04/2024: Received a major revision from Mathematics of Operations Research for our paper on “Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization” with Hassan Mortagy and Sebastian Pokutta.
04/2024: New paper! on “Equitably allocating wildfire resilience investments for power grids: The curse of aggregation and vulnerability indices” (arxiv) – with Madeleine, Ryan, Alyssa and Dan, on how just allocating budget based on vulnerability metrics like Justice40 fails to protect vulnerable communities.
03/2024: Really enjoyed visiting CMOR at Rice University and speaking about new recent work on hiring, facility location, and demand learning with constrained price movements! Made a detour via Atlanta to speak to lawyers at the cross-disciplinary Solving for X workshop organized by Deven Desai. Other workshops this month: CCC/INFORMS AI/OR Workshop in DC at the headquarters of CCC, on how AI and OR communities must come together to solve critical challenges we face today. + Talk at Oracle Retail Data Science on our recent work on bringing ethical decisions in retail.
03/2024: Received a minor revision from INFORMS Journal on Computing for our work “Fair and Reliable Reconnections for Temporary Disruptions in Electric Distribution Networks” with Dan Molzahn and Cyrus Hettle.
02/2024: New capstone projects started with Accenture and Cognira on the future of work with Generative AI and retail promotion effects! Really enjoyed visiting NYU Stern School of Business and speaking about new challenges in ethical decision-making.
01/09/2024: I gave a distinguished lecture at Aussois 2024, the 26th Workshop on Combinatorial Optimization. Really enjoyed being in the group of like-minded optimizers!
Happy new year!
12/2023: New paper! on Quantum Optimization: Potential, Challenges, and the Path Forward with a bunch of awesome people. (arxiv)
11/15/2023: Excited to join the scientific advisory board for Intrare! They provide pathways to vulnerable populations to find promising jobs.
11/08/2023: New paper on “Balancing Notions of Equity: Approximation Algorithms for Fair Portfolio of Solutions for Combinatorial Problems” – we explore trade-offs between sizes and achievable approximation factors for portfolios for various combinatorial problems (preprint available here).
11/06/2023: Our paper on “Hardness and Approximations for Submodular Minimum Linear Ordering Problems” just got accepted to Mathematical Programming!
09/01/2023: The newsletter OPTIMA 106 is out! The new issue brings together algorithmic fairness, optimization, and online learning.
08/29/2023: Our paper on “Warm-Started QAOA with Custom Mixers Provably Converges and Computationally Beats Goemans-Williamson’s Max-Cut at Low Circuit Depths” just got accepted to Quantum!
08/22/2023: New paper! On “Mixed-Integer Projections for Automated Data Correction of EMRs Improve Predictions of Sepsis among Hospitalized Patients” shows how one can model some of the clinical knowledge as high-dimensional mathematical constraints, and use projections onto these to correct for the errors in patient data and labs. Using these, we show a much improved pipeline for sepsis prediction, with high fidelity 6 hours before onset! (here)
07/07/2023: I received the NSF CAREER Award (here), for “Advancing Equity in Selection Problems Through Bias-Aware Optimization“, 2023. Looking forward to developing these ideas further!
05/02/2023: Two papers accepted to 24th ACM Conference on Economics and Computation, EC 2023! “Which Lp norm is the fairest? Approximations for fair facility location across all “p””, with Mohit Singh and Jai Moondra, and “Discovering Opportunities in New York City’s Discovery Program: Disadvantaged Students in Highly Competitive Markets” with Yuri Faenza and Xuan Zhang!
03/08/2023: Our paper on “Secretary Problems with Biased Evaluations using Partial Ordinal Information” has been accepted to Management Science!
03/08/2023: Our paper on “Reducing the Feeder Effect in Public School Admissions: A Bias-aware Analysis for Targeted Interventions” has been accepted to appear as a non-archival paper in 2023 SIAM Conference on Applied & Computational Discrete Algorithms.
02/2023: Madeleine Pollack is this year’s winner of the Davidson Family Tau Beta Pi Senior Engineering Award. This is the highest honor from the Georgia Tech College of Engineering given to only one graduating senior each year, recognizing academic excellence, leadership, and service. Congrats Madeleine!
