Rijeesh Keloth’s Post

🔍 Discovering Connections: #DataScience Meets #LaserPhysics! 📊✨ Excited to share a glimpse into my recent personal project where I dug into article titles from #Optica Publishing Group's journals, spanning 1962 to 2023. According to the findings, it's clear that the average length of titles has increased over time. What's cool about this project is how it combines my love for laser physics with data science. Even small details in titles can spark big discussions. While I'll share more later, I'm really interested in where data science and my field overlap. I'm keen to explore opportunities to work on exciting projects where I can use both my skills. If you're into laser industry and/or data science, let's connect! We can chat about our interests and maybe find ways to collaborate. #DataAnalytics #Research #Networking

I am delighted to share my insightful journey into the intricate domain of particle physics, where data science serves as our invaluable compass. Particle physics offers a profound glimpse into this enigma, where minuscule entities interact in ways that shape the very essence of reality. Amidst the vast expanse of data generated by particle collider experiments, lies the challenge of extracting meaningful insights and classifications. Through the strategic integration of machine learning algorithms and data preprocessing methodologies, embarking on a pioneering endeavor to classify particle physics events with unprecedented precision and efficacy. My methodology encompasses meticulous data acquisition, rigorous preprocessing, feature engineering, model development, and comprehensive evaluation protocols. Key Components: Data Acquisition: Rigorous collection of raw data from particle collider experiments, capturing intricate signatures of particle interactions. Preprocessing: Stringent preprocessing techniques to cleanse and refine the data, ensuring its integrity and reliability. Feature Engineering: Strategic feature selection and extraction to distill informative representations of particle physics phenomena. Model Development: Utilization of diverse machine learning models, including traditional algorithms to discern subtle patterns among particle events. Evaluation and Validation: Rigorous testing and validation procedures to assess the robustness and generalization capabilities of our models. Interpretation: Beyond classification, we strive to interpret the underlying physics behind our model predictions, elucidating fundamental mechanisms governing particle interactions. As navigating to the ever-evolving landscape of scientific inquiry, the synergy between particle physics and data science opens new vistas of exploration and understanding. Thank you Learnbay for your continued support and guidance. #DataScience#Machinelearning#Python#DataAnalytics

📣 Hello everyone, 🌟I am thrilled to announce my latest project in Machine Learning, focusing on the classification of physics particle events! 🎯📊 By utilizing various 🎖️classification algorithms such as Logistic Regression, Decision Tree classifier, Bagging Algorithm, Random Forest classifier, and XG boost algorithm, I delved deep into the 🌟science and research domain of physics particle classification.🌟 📌Throughout this project, I sharpened my skills in data preprocessing📈 and feature engineering, ensuring the robust performance of my models. The results of my project unveiled 📊intriguing insights, demonstrating distinct accuracies between the test and train datasets. This indicates the potential of each algorithm in accurately classifying the decay of particles as either signal or background noise signals. Always eager to explore new📈🎗️ approaches, I embraced the power of ensemble methods. I would like to express my gratitude to Kumar Sundram sir and Ashish som sir for their incredible sessions, which greatly contributed to the completion of this project and the establishment of reliable predictive models. I hope you all find this project useful. #machinelearningalgorithms #learnbay #supervisedmachinelearning #EDA #dataanalysis #datascience #DataScience #EnsembleMethods.#project

2030 !!!!! What will be the " Future of Jobs"? Choose the most essential skill for Jobs in 2030 ( Any 1 , write in comment): A. Information technology and Artificial Intelligence. B. Space Science. C. Softskills

I will most likely be looking for new opportunities in data science, machine learning, physics, research science and any combination of the above at the end of this year. I was wondering if there is any way to tell what sort of skills and techniques will be most sought after? My publications are currently listed. What will be most in demand by the end of this year?

Dear All, I am delighted to announce that I have successfully completed project on Particle Physics Event classification and have received Certificate of completion from Learnbay. The dataset used in this project was obtained from an experiment conducted by physicists which involved collision of subatomic particles in a particle accelerator over a short span of time to observe specific events. The primary objective was to classify these events into two categories: signal and background. A 'signal' event indicated a potential discovery of a new particle while a 'background' event indicated a process that could be described by known observations or theories. To accomplish this task, I utilized powerful Machine Learning and Deep Learning algorithms such as Random Forest, Decision Tree and Artificial Neural Network (ANN) after conducting thorough Exploratory Data Analysis and Data Preprocessing. This process allowed me to gain insights into handling large and complex datasets, detecting anomalies and optimizing model’s performance through fine-tuning techniques. I would like to extend my gratitude to Learnbay for offering extensive training, invaluable support and guidance throughout the program. Looking forward to applying the skills and knowledge gained from this project in the field of Data Science and predictive analytics. #datascience #particlephysics #learnbay #certificate #machinelearning #physics #ANN

