added teaching

_data/cv.yml

@@ -2,48 +2,46 @@
   type: map
   contents:
     - name: Full Name
-      value: Albert Einstein
-    - name: Date of Birth
-      value: 14th March 1879
+      value: Avishek Anand
     - name: Languages
-      value: English, German
+      value: English, Odiya, Hindi, German
 
 - title: Education
   type: time_table
   contents:
     - title: PhD
-      institution: University of Zurich, Zurich, Switzerland
-      year: 1905
+      institution: Saarland University, Saarbruecken, Germany
+      year: 2014
       description:
-        - Description 1.
-        - Description 2.
-        - title: Description 3.
+        - Researcher in the Max Planck Institute for Informatics
+        -
+        - title: Temporal Indexing for Web Archives
           contents:
-            - Sub-description 1.
-            - Sub-description 2.
-    - title: Federal teaching diploma
-      institution: Eidgenössische Technische Hochschule, Zurich, Switzerland
-      year: 1900
+            - Temporal Information Retrieval
+            - Indexing Text + Time
+    - title: Masters in Science
+      institution: Saarland University, Saarbruecken, Germany
+      year: 2009
       description:
-        - Description 1.
-        - Description 2.
+        - Funded by International Max Planck Research Scholarship (IMPRS)
+        - Msc Thesis: Indexing for Peer-to-Peer Web Archives
 
 - title: Experience
   type: time_table
   contents:
-    - title: Professor of Theoretical Physics
-      institution: Institute for Advanced Study, Princeton University
-      year: 1933 - 1955
+    - title: Associate Professor
+      institution: TU Delft
+      year: 2022 - now
       description:
         - Description 1.
         - Description 2.
         - title: Description 3.
           contents:
             - Sub-description 1.
             - Sub-description 2.
-    - title: Visiting Professor
-      institution: California Institute of Technology, Pasadena, California, US
-      year: 1933
+    - title: Assistant Professor
+      institution: Leibniz University, Hannover
+      year: 2017-2021
       description:
         - Description 1.
         - Description 2.

_news/ann_tkde-dine.md

@@ -5,4 +5,4 @@ inline: true
 related_posts: false
 ---
 
-Paper accepted at the TKDE Journal, titled *“DINE: Dimensional Interpretability of Node Embeddings”*. [Arxiv](https://arxiv.org/abs/2310.01162) 🎉
+Paper accepted at the [TKDE Journal](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=69), titled *“DINE: Dimensional Interpretability of Node Embeddings”*. [Arxiv](https://arxiv.org/abs/2310.01162) 🎉

_pages/cv.md

@@ -9,3 +9,15 @@ description: This is a description of the page. You can modify it in '_pages/cv.
 toc:
   sidebar: left
 ---
+## John Doe
+
+### Education
+- **PhD in Computer Science**, University of XYZ, 2020
+- **MSc in Artificial Intelligence**, University of ABC, 2015
+
+### Experience
+- **Senior Data Scientist**, Tech Corp, 2021-Present
+  - Developed machine learning models to improve user experience.
+
+### Publications
+- Doe, J., & Smith, A. (2020). Title of the Paper. *Journal of Important Research*.

_pages/teaching.md

@@ -2,8 +2,91 @@
 layout: page
 permalink: /teaching/
 title: teaching
-description: Materials for courses in the bachelors, and the masters level.
+description: Materials for courses in the bachelors, and the masters level at TU Delft.
 nav: true
 nav_order: 6
 ---
 
+### Data Mining
+
+**Level**: Bachelors  
+**Co-taught with**: Sicco Verwer, Nergin Tömen
+
+This course introduces students to the fundamental concepts and techniques of data mining. Topics include data preprocessing, probabilistic counting, dimensionality reduction, clustering, anomaly detection, and mining text and graph data. The course also includes practical sessions where students apply these techniques using popular data mining tools and software.
+
+[Studyguide Link](https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=67581)
+
+[Brightspace 2024](https://brightspace.tudelft.nl/d2l/home/595336)
+
+---
+
+### Information Retrieval
+**Level**: Masters
+
+**Co-taught with**: Sole Pera, Jie Yang
+
+In this course, students explore the principles and practices of information retrieval. The syllabus covers the design and implementation of search engines, text indexing, query processing, and evaluation of information retrieval systems. Special topics include learning-to-rank, neural ranking models, and recommender systems.
+
+[Studyguide Link](https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=70225)
+
+[Brightspace 2024](https://brightspace.tudelft.nl/d2l/home/596319)
+
+---
+
+### Natural Language Processing (NLP)
+**Level**: Masters
+
+**Co-taught with**: Jie Yang
+
+This course delves into the field of Natural Language Processing, covering both the theoretical and practical aspects. Students learn about text processing, language modeling, syntactic and semantic analysis, and large-language models. The course emphasizes hands-on projects and applications of NLP techniques in real-world scenarios.
+
+[Coursebase Link](https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=63115&_NotifyTextSearch_)
+
+[Brightspace 2024](https://brightspace.tudelft.nl/d2l/home/595262)  
+
+
+
+
+---
+### Past Courses (@Leibniz University Hannover)
+---
+
+
+
+### SS 2019 - 2021: Deep Learning
+**Level**: Masters
+
+**Course Description**: Foundations of Deep Learning with applications. The aim of this lecture is to provide a solid foundation about deep learning and its applications. We will first study regular machine learning and basic, simple deep learning architectures. Afterwards we will focus on the applications of deep learning/neural networks, including current cutting-edge research. The lecture has both theoretical and programmatic aspects. Students will be exposed to popular machine learning problems and datasets while being able to work hands on with frameworks such as TensorFlow and scikit-learn.
+
+---
+
+### SS 2016 - 2018: Algorithms for Big Data
+**Level**: Masters
+
+**Course Description**: Concepts and foundations of modern algorithms for Processing, Mining, and Learning for Big Datasets. The aim of this lecture is to learn efficient algorithms that are used for processing large datasets. The course involves learning scalable approaches to some of the fundamental problems involving finding similar items, clustering, recmmender systems and graph mining. The lecture has both theoretical and programmatic aspects. Students will be exposed to large distributed data processing frameworks like Hadoop and Spark.
+
+
+
+---
+
+### WS 2017 - 19: Foundations of Probabilistic Information Retrieval
+**Level**: Masters
+
+**Course Description**: The aim of this lecture is to learn the probabilistic underpinings of modern information retrieval techniques. We will study probabilistic modeling, machine learning, deep learning and how to apply concepts from these areas to improve search engines. The lecture has both theoretical and programmatic aspects. Students will be exposed to popular machine learning problems and datasets while being able to work hands on with frameworks such as tensorflow and scipy.
+
+---
+
+### WS 2014 - 2016: Temporal Information Retrieval
+**Level**: Masters
+
+**Course Description**: Masters level course on understanding temporal aspects and issues in Information Retrieval.
+
+---
+
+### Puzzling Problems in Computer Science
+**Level**: Bachelors
+
+**Course Description**: Bachelor course which presents computer science problems in the form of puzzles.
+
+---
+