NeurIPS, Transactions on Machine Learning Research (TMLR), and Journal of Computational Physics (JCP). He also has several preprints under review in leading venues like ICLR and CVPR. In addition to his research, Wenhan has served as a reviewer for top-tier conferences, including ICLR, NeurIPS, ICML, and KDD, and as a lecturer for undergraduate and graduate courses at Stony Brook University. Wenhan was awarded the NeurIPS Travel Award and Excellence in Teaching for Fall 2023.

Nam Nguyen

4-5pm, Dec 17 2020

https://stonybrook.zoom.us/j/94214254415?pwd=K1VoQml4cFdlVW51VW41dWtid2tJdz09



The molecular mechanisms and functions in complex biological systems
currently remain elusive. Recent high-throughput techniques, such as
next-generation sequencing, have generated a wide variety of
multiomics datasets that enable the identification of biological
functions and mechanisms via multiple facets. However, integrating
these large-scale multiomics data and discovering functional insights
are, nevertheless, challenging tasks. To address these challenges,
machine learning has been broadly applied to analyze multiomics. In
particular, multiview learning is more effective than previous
integrative methods for learning data's heterogeneity and revealing
cross-talk patterns. Although it has been applied to various contexts,
such as computer vision and speech recognition, multiview learning has
not yet been widely applied to biological data--specifically,
multiomics data. Therefore, we have developed a framework called
multiview empirical risk minimization (MV-ERM) for unifying multiview
learning methods (Nguyen, et al., PLoS Computational Biology, 2020).
MV-ERM enables potential applications to understand multiomics
including genomics, transcriptomics, and epigenomics, in an aim to
discover the functional and mechanistic interpretations across omics.
Based on MV-ERM, we have developed the following methods:
ManiNetCluster, Varmole and ECMarker.



(1) ManiNetCluster (Nguyen, et al., BMC Genomics, 2019) is a manifold
learning method which simultaneously aligns and clusters gene networks
(e.g., co-expression) to systematically reveal the links of genomic
function between different phenotypes. Specifically, ManiNetCluster
employs manifold alignment to uncover and match local and non-linear
structures among networks, and identifies cross-network functional
links. We demonstrated that ManiNetCluster better aligns the
orthologous genes from their developmental expression profiles across
model organisms than state-of-the-art methods. This indicates the
potential non-linear interactions of evolutionarily conserved genes
across species in development. Furthermore, we applied ManiNetCluster
to time series transcriptome data measured in the green alga
Chlamydomonas reinhardtii to discover the genomic functions linking
various metabolic processes between the light and dark periods of a
diurnally cycling culture;



(2) Varmole (Nguyen, et al., Bioinformatics, 2020) is an interpretable
deep learning method that simultaneously reveals genomic functions and
mechanisms while predicting phenotype from genotype. In particular,
Varmole embeds multi-omic networks into a deep neural network
architecture and prioritizes variants, genes and regulatory linkages
via biological drop-connect without needing prior feature selections.
With an application to schizophonia, we demonstrate that Varmole
provides an effective alternative for recent statistical methods that
associate functional omic data (e.g. gene expression) with genotype
and phenotype and that link variants to individual genes in population
studies such as genome-wide association study;



(3) ECMarker (Jin*, Nguyen*, et al., Bioinformatics, 2020) is an
interpretable and scalable machine learning model that predicts gene
expression biomarkers for disease phenotypes and simultaneously
reveals underlying regulatory mechanisms. Particularly, ECMarker is
built on the integration of semi- and discriminative- restricted
Boltzmann machines, a neural network model for classification allowing
lateral connections at the input gene layer. With application to the
gene expression data of non-small cell lung cancer (NSCLC) patients,
we found that ECMarker not only achieved a relatively high accuracy
for predicting cancer stages but also identified the biomarker genes
and gene networks implying the regulatory mechanisms in lung cancer
development.



Finally, we propose a novel multiview learning method, Malignomics, to
predict phenotypes from heterogeneous multi-omic features. Malignomics
will first align multi-omic features by deep manifold alignment onto a
common latent space, better predicting nonlinear relationships across
omics. This deep alignment aims to preserve both global consistency
and local smoothness across omics and reveal higher-order nonlinear
interactions (i.e., manifolds) among cross-omic features. Second, it
uses these manifold structures to regularize the classifiers for
predicting phenotypes. This manifold-regularization allows
highlighting cross-omic feature manifolds and prioritizing the
features and interactions for the phenotypes. The prioritized
multi-omic features will further reveal underlying phenotypic
functions and mechanisms and thus enhance the biological
interpretation of Malignomics. We will apply Malignomics to
multi-omics data in neuropsychiatric disorders, and prioritize gene
regulatory networks linking risk variants, regulatory elements, and
genes for the disorders. We will also compare Malignomics with the
state-of-the-arts, and investigate how the manifold regulation will
potentially improve understanding of multi-omics functions and
predicting diseases.

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

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.