K-DS 컨소시엄

강연안내

K-DS Distinguished Lecture Series

빅데이터 비즈니스 가치 창출

강연 일시
2025년 3월 28일 (금) 오후 2시
연사 이름
조성준 교수님
소속
서울대학교
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연사소개

서울대학교 조성준 교수님

현재 서울대학교 산업공학과 교수와 빅데이터 AI 센터장으로 재직하고 있다. 국가데이터정책위원회 위원을 맡고 있다. 공공데이터전략위원장, 정부3.0추진위원회 빅데이터전문위원장과 한국BI데이터마이닝학회 회장을 역임하였다. 인공지능, 뉴럴네트워크을 시작으로, 머신러닝, 데이터마이닝을 연구하여 왔고, 최근에는 딥러닝, 텍스트마이닝 등 빅데이터와 AI를 연구하고 있다. 이러한 방법론을 바탕으로 제조, 금융, 마케팅, 인사 분야에서 대량 생산되는 IoT 센서 데이터, 텍스트 데이터, 거래 데이터 등으로부터 인사이트를 도출하고 있다. 경험이나 감에 의존하는 주관적 의사결정을 넘어 데이터에 기반한 객관적 의사결정을 일상화하기 위해 필요한 프로세스 변화, 조직 변화, 임직원 교육에 대해서도 연구하고 있다.

서울대 산업공학과 학사, 석사학위 취득 후, 미 국무성의 풀브라이트 전액 장학생으로 선발되어 미국 워싱톤대학교 컴퓨터사이언스 학과에서 인공지능 석사학위 및 메릴랜드대학교 컴퓨터사이언스 학과에서 뉴럴네트워크, 머신러닝 분야로 박사학위를 받았다.

강연개요

빅데이터 비즈니스 가치 창출

빅데이터의 중요성, 분석의 목적과 방법, 그리고 실제 비즈니스 케이스 스터디를 통해 어떻게 데이터가 가치를 생성하는지를 보여줍니다. 또한, 빅데이터 분석을 통해 얻은 인사이트를 비즈니스 의사결정에 적용하는 프로세스와 담당 임직원의 역할에 대해서도 설명하고 있습니다.

K-DS Distinguished Lecture Series

Challenge and Issues in Biomedical Data Analysis

강연 일시
2025년 4월 18일 (금) 오전 10시
연사 이름
Zhezhen Jin, Ph.D.
소속
Columbia University
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Zhezhen Jin, Ph.D.

Zhezhen Jin is Professor of Biostatistics in the Department of Biostatistics in Mailman School of Public Health at Columbia University. He received his BS and MS in probability and statistics from Nankai University in 1989 and in 1992 respectively, MA in applied mathematics from the University of Southern California in 1994 and Ph.D. degree in Statistics from Columbia University in 1998. After 1998-2000 two years of postdoctoral studies at Harvard School of Public Health, he returned to Columbia as a faculty member in the Department of Biostatistics in 2000. He has been conducting statistical and biostatistical methodological research on resampling methods, survival analysis, nonparametric and semiparametric methods, smoothing methods, and statistical computing. He has also been collaborating with clinical investigators to address statistical issues in neurology, cardiology, oncology, transplantation, psychiatry, pathology and alternative medicine. He received Career Award from the National Science Foundation in 2002. He is a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and an elected member of International Statistical Institute. He served as the President of the International Chinese Statistical Association (ICSA) in 2022.

강연개요

Challenge and Issues in Biomedical Data Analysis

It is essential to incorporate basic statistical principles and ideas in data analysis. In the analysis of biomedical data, it is often encounter to compare and identify biomarkers that are more informative to disease diagnosis and monitoring, and to evaluate various treatment procedure and plan on health outcome. After a discussion on the issues and challenges with some real examples, I will review available statistical methods and present our newly developed semiparametric statistical methods that are useful for item reduction, differentiation of significant exposure factors and high dimensional data analysis.

K-DS Distinguished Lecture Series

Engineering for Intelligent Robotics

강연 일시
2025년 5월 16일 (금) 오후 4시 30분
연사 이름
Efi Psomopoulou, Ph.D.
소속
University of Bristol
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Efi Psomopoulou, Ph.D.

