My journey as a data scientist began when a national organization specializing in clandestine services came by Berkeley to do recruiting. While their interests were in taking raw data and analyzing it for intelligence purposes, they laid out for me a lifelong trajectory to ultimately become one of the unicorns of our time: a data scientist. As an interdisciplinary domain that incorporates mathematical sophistication, sound statistical reasoning, computer programming expertise, and a level of business savviness, data science has pushed me to focus on obtaining the relevant knowledge, skill sets, and experiences. Through Startup ML and the mentorship and real-world experiences that the fellowship has offered, I’ve taken one step closer to becoming a full-fledged data scientist.
I studied Statistics and Computer Science at the University of California, Berkeley and have had stints doing data mining at Unity Technologies, teaching data science at General Assembly, and doing machine learning and statistics research for the Haas School of Business and the School of Public Health at Berkeley.
Rewon Child, Baidu
I began working as a data scientist trying to make sense of the data I found in my career as a entrepreneur and software engineer. Throughout Startup.ML I’ve had the opportunity to formalize my knowledge and apply new techniques to a variety of problems. I’m looking forward to working with a team that is currently tackling challenging and important data problems.
Created and launched Lincoln English, a profitable app on the Japanese app store. Founded, raised money, and built product for Farmivore, a healthy foods subscription business in NY Interned in Equity Derivatives Sales and Trading at UBS Tokyo Worked for 3+ years as a software engineer for Yale faculty web applications. Senior thesis: NLP analysis of People’s Daily historical archives.
Fluent in Japanese and reading proficiency in Mandarin.
I deeply appreciate the rigor and creativity that machine learning adds to solving new and challenging problems. At Startup.ML, I had the opportunity to gain valuable practical experience in just that: making sense of raw data and the techniques to extend that understanding with meaningful and quantified results. I look forward to learning and working with a like-minded team to further explore the power and the boundaries of machine learning while building interesting solutions.
I have a passion for building functional and elegant systems that has increased through years of working as a software engineer with website server-side stacks. Prior to that, I earned a Bachelor of Science in Math from the University of Maryland College Park.
Trevor Lindsay, Facebook
George Manuelpillai, Orange
I am a consultant looking for a career change into machine learning. Having gained experience in software development, project management, requirements analysis and business development with client such as Amgen, Toyota, Aerospace Corporation, Pacific Bell, 20th Century Fox, Paramount Pictures, Hunt Wesson, International Rectifier and Southern California Edison, I would like to to use my broad range of expertise, along with my passion for data to build machine learning solutions.
I have a B.Sc. in Computer Engineer and Computer Science from USC and have consulted for Accenture and Oracle.
I have a passion for transforming data into information, and I’m especially motivated by opportunities to build real products that help real people. As a founder, investor and coach of more than a dozen start-ups, I have applied my skills and interests in a variety of areas — designing products, managing teams, writing marketing and technical literature, programming, etc. After selling my most recent company (Cozio) in 2012, I discovered ML and have been learning as fast as I can about this fascinating field via online courses and Kaggle competitions (currently ranked 19th among all Kagglers). I love the energy and possibilities of start-up companies and am looking forward to applying my new skills to real-world problems.
Alex Miller, Yelp
I am always looking for problems to solve. As a math major, I enjoyed staying up late finishing homework sets, and applying statistical methods in econometrics papers. After college, I wrote and debugged code that helped large hospitals be more efficient at Epic, a medical software company. And while tutoring in New York, I created SQL scripts to help my company figure out where its referrals and revenue were coming from.
Since working at Epic, I have become fascinated by the power and usefulness of data on a large scale. After many tech meetups, machine learning lectures, and side projects on Github, I am excited to be applying my skills to messy, real world problems and datasets at Startup.ML.
My background is in probability and statistical physics. Mostly theoretical questions, but recently, doing a post doc, I worked on a project that involved simulations of a particle system in a finite box on an integer lattice. Each successive approximation was not conclusive enough and a larger one had to be simulated. I was immersed solving problems associated with generating, processing, and analyzing large amounts of data that did not fit on my desktop. I was absorbed by the task, enjoyed the project much more than the other ones, and decided to switch to data science.
Following various MOOCs on machine learning I came to appreciate its approach to solve problems. After trying various DIY data projects I am very excited to join Startup.ML to tackle the real world problems and learn cutting edge techniques.