01/2023: Our work on “Algorithmic Challenges in Ensuring Fairness at the Time of Decision” received a major revision from Operations Research, and our work on “Reducing the Feeder Effect in Public School Admissions: A Bias-aware Analysis for Targeted Interventions” received a major revision from M&SOM.
11/2022: New paper on “Socially Fair and Heirarchical Facility Location Problem” with Mohit Singh and Jai Moondra available here: https://arxiv.org/abs/2211.14873
11/2022: Hassan Mortagy received the Shabbir Ahmed Research Excellence Award at ISyE, Georgia Tech! Congratulations Hassan!
09/2022: Excited to serve as the co-editor of OPTIMA, the newsletter of the Mathematical Optimization Society, along with Sebastian Pokutta and Omid Nohadani. Our first issue is online: http://www.mathopt.org/Optima-Issues/optima105.pdf
09/2022: Our work on “Hardness and Approximation of Submodular Minimum Linear Ordering Problems” received a major revision from Mathematical Programming, and on “Secretary Problems with Biased Evaluations using Partial Ordinal Information” received a minor revision from Management Science.
09/2022: Our work on “Algorithmic Challenges in Ensuring Fairness at the Time of Decision” is accepted to WINE 2022! We also have first online views of “Generating Target Graph Couplings for QAOA from Native Quantum Hardware Couplings” at Physical Review A (here) and “Bridging Classical and Quantum using SDP initialized warm-starts for QAOA” at ACM Transactions in Quantum Computing (here).
09/2022: Upcoming workshops to look out for and participate in (also those I’m organizing):
– Workshop on Quantum Computing and Operations Research (here), Fields Institute, October 13-14, 2022, Toronto,
– Los Alamos National Lab 2023 Grid Science Winter School and Conference (here), Jan 9 – 13, 2023, New Mexico,
– ICERM Discrete Optimization: Mathematics, Algorithms and Computation (here), Jan 2023 – May 2023, Providence RI.
05/2022: Hassan Mortagy and Jai Moondra got an honorable mention for best poster award on “Reusing Combinatorial Structure for Faster Iterative Projections on Submodular Polytopes“, at Mixed Integer Programing Workshop 2022! Congratulations!
05/2022: I’m excited to be giving a keynote at the Lorentz Center’s workshop on “Advanced Optimization for Social Choice” this July 2022! I’ll also be giving an invited talk at the Future of data-Centric AI 2022 organized by Snorkel, along with fantastic academics including Hamsa Bastani and James Zou!
05/2022: On the program committee for Coherent Network Computing 2022 (Stanford), and invited to speak at ICERM’s workshop on Combinatorics and Optimization, March 2023. I’ll be attending the ACO@CMU celebration of Gerard Cornuejols’ 71st birthday.
04/2022: Speaking at the “Trustworthy AI: Practical Roadmap for the Government“, organized by Snorkel AI, along with the amazing Sakshi Jain (Responsible AI, Linkedin), Gregory Ihrie (CTO of the FBI), Thomas Sasala (Chief Data Officer of Dept of Navy), Skip McCormick (BNY Mellon), Alexander Ratner (CEO Snorkel and UW faculty), Braden Hancock (CTO Snorkel) and Alexis Zumwalt (Strategy, Snorkel).
04/2022: Our first law review article on “Using Algorithms to Tame Discrimination: A Path to Algorithmic Diversity, Equity and Inclusion” is accepted to UC Davis Law Review! See paper here.
04/2022: Our work on “Don’t let Ricci v. DeStefano Hold You Back: A Bias-Aware Legal Solution to the Hiring Paradox” (arxiv) has been accepted to ACM FAccT 2022!
03/2022: New report on “Mathematically Quantifying Gerrymandering and the Non-Responsiveness of the 2021 Georgia Congressional Districting Plan” (arxiv) shows our mathematical analysis on a huge number of non-partisan maps for congressional districting on Georgia, and how the enacted 2021 plan is in fact quite unresponsive to the changes in voting outcomes. Accepted to appear at EAAMO 2022!
