Ali Vakilian

I am a Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC).

CV [last update: 9/23] (Google Scholar profile & DBLP profile)
Email: vakilian[at]ttic[dot]edu

Education

• Ph.D., EECS Department, MIT
  Advisors: Erik Demaine and Piotr Indyk
  
• M.S., CS Department, University of Illinois at Urbana-Champaign (UIUC)
  Advisor: Chandra Chekuri
  
• B.S., CE Department, Sharif University

Research Interests

• Algorithmic Fairness
• Design and Analysis of Algorithms for Massive Data
• Learning-Augmented Algorithms, Algorithms with Predictions
• Approximation Algorithms, Combinatorial Optimization, and Graph Algorithms

Teaching & Mentorship

• Courses:
  • TTIC 31150/CMSC 31150 - Mathematical Toolkit (Spring 2023)
• Past Interns:
  • Summer Interns at TTIC (for internship opportunities at TTIC, apply here):
    • Madhusudhan Pittu (PhD at CMU; 2023)
    • Erasmo Tani (PhD at UChicago; 2023)
  • Fatima Fellows (check the Fatima Fellow program):
    • Sèdjro Hotegni (MS at African Institute for Mathematical Sciences—Rwanda; 2022)
    • Sougata Sarkar (MS at IISER Kolkata; 2023)

Academic Service

• Program Committee:
• PC Member at ICALP 2024 and PODS 2022
• Area Chair at AISTATS 2024 and AISTATS 2023
• Junior PC Member at ALT 2023 and ALT 2022
• Workshop Co-organizer:
• SoCG’23 Workshop on “Recent Developments in Geometric Clustering”, June’23.
• Chicago Junior Theorists Workshop, January’23.
• STOC’20 Workshop on “Algorithms with Predictions”, June’20.
• TTIC Workshop on “Learning-Based Algorithms”, August’19.

Publications

• Conference Papers
  • Constant Approximation for Individual Preference Stable Clustering
    Anders Aamand, Justin Chen, Allen Liu, Sandeep Silwal, Pattara Sukprasert, Ali Vakilian, Fred Zhang.
    NeurIPS 2023; selected as a spotlight paper.
  • Improved Frequency Estimation Algorithms with and without Predictions
    Anders Aamand, Justin Chen, Huy Nguyen, Sandeep Silwal, Ali Vakilian.
    NeurIPS 2023; selected as a spotlight paper.
  • Tight Bounds for Volumetric Spanners and Applications
    Aditya Bhaskara, Sepideh Mahabadi, Ali Vakilian.
    NeurIPS 2023.
  • Approximating Red-Blue Set Cover
    Eden Chlamtáč, Yury Makarychev, Ali Vakilian.
    APPROX 2023.
  • Sequential Strategic Screening
    Lee Cohen, Saeed Sharifi-Malvajerdi, Kevin Stangl, Ali Vakilian, Juba Ziani.
    ICML 2023.
  • Approximation Algorithms for Fair Range Clustering
    Sèdjro Hotegni, Sepideh Mahabadi, Ali Vakilian.
    ICML 2023.
  • Learning the Positions in CountSketch [code]
    Yi Li, Honghao Lin, Simin Liu, Ali Vakilian, David Woodruff.
    ICLR 2023; selected as a Notable-top-25% paper.
  • Individual Preference Stability for Clustering [code]
    Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian.
    ICML 2022; selected as a long presentation.
  • Faster Fundamental Graph Algorithms via Learned Predictions
    Justin Chen, Sandeep Silwal, Ali Vakilian, Fred Zhang.
    ICML 2022.
  • Multi Stage Screening: Enforcing Fairness and Maximizing Efficiency in a Pre-Existing Pipeline
    Avrim Blum, Kevin Stangl, Ali Vakilian.
    FAccT 2022.
  • Fair Representation Clustering with Several Protected Classes
    Zhen Dai, Yury Makarychev, Ali Vakilian.
    FAccT 2022.
  • Improved Approximation Algorithms for Individually Fair Clustering
    Ali Vakilian, Mustafa Yalçıner.
    AISTATS 2022.
  • Approximating Fair Clustering with Cascaded Norm Objectives
    Eden Chlamtáč, Yury Makarychev, Ali Vakilian.
    SODA 2022.
  • Approximation Algorithms for Socially Fair Clustering
    Yury Makarychev, Ali Vakilian.
    COLT 2021.
  • Learning Online Algorithms with Distributional Advice
    Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Ali Vakilian, Nikos Zarifis.
    ICML 2021.
  • Individual Fairness for k-Clustering
    Sepideh Mahabadi, Ali Vakilian.
    ICML 2020.
  • Improved Local Computation Algorithm for Set Cover via Sparsification
    Christoph Grunau, Slobodan Mitrović, Ronitt Rubinfeld, Ali Vakilian.
    SODA 2020.
  • Learning-Based Low-Rank Approximations
    Piotr Indyk, Ali Vakilian, Yang Yuan.
    NeurIPS 2019.
  • Structural Rounding: Approximation Algorithms for Graphs Near an Algorithmically Tractable Class
    Erik Demaine, Timothy Goodrich, Kyle Kloster, Brian Lavallee, Quanquan Liu, Blair Sullivan, Ali Vakilian, Andrew van der Poel.
    ESA 2019.
  • Sample-Optimal Low-Rank Approximation of Distance Matrices
    Piotr Indyk, Ali Vakilian, Tal Wagner, David Woodruff.
    COLT 2019.
  • Scalable Fair Clustering
    Arturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian, Tal Wagner.
    ICML 2019.
  • Tight Trade-offs for Maximum k-Coverage Problem in the General Streaming Model
    Piotr Indyk, Ali Vakilian.
    PODS 2019.
  • Learning-Based Frequency Estimation Algorithms [website] [code] [MIT News] [Forbes]
    Chen-Yu Hsu, Piotr Indyk, Dina Katabi, Ali Vakilian.
    ICLR 2019.
  • Local Computation Algorithms for Spanners
    Merav Parter, Ronitt Rubinfeld, Ali Vakilian, Anak Yodpinyanee.
    ITCS 2019.
  • Set Cover in Sub-linear Time
    Piotr Indyk, Sepideh Mahabadi, Ronitt Rubinfeld, Ali Vakilian, Anak Yodpinyanee.
    SODA 2018.
  • Fractional Set Cover in the Streaming Model
    Piotr Indyk, Sepideh Mahabadi, Ronitt Rubinfeld, Jonathan Ullman, Ali Vakilian, Anak Yodpinyanee.
    APPROX 2017.
  • Cost-Effective Conceptual Design Over Taxonomies
    Ali Vakilian, Yodsawalai Chodpathumwan, Arash Termehchy, Amir Nayyeri.
    WebDB 2017.
  • Towards Tight Bounds for the Streaming Set Cover Problem
    Sariel Har-Peled, Piotr Indyk, Sepideh Mahabadi, Ali Vakilian.
    PODS 2016.
  • On Streaming and Communication Complexity of The Set Cover Problem
    Erik Demaine, Piotr Indyk, Sepideh Mahabadi, Ali Vakilian.
    DISC 2014.
  • Which Concepts are Worth Extracting?
    Arash Termehchy, Ali Vakilian, Yodsawalai Chodpathumwan, Marianne Winslett.
    SIGMOD 2014.
  • Improved Approximation Algorithms for Degree-bounded Network Design Problems with Node Connectivity Requirements
    Alina Ene, Ali Vakilian.
    STOC 2014.
  • Prize-collecting Survivable Network Design in Node-weighted Graphs
    Chandra Chekuri, Alina Ene, Ali Vakilian.
    APPROX 2012 .
  • Node-weighted Network Design in Planar and Minor-closed Families of Graphs
    Chandra Chekuri, Alina Ene, Ali Vakilian.
    ICALP 2012.
• Journal Papers
  • Node-weighted Network Design in Planar and Minor-closed Families of Graphs
    Chandra Chekuri, Alina Ene, Ali Vakilian.
    Transactions on Algorithms (TALG) 2021.
    builds upon ICALP'12 paper and extends to the element-connectivity requirements.
  • Cost-Effective Conceptual Design Using Taxonomies
    Ali Vakilian, Yodsawalai Chodpathumwan, Arash Termehchy, Amir Nayyeri.
    VLDB Journal 2018.
  • Cost-Effective Database Design For Information Extraction Applications
    Arash Termehchy, Ali Vakilian, Yodsawalai Chodpathumwan, Marianne Winslett.
    Transactions on Database Systems (TODS) 2015.
• Manuscripts
  • (Learned) Frequency Estimation Algorithms under Zipfian Distribution
    Anders Aamand, Piotr Indyk, Ali Vakilian. February 2020.
  • Approximation Algorithms for Nearly H-Minor-Free Graphs
    Erik Demaine, Quanquan Liu, Ali Vakilian. November 2018.
  • Connected Domatic Packing in Node-capacitated Graphs
    Alina Ene, Nitish Korula, Ali Vakilian. July 2013.
• Theses
  • New Directions in Streaming Algorithms [DSpace@MIT]
    Ph.D. thesis, Department of EECS, MIT, 2019.
  • Node-Weighted Prize-Collecting Survivable Network Design Problems [IDEALS@UIUC]
    M.S. thesis, Department of Computer Science, UIUC, 2013.