I have a PhD in Math from Courant Institute of Mathematical Science, New York University.
Saad Eddin Al Orjany, 6sense
During my MSc course work I applied some machine learning algorithms on different computer vision and data mining problems. As I continue learning about different machine learning algorithms and applying them to problems from different domains, I want to build software products that utilize the power of machine learning and to find insights in data to help us understand and tackle real-world problems that touch people’s lives, to this end I have joined Startup.ML as a fellow.
The agile methodology and unique structure of the fellowship at Startup.ML provides an essential expertise on building data-driven machine learning products and exposes the fellows to real-world scenarios that machine learning practitioners in the industry have to deal with on a daily basis and how these scenarios are approached.
It is said that “We are drowning in data, and starved for information”. I am excited to start making information out of data at Startup.ML!
Shonket Ray, Lumiata
My previous research combined quantitative data analysis with medical image processing and visualization in clinically relevant breast cancer imaging applications. As a post-doctoral researcher, I investigated the potential of novel breast parenchymal texture-based imaging biomarkers to improve individualized breast cancer risk assessment using large sets of clinical screening imaging data representative of a “Big Data” project. Similarly, my Ph.D. project involved performing quantitative image analysis of clinical breast CT data sets where I designed and implemented a computer-aided diagnosis system consisting of image preprocessing, lesion segmentation, structural analysis and lesion-type malignant/benign classification using Multilayer Perceptrons.
I am excited to join Startup.ML as a fellow in order to help translate my previous knowledge and skillset from academia and clinical research to industry and startups. Many companies desire the latest and greatest developments and innovation in data science and machine learning for their specific data scenarios; this program offers a great opportunity for training in the latest algorithm design and software tools, mentoring by experienced industry data scientists and hands-on learning by directly working with data from real-world scenarios. I look forward to beginning this journey into the fascinating world of machine learning and data science.
Rory Hartong-Redden, Allstate
Daniel Saltiel, Engineers Gate
I am fascinated by the concept and application of machine learning, a beautiful example of ‘more than the sum of its parts’: nuanced predictions and decisions based on complex interactions of simple rules. My aim is to master and apply these techniques to important and novel problems; the next step along this path is to join a data science team where I can hit the ground running - helping tackle interesting projects while I learn and develop my skills along the way.
I have a B.A. in Physics with a minor in English from the University of California Berkeley and multiple years of experience in scientific research.
Adrian Sarno, Corax Cyber Security
Marjorie Sayer, CloudGenix
The lever, the punchline, the tipping point, the path out of the weeds: I'm interested in finding and creating value. Value can be profit, or a work of art, or a way of deepening our knowledge of the world. It takes many forms, but it's what ultimately feeds us. What excites me about data science is that it brings together mathematics, technology, aesthetics (who doesn't love an elegant algorithm) and a bit of jungle survival skills to the search for value.
There's value in your data, and I'd love to help you find it.
I have a masters in mathematics, and am finishing a masters in data science, both from UC Berkeley.
Peter Skipper, Sentient Technologies
When it comes to data, I love to get my hands dirty. A useful solution to a business problem requires really understanding the domain, examining outliers for insight rather than discarding them as inconveniences. The fellowship has given me an opportunity to take my quantitative background and combine it with a growing set of software-engineering skills.
I have a Master of Science (MS), Statistics from University of California, Los Angeles. You can check out some of the open-source software projects I’ve built on Github (github.com/peterskipper) and data visualizations I’ve created at my blog, theunchartedblog.net
Layla Tadjpour, Sabus Technologies
Eric Wayman, Pivotal
My background is in probability theory and stochastic processes. I first came across some machine learning articles while doing research as a math Ph.D. student at UC Berkeley. This immediately piqued my interest, and I continued to learn machine learning on the side the last few years of grad school. After a brief stint teaching probability at Berkeley, I decided to make the switch to machine learning, and I joined Startup.ml as a fellow.
My interests and goals center on developing cutting edge machine learning methods and applying them to real world problems in industry. My probability and research background serve me well when learning a new method, and through the projects at Startup.ml I’m learning the tools and techniques need to tackle the computational and modeling challenges one faces when dealing with massive and messy data sets. Through the fellowship, I’ve been enjoying keeping my math skills sharp while also seeing my work being used for real projects.