Hi, my name is Yannan. I am a research scientist with 5+ years of experience in neuroscience with a specialization in computational neuroscience and data science. I am experienced in data analysis of diverse neural and behavioral data (fMRI, neural oscillations, psychometrics...), mathematical modeling, Bayesian inference, and statistics. My teaching and supervising skills have been enhanced in tutoring bachelor and master students. Besides, I highly value my diverse education and research background which allows me to transition between multiple fields and leverage unique perspectives (biological+psychological+computational+systemic). As a big fan of mentoring culture, I am glad to share my career paths, my hacks for Ph.D. and academic life, and/or my experience as a non-EU student moving to Germany and immersed in an international environment.

My Mentoring Topics

  • Career in Academia
  • PhD
  • Neuroscience
  • Interdisciplinary Research
S.
26.October 2023

Thanks Yannan, for your time and guidance. She has a great empathy and listens mentees very patiently. She listened to all my doubts and gave me the best advice. She provided me some very good insights about neurotech industry and explained me about the possible career paths in neuroengineering. She informed me about the various programs available for me. She is very supportive and encouraged me to go forward.

N.
25.July 2023

First of all, I want to thank you, Yannan, for your time and for your great advice. The mentoring session with you was of great help to me. It provided me with the needed structure regarding which steps to take and how I could analyze my data. I can only recommend booking a mentoring session with Yannan, and I am sure you will benefit from it.

Models of the Mind - How Physics, Engineering and Mathematics Have Shaped Our Understanding of the Brain
Grace Lindsay

Key Facts or Insights from the Book Our understanding of the brain is greatly enhanced by concepts borrowed from physics, engineering, and mathematics. Models of the mind are essential tools for neuroscientists to study and predict brain behavior. Neurons, the basic units of the brain, function much like electrical circuits, an idea borrowed from engineering. Mathematical models are used to analyze the complex network of neurons in the brain. Quantum physics has been proposed as a potential explanation for some of the brain's most perplexing phenomena, like consciousness. Computational neuroscience, a field that uses mathematical tools to study the brain, has made significant strides in understanding how the brain processes information. Machine learning and artificial intelligence have their foundations in our understanding of the brain's structure and function. Despite the advancements in brain modeling, our understanding of the brain is still incomplete, and many mysteries remain. Models of the mind have practical applications in diagnosing and treating brain disorders. Interdisciplinary collaboration between neuroscientists, engineers, physicists, and mathematicians is key to furthering our understanding of the brain. An In-depth Analysis of the Book's Contents "Models of the Mind" by Grace Lindsay is a fascinating exploration of how physics, engineering, and mathematics have shaped our understanding of the brain. Drawing on her experience as a computational neuroscientist, Lindsay provides a rich explanation of how these fields contribute to neuroscience. One of the book's central ideas is that neurons, the basic units of the brain, function much like electrical circuits. This idea, borrowed from engineering, provides a framework for understanding how neurons transmit and process information. Lindsay explains the mathematics behind the electrical properties of neurons, making the complex scientific concepts accessible to readers without a background in neuroscience. The book also delves into how mathematical models are used to analyze the brain's complex network of neurons. These models help scientists predict how the brain will behave under different conditions and offer insights into how it processes information. The advent of computational neuroscience, a field that uses mathematical tools to study the brain, has significantly advanced our understanding of brain function. One of the more controversial topics Lindsay discusses is the role quantum physics may play in explaining some of the brain's most perplexing phenomena. Quantum physics, with its ability to describe the behavior of particles at the smallest scales, has been proposed as a potential explanation for consciousness and other cognitive processes. However, this remains a contentious issue in neuroscience, with many scientists skeptical of quantum theories of mind. Despite the advancements in brain modeling, Lindsay emphasizes that our understanding of the brain is still incomplete. The brain's complexity far outpaces our current mathematical and computational models. This gap in our knowledge underscores the importance of continued research and interdisciplinary collaboration. Lindsay also explores the practical applications of brain models, such as diagnosing and treating brain disorders. For example, models of neural networks can help identify abnormalities associated with conditions like epilepsy or Parkinson's disease. Moreover, understanding the brain's workings can inform the development of artificial intelligence and machine learning algorithms, further demonstrating the real-world implications of brain research. In conclusion, "Models of the Mind" offers a compelling look at how physics, engineering, and mathematics contribute to our understanding of the brain. Lindsay's book underscores the importance of interdisciplinary collaboration and the ongoing need for research in this complex and fascinating field. Although we have made significant strides in understanding the brain, many mysteries remain, and future advancements will likely depend on our ability to develop more sophisticated models of the mind. The book is a must-read for anyone interested in the intersection of neuroscience, engineering, and mathematics. It provides a unique perspective on the brain, offering insights that can be beneficial for researchers, clinicians, and anyone curious about the complexities of the human mind.

