Mohammad S. Parsa

I am a PhD student in Applied Science and Technology at UC Berkeley, where I work at the intersection of artificial intelligence and biology. Under the guidance of Professor M. R. K. Mofrad, my research centers on developing protein language models and deep learning tools for protein engineering and cellular mechanics.

I previously interned as a Computational Biologist at 310.Ai in San Francisco, where I developed multimodal models for protein design. Before that, I served as a Graduate Research Assistant at the University of Waterloo’s Data Science Lab, analyzing millions of social media posts to investigate COVID-19’s societal impacts under Professor Lukasz Golab. I earned an M.Sc. in Management Sciences from the University of Waterloo and a B.Sc. in Industrial Engineering from Iran University of Science and Technology. My PhD is funded in-part by the prestigious NSERC award and NSF AI Research Resource Pilot Funding.

Beyond research, I swap my hoodie for an apron to cook Persian dishes and experiment with different recipes—like my tiramisu, a hit among friends. I’m an avid hiker and backcountry camper, always bringing my Canon EOS R to photograph nature, especially the stars above my tent. At home, I nurture an herb garden and wrestle with stubborn tomatoes that keep me grounded. When I’m not earthbound, I’m soaring through the skies with my FPV drone.

Email  /  GitHub  /  Google Scholar  /  LinkedIn

Research

I'm interested in protein language models, multimodal deep learning, variational autoencoders, graph theory, and natural language processing.

A generalized protein design ML model enables generation of functional de novo proteins

We introduce MP4, a transformer-based AI model that generates novel protein sequences from functional text prompts. Trained on a diverse dataset of 138,000 tokens and 3.2 billion data points, MP4 creates proteins with specified functions like binding or catalysis, achieving high structural stability and experimental success. We tested over 1,000 unique prompts, producing sequences with natural amino acid distributions and predicted foldability (average pLDDT 82.6). Experimentally, 84% of 94 selected sequences expressed well in a cell-free system.

Identification of acrylamide-based covalent inhibitors of SARS-CoV-2 (SCoV-2) Nsp15 using high-throughput screening and machine learning

We studied Nsp15, a SARS-CoV-2 endoribonuclease crucial for immune evasion and a promising drug target. Despite its complex structure, we screened 2640 acrylamide compounds, finding 10 that inhibited Nsp15 activity (IC50 < 5 μM) with favorable properties like low molecular weight and Lipinski compliance. Using this data, we built an AI model with 73% accuracy to predict new inhibitors, showing acrylamide fragments’ potential for Nsp15 drug development.

Four Things People Should Know About Migraines

We explored Reddit discussions to highlight public misunderstandings about migraines and their impact on migraineurs. Migraines are a serious condition affecting all ages, triggered by diverse factors like stress or caffeine, disproportionately impact women (especially during menstruation or pregnancy), and worsened during COVID-19 due to intensified attacks and lockdown stress. These findings, drawn from subreddits like r/migraine and r/AskWomen, reveal unique social and gender dynamics absent from our environmental studies, emphasizing personal struggles over policy debates.

Analyzing Climate Change Discussions on Reddit

We explored the relationship between climate change discussions on Reddit and the COVID-19 pandemic across subreddits. Before the pandemic, we observed steady conversations focusing on skepticism, consequences, and solutions, tied to events like the Paris Agreement. After COVID-19 hit, we noted a shift—discussions in r/climateskeptics grew more doubtful, linking lockdowns to reduced emissions as “proof” against climate urgency, while r/climateOffensive and r/environment pivoted to pandemic-driven solutions like remote activism and economic recovery tied to green initiatives, amplifying both division and innovation in climate discourse.

Social media mining to understand the impact of cooperative education on mental health

In our study, we investigated the impact of cooperative education (co-op) on students’ mental well-being by analyzing Reddit discussion communities from over 50 U.S. and Canadian universities. We discovered that students frequently grapple with self-doubt, anxiety, and feelings of inadequacy due to the fiercely competitive co-op job market, especially when they end up in entry-level roles unrelated to their academic programs or endure stressful job interviews clashing with academic deadlines. For instance, we noted students expressing overwhelming disappointment and helplessness when unable to secure coveted positions, alongside loneliness from relocating away from friends and family for work terms. The COVID-19 pandemic intensified these struggles, as cancelled work terms sparked heightened competition, financial strain, and desperation to devise alternative plans, often leaving students feeling unmotivated, judged by peers, and fearful of falling behind.

Academic Integrity during the COVID-19 Pandemic: a Social Media Mining Study

We examined Reddit discussions on academic subreddits from Fall 2019 and Fall 2020 to understand students’ feelings about online cheating. We found a threefold increase in cheating-related posts in 2020, shifting from plagiarism in programming (pre-COVID) to broader online assessment concerns during the pandemic. We identified three unique themes in 2020: cheating inflates grades, prompting tougher exams; unpunished cheating demotivates students; and unclear integrity policies confuse them.

Climate Action During COVID-19 Recovery and Beyond: A Twitter Text Mining Study

In this study, we analyzed tweets to understand public attitudes toward climate action during the pandemic, contrasting with our Reddit study’s broader pre- and post-COVID scope. Unlike the Reddit findings, where we saw a split between heightened skepticism and solution-focused shifts, here we found a stronger tilt toward optimism, with over 60% of tweets supporting climate action in recovery, like sustainable infrastructure and cycling, despite unique concerns about public transit’s COVID-19 risks. We also identified a distinct theme absent from Reddit: lessons from the pandemic, such as global collaboration and science-driven policy, shaping future climate strategies, though skeptics still echoed doubts seen on Reddit, now tied to economic recovery debates.

Text mining of COVID-19 discussions on Reddit

We analyzed posts from January to May 2020 across various subreddits to uncover how the pandemic affected online communities. We identified key discussion hubs beyond dedicated COVID-19 forums, including general Q&A (r/AskReddit), teen spaces (r/teenagers), and advice forums for medical, mental health, relationship, and legal issues. Using topic modeling, we found recurring themes: fear of infection, job insecurity, loss of motivation, struggles with online life, and frustration with non-compliance to social distancing. These insights reflect the profound personal and social disruptions faced by Reddit users during the early pandemic.

Teaching

During my graduate studies, I had the opportunity to teach:

University of California, Berkeley

• Spring 2023 - DATA C100: Principles & Techniques of Data Science
• Fall 2023 - ENGIN 270C: Teaming & Project Management
• Spring 2024 - ENGIN 270K-101: Coaching for High Performance Teams
• Fall 2024 - ENGIN 270C: Teaming & Project Management
• Spring 2025 - ENGIN 270K-101: Coaching for High Performance Teams

University of Waterloo

• Winter 2022 - MSCI 541: Big Data Analytics
• Fall 2021 - MSCI 442: Engineering Economics
• Winter 2021 - MSCI 542: Data Warehousing & Mining
• Fall 2020 - MSCI 542: Data Warehousing & Mining
• Spring 2020 - MSCI 541: Big Data Analytics
• Winter 2020 - MSCI 446: International Project Management
• Fall 2019 - MSCI 542: Data Warehousing & Mining

Iran University of Science and Technology (IUST)

• Spring 2018 - CEN 101: Introduction to Computer Programming (Java)
• Fall 2017 - CEN 101: Introduction to Computer Programming (Java)

Design and source code from Jon Barron's website