Scholar
Alishba Imran
Google Scholar ID: t1YQ65kAAAAJ
UC Berkeley
Citations & Impact
Citations
245
H-index
6
i10-index
3
Publications
20
Co-authors
6
list available
Publications
Resume (English only)
Academic Achievements
- 1. Published paper 'Contrastive learning of cell state dynamics in response to perturbations', co-authored with Soorya Pradeep, Alishba Imran, et al., arXiv preprint, 2024/10/15, under review at Cell Patterns.
- 2. Authored the textbook 'AI for Robotics' with Keerthana Gopalakrishnan, published by Apress, Springer Nature, expected in 2025.
- 3. Published paper 'How GPT Learns Layer by Layer', co-authored with Jason Du, Kelly Hong, Alishba Imran, et al., arXiv:2501.07108.
- 4. Published paper '14 Examples of How LLMs Can Transform Materials Science and Chemistry', Kevin Maik Jablonka et al., Digital Discovery, 2023, 2, 1233-1250.
Research Experience
- 1. Worked at Arc Institute with Yusuf Roohani and Hani Goodarzi on foundation models for genetic and chemical perturbation effects on single-cells.
- 2. Worked at Chan Zuckerberg Biohub with Shalin Mehta, modeling cell dynamics using contrastive learning on time-lapse images.
- 3. Served as a lead engineer at Tesla on physics models and ML methods for battery materials discovery and founded Voltx, an ML and physics platform to speed up battery development.
- 4. Joined the AI research team at Cruise and worked with Vector Institute and NVIDIA AI on RL-based research.
- 5. Co-led neuro-symbolic AI research at Hanson Robotics during high school.
Education
- Insufficient information
Background
- Research interests: Machine learning and robotics methods to accelerate scientific exploration within biology and materials discovery. Professional field: AI, biology, and robotics. Brief introduction: Currently an incoming Member of Technical Staff at EvolutionaryScale, working with the team that created ESM. Previously worked at Arc Institute with Yusuf Roohani and Hani Goodarzi on foundation models for genetic and chemical perturbation effects on single-cells.
Miscellany
- Supported by communities: Masason Foundation, Interact Fellowship, Accel Scholars, Emergent Ventures. Involved with the external and research teams at Machine Learning at Berkeley, supporting research initiatives and organizing events connecting students with founders and startups across the Bay Area.
