Update about me and fix error in publications

Author: apointa

content/authors/admin/_index.md

@@ -91,11 +91,12 @@ highlight_name: true
 
 **About me**
 
-Hi, I am Andreas. I am a **PhD student** in the field of **Machine Learning** at the [Institute for Machine Learning](https://ml-jku.github.io/) at Johannes Kepler University, Linz, Austria, in the Team of Sepp Hochreiter. I am also part of the [ELLIS PhD Program](https://ellis.eu/phd-postdoc).
-Previously, I completed my bachelor's and master's degrees in Computer Science at the Technical University of Vienna.
+Hi, I am Andreas. I am a **PhD student** in the field of **Machine Learning** at the [Institute for Machine Learning](https://ml-jku.github.io/) at Johannes Kepler University, Linz, Austria, advised by Sepp Hochreiter. I am also part of the [ELLIS PhD Program](https://ellis.eu/phd-postdoc).
 
-In my research I am most interested in **Deep Learning** in the context of **Time Series** or more broadly said --- sequential data.
-Currently I am specifically interested in **Foundational Time Series Models**.
+In my research I am most interested in **Deep Learning** in the context of **Time Series** or, more broadly, sequential data.
+Currently I am specifically interested in **Foundational Time Series Models**:
 
+I led the development of **[TiRex](https://arxiv.org/abs/2505.23719)**, a state-of-the-art foundational forecasting model built with xLSTM, and **[COSMIC](https://arxiv.org/abs/2506.03128)**, the first foundational forecasting model that beneficially utilized covariates in a zero-shot setting.
+Further, I am the co-author of **[Chronos-2](https://arxiv.org/abs/2510.15821)** and **[xLSTM](https://arxiv.org/abs/2405.04517)**.
 
-I have also experience as professional Software Developer.
+Prior to my PhD, I completed my BSc and MSc degrees in Computer Science at the Technical University of Vienna and gathered experience as professional software developer.

content/publication/2025-chronos2/index.md

@@ -2,7 +2,7 @@
 title: "Chronos-2: From Univariate to Universal Forecasting"
 date: 2025-10-01
 publishDate:  2025-10-01
-authors: ["**Abdul Fatir Ansari, Oleksandr Shchur, Jaris Küken, **Andreas Auer**, Boran Han, Pedro Mercado, Syama Sundar Rangapuram, Huibin Shen, Lorenzo Stella, Xiyuan Zhang, Mononito Goswami, Shubham Kapoor, Danielle C. Maddix, Pablo Guerron, Tony Hu, Junming Yin, Nick Erickson, Prateek Mutalik Desai, Hao Wang, Huzefa Rangwala, George Karypis, Yuyang Wang, Michael Bohlke-Schneider"]
+authors: ["Abdul Fatir Ansari, Oleksandr Shchur, Jaris Küken, **Andreas Auer**, Boran Han, Pedro Mercado, Syama Sundar Rangapuram, Huibin Shen, Lorenzo Stella, Xiyuan Zhang, Mononito Goswami, Shubham Kapoor, Danielle C. Maddix, Pablo Guerron, Tony Hu, Junming Yin, Nick Erickson, Prateek Mutalik Desai, Hao Wang, Huzefa Rangwala, George Karypis, Yuyang Wang, Michael Bohlke-Schneider"]
 publication_types: ["2"]
 abstract: "Pretrained time series models have enabled inference-only forecasting systems that produce accurate predictions without task-specific training. However, existing approaches largely focus on univariate forecasting, limiting their applicability in real-world scenarios where multivariate data and covariates play a crucial role. We present Chronos-2, a pretrained model capable of handling univariate, multivariate, and covariate-informed forecasting tasks in a zero-shot manner. Chronos-2 employs a group attention mechanism that facilitates in-context learning (ICL) through efficient information sharing across multiple time series within a group, which may represent sets of related series, variates of a multivariate series, or targets and covariates in a forecasting task. These general capabilities are achieved through training on synthetic datasets that impose diverse multivariate structures on univariate series. Chronos-2 delivers state-of-the-art performance across three comprehensive benchmarks: fev-bench, GIFT-Eval, and Chronos Benchmark II. On fev-bench, which emphasizes multivariate and covariate-informed forecasting, Chronos-2's universal ICL capabilities lead to substantial improvements over existing models. On tasks involving covariates, it consistently outperforms baselines by a wide margin. Case studies in the energy and retail domains further highlight its practical advantages. The in-context learning capabilities of Chronos-2 establish it as a general-purpose forecasting model that can be used "as is" in real-world forecasting pipelines."
 featured: true