Postdoctoral Fellow in Bioinformatics, Deep Learning

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The successful candidate is expected to join an established bioinformatics team. The ongoing projects in BSML focus on precision medicine, functional roles of genetic variants in complex disease, next-generation sequencing and single cell RNA sequencing method development and data analyses, deep learning, and regulatory networks. Integrative genomics and deep learning approaches are often applied. Funding (NIH grants, CPRIT, and lab/center startup) is available to support this position for 3 years and promotion to faculty positions is possible. The candidate will have the opportunity to access many high throughput datasets and interact with investigators across UTHealth and Texas Medical Center.


Spectroscopy and Chemometrics News Weekly #50, 2019

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Check out their product page … link Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us. Near Infrared " Raman Spectroscopy and NIR Spectroscopy as Possible AID in Localisation of Solitary Pulmonary Nodules" LINK NIR spectroscopy has potential for rapid on farm analysis of slurry nutrient content. Wouter Saeys, IFSConf LINK "Modeling for SSC and Firmness Detection of Persimmon Based on NIR Hyperspectral Imaging by Sample Partitioning and Variables Selection" LINK " Application of the NIR Spectroscopy in the Researches of Orthopedics Diseases" LINK "FT-NIR による油脂の迅速な品質管理" LINK "Accuracy improvement of quantitative analysis in VIS-NIR spectroscopy using the GKF-WTEF algorithm." LINK "Rapid determination of the content of digestible energy and metabolizable energy in sorghum fed to growing pigs by near-infrared reflectance spectroscopy." LINK "Characterization of the Processing Conditions upon Textural Profile Analysis (TPA) Parameters of Processed Cheese Using Near-Infrared Hyperspectral Imaging" LINK "Total aromatics of diesel fuels analysis by deep learning and near-infrared spectroscopy" LINK "Rapid Assessment of Soil Quality Indices Using Infrared Reflectance Spectroscopy" LINK "Quantitative Determination of the Fiber Components in Textiles by Near-Infrared Spectroscopy and Extreme Learning Machine" LINK "Non-Destructive Method for Predicting Sapodilla Fruit Quality Using Near Infrared Spectroscopy" LINK "Qualitative analysis for sweetness classification of longan by near infrared hyperspectral imaging" LINK " MENGUKUR BERAT VOLUME TANAH DI LAPANGAN MENGGUNAKAN NEAR INFRARED SPECTROSCOPY MEASUREMENT OF SOIL BULK DENSITY IN …" LINK "Hyperspectral Characteristics of Coastal Saline Soil with Visible/near Infrared Spectroscopy" LINK "Monitoring Soil Surface Mineralogy at Different Moisture Conditions Using Visible Near-Infrared Spectroscopy Data" LINK "Near infrared spectroscopy for assessing mechanical properties of Castanea sativa wood samples" Modulus of elasticity LINK " Development of near-infrared spectroscopic sensing system for online real-time monitoring of milk quality during milking" LINK " Advances in Near-Infrared Spectroscopy and Related Computational Methods" LINK "Morphological, Physicochemical and FTIR Spectroscopic Properties of Bee Pollen Loads from Different Botanical Origin" LINK "Fourier transform infrared imaging and quantitative analysis of pre-treated wood fibers: A comparison between partial least squares and multivariate curve resolution with alternating least squares methods in a case study" LINK "Antioxidant Activity of Blueberry (Vaccinium spp.)


An in-depth guide to supervised machine learning classification

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In supervised learning, algorithms learn from labeled data. After understanding the data, the algorithm determines which label should be given to new data by associating patterns to the unlabeled new data. Supervised learning can be divided into two categories: classification and regression. Some examples of classification include spam detection, churn prediction, sentiment analysis, dog breed detection and so on. Some examples of regression include house price prediction, stock price prediction, height-weight prediction and so on.


7 Regression Types and Techniques in Data Science

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Linear and Logistic regressions are usually the first algorithms people learn in data science. Due to their popularity, a lot of analysts even end up thinking that they are the only form of regressions. The ones who are slightly more involved think that they are the most important among all forms of regression analysis. The truth is that there are innumerable forms of regressions, which can be performed. Each form has its own importance and a specific condition where they are best suited to apply.


Reflections on NeurIPs 2019

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There is a huge push among the researchers here for accountability. I was presenting a poster on "Objective Mismatch in Model-based Reinforcement Learning" at the Deep RL Workshop, and the crowd was very receptive to the idea that some of our underlying assumptions of how RL works may be flawed. I also happened to be presenting my poster next to a researcher at Google pushing for more metrics of reliability in RL algorithms. This means: how consistent is the performance papers propose when they claim a new "state-of-the-art" across environments and random seeds. This realistic robustness may be the key to getting these algorithms to be more useful on real applications (such as robotics which I will always bring up as a great interpretable platform for RL).


How This 3-Year-Old Startup Is Using AI/ML To Deploy & Manage 25,000 Battery Management Systems

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Since the last decade, the auto industry has experienced technological breakthroughs that have transformed the automotive landscape. Keeping up with the growing demand, almost every major carmaker has launched or has plans to dive into the electric vehicles market. Mumbai -based ION Energy was born out of the desire to tackle the threat of climate degradation by enabling a much more environment-friendly mobility solution. Founded in 2016, ION acquired an 8-year-old French Battery Management System (BMS) developer – Freemens SAS, in a first of its kind cross-border acquisition. In 2018, ION came out of stealth mode and unveiled its first product UDYR, a portable battery for electric scooters and started commercialising its flagship BMS platform.


The ELIZA Effect - 99% Invisible

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Throughout Joseph Weizenbaum's life, he liked to tell this story about a computer program he'd created back in the 1960s as a professor at MIT. It was a simple chatbot named ELIZA that could interact with users in a typed conversation. As he enlisted people to try it out, Weizenbaum saw similar reactions again and again -- people were entranced by the program. They would reveal very intimate details about their lives. It was as if they'd just been waiting for someone (or something) to ask.


B.Tech students from leading institutions including IITs interning at Bennett University on AI projects - Times of India

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Shounak Banerjee and Pradeep Chandra from IIT Kharagpur opted for winter internship 2019 at Leadingindia.ai, a hotspot for Artificial Intelligence (AI) mentoring at Bennett University, Greater Noida. Similarly, Lovepreet Singh from IIT Gandhinagar selected Bennett University to learn Artificial Intelligence and Data Science after receiving outstanding feedback from the previous interns. As per'MARKETSANDMARKETS' report, the artificial intelligence (AI) market is expected to reach USD 190 billion by 2025, at a CAGR of 36.62%. Linkedin, in a recent report, mentions Artificial intelligence as one of the most sought after technical skills by businesses in the coming year. The trend is no different at Bennett University with a growing number of internship applications compared to last year.


A National Initiative on AI Skilling and Research

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World is on the cusp of a revolution about new possibilities in AI and Machine Learning. Deep Learning is being used to solve many critical healthcare related issues apart from other important areas that impact society. India has aspiring young students in thousands of educational institutions in the country. Due to lack of quality faculty and curriculum design issues many of these students are not able to get access to latest skill sets required by the industry. Industry all over the world is facing huge scarcity of trained manpower in machine intelligence.


Overcoming the Patient Healthcare Challenges with AI Chatbots

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Chatbots are replacing phone call-based customer service in various sectors. In the healthcare industry is opting Chatbots to help patients in resolving their concerns faster than traditional methods. In the healthcare sector, the patient's experience remains at the top priority. These days the patient-driven healthcare environment is helping the patient to make better decisions. To remain spontaneous, it is the time for the healthcare services to embrace consumer readiness for digital communication via AI chatbot.