03/2022: Exciting news from FATHOM Research group: Cyrus Hettle secured a summer internship at Los Alamos National Labs, Reuben Tate at NASA Quantum AI Lab, and Hassan Mortagy at Amazon. Also, our second phase of funding via the OPTIQ project just got approved by DARPA (read here)!
03/2022: New paper on “Discovering Opportunities in New York City’s Discovery Program: An Analysis of Affirmative Action Mechanisms” (arxiv) shows although the discovery program in NY has helped a large number of disadvantaged students get admissions to the top high schools in NY, it has also created in-group blocking pairs amongst disadvantaged students as well as created incentives for students to underperform. We explore characteristics of the discovery program, and show how this mechanism can be minimally changed to remove these drawbacks.
02/2022: Upcoming invited talks: UBC Sauder, Global Privacy Summit (IAPP), IISc-MSR Theory.
01/2022: New paper on solving the disparate treatment/impact paradox faced by companies when hiring a diverse workforce: Don’t let Ricci v. DeStefano Hold You Back: A Bias-Aware Legal Solution to the Hiring Paradox (arxiv). Most of our algorithms (e.g., resume screening) reduce people to numbers, but this is not something that the Title VII supports. Read how data-driven partially-ordered sets can be used to bridge law, algorithms and practice.
12/2021: I’ll be serving as an area chair for the cross-disciplinary ACM Conference on Fairness, Accountability and Transparency 2022. Consider sending your best work! https://facctconference.org
11/2021: I’ll be giving an invited talk on “Opportunities for Ethical ML and Supply Chains” at the NSF Tripods-X workshop on “Machine Learning and Supply Chain Management Workshop” at Lehigh University, December 13-14, 2021. (video available here)
09/2021: Our paper on“Reusing Combinatorial Structure: Faster Projections over Submodular Base Polytopes” (here) has been accepted to NeurIPS 2021!
08/2021: Upcoming Invited talks: on Quantum Devices and Algorithms at NSF-FET Workshop on Ising Machines 2022, on Mathematics of Bias and Fairness (October 2021) at GTRI, on Developing the QAOA at IEEE Quantum Week 2021 (October 2021).
07/2021: New paper: On Hardness and Approximation of Submodular Minimum Linear Ordering Problems: we settle the NP-hardness of graphic matroid MLOP and minimum vertex latency cover. We further bring in techniques from the theory of principal partitions and improve known approximation ratios for monotone submodular MLOP and MLVC. Check out our work here, we include many interesting open questions!
07/2021: Super thrilled to be a part of the newly-awarded multi-institution NSF AI Institute (ai4opt.org) on Advances in Optimization (led by Pascal Van Hentenryck), and as a lead of the Ethical AI thrust (co-lead with Justin Biddle)!
07/2021: I’m serving as a guest editor for a special issue on Analytical Fairness in Healthcare, at Healthcare Management Science. Consider submitting your best work at the intersection of fairness and healthcare! Deadline is February 2022.
06/2021: New paper: On Reusing Combinatorial Structure: Faster Projections over Submodular Base Polytopes (here), joint work with Hassan Mortagy and Jai Moondra. Iterative algorithms like online mirror descent repeated solve projection subproblems. Though current methods solve each subproblem from scratch, reusing combinatorial information about tight inequalities can help speed up subsequent projections significantly. Check out our set of tools to see how speed-ups can be achieved within iterative optimization framework.
06/2021: I received the JP Morgan Chase Early Career Award 2021!
05/2021: I’ll be serving on the program committee for WINE 2021 (https://hpi.de/wine2021/).
04/2021: New paper: On Fair and Reliable Reconnections for Temporary Disruptions in Electric Distribution Networks using Submodularity (here). We show that minimizing metrics for reconnection times – something that utility companies get fined for – form special cases of a submodular optimization problem called the “minimum sum set cover”. Is this special case NP-hard? Can it be solved in practice? What about multiple objectives that utility companies care about? Check out our joint work with Cyrus Hettle and Daniel Molzahn to find out more!
04/2021: I’ll be presenting at the junior researcher’s workshop “Crossing Disciplines: Studying Fairness, Bias, and Inequality in Management and Decision Sciences Research (link)” at Harvard Business School on May 21, 2021.
more03/2021: Our paper on Balanced Districting on Grid Graphs with Provable Compactness and Contiguity (here) got accepted to FORC 2021 for a non-archival track presentation! Congratulations to Cyrus, Yao and Woody!