Efi Psomopoulou is Assistant Professor in the School of Engineering Mathematics and Technology at the University of Bristol and the Bristol Robotics Laboratory. She investigates and controls the physical interactions of robots with the environment in unstructured scenarios by focusing on stable grasping and dexterous in-hand manipulation using tactile sensing. She has also worked on building underactuated robot hands, on control of variable stiffness joint robots and haptic feedback during minimally invasive surgery. She is the Bristol PI in the Royal Society International Collaboration with Kyungpook National University in South Korea through a £250k award. She is the Bristol PI in a £1.5M ARIA Robot Dexterity project and the Bristol co-I in the £7M Horizon Europe MANiBOT project. She is the publications committee co-chair of the IEEE RAS Women in Engineering and elected member of the UK-RAS Network's Early Career Forum.

More information here : https://efi-robotics.com

강연개요

Engineering for Intelligent Robotics

For robot manipulators to move out of industrial settings and into human environments, they will need physical intelligence for their interactions with the environment and humans and they will also need the dexterous capabilities of the human hand. This raises many unsolved problems, from designing the mechanisms and actuator technologies for such dexterous manipulators to their fine motor control with force and tactile sensing capabilities. These problems are interlinked: the mechanism for a manipulator is interdependent with its control which is interdependent with its sensing capabilities. This talk will present my past and recent work towards solving these problems.

K-DS Distinguished Lecture Series

Data Science: From Modeling and Simulation’s Perspective

강연 일시
2025년 5월 30일 (금) 오전 10시
연사 이름
Joon-Seok Kim, Ph.D.
소속
Emory University
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Joon-Seok Kim, Ph.D.

Joon-Seok Kim is an Assistant Professor in Computer Science at Emory University, focusing on AI and simulation. He received his Ph.D. in Computer Science, from Pusan National University (PNU), South Korea in 2016, specializing in spatial databases. Before joining Emory, he was an R&D Scientist (2022-2024) at Oak Ridge National Laboratory (ORNL) in the Geospatial Science and Human Security Division of the National Security Science Directorate. He worked at Pacific Northwest National Laboratory (PNNL) as a postdoc (2021-2022) in National Security Directorate, developing predictive and prescriptive analytics tools leveraging AI/ML and simulations for nonproliferation and cybersecurity in power grids. Dr. Kim worked at George Mason University as a postdoc (2018-2021), leading development of an urban simulation that models social networks, human mobility, and dynamics in the urban environment.

K-DS Distinguished Lecture Series

Making Discoveries for Humanity and Society
with Data Science

강연 일시
2024년 3월 29일 (금) 오후 2시
연사 이름
Meeyoung Cha
소속
Professor, KAIST Chief Investigator, IBS Scientific Director, MPI-SP
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Meeyoung Cha

(Current) Professor at School of Computing, KAIST

(Current) Chief Investigator at the Institute for Basic Science (IBS)

(Current) Director, at Max Planck Institute for Security and Privacy (MPI-SP), Germany

(Former) Visiting Professor, Facebook Data Science Team, USA

Received ACM IMC Test-of-Time Award, AAAI ICWSM Test-of-Time Award, MSIT Award

강연개요

Making Discoveries for Humanity and Society with Data Science

Data science is an interdisciplinary field of study that extracts knowledge and insight from diverse forms of data through the use of scientific methods, algorithms, and systems. In particular, data science based on mathematical modeling and artificial intelligence (AI) has become critical for effectively handling astronomically growing data in various fields.

In this talk, I will introduce two research themes—poverty mapping and fake news detection—that address global issues and have a social impact. These research themes are examples of computational social science.

I will also talk about life as a data scientist, based on my experiences collaborating with world-class scientists at Facebook, AT&T Research, and Max Planck Institute, as well as NGOs like the United Nations Pulse Lab and the World Customs Organization.

K-DS Distinguished Lecture Series

How to Manage Societies Better than Optimal?

강연 일시
2024년 4월 26일 (금) 오후 4시
연사 이름
Dirk Helbing
소속
Professor of Computational Social Science, ETH Zurich
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Dirk Helbing

Professor of Computational Social Science at ETH Zurich

More than 10 Publications in Nature, Science, and PNAS

Recipient of Honorary Ph.D. from TU Delft

Former Director of the Ph.D. School in "Engineering Social Technologies for a Responsible Digital Future" at TU Delft

Active Member of the External Faculty of the Complexity Science Hub Vienna

강연개요

How to Manage Societies Better than Optimal?

