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Rakesh VenkatView profile

Assistant Professor

• Theoretical Computer Science
• Approximation Algorithms
• Hardness of Approximation
• +1 more

Rakesh Venkat is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad. His research focuses on Theoretical Computer Science, including approximation algorithms, hardness of approximation, and communication complexity. Education : Ph.D., Tata Institute of Fundamental Research (TIFR), Mumbai. Research Trends : His work addresses fundamental challenges in algorithm design, such as optimizing cache misses, improving clustering algorithms, analyzing graph expansion, and exploring embedding techniques. Publications span top-tier conferences like APPROX, FSTTCS, ICALP, and ITCS, with collaborations at institutions including HUJI, TIFR, and IIT-Bombay. Teaching : Courses taught include Approximation Algorithms, Advanced Data Structures, Discrete Mathematics, and Spectral Graph Theory.

Andreas GalanisView profile

Associate Professor

• Algorithms
• Combinatorics
• Statistical Physics
• +3 more

Dr. Andreas Galanis is an Associate Professor in Computer Science at the University of Oxford, where he serves as a Tutorial Fellow at Hertford College. He earned his PhD from Georgia Tech University in 2015 with a focus on Algorithms, Combinatorics, and Optimization. Research Interests: His work bridges approximate counting/sampling problems in computer science and phase transitions in statistical physics, with a strong emphasis on the computational complexity of partition functions, stochastic processes, and spin systems. Recent Publications: His research spans approximate counting algorithms ( ICALP 2019 , APPROX/RANDOM 2019 ), hardness results in Potts and random-cluster models ( Electronic Journal of Probability 2018 , APPROX/RANDOM 2018 ), and theoretical insights into correlation decay and spatial mixing ( SIAM Journal on Computing 2019 ). Scientific Awards: Best Paper Award, ICALP 2016 Collaborations: He frequently collaborates with researchers like Leslie A. Goldberg and Eric Vigoda on topics involving Markov chains, graph theory, and computational complexity.

• Global Optimization
• Numerical Analysis
• Data Analysis
• +3 more

Waltraud Huyer is a faculty member at the Department of Mathematics, Faculty of Mathematics, University of Vienna. Her office is located in Room 04.120 at Oskar-Morgenstern-Platz 1, 1090 Wien, Austria. She actively teaches courses including Numerical Mathematics and Introduction to Mathematics for both undergraduate and secondary school teacher accreditation programs. Her research spans Global and Local Optimization , Numerical Analysis , Data Analysis , Protein Folding , and Population Dynamics with structured populations. She develops advanced optimization algorithms like SNOBFIT for noisy environments and MINQ8 for quadratic programming, while applying mathematical techniques to biological problems such as protein structure prediction and age-structured population modeling. Analysis of her 14 most recent publications (1994-2018) reveals three dominant research thrusts: (1) Algorithmic development in global optimization (7 papers), including multilevel coordinate search and exact penalty functions; (2) Mathematical biology applications (5 papers), particularly in protein folding and population dynamics; (3) Computational verification methods (2 papers) for linear systems and feasibility problems. Her work consistently bridges theoretical mathematics with practical computational implementations. Her scientific contributions include widely-used algorithms implemented in MATLAB, such as the MCS global optimizer and SNOBFIT for noisy optimization, available through the University of Vienna's software repository. Huyer supervises teaching activities for foundational mathematics courses, emphasizing practical computational skills using MATLAB for numerical analysis and regression problems. Her teaching materials include detailed exercise sets with structured assessment criteria requiring both theoretical understanding and programming implementation.