03/2021: I’ll be serving as FORC Publications Chair 2021. We have put together an amazing program: https://responsiblecomputing.org/forc-2021-accepted-papers/.
03/2021: New paper: On taming wild price fluctuations in demand learning – via monotone stochastic convex optimization with bandit feedback. Price exploration is often construed as unfair by consumers. Can one maximize revenue while monotonically decreasing prices (or increasing prices)? Check out our joint work with Vijay Kamble and Jad Salem here.
03/2021: Jad Salem, Deven Desai and I have been selected to present at the Data Law and Ethics Research Workshop 2021 our new work (which is still baking) on Hiring Practices: Biased Data, Fairer Algorithms and Discrimination Law. Also, this paper will be presented at Privacy Law Scholars Conference 2021.
02/2021: New paper: on “Balanced Districting on Grid Graphs with Provable Compactness and Contiguity”, with Cyrus Hettle, Shixiang Zhu and Yao Xie on arxiv! We give the first multi-criteria approximation gaurantee for planar grid graphs to achieve a balanced, compact and contiguous districting, and apply this on 911 calls data from South Fulton City to reduce workload for the fire department, as well as to reduce overpolicing of certain areas.
02/2021: Upcoming talks at Lehigh University (ISE) April 27th, Wharton (guest lecture) April 16th and National University of Singapore (IORA) March 25th (ET).
03/2021: I gave an invited talk organized by the INFORMS Student Chapter at Virginia Tech (ISE) on electrical flows over spanning trees.
01/2021: I’m honored to receive the Student Recognition of Excellence in Teaching: Class of 1934 CIOS Award at Georgia Tech for the new undergraduate course I developed on “Online Learning”. With my student Jad Salem, I am writing a textbook at the undergraduate level, which we would be happy to share and get feedback on.
01/2020: I’ll be speaking on “Electrical Flows over Spanning Trees” in ARC Annual Day at Georgia Tech, February 8, 2020.
12/2020: Upcoming talks: “Mitigating the Impact of Bias in Selection Algorithms”, MIT Operations Research Center IAP Seminar on “Policymaking in OR” and at Amazon Research in January 2020.
12/2020: “Electrical Flows over Spanning Trees” accepted to Mathematical Programming B for publication, and we received a major revision in Management Science for “Closing the GAP: Online Selection of Candidates with Biased Evaluations”. Congratulations to amazing collaborators!
12/2020: We have formed an “ICS Quantum Computing (QC) Working group“, comprised of Sven Leyffer, Giacomo Nannicini, Jim Ostrowski, Luis Zuluaga and myself, for advancing opportunities (e.g., funding, internships, jobs) in QC for INFORMS Computing Society members.
11/2020: Our online learning class made it into the GT CoE news: Teaching in the time of COVID!
11/2020: Two new papers on bridging classical optimization and quantum computing: one of them introduces warm-starts within quantum approx. optimization algorithm (QAOA) (here) and the other solves a large MIP with a big-M approximated combinatorially to set up the graph instances on an ion-trap hardware for running QAOA (here). These are joint papers with Creston Herold (GTRI), Greg Mohler (GTRI), Reuben Tate, Majid Farhadi, Jai Moondra and Joel Rajakumar.
09/2020: Two papers accepted to NeurIPS 2020 – “Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization” with Hassan Mortagy and Sebastian Pokutta, and “Group-Fair Online Allocation in Continuous Time” with Semih Cayci and Atilla Ermilyaz. Congratulations to all collaborators!
08/2020: Our paper on “Electrical Flows over Spanning Trees“ got a minor revision from Math Programming B. Congratulations Hassan, Eddie and Ali! Also, our paper on “Closing the Gap: Mitigating Bias in Online Resume-Filtering” got accepted as an extended abstract to WINE 2020. Congratulations Jad!
08/2020: I’m on the program committee for FORC 2021. The Symposium aims to catalyze the formation of a community supportive of the application of theoretical computer science, statistics, economics and other relevant analytical fields to problems of pressing and anticipated societal concern. Here’s a link for FORC 2020.
