data science panel

Saturday, June 9, 2018 | 11:05 AM - 12:05 PM | Monadnock Room, Katadhin Room

In this age of information, large amounts of data are easily amassed. Thus, in recent years, the demand for data analysis has been on the rise. As more and more companies begin working with large amounts of data, organizations are motivated to use these vast amounts information to improve their offered services by analyzing available data. Recently, data science has increasingly moved from academic research into industry and has become an attractive career choice. In this panel we aim to define what is data science, the skills and practicalities it encompasses and potential career paths in this ever-growing field of work.   


Ram Barankin, PhD, MSc, LLB

Data Scientist at Liberty Mutual Insurance

Data Scientist in the Customer Advocacy group at Liberty Mutual Insurance, working on analyzing unstructured big data, mainly applying text analytics methodologies to get business insights.  Ram has a PhD in Environmental Science from the University of Massachusetts Boston, where he studied Econometrics aspects of adaptation to climate change and natural disasters. In addition, Ram has a Master of Science in Biology (Summa Cum Laude) from Tel-Aviv University, a Law degree and a Business Administration Degree.  In the past, Ram also worked as a data science consultant to the Massachusetts Department of Transportation, as a Statistics lecturer, and as an attorney.

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Amir Handzel, PhD

Co-founder and CEO of Pangea Diagnostics

Dr. Handzel is co-founder and CEO of Pangea Diagnostics, a start-up bringing the practice of pathology to the 21st century by automating the diagnostic process using deep machine learning and advanced image processing.  He is also Principal at Quantorum, a boutique consultancy in precision medicine. Previously Dr. Handzel served as Statistical Science Director at Astrazeneca where he was member of its Precision Medicine & Genomics extended leadership team and member of the team leading multi-drug, multi-party – Umbrella & Basket – clinical trials. He had served as the head of the bioinformatics and biostatistics group at OSI Pharmaceuticals, a former subsidiary of Astellas Pharma, where he led the development of multivariate predictive biomarkers of drug efficacy for the oncology portfolio. He had also worked at Merck & Co. in the Biometrics Research department and at Beyond Genomics, a biotechnology company that specialised in broad profiling of high dimensional “omics” biomarkers. Dr. Handzel obtained his M.Sc. in Physics and Ph.D. in Applied Mathematics from the Weizmann Institute of Science and he conducted post-doctoral research at the Institute for Systems Research at the University of Maryland.


Francesca Lazzeri, PhD

data scientist at microsoft

Francesca Lazzeri is a Data Scientist II at Microsoft, where she is part of the Algorithms and Data Science team. She is passionate about innovations in big data technologies and applications of advanced analytics to real-world problems. Her work focuses on the deployment of machine learning algorithms and web service based solutions to solve real business problems for customers in the energy, retail, and HR analytics sectors. Before joining Microsoft, she was a Research Fellow in Business Economics in the Technology and Operations Management Unit at Harvard Business School (HBS). She holds a Ph.D. degree in Innovation Management from Sant’Anna School of Advanced Studies (Pisa, Italy).


Lorena Pantano, PhD

Research Scientist at Harvard School of Public Health

Dr. Lorena Pantano received her MSc and Ph.D. from the University of Pompeu Fabra – Barcelona – Spain. She then worked at the Institute of Biotechnology and Biomedicine and, subsequently, served as Research Associate at Harvard Chan School for 3 years. Dr. Pantano currently is a Research Scientist at the same school, working on multiple bioinformatics projects. She has contributed to the development of multiple open source tools to analyze high throughput sequencing data based on python and R. Moreover, she has a great interest in data analysis through interactive visualization. In total, she has contributed to 27 published papers in well established international journals.


Ethan Xu, PhD

Director of Translational Genomics at Sanofi Genzyme

Dr. Ethan Xu obtained his Ph.D. degree in Biochemistry, Cellular and Molecular Biology from Johns Hopkins University School of Medicine.  He has followed a R&D career path as a computational biologist in the biopharmaceutical industry since 2001. While working in the Systems Toxicology Group of Merck Research Laboratories, Dr. Xu developed a new method to integrate metabolomics with transcriptomics data to build detailed models of drug-induced nephrotoxicity.  During his tenure at Infinity Pharmaceuticals between 2012 and 2016, Dr. Xu dived into genomics data science and led the initiative to build a scalable cloud-based multi-omics analysis pipeline to support several oncology clinical trials. Since 2016, Dr. Xu has been leading the computational genomics efforts in the Translational Sciences Unit of Sanofi Genzyme.


Ran Xue, MS

data scientist at amazon

Being passionate about making beautiful things with data, Ran Xue is always enthusiastic about machine learning and data analytics in various domains. In 2010, Ran started his journey in data science by pursuing a master degree in Bioinformatics at Boston University. After graduation, Ran joined GNS Healthcare as a bioinformatics scientist. At GNS Healthcare, he led and participated in multiple client-facing projects in precision medicine and healthcare. By focusing on delivering intuitive results from analyzing large, multi-modal datasets, he approached problems in different disease areas, including but not limited to preterm birth, autoimmune disease, and lung cancer.

Earlier in 2018, Ran took a big transition by joining Amazon as a Data Scientist in the Alexa Artificial Intelligence department. With Amazon’s heterogeneous speech and text data sources, and large-scale computing resources, Ran participates in building and releasing natural language understanding production models to provide the best service to Alexa customers.