Given the ongoing digital revolution and our present-day sustainability challenges, the ways cities and societies are operated are currently being reinvented. Societies are complex systems. This has important implications for how a society should be managed: Not like a company, and also not like a machine! I will illustrate our insights by means of pattern formation and self-organisation phenomena in social systems as well as applications to self-governance and self-control. Based on this, I will argue that the requirement of organizing societies in a more resilient way implies the need for more decentralized solutions based on digitally assisted self-organization – a concept, which is also compatible with sustainability requirements and greater participation. I will further discuss, how collective intelligence and co-creation can be supported in ways that promote favourable systemic outcomes – outcomes that are better than optimal, i.e. better than when optimization is applied. As an application example, I will present a field study on participatory budgeting, which has been recently carried out in Aarau, Switzerland. Specifically, I will show, how voting rules can be improved to promote individual and systemic benefits, such as inclusion and fairness.

K-DS Distinguished Lecture Series

Human Mobility Science

강연 일시
2024년 5월 24일 (금) 오전 10시
연사 이름
Gautam Malviya Thakur
소속
Group Leader, Location Intelligence R&D, Oak Ridge National Laboratory
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Gautam Malviya Thakur

(Current) Senior Staff Scientist and founding group leader of the Location Intelligence Group in the Geospatial Science and Human Security Division.

(Former) Deutsche Telekom Research Laboratories, Berlin, on transportation system modeling and understanding the network anatomy of major cities worldwide

(Former) Disney Research Laboratory, Zürich, on activity-driven mobility modeling of guests visiting the Disney theme parks

Research Interests in interconnected topics related to activity-driven human mobility modeling, place-based characterization, multi-scale global land use modeling, passive sensing, and spatially explicit disinformation detection.

Senior member of both ACM and IEEE

강연개요

Human Mobility Science

Human Mobility Modeling enables the understanding and characterization of time-variant movement and place visitation of individuals (or groups) in the real world. This knowledge is critical for domains like transportation planning, epidemic modeling, and energy use forecasting. Traditionally, simplistic statistical models such as a random walk or power law distribution were used to model these mobility patterns; however, it was determined that real-world mobility patterns are more complex and, therefore, require data-informed approaches for an accurate depiction. This talk will focus on the art and science of human mobility modeling with an emphasis on the use of Foundation Geoint data (demographics, points of interest, building footprint, etc.), behavior-driven movement-based patterns life generation, design and construction of data-informed human mobility model (HumoNet), spatio-temporal mobility interventions, and approaches to accurately benchmark mobility data to evaluate the presence of real-world mobility kinematics. The talk will conclude with the characterization of human mobility patterns from real-world scenarios, including disaster mitigation and cyber security attacks on critical infrastructure systems.

K-DS Distinguished Lecture Series

Frontiers of Collective Intelligence

강연 일시
2024년 6월 7일 (금) 오후 4시
연사 이름
Manuel Cebrián Ramos
소속
Senior Research Scientist, Spanish National Research Council
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Manuel Cebrián Ramos

Current Position: Senior Research Scientist at the Center for Automation and Robotics, Spanish National Research Council; Member of the National Advisory Board for Artificial Intelligence, Spanish Government

Research Focus: Computational Social Science, Network Science, Artificial Intelligence

Past Affiliations: Max Planck Society, Massachusetts Institute of Technology (MIT), Commonwealth Scientific and Industrial Research Organisation (CSIRO), University of California at San Diego, Brown University

Publications: Articles in Science, Nature, and Proceedings of the National Academy of Sciences on computational approaches to societal challenges

Recognition: Research featured in The New York Times, The Economist, The Guardian

강연개요

Frontiers of Collective Intelligence

In today’s world, complex systems capable of solving problems under significant pressure are omnipresent, including social networks, cities, and the Internet. The advent of Artificial Intelligence (AI) has added a new dimension to these systems, igniting debates about its deployment and long-term societal impacts. This seminar diverges from the conventional AI discourse, spotlighting an alternative form of experimental intelligence: Collective Intelligence. Collective Intelligence, which has evolved alongside the Internet, is distinguished by the collective capacity to address challenges and make decisions in ways that surpass the capabilities of individual members. In this seminar, I will share insights from several pivotal situations I have encountered, which illustrate the application of Collective Intelligence. In this seminar, we will explore the versatile applications of Collective Intelligence, including locating hidden entities across vast geographies, coordinating global searches for missing individuals, reassembling critically important shredded documents, and directing human actors in real-time via the Internet. By examining both the untapped potential and the inherent challenges of Collective Intelligence, the seminar aims to foster a dialogue on how the lessons learned from these experiences can inform the future development and strategic deployment of Artificial Intelligence.