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The Hundred-page Machine Learning Book
Andriy Burkov

Key Insights from "The Hundred-page Machine Learning Book" by Andriy Burkov Demystification of Machine Learning: The book simplifies the complex concepts of machine learning, making it accessible for beginners as well as advanced readers. Comprehensive coverage: Despite its brevity, the book covers all the essential aspects of machine learning, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Practical Implementation: Burkov emphasizes the practical implementation of machine learning algorithms rather than just the theoretical concepts. Real-world examples: The book uses real-world examples to explain abstract concepts, aiding in better understanding. Python Code: The book includes Python code for different machine learning algorithms, making it easier for readers to apply the knowledge practically. Mathematical Foundations: The book provides a clear explanation of the mathematical foundations of machine learning, which are essential for understanding the algorithms. Model Evaluation: Burkov dedicates a significant part of the book to model evaluation, discussing the importance of performance metrics and validation techniques. Feature Engineering: The book highlights the significance of feature engineering in improving the performance of machine learning models. Deep Learning: The book also introduces the concept of deep learning, providing a brief overview of neural networks and their applications. Future of Machine Learning: Burkov concludes the book by discussing the future of machine learning and its potential impact on various industries. Detailed Analysis of "The Hundred-page Machine Learning Book" "The Hundred-page Machine Learning Book" by Andriy Burkov is a concise yet comprehensive guide to machine learning. This makes it an ideal resource for beginners who want to understand the basics of machine learning, as well as for experienced practitioners looking for a quick reference. The book starts by demystifying machine learning, explaining that it is not a complex, inaccessible field reserved for computer scientists or mathematicians. Instead, Burkov argues that machine learning can be understood and applied by anyone with a basic understanding of mathematics and programming. Despite its brevity, the book covers all the essential aspects of machine learning. This includes supervised learning, where the algorithm learns from labeled data; unsupervised learning, where the algorithm learns from unlabeled data; semi-supervised learning, which combines both approaches; and reinforcement learning, where the algorithm learns by interacting with its environment. One of the major strengths of the book is its emphasis on the practical implementation of machine learning algorithms. While many books focus on the theoretical aspects of machine learning, Burkov provides Python code for different algorithms, allowing readers to apply their knowledge practically. This hands-on approach is particularly useful for beginners, who often struggle to bridge the gap between theory and practice. The book also excels in using real-world examples to explain abstract concepts. For instance, Burkov uses the example of a spam filter to explain supervised learning, making it easier for readers to understand the concept. While the book is accessible for beginners, it does not shy away from the mathematical foundations of machine learning. Burkov provides a clear explanation of these foundations, making complex concepts like gradient descent and backpropagation more understandable. This is a valuable resource for readers who want to delve deeper into machine learning. Model evaluation is another significant topic covered in the book. Burkov explains the importance of performance metrics and validation techniques, showing how they can be used to assess the accuracy of a machine learning model. This is crucial for practitioners, as it helps them judge the quality of their models. The book also highlights the importance of feature engineering in improving the performance of machine learning models. Burkov explains how selecting the right features can make a significant difference in the model's performance, providing tips and techniques for effective feature engineering. In the final chapters of the book, Burkov introduces the concept of deep learning, providing a brief overview of neural networks and their applications. This serves as a good introduction to the topic, paving the way for readers to explore more advanced resources. Lastly, Burkov discusses the future of machine learning, looking at how it could impact various industries. This is particularly relevant in today's rapidly changing technological landscape, where machine learning is expected to play a pivotal role. In conclusion, "The Hundred-page Machine Learning Book" by Andriy Burkov is a concise and comprehensive introduction to machine learning. It covers all the essential aspects of machine learning, from the basics to advanced concepts, making it an ideal resource for anyone interested in this exciting field.

