Operations Research Center Massachusetts Institute of Technology
GRADUATE STUDENT DIRECTORY
November 2020 Moïse Blanchard
Operations Research Center 275 Medford Street Massachusetts Institute of Technology Somerville, 02143 77 Massachusetts Avenue, E40-103 530-302-6432; +33 7 68 83 69 35 Cambridge, MA 02139 Email: [email protected]
Education Massachusetts Institute of Technology, Cambridge, MA Candidate for PhD in Operations Research; expected completion, May 2023. GPA: 4.0 Advisors: Professor Patrick Jaillet and Professor Alexandre Jacquillat
Ecole Polytechnique, Palaiseau, France MS and BS in Applied Mathematics, completion July 2019. GPA: 4.0 Valedictorian
Prépa at Lycée Louis-le-Grand, Paris, France Mathematics, Computer Science and Physics, completion July 2016. Prépa is selective post-secondary scientific studies leading to the Grandes Ecoles.
Work Experience
2018 BNP Paribas Cardif, Lima, Peru Actuarial Intern Pricing and technical studies intern in the Actuarial Area. Classification automatization.
2016-2017 French Foreign Legion, Mayotte, France Land forces officer (Lieutenant) at Détachement de Légion Etrangère de Mayotte Group leader, operational and tactical instructor for malgach soldiers, and coordinator for operations against illegal immigration.
Research Experience
2019–Present Massachusetts Institute of Technology, Cambridge, MA Research Assistant Advisors: Professor Patrick Jaillet and Professor Alexandre Jacquillat Optimization under uncertainty on combinatorial problems including optimal matchings, TSP and other routing problems.
2019 University of California, Davis, CA Research Intern Supervisor: Professor Jesus De Loera Research on the Simplex method for linear optimization and monotone diameters of polytopes.
2018-2019 Ecole Plytechnique, Palaiseau, France Research Intern Supervisor: Professor Laurent Massoulié Reconstruction and assembly of graphs from node neighborhoods. Reconstructibility of several classes of graphs
2018-2019 Ecole Polytechnique, Palaiseau, France Research Project Supervisor: Professor Gabriel Peyré Optimal Transport for Natural Language recognition, process and classification.
Teaching Experience
2020 Massachusetts Institute of Technology, Cambridge, MA (Fall) Teaching Assistant for 15.072 Advanced Analytics Edge Recitations, office hours, grading and supervising group projects.
2017-2019 Lycée Condorcet, Paris, France Teaching Assistant for "Classes Préparatoires" students Provided weekly training courses for undergraduates in Mathematics.
Publications
"Online matchings on unknown bipartite graphs", with Prof. Alexandre Jacquillat and Patrick Jaillet, submitted to INFORMS Mathematics of Operations Research, September 2020.
"On the Length of Monotone Paths in Poyhedra", with Prof. Jesus De Loera and Prof. Quentin Louveaux, submitted to SIAM Journal of Discrete Mathematics, January 2020.
"Shotgun Assembly of Graphs and Lattices", with Romain Cosson and Prof. Laurent Massoulié, ongoing work.
Honors and Awards
2020 2nd place at "The East Coast Data Open" "The Data Open" is a datathon organized by Citadel.
2019 Laplace Medal Medal from the French Science Academia awarding the valedictorian of Ecole Polytechnique
2019 Rivot Medal "On the Length of Monotone Paths in Polyhedra" Award from the French Science Academia honoring the quality of research work at Ecole Polytechnique.
2015 Bronze medal at 46th International Physics Olympiad (IPhO)
2014 Bronze medal at 55th International Mathematics Olympiad (IMO)
2014 Silver medal at 18th Junior Balkan Mathematical Olympiad (JBMO)
2014 1st prize at "Concours Général" in Mathematics "Concours Général" is a nationwide Mathematics Olympiad in France
2013 Baccalaureate with distinction, youngest national laureate Skills and Activities
Languages: French (mothertongue), English (fluent), Spanish (fluent) Programming: Python, Julia, R, C++, SQL Interests: Cello, Piano, Swimming, Cycling (bicycle trip across the US in 2016)
Citizenship Citizen of France Ryan Cory-Wright
Operations Research Center Email: [email protected] Massachusetts Institute of Technology Cell: 617-955-5710 77 Massachusetts Avenue, E40-103 Website: ryancorywright.github.io Cambridge, MA 02139
Education Massachusetts Institute of Technology, Cambridge, MA Candidate for PhD in Operations Research; expected completion, May 2022. GPA: 5.0/5.0 Advisor: Dimitris Bertsimas
University of Auckland, Auckland, New Zealand B.E. (1st Class Hons) in Engineering Science, May 2017. GPA 8.84/9.00 Advisors: Golbon Zakeri, Andy Philpott.