Talks

• Algorithms for Socially Fair Clustering: Min-Max Fairness to Cascaded Norms
  • UW Seattle, Theory Seminar (11/13/23)
  • INFORMS Annual Meeting (10/15/23)
  • UT Austin, Theory Seminar (10/6/23)
  • Stanford, Algorithmic Fairness Seminar (10/2/23)
• Learning-Augmented Algorithms for Massive Data
  • INFORMS Annual Meeting (10/18/23)
• Tight Bounds for Volumetric Spanners in All Norms
  • Simons Institute at Berkeley, Sketching and Algorithm Design Workshop (10/11/23)
• Individual Preference Stability for Clustering
  • TRIPODS Postdoc Workshop (8/22/23)
  • Research at TTIC (2/24/23)
• Graph Algorithms with Learned Duals
  • “Scheduling” Seminar at Schloss Dagstuhl (2/9/23)
• Learning Online Algorithms with Distributional Advice
  • Algorithms Under Uncertainty Workshop at FSTTCS'22, IIT Madras (12/6/22)
• Algorithm Design in the Machine Learning Era
  • Research at TTIC (5/13/22)
• Individually Fair Clustering
  • IDEAL Workshop on Clustering, Northwestern (4/23/22)
• Algorithms for Socially Fair Clustering
  • University of Wisconsin—Madison, IFDS (6/10/21)
• Approximation Algorithms for Fair Clustering
  • UCSD, Theory Seminar (5/17/21)
  • UWaterloo, Combinatorics & Optimization Department (5/17/21)
  • MIT, A&C Seminar (5/10/21)
  • TOC4Fairness Seminar (4/28/21)
  • Joint Purdue and UMichigan Theory Seminar (4/23/21)
  • UW Seattle, Theory Seminar (4/20/21)
  • UIUC, Theory Seminar (4/19/21)
  • Google Research (4/15/21)
• Learning-based Algorithms For Massive Data
  • INFORMS Annual Meeting (10/10/20)

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