07/2020: I’ll be speaking at the GT Library’s Symposium on the Interaction of Privacy and Autonomy, August 2020.
07/2020: I currently serve as an Associate Editor for the Open Journal of Mathematical Optimization. Consider submitting your best work in mathematical optimization to the journal!
07/2020: New podcast online! Keeping Bias Out of Job Applications and School Admissions, Resoundingly Human, INFORMS OR/MS Magazine, June 2020.
06/2020: DARPA awarded up to $9.2 million to our inter-agency team involving GTRI, GT, NIST and ORNL for Optimization with Trapped Ion Qubits (OPTIQ)! Here’s a news article from GT ISyE on this award.
06/2020: What is the best feasible direction of descent for constrained minimization? Does it get linear convergence, with rate independent of geometric constants (like pyramidal width)? Check out our latest research that partially answers these questions: Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization – joint work with Hassan Mortagy and Sebastian Pokutta. abstract
Descent directions such as movement towards Frank-Wolfe vertices, away steps, in-face away steps and pairwise directions have been an important design consideration in conditional gradient descent (CGD) variants. In this work, we attempt to demystify the impact of movement in these directions towards attaining constrained minimizers. The best local direction of descent is the directional derivative of the projection of the gradient, which we refer to as the shadow of the gradient. We show that the continuous-time dynamics of moving in the shadow are equivalent to those of PGD however non-trivial to discretize. By projecting gradients in PGD, one not only ensures feasibility but also is able to “wrap” around the convex region. We show that Frank-Wolfe (FW) vertices in fact recover the maximal wrap one can obtain by projecting gradients, thus providing a new perspective to these steps. We also claim that the shadow steps give the best direction of descent emanating from the convex hull of all possible away-vertices. Opening up the PGD movements in terms of shadow steps gives linear convergence, dependent on the number of faces. We combine these insights into a novel Shadow-CG method that uses FW steps (i.e., wrap around the polytope) and shadow steps (i.e., optimal local descent direction), while enjoying linear convergence. Our analysis develops properties of directional derivatives of projections (which may be of independent interest), while providing a unifying view of various descent directions in the CGD literature.
06/2020: Online learning frameworks often operate under the assumption that the feedback from an action takes a fixed (unit) amount of time, but what if the time for feedback is also stochastic? Can we learn the best group of users (arms) to allocate jobs to, so that the rate of reward is maximized? Can we also ensure “some” fairness in allocation? In a recent work: Group-fair Online Allocation in Continuous Time, jointly with Semih Cayci and Atilla Eryilmaz, we develop fair online allocation policies under stochastic times for task completion.
abstract05/2020: We made some virtual pyramids at MIP Workshop 2020!
05/2020: I’m on IPCO 2021 organizing committee, alongside Mohit Singh (chair), Alejandro Toriello and Santanu Dey. Stay tuned for the call for papers!
04/2020: Our paper on Individual Fairness in Hindsight, joint work with Vijay Kamble, got accepted with minor revision to Journal of Machine Learning Research!
04/2020: What is the impact of bias in student application evaluations in college admissions? Can we quantify the impact on students to know who’s impact the most? How should we distribute limited resources to reduce disparate impact? Check out our latest draft on quantifying the Impact of Bias on School Admissions and Targeted Interventions, joint work with Yuri Faenza and Xuan Zhang.
abstractWe show that under a natural matching mechanism, the ranking of the matched schools of both unbiased group and biased group of students are affected, with some of the latter being heavily penalized even for relatively small bias. Further, we find that schools have little incentive to change their evaluation mechanism, if their goal is the maximize the total potential of accepted students. Armed with this basic model and inferences, we show that the students who are most in need of additional resources to achieve their true potential are average-performing students, as opposed to high performers, thus questioning existing scholarship/aide mechanisms focusing on top performers. We further show, using computational experiments, that the qualitative take-aways from our model remain the same even if some of the assumptions are relaxed and we move, e.g., from a continuous to a discrete model and allow the bias factor to vary for each student.
03/2020: Jad Salem presented our ongoing work on bias in hiring as a poster at the Celebrating Teaching Day 2020.
