Deaf STEMist Network


Adam Munder

CEO and Co-Founder

Comm-Verse (an Intel Company)


Phoenix, AZ, USA


Nano-fabrication, System Architecture, Systems Engineering, AI, Deep Learning, Computer Vision System


I’m an engineer and the CEO and Co-Founder of Comm-Verse backed by Intel Corporation. My company is dedicated to empowering the Deaf to communicate with anyone, anywhere, anytime. Utilizing technology and world class engineers, we are striving to deliver an Artificial Intelligence (AI) based solution, enabling real-time conversation between people using ASL and those who speak English.

Adam Skwersky

Software Architect



Marlborough, MA, USA


Computers. Robotics. Mechanical Engineering.


I write high quality code and teach others to write high quality code.

Adam Stone

Data Scientist



Edinburgh, UK


data science, cognitive neuroscience, psychology


A full-time data scientist with a background in cognitive neuroscience, language acquisition, sign language linguistics, and childhood education.

Ainsley Latour

Clinical Genetic Technologist, background in molecular ecology and evolution

Kingston General Hospital


Kingston, Ontario, Canada


Biology, medical genetics, molecular ecology


I am a clinical genetic technologist with skills in both cyto and molecular genetics.  I also work as a research consultant in the accessibility space with a focus on barriers encountered by scientists with disabilities or who are Deaf. I also train and consult with organizations around inclusion, diversity, equity and accessibility as part of an organization I co-founded, IDEA-STEM.  I am an Ontario trained teacher of the Deaf/Hard of Hearing.  My masters is in marine evolutionary biology from Memorial University of Newfoundland.  



Alex Lu

Senior Researcher

Microsoft Research


Cambridge, MA, USA


Computational Biology, Machine Learning, Computer Science, Artificial Intelligence


I’m a Senior Researcher at Microsoft Research New England. I lead a research program around the application of artificial intelligence and machine learning to big biological datasets. 

Modern biological experiments generate an unprecedented amount of data. How do we discover new biology when we have millions of microscopy images or protein sequences, and it becomes impossible to look at data at a one-by-one basis anymore? My research develops machine learning methods for discovering hypotheses in biology.

I have broad research interests, but central themes include:

  • Reducing effort and bias in applying machine learning: The best-performing machine learning methods often require a large volume of labeled training data. Not only is this time-consuming, but it biases models to be more sensitive to biology we have prior knowledge of – we might not be able to discover unknown biology, since we can’t label it. To address these barriers, I focus on self-supervised machine learning methods.
  • Learning relevant signal without direct specification: In biology, one scientist’s signal is another scientist’s noise: different biologists simply have different questions and are interested in learning different things from even the same data. I research how we can train machine learning models to extract relevant biological signal from data (even when we don’t know how to directly specify this), and robust to non-biological noise.
  • Interpretation and visualization: After we’ve trained a model, how do we extract insights? I’m interested in how we can use interpretation and visualization techniques to identify new biological hypotheses.

Alexander Lin

PhD Candidate

UT Austin


Austin, TX, USA


Synthetic Biology, Tissue Engineering, Cell Adhesion, Mechanobiology


I am a 3rd-year Chemistry PhD candidate at UT Austin in Prof. Brian Belardi’s lab (located in the McKetta Department of Chemical Engineering). In my research, I am currently trying to engineer bio-like adhesions into minimal synthetic cells in order to construct synthetic tissue.

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Are you curious who the scientists, technologists, engineers, and mathematicians (STEMists) in our community are? Look no further! This is the place for you to search and connect with fellow STEMists.

Are you a STEMist? Come and join the network (deaf, deafblind, deafdisabled, hard of hearing, late-deafened, hearing impaired, and CODAs welcome)!



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