BEAMLAB - Key Persons
Job Titles:
- Postdoctoral Fellow
- Postdoctoral Research Fellow
Postdoctoral Fellow
Allen is a Postdoctoral Research Fellow at the Harvard T.H. Chan School of Public Health.
Allen Schmaltz is a Postdoctoral Research Fellow in the Department of Epidemiology at Harvard, working in the area of AI/machine learning for medicine and public health. His research agenda is oriented toward the larger goal of automated reasoning in the medical setting, including approaches for interpretability and explainability for enabling the reliable use of learned models in real-world medical settings.
He recently completed his Ph.D. in Computer Science at Harvard, with a focus on natural language processing. As a graduate student, his research primarily focused on natural language generation, working on tasks such as grammatical error correction and detection, as well as the diagnostic task of word ordering. In addition to his work in CS at Harvard, he has also been fortunate to work with researchers at IQSS, Northwestern, Rakuten, Disney Research, and the Information Sciences Institute. His previous degrees are from Stanford and Northwestern.
Amita is a master's student of Health Data Science at the Harvard T.H Chan School of Public Health. Read more
Amita is a master's student of Health Data Science at the Harvard T.H Chan School of Public Health.
Her undergraduate background is in Biomedical Engineering and she is interested in exploring the applications of deep learning and natural language processing to healthcare data. When she's not hunched over her computer, she can be found baking or playing with her roommates' dogs.
Andrew is an assistant professor in the Department of Epidemiology at the Harvard T.H. Chan School of Public Health. Read more
Andrew Beam, PhD is an assistant professor in the Department of Epidemiology at the Harvard T.H. Chan School of Public Health, with secondary appointments in the Department of Biomedical Informatics at Harvard Medical School and the Department of Newborn Medicine at Brigham and Women's Hospital. His research develops and applies machine-learning methods to extract meaningful insights from clinical and biological datasets, and he is the recipient of a Pioneer Award from the Robert Wood Johnson Foundation for his work on medical artificial intelligence.
Previously he was a Senior Fellow at Flagship Pioneering and the founding head of machine learning at VL56, a Flagship-backed venture that seeks to use machine learning to improve our ability to engineer proteins.
He earned his PhD in 2014 from N.C. State University for work on Bayesian neural networks, and he holds degrees in computer science (BS), computer engineering (BS), electrical engineering (BS), and statistics (MS), also from N.C. State. He completed a postdoctoral fellowship in Biomedical Informatics at Harvard Medical School and then served as a junior faculty member.
Ben is a PhD student in the Bioinformatics and Integrative Genomics (BIG) graduate program at Harvard Medical School. Read more
Beam Lab gives him the freedom to explore a diverse array of research ideas. He's worked on natural language processing for electronic health records and is interested in pursuing projects in causal inference and uncertainty quantification.
Prior to Beam Lab, he studied Math & Computer Science at UNC Chapel Hill. As a Churchill Scholar at the University of Cambridge, he earned an M.Phil. in Computational Biology. Outside of school, he likes to run and play bridge, the card game your grandma might play.
Job Titles:
- Designer
- Designer at State Street
Caleb is a designer at State Street financial and designed the beamlab logo. Read more
Caleb's expertise lies in translating a brand's core values, strategy and voice into striking visual executions. Noteworthy projects include the global rebrand of National Geographic, ad-campaigns for The New York Times and video installations for the opening of the Nike flagship in Soho. His work has been honored by the D&AD, the Art Directors Club, and The Type Directors Club.
Emily is a graduate student in the HST program at Harvard and MIT. Read more
Emily is a PhD student in the Harvard-MIT Health Science and Technology program. Her research involves natural language processing for clinical text with a focus on summarization, question answering, and transfer learning. Emily is particularly interested in developing methods that can combine neural networks with external knowledge. Emily previously interned at Microsoft Research and Verily, and prior to pursuing her PhD, she received a BS in computer science and MS in biomedical informatics from Stanford.
Eric Chen is a medical student in the Harvard-MIT Program in Health Sciences and Technology. Read more
Eric Chen is a medical student in the Harvard-MIT Program in Health Sciences and Technology. He seeks to improve patient care by applying deep learning techniques to address challenges in the diagnosis of medical conditions in the neonatal intensive care unit and the ophthalmology clinic.
