معرفی
Alina Ene is an Associate Professor in the Department of Computer Science at Boston University, within the Faculty of Arts & Sciences. She holds a BSE in Computer Science (High Honors) from Princeton University (2008) and a PhD from the University of Illinois at Urbana-Champaign (2013), advised by Chandra Chekuri. Her research focuses on algorithms, combinatorial optimization (submodularity, graph theory), and their applications to machine learning. She previously served as Assistant Professor at the University of Warwick, Faculty Fellow at the Alan Turing Institute, and postdoctoral researcher at Princeton's Center for Computational Intractability.
Key research themes include distributed submodular maximization, streaming algorithms, optimization theory, and machine learning. Her work bridges theoretical guarantees with practical applications in data science and distributed systems. Notable contributions include frameworks for submodular maximization under constraints, randomized coordinate descent methods, and routing algorithms with balance considerations.
Publications emphasize algorithm design for submodular functions, stochastic optimization, and graph problems. Recent work explores high-probability convergence in stochastic gradient methods, clipped gradient techniques, and online ad allocation strategies. Her 2016 FOCS paper on distributed submodular maximization remains influential in large-scale optimization.
Professional service includes editorial work for conferences like FOCS/STOC/SODA and grants from NSF's CAREER program. No explicit scientific awards are listed, though her fellowships and postdoctoral appointments reflect scholarly recognition.