02/2020: Our paper on “Too many fairness metrics: Is there a solution?” got accepted to Ethics of Data Science Conference 2020, Sydney! Congratulations Gireeja, Akhil, Helen and Simon!
abstractTo demonstrate our framework, we consider the placement of emergency rooms in two neighboring California counties. We consider “fairness” with respect to different groups of people (e.g. grouped by race, income etc.). The distance traveled an emergency room for groups of people is the basis of the fairness metrics, and there are at least 24 group metrics of fairness in the literature. In this, one must ensure equity across multiple groupings of the population. For example, it is important to simultaneously consider equity across both racial groups as well as across economic groups.
In this paper we discuss: (i) a problem formulation with composite objectives that capture interplay of equity and efficiency, (ii) problem instances when two or more equity metrics can be simultaneously minimized, (iii) solutions that are approximately-optimal for multiple definitions of fairness simultaneously. We find that in practice, rather than deciding on a single equity metric, one can compute approximately-optimal solutions that may not be perfect with respect to any single metric, yet can be near-perfect with respect to multiple metrics. We hope that these tools can be useful for policymakers to find operational solutions amidst the myriad of fairness definitions.
02/2020: Upcoming talks: IOS Conference 2020 (March 15-17), CDC Workshop on AI4PH (March 26-27), Classification Society Meeting (June 17-20). (cancelled due to COVID)
01/2020: The New Year 2020 started with receiving a bundle of letters from students at the Peeples Elementary school, following my 11/2019 visit. What an amazing gift in the new year!
12/2019: Invited talk on Impact of Bias on Hiring Decisions, in the Optimization for ML Workshop, in Vancouver, co-located with NeurIPS 2019.
12/2019: Coming up soon – podcast with our College of Engineering Dean Steve McLaughlin on bias/fairness in AI/ML – the uncommon engineer!
11/2019: I’ll be speaking to a large number of fifth graders in the Fayette County through a webcast, while in person at the Peeples Elementary School, about our research on Bias and Fairness in Algorithms, on November 12, 2019. This visit conspired from a letter from a fifth grader from Peeples about bias in algorithms!
Talking about bias/fairness to super enthusiastic fifth graders is one of the highlights of being in academia! I am amazed at how many fairness solutions these kids came up with! #bias #fairness @POGFayette @PeeplesFCBOE @AprilDeGennaro @GeorgiaTechISyE @gatechengineers @mlatgt pic.twitter.com/FAcH7o96Xn
— Swati Gupta (@swati1729) November 17, 2019
11/2019: I’ll be speaking at AI@GT Symposium on November 4th as well as on a panel discussion on Fairness in Machine Learning at Georgia Tech, along with my wonderful colleagues Judy Hoffman and Rachel Cummings, on November 6th from 12:15 – 1:15 PM. (link)
09/2019: How can one find a tree of links to send electricity from substations to various houses so that the power loss is minimized? Check out our new paper on Electrical Flows over Spanning Trees for interesting analysis and the first approximation guarantees! Joint work with Ali Khodabakhsh, Hassan Mortagy and Evdokia Nikolova.
09/2019: Our proposal for organizing a Focus Program on Data Science and Optimization got accepted by the Fields Institute for Research in Mathematical Sciences! Stay tuned for the program.
08/2019: There is bias in evaluations of candidates – often dependent on socio-economic status, gender, race and nationality. Can a written test be useful? Existing stereotypes and perceived difficulty can impact test performance. How to break the cycle and improve quality of hires? Check out our new paper on Closing the GAP: Group-Aware Parallelization for Online Selection of Candidates with Biased Evaluations, joint work with Jad Salem. Lot more work to be done on incorporating bias in online learning!
08/2019: Georgia Tech College of Engineering’s News Article about some of my ongoing research in algorithmic fairness!
07/2019: I am on the program committee for AAAI 2020 and AAAI-20 AI for Social Impact Track.
05/2019: Check out our new paper on Robust Classification using Robust Feature Augmentation, joint work with Atul Prakash, Kevin Eykholt and Haizhong Zheng. Lot of interesting open questions regarding certifiable robustness of classifiers against adversarial attacks!
