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Modelling the Mind with Computational Biology

Since Dolly the sheep was first cloned, humans have had a love-hate relationship with machines. Ok, maybe even before we asked a machine to make a living thing. In a variety of industries, machine learning systems, AI, and robotics are taking on the routine, mundane tasks once reserved for humans. But they’re doing this not to take away from humans, but to give them an opportunity to operate at a higher, creative level.  So, when you’re modelling the mind using Machine Learning and Computational Data in Neuroscience for mind blowing breakthroughs, we sit up and pay attention.  When it comes neuroscience, the benefits far outweigh the pitfalls. Just ask the researchers in China, who’ve developed a way to spot whether or not a child has autism from imaging the back of their eye. Other neurological orders such as dementia and Alzheimer’s falls under the computational neuroscience spectrum as well. From the 1970s to today, computational biology, using analytical, mathematical modelling, and simulation techniques to study behavioral and biological systems has evolved into a variety of subgenres. And it's within these subgenres we get a sneak peek into the mind of man that creates computers that can understand the mind of man. Can you wrap your head around it? Engineering the Mind – Mathematical Relationships The Life Sciences, Biostatistics, and Computational Biology all play a role in physical and mental health care. In seeking to understand the makings of the human mind, to study its syntactic rules, and to help explain how we think, human and machine have come together again. This time in the form of Computational Psychiatry. It’s here we realize our computational theories have often mirrored what we hoped to accomplish in building computers that could think with reason and logic. By understanding how we think, how the brain performs, and how it solves problems, can also help us to identify what we see as abnormalities of the mind – autism, schizophrenia, Alzheimer’s, dementia, and Parkinson’s disease just to name a few. At its heart, the fundamental message is that the brain’s way solving of inferred problems can be useful in determining hypotheses around neurological disorders.  Even within these subgenres there are varying degrees of theoretical concepts and with the data Computational Biologists and Computational Psychiatrists are able to conduct to navigate the complex inner workings of the brain. But much like the gathering, collecting, and analyzing of the data for the pandemic, the same can be done for in the mental health arena. Not the least of these theorems newly determined comes from a new theoretical model in the journal Medical Hypotheses. In it, T.A. Meridian McDonald, PhD, a research instructor in Neurology at Vanderbilt University Medical Center describes the positive traits of autism.  These positive traits she puts forth include but are not limited to increased attention, increased memory, increase differences in sensory and perception.  Building Computational Relationships Building relationships between neurobiology, environment, and mental signals in computational terms provides a cognitive model to understand the current state of one’s environment. It’s this building of relationships upon which human minds and the inner workings of the machine come together for the common good. There are positives in the negative. Mindset shifts aren’t just for learning how to work online or be more mindful, but are how best to present, and put your best foot forward. If you’re interested in Life Science Analytics, Computational Biology, Decision Science, Machine Learning, or Robotics just to name a few, Harnham may have a role for you. Check out our current vacancies or contact one of our expert consultants to learn more.    For our West Coast Team, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

Why You Should Always Be Learning In Data Science: Tips From Kevin Tran

Last month we sat down with Kevin Tran, a Senior Data Scientist at Stanford University, to chat about Data Science trends, improvements in the industry, and his top tips for success in the market.  As one of LinkedIn’s Top Voices of 2019 within Data & Analytics. his thoughts on the industry regularly garner hundreds of responses, with debates and discussions bubbling up in the comments from colleagues eager to offer their input.  This online reputation has allowed him to make a name for himself, building out his own little corner of the internet with his expertise. But for Tran, it’s never been about popularity. “It’s not about the numbers,” he says without hesitation. “I don’t care about posting things just to see the number of likes go up.” His goal is always connection, to speak with others and learn from them while teaching from his own background. He’s got plenty of stories from his own experiences. For him, sharing is a powerful way to lead others down a path he himself is still discovering.  When asked about the most important lesson he’s learned in the industry, he says it all boils down to staying open to new ideas.  “You have to continue to learn, and you have to learn how to learn. If you stop learning, you’ll become obsolete pretty soon, particularly in Data Science. These technologies are evolving every day. Syntax changes, model frameworks change, and you have to constantly keep yourself updated.”  He believes that one of the best ways to do that is through open discussion. His process is to share in order to help others. When he has a realisation, he wants to set it in front of others to pass along what he’s learned; he wants to see how others react to the same problem, if they agree or see a different angle. It’s vital to consider what you needed to know at that stage. Additionally, this exchange of ideas allows Tran to learn from how others tackle the same problems, as well as get a glimpse into other challenges he may have not yet encountered.  “When I mentor people, I’m still learning, myself,” Tran confesses. “There’s so much out there to learn, you can’t know it all. Data Science is so broad." At the end of the day, it all comes down to helping each other and bringing humanity back to the forefront. In fact, this was his biggest advice for both how to improve the industry and how to succeed in it. It’s a point he comes back to with some regularity in his writing. “It doesn’t matter how smart you are, stay humble and respect everyone,” one post reads. “Everyone can teach you something you don’t know.” Treating people well, understanding their needs, and consciously working to see them as people instead of numbers or titles—this, Tran argues, is how you succeed in the business. To learn and grow, you must work with people, especially people with different skills and mindsets. Navigating your career is not all technical, even in the world of Data. “The thing that cannot be automated is having a heart,” he tells me sagely. Beyond this, Tran stresses the need for a solid foundation. The one thing you can’t afford to do is take shortcuts. You have to learn the practicalities and how to apply them, but to be strong in theory as well.  Understanding what is happening underneath the code will keep you moving forward. He compares knowing the tools to learning math with a calculator. “If you take the calculator away, you still need to be able to do the work. You need the underlying skills too, so that when you’re in a situation without the calculator, you can still provide solutions.” By constantly striving to collaborate and improve, Tran believes the Data industry has the best chance of innovating successfully.  If you’re looking for a new challenge in an innovative and collaborative environment, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

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