Research Interests
Methodological: Optimization (discrete/conic/stochastic/robust), machine learning, statistics Applications: Finance, energy (market design/renewable integration)
Research Experience
2017-Present Massachusetts Institute of Technology, Cambridge, MA Research Assistant Advisor: Dimitris Bertsimas • Developing optimization techniques for solving central problems in the operations research, machine learning and statistics literatures, with focus on certifiable optimality and scalability. • Aiming to make methodological and algorithmic contributions to the fields of discrete and conic optimization, including developing new algorithms for solving cardinality and rank constrained optimization problems to certifiable optimality.
2016-2017 University of Auckland, Auckland, New Zealand Research Assistant Advisor: Golbon Zakeri • Designed techniques for pricing electricity in markets with uncertain supply, risk-aversion. • Implemented pricing mechanism on a full-scale replica of the New Zealand Market. • Corresponding author on two papers.
Teaching Experience
2020 15.071 The Analytics Edge (MBA level) (Fall) Instructor: Bart van Parys Head TA for a class which introduces Sloan MBA students to data analytics. Duties: preparing and leading recitations, developing and grading assignments, holding office hours and supervising final projects.
2020 15.S60 Computing in Operations Research and Statistics Instructor (MSc/PhD level) (IAP) Taught a 3-hour session which aims to provide PhD students with an overview of state-of-the-art software tools used in optimization and statistics. Material available here. 2019 15.095 Machine Learning Under a Modern Optimization Lens TA (MBaN/MSc/PhD level) (Fall) Instructor: Dimitris Bertsimas. Syllabus Teaching Assistant for a course which provides masters/PhD students with a modern treatment of Machine Learning using the lenses of convex, robust and mixed-integer optimization. Duties: preparing and leading recitations, developing and grading assignments and exams, holding office hours, and supervising final projects.
2019 15.S60 Computing in Operations Research and Statistics Instructor (MSc/PhD level) (IAP) Taught a 3-hour session which aims to provide PhD students with an overview of state-of-the-art software tools used in optimization and statistics. Material available here.
2018 15.093 Optimization Methods TA (MBaN/MSc level). Instructor: Bart van Parys. Syllabus (Fall) Teaching Assistant for a course which aims to provide masters students with a unified overview of the main algorithms and areas of application in optimization. Duties: preparing and leading recitations, developing and grading assignments and exams, answering Piazza questions, and holding office hours.
Publications “From Predictions to Prescriptions: A Data-Driven Response to COVID-19”, with Dimitris Bertsimas et. al., to appear in Health Care Management Science, 2020. • Winner, INFORMS Healthcare Applications Society Pierskalla Best Paper Award (2020).
“On Stochastic Auctions in Risk-Averse Electricity Markets With Uncertain Supply”, with Golbon Zakeri, Operations Research Letters, 48(3):376-384, 2020.
“On Polyhedral and Second-Order Cone Decompositions of Semidefinite Optimization Problems”, with Dimitris Bertsimas, Operations Research Letters. 48(1):78-85, 2020.
“Payment Mechanisms for Electricity Markets With Uncertain Supply”, with Andy Philpott and Golbon Zakeri, Operations Research Letters. 46(1):116-121, 2018. • Winner, Operations Research Society of New Zealand Young Practitioner’s Prize (2016).
Preprints “Mixed-Projection Conic Optimization: A New Paradigm for Modeling Rank Constraints”, with Dimitris Bertsimas and Jean Pauphilet, Operations Research, under review. • Finalist, INFORMS George Nicholson Student Paper Competition.
“Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality”, with Dimitris Bertsimas and Jean Pauphilet, JMLR, under review.
“A Unified Approach to Mixed-Integer Optimization: Nonlinear Formulations and Scalable Algorithms”, with Dimitris Bertsimas and Jean Pauphilet, SIAM Journal on Optimization, under review. • Winner, INFORMS Computing Society Student Paper Competition (2019). • Abridged version appeared in the Spring 2020 INFORMS Computing Society Newsletter. • Finalist, MIP Workshop student poster competition (2020).