05/2019: I’m speaking at the Little Big Stage at Global Privacy Summit in Washington, DC on “Individual Fairness in Hindsight“. This event is attended by around 3600 privacy professionals from all over the world.
04/2019: Our paper on Individual Fairness in Hindsight got accepted in EC 2019!
02/2019: We just uploaded on arxiv our paper on Individual Fairness in Hindsight, joint work with Vijay Kamble. Looking forward to extensions and connections!
02/2019: Our paper on “Robust Look-ahead Three-phase Balancing of Uncertain Distribution Loads” won the 5th place at the ISSIP-IBM-CBA student paper award for the “Best Industry Studies Paper” 2019. Congratulations Xinbo and Le!
01/2019: I received the NSF CISE CRII Research Award on Faster Iterative Decisions within First-Order Optimization Methods!
01/2019: I am a program committee member of APPROX 2019.
01/2019: I am invited to speak at a panel on “Profiling, micro-targeting and a right to reasonable algorithmic inferences” organized by Microsoft at the International Conference on Computers, Privacy and Data Protection, in Brussels, January 30 – Feb 1, 2019. (slides)
01/2019: I will be a long-term visitor during the program on Fairness at the Simons Institute in Berkeley from May 30th to July 10th, 2019.
12/2018: Simons Reunion Workshop for Bridging Continuous and Discrete Optimization Program: presented joint work with Madeleine Udell and Sam Zhou on Limited Memory Kelley’s Method Converges for Composite Convex and Submodular Functions.
11/2018: Our workshop papers on Temporal Aspects of Individual Fairness (with Vijay Kamble, spotlight talk) and Fairness in the Face of Uncertainty (with Michael Wang) got accepted in the NIPS Workshop on Ethical, Social and Governance Issues in AI. Congrats Vijay and Michael! Arxiv versions coming up soon!
10/2018: Posted slides of my Tutorial on Bias/Fairness in AI/ML geared towards the members of the European Parliament and privacy professionals! The talk video will be posted here (fpf.org/classes). Pictures from the event coming up soon!
09/2018: Our book chapter on “Computational Comparison of Metaheuristics” is online! Joint work with John Silberholz, Bruce Golden and Xingyin Wang.
09/2018: I am invited to speak in the panel session on Optimization in Power Systems, at the Power and Energy Systems Annual Meeting, August 2019.
09/2018: I am invited by the Future of Privacy Forum to give an introduction to Machine Learning and Fairness/Bias to members of the European parliament at their official side event at the 40th International Conference of Data Protection and Privacy Commissioners in Brussels (October 25th, 2018). Super excited to visit the European Parliament for the main event, as well as to speak at the side event! Stay tuned for the live stream of the event!
09/2018: Our paper on Robust Look-ahead Three-phase Balancing of Uncertain Distribution Loads got accepted in the Hawaii International Conference on System Sciences (HICSS-52)! Congratulations Le Xie and Xinbo!
09/2018: I will be giving an invited talk at the AMS Sectional Meeting (University of Hawaii at Manoa, March 22-24, 2019) and Mixed Integer Programming Workshop (MIP) 2019 (Sloan School of Management, MIT, July 2019).
09/2018: Sam Zhou qualified as a finalist for the INFORMS Undergraduate Operations Research Prize for our paper on Limited Memory Kelley’s Method (below), presentation @INFORMS Annual Meeting on Sunday, November 4th in the session SD26 (4:30pm in room 132A North Building)! Congratulations Sam!
09/2018: Our paper on Limited Memory Kelley’s Method Converges for Composite Convex and Submodular Objectives got accepted in NIPS 2018, with a spotlight presentation! Congratulations Madeleine and Sam!
07/2018: We arxived a new result with Madeleine Udell and Song Zhou: Limited Memory Kelley’s Method Converges for Composite Convex and Submodular Objectives! <convergence rate for novel ltd memory variant of Bach’s simpicial method –> solves Bach’s conjecture (2015), dual is also limited memory, variant of FCFW –> provably small subproblems, better run time!>
07/2018: Officially started as an Assistant Professor at Georgia Tech.