K-DS Distinguished Lecture Series

Generative AI meets Vector databases and RAG techniques

강연 일시
2024년 9월 20일 (금) 오후 2시
연사 이름
Dr. Min-Soo Kim
소속
Professor at School of Computing, KAIST
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연사소개

Dr. Min-Soo Kim

(Current) Professor at School of Computing, KAIST

(Current) Co-Founder and CEO of GraphAI Co. Ltd.

(Current) Vice President, Office of Academic Information, KAIST

(Former) Director of Research Center for Extreme Exploitation of Dark Data, ERC, NRF, Korea

(Former) Professor at Electrical Engineering and Computer Science, DGIST

(Former) Postdoctoral Research Staff, IBM Almaden Research Center, USA

강연개요

Generative AI meets Vector databases and RAG techniques

In numerous industrial sectors, significant efforts are underway to enhance efficiency and automate operations utilizing generative AI models such as ChatGPT. Despite their potential, these technologies encounter three primary challenges: hallucination, data recency, and high cost. In response, the Retrieval-Augmented Generation (RAG) technique has emerged as a promising solution, receiving considerable attention for its potential to mitigate these challenges. RAG addresses hallucination and data recency problems by enabling generative AI models to access and retrieve relevant facts from databases, subsequently generating responses based on this retrieved data. Vector databases, which are extensively employed for this purpose, embed diverse data types, including documents, images, and videos, into multi-dimensional vectors. To effectively counter the curse of dimensionality, vector databases typically employ graph-based indexes. RAG has been enhanced with variations like iterative RAG and plan RAG to tackle more complex challenges. Iterative RAG repeatedly executes the retrieval and generation cycle, while plan RAG strategizes to solve intricate corporate decision-making problems prior to retrieval and generation. In this talk, I will introduce these innovative methodologies about vector databases and RAG techniques.

K-DS Distinguished Lecture Series

Query optimization in cloud database, and Graph neural networks: techniques and benchmarks

강연 일시
2024년 10월 25일 (금) 오전 10시
연사 이름
Makoto Onizuka
소속
Professor, Graduate School of Information Science and Technology, Osaka University
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Makoto Onizuka

Makoto Onizuka is a Professor at Graduate School of Information Science and Technology, Osaka University. He is the leader of Big data engineering Laboratory and conducts research on graph mining algorithms and AI-driven database query optimization techniques. Prior to joining Osaka University, he worked at Nippon Telegraph and Telephone Corporation (NTT) for more than 20 years being served as a distinguished technical member from 2010 to 2014. He also worked as a visiting scholar at the University of Washington from 2000 to 2001. He developed research prototype systems and some of them were used in production: LiteObject (object-relational main memory database system), XMLToolkit (XML stream engine), CBoC type2 (Common IT Bases over Cloud Computing at NTT), and Grapon (Graph mining techniques). He serves as Director at the database society of Japan (2024-present), Information Processing Society of Japan (2019-2021), Academic committee at Shonan meeting (2016-present), PC co-chair at DASFAA 2024 and KJDB 2024, Publicity co-chair at MIPR 2021, Workshop co-chair at VLDB 2020, Best demonstration award committee at VLDB 2024 and DASFAA 2010, Best paper award committee at DASFAA 2012, and program committee at international conferences, including VLDB(2009-2010 industrial track, 2024 demonstration track), SIGMOD(2018, 2020), ICDE(2015 industrial track, 2023 demonstration track), AAAI(2021-2025), NeurIPS(2022-2025), ICLR(2024), IJCAI(2023-2024), ICML(2023-2024), ECML(2022, 2024), CIKM(2017-2018,2020-2023), DASFAA(2010-2016,2020-2023).

강연개요

Query optimization in cloud database, and Graph neural networks: techniques and benchmarks

In this talk, I will introduce recent advancements in cloud database management and graph neural networks.