“A Scalable Algorithm for Sparse Portfolio Selection”, with Dimitris Bertsimas, INFORMS Journal on Computing, reject and resubmit.
Books in Preparation
“Scalable Algorithms for Sparse and Low-Rank Optimization Problems [working title]”, with Dimitris Bertsimas and Jean Pauphilet, Dynamic Ideas LLC, to appear 2022.
Selected Talks
“Mixed-Projection Conic Optimization: A New Paradigm for Modeling Low-Rank Constraints”, Presented at INFORMS George Nicholson Finalists Session, November 2020.
“A Unified Approach to Mixed-Integer Optimization: Nonlinear Formulations and Scalable Algorithms”, Presented at: ICCOPT, August 2019; INFORMS, October 2019; MIT ORC Student Seminar Series, November 2019; MIT LIDS student conference, January 2020; MIP Workshop, May 2020.
“A Scalable Algorithm for Sparse Portfolio Selection”, Presented at: INFORMS Annual Meeting, November 2018; ORC 65th anniversary, November 2018 (poster); LIDS student conference, January 2019; MIP Workshop, June 2019 (poster).
“Payment Mechanisms and Risk-Aversion in Electricity Markets with Uncertain Supply”, Presented at: ORSNZ, December 2016, EPOC mini workshop, July 2017; ISMP, July 2018.
Selected Honors and Awards
2020 Finalist, INFORMS Nicholson Student Paper Competition (1 of 5 finalists/119 submissions) For: “Mixed-Projection Conic Optimization: A New Paradigm for Modeling Rank Constraints”
2020 Winner, INFORMS Healthcare Applications Society Pierskalla Best Paper Award For: “From Predictions to Prescriptions: A Data-Driven Response to COVID-19”
2020 Finalist, MIP Workshop 2020 Best Student Poster Competition For: “A Unified Approach to Mixed-Integer Optimization: Nonlinear Formulations and Scalable Algorithms”
2019 Winner, INFORMS Computing Society (ICS) Student Paper Award For: “A Unified Approach to Mixed-Integer Optimization: Nonlinear Formulations and Scalable Algorithms”
2017 Senior Scholar Award, University of Auckland (top of graduating class).
2016 Winner, Young Practitioner’s Prize, Operations Research Society of New Zealand. For: “Payment Mechanisms for Electricity Markets with Uncertain Supply”
2014-2016 Deans Honours List, Faculty of Engineering, University of Auckland (top 5% of class).
2014-2016 First in Course Award x5, University of Auckland.
2013 NZQA Outstanding Scholar Award (top 50 high school students in New Zealand).
Mentoring Experience
Summer 2019 15.089 Analytics Capstone Project: Student Mentor Instructor in charge: Dimitris Bertsimas Advised a project completed by two MBaN students, who applied prescriptive analytics to prescribe actions which optimize fund flows for a large investment management company.
Summer 2018 15.089 Analytics Capstone Project: Student Mentor Instructor in charge: Dimitris Bertsimas Advised a project completed by two MBaN students, who applied machine learning techniques to predict fund flows at the financial advisor level for a large investment management company. • Mentees received award for the best capstone presentation in graduating class.
Professional Activities and Service
2019-2020 Coordinator, MIT ORC Student Seminar Series 2019 Session Chair, INFORMS 2019 Annual Meeting 2019 Tester and Proctor, MIT Operations Research Center Qualifying Exam 2018-present Reviewer, European Journal of Operational Research; IEEE Transactions on Power Systems; INFORMS Journal On Computing; INFORMS Journal on Optimization; Journal on Global Optimization; Omega.
Skills and Activities
Programming Languages: Julia (preferred), R, VBA, SQL, MATLAB, C++, HTML, CSS. Optimization Software: JuMP (preferred), CPLEX (preferred), Gurobi (preferred), MOSEK (preferred), most languages/solvers. Languages: French (conversational), German (beginner). Extracurriculars: Skiing, Running, Hiking.