06/2018: I conducted an interactive session on Predictability and Learning during the Mission Possible Summer Camp for high school students at Georgia Tech! <combinatorial explosion is awesome, and yet we can learn>
04/2018: As the Microsoft Research Fellow, I will present a highlights of my work at the Industry Day, an all-day event at the Simons Institute with visitors from the companies that sponsor the institute.
04/2018: I am giving a talk in the next Simons Institute workshop on Mathematical and Computational Challenges in Real-time Decision Making on May 2, 2018.
04/2018: Our paper on “4/3 approximation for TSP on cubic 3-edge-connected graphs” has been accepted for publication in Operations Research Letters!
04/2018: I am visiting Microsoft Research, Redmond on April 23 and 24, 2018.
04/2018: I am giving a talk at the Stanford Theory Seminar on Thursday April 5, 4:15 PM, Gates 463A.
03/2018: I am giving a talk in the Session FB11: New Techniques in Discrete and Mixed Discrete Optimization, on February 23, Optimization Society Meeting, Denver.
03/2018: I am participating in the 1st Transatlantic-Transpacific Workshop of the ML Research Triangle, at Georgia Tech. I led the discussion on Grand Challenges in AI/ML, with my focus on Bias/Fairness in Optimization/ML.
02/2018: I am invited to the NSF workshop on Real-Time Learning and Decision Making in Dynamical Systems, Feb 12-13, Alexandria, VA.
02/2018: I will be presenting my research at the Discrete Optimization and Machine Learning Workshop in July, Tokyo.
02/2018: I am giving a talk on Learning Combinatorial Structures at Google Research, Mountain View.
12/2017: Our book chapter on “Computational Comparison of Metaheuristics” is soon coming up! Joint work with John Silberholz, Bruce Golden and Xingyin Wang.
12/2017: “In Order Not to Discriminate, We Might Have to Discriminate“, an article by Christoph Drosser stemming from the discussions and talks at our Optimization and Fairness mini-symposium at the Simons Institute.
11/2017: I am giving a talk about “Learning What Works Best When” at Visa Research, Palo Alto.
11/2017: I am co-organizing an Optimization and Fairness Mini-symposium at the Simons Institute, along with David Williamson! Looking forward to a half-day of exciting talks!
11/2017: How can one explain their research to an average educated person? To meet this challenge, I will give a talk on “Alexa, What Should I Read Next?”, during the Fireside Chats competition at the Simons Institute.
10/2017: I am invited to give a talk at the Workshop on Algorithms and Optimization (January 2018), International Centre for Theoretical Sciences (ICTS), Bangalore.
10/2017: I am giving a talk on Learning Combinatorial Structures at the Simons/Google Visit Day @Google Mountain View.
09/2017: Our paper on “What Works Best When? A Systematic Evaluation of Heuristics for Max-Cut and QUBO” got accepted for publication in the INFORMS Journal on Computing!
08/2017: I will teach a module on the “Growth and Decay of Functions” at the Berkeley Math Circle (a weekly program for around 500 San Francisco area elementary, middle and high school students) in January, based off a BLOSSOMS video we shot earlier at MIT.
07/2017: I have been selected as the Microsoft Research Fellow, for the Real-Time Decision Making program at the Simons Institute!
05/2017: I have successfully defended my thesis titled, Combinatorial Structures in Online and Convex Optimization! I extend a heartfelt thanks to my advisors, committee members, friends and family for their encouragement and support.
01/2017: Our paper on “Newton’s Method for Parametric Submodular Function Minimization” got accepted for IPCO 2017!
12/2016: I am looking forward to attending a short winter course at the Harvard Law School on Internet & Society: The Technologies and Politics of Control, taught by Jonathan Zittrain and Joi Ito!
10/2016: I am on the job market this Fall!Here is a link to my Publications, Presentations, CV and Research Statement.
10/2016: What works best when? A Framework for Systematic Heuristic Evaluation (with John Silberholz, Iain Dunning) received a special recognition by INFORMS Computing Society in 2016 as a part of the student paper competition!
10/2016: An Efficient Algorithm for Dynamic Pricing using a Graphical Representation (with Maxime Cohen, Jeremy Kalas, Georgia Perakis) is a Finalist in the INFORMS Service Science Section student paper award 2016!
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