Subject to: Katya Scheinberg

2026-05-21 · Source: Subject to · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, extended

Summary

Katya Scheinberg, Coca-Cola Foundation Chair and Professor at Georgia Tech, discusses her journey from Soviet Moscow to a leading role in continuous optimization. Born in 1971, she navigated a childhood marked by Soviet restrictions, including name changes to conceal Jewish identity and limited access to Western culture, which she circumvented via VCR clubs. After attending a specialized math high school and Moscow University, she pursued a PhD at Columbia University, graduating in 1997. Her career included a significant tenure at IBM TJ Watson Research Center from 1995 to 2009, where she pioneered derivative-free optimization and co-authored the highly cited book "Introduction to Derivative-Free Optimization" (over 2,700 citations). Following an unexpected layoff from IBM in 2009, she transitioned to academia, holding positions at Lehigh University (2010-2019) and Cornell University (2019-2023) before joining Georgia Tech in 2024. Scheinberg also highlights her current leadership as Chair of the Mathematical Optimization Society and co-editor of Mathematical Programming, emphasizing the importance of recognizing intellectual contributions in peer review.

Key takeaway

For research scientists and AI students navigating complex career paths, Katya Scheinberg's journey underscores the value of embracing unexpected transitions and diverse research challenges. You should prioritize intellectual curiosity over chasing "hot" topics, as genuine excitement often leads to impactful, long-term contributions. Actively seek strong mentorship and advocate for the recognition of critical, often overlooked, academic contributions like peer review, which strengthens the entire research community.

Key insights

Katya Scheinberg's career exemplifies resilience and adaptability, transforming challenges into opportunities in continuous optimization and academic leadership.

Principles

• Adaptability is crucial for navigating career shifts.
• Mentorship and collaboration accelerate research impact.
• Intellectual curiosity drives significant contributions.

Method

Extending Kar's method to second-order cone programming involves building a transformation using a barrier function's Hessian and its square root.

In practice

• Embrace interdisciplinary problems, like SVMs, to identify new research gaps.
• Actively seek out and learn from leading experts in your field.
• Advocate for formal recognition of peer review as an intellectual contribution.

Topics

• Continuous Optimization
• Derivative-Free Optimization
• Stochastic Optimization
• Support Vector Machines
• Interior Point Methods
• Mathematical Optimization Society

Best for: AI Scientist, Research Scientist, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Subject to.