Citizenship Citizen of New Zealand, Ireland Victor Gonzalez
Operations Research Center 235 Albany St., 4015 Massachusetts Institute of Technology Cambridge, MA 02139 77 Massachusetts Avenue, E40-103 512-769-5200 Cambridge, MA 02139 Email: [email protected]
Education Massachusetts Institute of Technology, Cambridge, MA Candidate for PhD in Operations Research; expected completion, June, 2024 GPA: 5.0/5.0 Advisor: Patrick Jaillet
Rice University, Houston, TX BA in Computational and Applied Mathematics, June, 2019. Summa Cum Laude
Work Experience
2016 Piney Point, Milwaukee, WI (Summer) Hedge Fund Intern Researched energy companies and worldwide factors to determine catalysts for movement in stock prices.
Research Experience
2019–Present Massachusetts Institute of Technology, Cambridge, MA Research Assistant Advisor: Patrick Jaillet Developing algorithms which will solve problems related to online matching. I am studying how competitive these algorithms are, and I am working to improve the efficiency of these algorithms.
2017-2019 Rice University, Houston, TX Research Assistant Supervisor: Andrew Schaefer Worked on generalizing facet-defining inequalities in a subset of variable upper bound problems under uncertainty. This could be used improve computational time for certain network optimization problems under uncertainty. This could also be extended to variable upper bound problems in general.
Teaching Experience
2016-2019 Rice University, Houston, TX Teaching Assistant for Introduction to Engineering Compuation (CAAM 210) Helped organize an introductory class teaching students Matlab and its applications to engineering. Held a weekly recitation and office hours to help students understand the week's material. Graded the students' assignments.
Honors and Awards
2019 James W. Waters Award, Rice University (Spring) Awarded for creativity in research
2018 CAAM Chevron Award, Rice University (Fall) Awarded for class performance and research
2017-2019 LJ Walsh Scholarship
2015-2019 Rice University President's Honor Roll
Skills and Activities
Programming: Python, C, C++, Matlab, Gurobi, CPLEX, LaTex Baker Academic Mentor, 2016-2019 Baker Head Academic Mentor, 2017-2019 Hispanic Association for Cultural Enrichment at Rice
Citizenship Citizen of United States of America Zachery Halem
Operations Research Center 15 Justin Road Massachusetts Institute of Technology Harrison, NY 10528 77 Massachusetts Avenue, E40-103 914-715-1870 Cambridge, MA 02139 Email: [email protected]
Education Massachusetts Institute of Technology, Cambridge, MA Candidate for SM in Operations Research; expected completion, June 2020. Academic distinctions, i.e., magna cum laude Advisor: Dr. Andrew Lo
Princeton University, Princeton, NJ BSE in Operations Research and Financial Engineering, High Honors, Elected to Phi Beta Kappa, June 2018. GPA: 3.93/4.00.
Work Experience
2020 Cinemagic, Cambridge, MA Founder and CEO Developed a startup that produces a custom-designed, film-specific unmanned aerial vehicle (UAV) fleet that allows filmmakers to shoot content with exquisite precision and without the need of a crew.
2015-2019 PayRay, New York, NY Founder and CEO Formed a retail tech start-up that allows customers to enter a store, scan products they wish to purchase with their smartphone, and exit the store without having to wait in a checkout line.
2018 Blue Labs, Washington, D.C. Associate Campaign Analyst (Midterms Developed and managed an election night tracker; leveraged internal polls to target underperforming demographics and redistribute advertisement spending in major media markets for several major Senate and Gubernatorial races.
2016 BoomBang, Los Angeles, CA Intern Conceptualized an innovative recovery-enhancing wearable technology for a major athletic company.
2015 DidIt Labs, New York, NY (Summer) Intern Developed metrics around user intent signaling that formed the basis of the social media startup's revenue model.
Research Experience
2019–Present Massachusetts Institute of Technology, Cambridge, MA Research Assistant Advisor: Dr. Andrew Lo Developing portfolio and investment models by utilizing creative financial engineering techniques to incentivize the funding of nuclear fusion in the private sector; examining how market dynamics of Fortune 500 companies have induced energy transitions and GHG reduction efforts.
2017-2018 Princeton University, Princeton, NJ Senior Thesis Research Supervisor: Professor Matthew Weinberg Formulated a game theoretical model for allowance flows in a cap and trade scheme, and subsequently evaluate different auction, reserve sale, and price ceiling formats.
