중앙대 응용통계학과

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2025 웰에이징데이터과학융합연구소 추계 학술대회 개최

응용통계학과 2025.10.13 조회수 85

중앙대학교 웰에이징데이터사이언스융합연구소에서 2025년 10월 24일 학술대회를 개최합니다.

연사 : 중앙대학교 김원영 교수, 중앙대학교 곽일엽 교수, 연세대학교 신민석 교수

일시: 2025년 10월 24일 15:00 - 17:00 PM

장소: 310관 413호

김원영 교수 중앙대학교 AI학과

Statistical Missing Data Approach for Sequential Decision Making Problems

Sequential decision-making problems have been widely applied in clinical trials, robotics, e-commerce, recommender systems, and more. Although many advanced algorithms exist for these problems, current approaches have limitations: they often fail to achieve statistically optimal designs and tend to discard information about unchosen actions. In this talk, we reformulate sequential decision-making as a missing data problem and propose new algorithms that efficiently incorporate the information previously discarded. The proposed algorithms offer advantages in both theoretical analysis and empirical performance.

곽일엽 교수 중앙대학교 응용통계학과

Advances in Sequence-Based Deep Learning for Gene Expression Prediction from Promoter Regions

In this presentation, we will discuss our research applying deep learning methods to genomic data. The DREAM Challenge (https://dreamchallenges.org/), organized by IBM Research, provides open platforms where important problems in biomedical science are released with corresponding datasets, enabling researchers worldwide to collaboratively develop solutions and publish findings. Our team participated in the 2022 competition Predicting Gene Expression Using Millions of Random Promoter Sequences, which focused on predicting gene expression levels from promoter region DNA sequences. With access to approximately six million data samples, we developed a variant of the Transformer architecture, revising Confermer architecture, and achieved third place in the challenge. In this presentation, we will outline how our model was constructed and refined during the competition, and describe how subsequent collaborations with the organizers and other top-performing teams have led to extended joint research efforts.

신민석 교수 연세대학교 응용통계학과

Generative Quantile Regression with Variability Penalty

Quantile regression and conditional density estimation can reveal structure that is missed by mean regression, such as multimodality and skewness. In this article, we introduce a deep learning generative model for joint quantile estimation called Penalized Generative Quantile Regression (PGQR). Our approach simultaneously generates samples from many random quantile levels, allowing us to infer the conditional distribution of a response variable given a set of covariates. Our method employs a novel variability penalty to avoid the problem of vanishing variability, or memorization, in deep generative models. Further, we introduce a new family of partial monotonic neural networks (PMNN) to circumvent the problem of crossing quantile curves. A major benefit of PGQR is that it can be fit using a single optimization, thus, bypassing the need to repeatedly train the model at multiple quantile levels or use computationally expensive cross-validation to tune the penalty parameter. We illustrate the efficacy of PGQR through extensive simulation studies and analysis of real datasets.