There were times during my PhD that I felt isolated. I was not running experiments and was often in the office by myself for days at a time. I did not specialize in a single computational method such as #DFT or #MolecularDynamics and other computational chemists in the department were often too busy to troubleshoot problems I would run into. Despite this perceived isolation, I felt fortunate during my PhD. I learned to model experimental observations. I was a theoretician in laser lab. I learned to dissect spectroscopic signals of multidimensional experiments. I learned to distinguish desired signals from a multitude of interactions, errors, and artifacts that might occur during an optical measurement. I am fortunate for all of this because it gave me the tools I needed to begin my work with Frances Houle at Berkeley Lab. Frances had a new project exploring the #photophysics of a particular design of solar harvesting systems. That is, she wanted to use my expertise to understand how molecules on a semiconductor converted light from the sun to generate electricity or even solar fuels. She gave me the space to imagine how the project should be started. She used the #SocraticMethod to guide my initial thinking and to help me explore the questions we would want to ask, the data needed to ask such questions, and the analysis needed to answer those questions. I did not realize it at the time, but she was teaching me how to start a #datascience project, which is truly just the contemporary #scientificmethod. I did a literature review to really understand the scope of the field and identified that we needed #timeseries data for the changing spectra for a set of compounds. Frances gave me the space to contact the principal investigators that would have the relevant datasets, and I was off. The only major obstacle I had left was that I did not have free access to MATLAB Coding like I did at University of North Carolina at Chapel Hill. Fortunately, I shared an office with Marielle Soniat Pointer who turned me onto #python and mentored me in my first steps as an #independentresearcher and data scientist. With her guidance, I learned to manage the raw data and preprocess it for #visualization and #analysis. Over the course of the project, we published four remarkable peer-reviewed papers and presented posters and talks at both domestic and international conferences. The daily conversations I had with Marielle made me a better communicator and keener data scientist. My weekly meetings with Frances evolved over time from mini-lessons to collegial updates. Brooklyn College #AcademicJourney #CareerTransition #ChemistryPhD #IndustryJobs #PhDLife #ResearchJourney #GraduateSchool #ScienceLife #CareerGrowth #PostdocLife #ResearchLife

🌟 Exciting News! 🌟 I'm thrilled to share that I've been published in Towards Data Science! 📝 In my latest article, "Unveiling Metadynamics: A Beginner’s Guide to Mastering PLUMED (Part 1 of 3)," I explore the general concept of metadynamics as it relates to molecular dynamics. This piece takes a mathematics-light approach to understanding the physics behind metadynamics and includes basic code structure with appropriate syntax that should be enough to run a general metadynamics experiment on your own. Writing for Towards Data Science is an honor, and I’m grateful for the opportunity to contribute to such a respected platform in the data science community. Check out the article here: https://lnkd.in/e9ps3_34 and let me know your thoughts! I look forward to sharing more insights and continuing the conversation around metadynamics in part two of the 3 part series, where I'll delve into the mathematics that make metadynamics work as an experimental technique. #DataScience #Metadynamics #MolecularDynamics #TowardsDataScience #Writing #Research #Innovation

It's been 20 months since I embarked on my PhD journey, and what a ride it has been! 🚀 I've had the chance to dive into various fields, from OMICS to database management, but I’ve always been especially excited about image analysis and spatial data. Last month, I finally got access to some existing spatial data from another project—an incredible opportunity to learn something new. 🌟 Now, I'm fully immersed in understanding the entire analysis pipeline for spatial transcriptomics, and I'm eager to apply the insights I've gained from bulk data. 🧠 I'm also picking up some basic imaging analysis skills to get a better grasp of my staining data. These days, I am working in the lab alongside our super helpful technicians, trying to do everything from tissue cutting and staining to scanning and analyzing images. 🔬 My goal is to really get the full picture of the process, and then dive deep into spatial data analysis. There's still a lot to learn, but I'm excited about where this journey is taking me. Here’s to continuous learning—no stopping now! 💪😊 #PhDLife #SpatialTranscriptomics #ImageAnalysis #ResearchJourney #ContinuousLearning #Science

We are collaborating with an expert in Quantum Applications specialized in creating algorithms through the use of optimization and machine learning methods to secure a fresh job opportunity. For further details regarding this candidate, please reach out to us at: amir.rasool@orioneng.com | monika.zbierska@orioneng.com #quantum #quantumcareer #quantumcomputing #quantumresearch #quantumtechnologies #quantumcomputer