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Designing Machine Learning Systems
Chip Huyen

Key Insights from "Designing Machine Learning Systems" Machine Learning (ML) is not an isolated discipline: It involves a blend of mathematics, statistics, computer science, and domain-specific knowledge. Understanding the problem at hand is crucial: The book emphasizes the importance of understanding the problem you are trying to solve before you start coding. Real-world ML projects are messy: Real-world ML problems are often unstructured, and require a fair amount of data cleaning and preprocessing. Iterative development is key: The process of developing a machine learning system is iterative, involving data collection, feature extraction, model selection, training, evaluation, and deployment. Choosing the right model is fundamental: The choice of model is crucial and should depend on the problem, the data, and the computational resources at hand. Evaluation of an ML system is complex: It involves understanding the trade-off between bias and variance, precision and recall, and other metrics. Deployment is a crucial phase: Deploying a machine learning system is not the end, but rather the beginning of a new phase that involves monitoring, maintenance, and continuous learning. Machine Learning is evolving: It is important to stay updated with the latest trends and advancements in the field. Detailed Analysis of "Designing Machine Learning Systems" The author, Chip Huyen, is a well-known figure in the field of machine learning. She has combined her practical experiences and theoretical knowledge to provide a comprehensive guide to designing machine learning systems. The book begins by emphasizing that machine learning is not an isolated discipline, but a combination of several fields. It requires a blend of mathematics for understanding algorithms, statistics for interpreting results, computer science for implementing algorithms, and domain knowledge for applying machine learning to specific problems. This perspective is important as it sets the tone for the interdisciplinary nature of machine learning. One of the key insights from the book is the importance of understanding the problem at hand. It is essential to understand the problem you are trying to solve, the available data, and the desired outcome before you start coding. This is a clear departure from the common practice of jumping straight into coding without a clear understanding of the problem. The author also provides a realistic view of how messy real-world ML projects can be. Real-world problems are often unstructured and involve messy data that requires significant preprocessing. This includes dealing with missing data, outliers, and unbalanced datasets. The book also emphasizes the importance of iterative development in machine learning. The process of building a machine learning system involves several stages – data collection, feature extraction, model selection, training, evaluation, and deployment. Each stage requires careful planning and execution, and the process is often iterative, with each stage feeding back into the previous one. One of the most important aspects of machine learning, according to the book, is choosing the right model. The choice of model should be based on the nature of the problem, the available data, and the computational resources at hand. The book provides practical tips on how to choose the right model for a given problem. The evaluation of a machine learning system is another complex process that the book delves into. It discusses various metrics for evaluating the performance of a machine learning system, and the trade-offs between them. For example, it discusses the trade-off between bias and variance, and between precision and recall. Another important aspect that the book focuses on is the deployment phase of a machine learning system. It emphasizes that deployment is not the end, but rather the beginning of a new phase that involves monitoring, maintenance, and continuous learning. It also discusses the challenges of deploying machine learning systems in production. Lastly, the book emphasizes that machine learning is constantly evolving, and it is important to stay updated with the latest trends and advancements in the field. In conclusion, "Designing Machine Learning Systems" provides a comprehensive, practical, and realistic guide to building machine learning systems. It emphasizes the importance of understanding the problem at hand, iterative development, choosing the right model, evaluating the system, and the deployment phase. By focusing on these aspects, the book provides a valuable resource for anyone interested in machine learning.

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