He recently completed his BS/MS in Biomedical Engineering at Yale, in addition to a certificate in Global Health Studies. His research at Yale focused on understanding the mechanism of epileptogenesis in mesial temporal lobe epilepsy. There, he studied the role of glutamine synthetase, glutamine supplementation, and branched-chain amino acids on seizure frequency and severity. Eric also completed a thesis project on the breast cancer immune microenvironment through the Biomedical Engineering department and a capstone project on farmworker justice in California through the Global Health department.
He has had the opportunity to work at the Office of the Chief Economist of the U.S. Department of Agriculture, Wolfram Research Inc., and the San Diego Supercomputer Center. Outside of lab, he loves to sing, cook, and play guitar.
Kristyn is a clinical fellow in the Harvard Neonatal-Perinatal Medicine Training Program. Read more
Kristyn Beam, MD graduated from the University of North Carolina at Chapel Hill and continued there for medical school (Go Heels!). During her time in Chapel Hill, she participated in the Doris Duke Research Fellowship where she came to love research and Neonatology and has pursued these areas since.
She completed a pediatric residency at The Floating Hospital for Children at Tufts Medical Center and then began a Neonatal Fellowship at the Harvard Combined Neonatal-Perinatal Fellowship program, in which she is now a third year fellow. Her research interests center on improving clinical decision making in the NICU using artificial intelligence and machine learning.
She is currently pursuing a Masters of Public Health in Quantitative Methods the Harvard School of Public Health. She is excited to help build new communities between data scientists, epidemiologists, machine learning experts, and clinicians to improve NICU outcomes.
MD/PhD Student
Marika is an MD/PhD Student at Albert Einstein College of Medicine.
Marika is an MD/PhD candidate in her final year of medical school at the Albert Einstein College of Medicine in The Bronx, NY. Her thesis research focused on congenital Long QT Syndrome and understanding the role that synonymous mutation can play in the hERG protein function. She is currently submitting applications for residency training in Obstetrics and Gynecology, where she plans to specialize in high-risk obstetrics and is interesting in utilizing integrated healthcare data to further understand factors involved in risk of pregnancy complications.
Prior to starting the MD/PhD program at Einstein, she graduated from the University of Rochester with a BS in Microbiology with distinction in research.
Matthew is an undergraduate student in the Summer Institute for Biomedical Informatics (SIBMI) program at Harvard Medical School. Read more
He is interested in advancing patient outcome and care in pediatrics through deep learning projects that utilize genomic, image, and/or clinical data. Prior to the BeamLab, his research largely focused on neuro-oncology in the context of precision medicine where he utilized bioinformatics and machine learning techniques on multi-omics data (genomic, transcriptomic, proteomic, epigenomic). Matthew also worked on drug repurposing for Huntington's Disease and assisted in the development of a deep learning model to better understand transcriptional regulation. Although his hometown is Seattle, WA, Matthew is pursuing a BS in Biomedical Informatics and Business at Arizona State University where he helped (and later assisted teaching) in the development of a Clinical Informatics course for other undergraduates (BMI 201). He will graduate in May, 2021 where he then intends to pursue a Ph.D. in a discipline associated with Biomedical Informatics and/or Artificial Intelligence.
Outside of the lab, Matthew enjoys running, reading, film, and computer animation.
Michael Chen is an undergraduate at Harvard University with a concentration in applied mathematics and a secondary in computer science. Read more
Michael Chen is currently an undergraduate at Harvard University with a concentration in applied mathematics and a secondary in computer science. He is originally from Honolulu, Hawaii and is interested in the intersection between computational genomics, deep learning, and medicine. He has worked as a teaching fellow in CS 50 (Introduction to Computer Science) and MCB 112 (Biological Data Analysis).
He is currently developing deep learning methods to identify tuberculosis antibiotic resistance from whole genome sequencing. His work has been supported by the Herchel Smith-Harvard Undergraduate Science Research Fellowship, the Harvard College Research Program (HCRP), and the Harvard PRISE Fellowship.
Outside of lab, Michael can be found volunteering in the hospital, playing tennis with the Harvard club team, conducting/playing clarinet with the pops orchestra, and eating acai bowls.