Regarding cloud database management, many companies are striving to manage their data in the cloud and are working on improving sales and cost reduction through real-time behavioral analysis. I will discuss technological trends in this area and our efforts, including cloud-scale database testing, schema optimization problem for time-dependent workloads, and join query optimization using cardinality estimation.

Next, in the context of graph neural networks (GNNs), we will address the explosive growth of research papers in this field, driven by recent advances in AI. We will introduce trends in GNNs and our efforts on improving the performance of GNNs, GNN benchmarking by synthetic graph generators, and patent search using GNNs.

K-DS Distinguished Lecture Series

Privacy in the Age of AI and Large Language Models

강연 일시
2024년 11월 22일 (금) 오전 10시
연사 이름
Professor Li Xiong
소속
Samuel Candler Professor of Computer Science and Biomedical Informatics, Emory University
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Professor Li XIONG

Li Xiong is a Samuel Candler Professor of Computer Science and Biomedical Informatics at Emory University. She has a Ph.D. from Georgia Institute of Technology, an MS from Johns Hopkins University, and a BS from the University of Science and Technology of China. Her research lab, Assured Information Management and Sharing (AIMS), conducts research on trustworthy and privacy-enhancing data-driven AI solutions for healthcare, public health, and spatial intelligence. She is recognized as an IEEE fellow for her contributions on privacy-preserving and secure data sharing and analytics. She has published over 200 papers and received six best paper or runner up awards. She has served and serves as associate editor for TKDE, TDSC, VLDBJ, general or program chair for SIGSPATIAL, CIKM, BigData, and program vice chair for SIGMOD, VLDB, and ICDE. Her research has been supported by both governments (NSF, NIH, IARPA, AFOSR) and industry/foundations (Mistubishi, Cisco, AT&T, Google, IBM). More details are at http://www. cs.emory.edu/~lxiong.

강연개요

Privacy in the Age of AI and Large Language Models

As artificial intelligence (AI) and large language models (LLMs) increasingly influence every facet of our lives, ensuring the privacy of user data has become paramount. In this talk, I will review the commonly used technique for ensuring differential privacy of machine learning models built from privacy sensitive data and introduce several of our recent works on:
1) ensuring differential privacy for training graph neural networks from correlated graph data,
2) ensuring personalized differential privacy for federated learning from decentralized data, and
3) new privacy risks and defenses in the emerging fine-tuning paradigm using pre-trained LLMs.

K-DS Distinguished Lecture Series

Under the Ground, Over the Oceans, and through the Space: Exploring the World with Imaging Beyond the Visible Spectrum.

강연 일시
2024년 12월 13일 (금) 오후 4시
연사 이름
Professor Seniha Esen Yuksel
소속
Department of Electrical and Electronics Engineering, Hacettepe University
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Professor Seniha Esen Yuksel

Dr. Seniha Esen Yuksel received her Ph.D. degree from the University of Florida, Department of Computer Information, Science and Engineering, USA in 2011. Currently, she is an associate professor at Hacettepe University, Department of Electrical and Electronics Engineering. She also serves as the director of the Pattern Recognition and Remote Sensing Laboratory (PARRSLAB), where her research focuses on machine learning and computer vision utilizing sensors that extend beyond the visible spectrum, such as hyperspectral, radar, X-ray, thermal, SAR, and LiDAR. Dr. Yuksel is recognized as an IEEE Senior member and has been honored with the BAGEP Outstanding Young Scientist Award by the Science Academy. She has contributed to the scientific community by authoring or coauthoring over 100 articles published in peer-reviewed journals and conferences.

강연개요

Under the Ground, Over the Oceans, and through the Space: Exploring the World with Imaging Beyond the Visible Spectrum.

Hyperspectral cameras are specialized devices that capture hundreds of images from across the electromagnetic spectrum. This comprehensive collection allows for the identification and labeling of each pixel based on its material composition, be it grass, water, gold, cotton, and more. The wealth of information provided by hyperspectral imaging has far-reaching applications in diverse fields such as agriculture, astronomy, medical imaging, and defense. In this presentation, I will introduce our research which ventures beyond the limitations of the visible spectrum using radar and hyperspectral images, seeing under the ground, over the oceans and through space. Additionally, I will outline our current research on deep denoising networks for hyperspectral imaging (HSI), introduce the current trends, discuss the future of HSI with the current paradigm shifts in the imaging pipeline, and touch upon the low-cost programmable cameras that put the HSI capability to our cell-phones.

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