2017 Princeton University, Princeton, NJ Junior Thesis Resarch Supervisor: Professor Warren Powell Developed a novel sequential decision-making framework that uses stochastic modeling and Markov dependent processes to optimize the manner in which a baseball manager utilizes relief pitchers in his bullpen
2017-Present Lisman Research Laboratories at Toolik Field Station, Fairbanks, AK Researcher Supervisor: Dr. Rachel Cox Collecting yearly spruce samples along a latitudinal transect in northern boreal forest, and performing methylation, acetylation, photosynthetic rate, and morphological measurements to investigate whether epigenetic changes due to the harsh environmental conditions are manifested in mitosis and/or meiosis
2012-2014 Lisman Molecular Ecology Research Laboratories, New York, NY Researcher Supervisor: Dr. Rachel Cox Performed a study on the effect of stressful environmental conditions on endocrine functions of the Atlantic ribbed mussel, which was the first to demonstrate a correlation between low dissolved oxygen and disruptions in androgynous steroid levels, physiological development, and maturation
Publications
“A Game Theoretical Approach to Cap and Trade System Modeling and Auction Design.” Princeton Senior Thesis, with Matthew Weinberg, working paper.
” Quantification of heat shock protein 70 and acetylcholinesterase over a time course suggests environmental adaptation in a foundational molluscan species”, with Andrew Ravaschiere, Caroline Cutler, and Rachel Cox, in Ecotoxicology and Environmental, April, 2017.
” Evidence for intraspecific endocrine disruption of Geukensia Desmissa (Atlantic ribbed mussel) in an urban watershed”, with Dustin Ross & Rachel Cox, in Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, May, 2014.
Honors and Awards
2018 Selected to the Phil Beta Kappa Honor Society, Tau Beta Pi Engineering Honor Society, and Sigma Xi Scientific Research Honor Society
2018 Gates Scholarship Finalist
2016 Participant in competitive Silicon Valley TigerTrek program; named Princeton Entrepreneur of the Month
2014 Awarded Marjot Foundation grant, Bausch & Lomb Honorary Science Award, and Charmatz Science Award
Skills and Activities
Technical: Java, R, MATLAB, SQL and AMPL Skills, Proficient with Adobe InDesign and Microsoft Office Language: English (native), Mandarin (proficient) Composer of jazz, classical, and lyrical music, including completion of a symphony entitled A Centennial Overture, which premiered at the Taplin Auditorium in Princeton University Member of Princeton Polo Team, 2014-2018 Founding Member & Donation Manager of Effective Altruism Investments at Princeton University, 2015-2017 Volunteer, Peace Memorial Foundation, 2015-2017
Citizenship Citizen of United States of America Nicholas André G. Johnson
Operations Research Center 88 Ames Street Unit 1812 Massachusetts Institute of Technology Cambridge, MA 02142 77 Massachusetts Avenue, E40-103 514-653-8192 Cambridge, MA 02139 Email: [email protected]
Education Massachusetts Institute of Technology, Cambridge, MA Candidate for PhD in Operations Research; expected completion, June 2024. Academic distinctions, i.e., magna cum laude Advisor: Dimitris Bertsimas
Princeton University, Princeton, NJ BS in Engineering, June 2020. GPA: 4.0/4.0. Summa Cum Laude. Valedictorian. Thesis title: Sequential Stochastic Network Structure Optimization With Applications To Addressing Canada's Obesity Epidemic
Work Experience
2020 D. E. Shaw Group, New York City, NY (Summer) Hybrid Quantitative Research and Software Developer Intern Developed a simulated exchange environment and studied the optimal trading behavior of reinforcement learning agents trading against each other in this environment when given various forecasts.
2018-2020 Princeton University - Whitman College, Princeton, NJ Residential College Adviser (RCA) Worked closely with a group of first year advisees to help them transition to life as Princeton students and develop responsible personal, academic, and social decision-making skills.
2017-2020 Princeton University Writing Center, Princeton, NJ Writing Fellow Offered 150+ individual 50-minute conferences to undergraduate and graduate students across all disciplines to workshop various forms of written work and develop their writing skills.
2019 Google, Mountain View, CA (Summer) Machine Learning Software Engineering Intern Developed a pipeline to incorporate the image content of image based advertisements when deciding which advertisement(s) to display to a user following a Google search query. This enhances user experience and optimizes Google’s advertisement revenue, which comprises 70% of total revenue.
Research Experience
2020–Present Massachusetts Institute of Technology, Cambridge, MA Research Assistant Advisor: Dimitris Bertsimas Research focused on making methodological and algorithmic contributions to discrete optimization and leveraging modern advances in discrete optimization to solve central machine learning problems exactly at scale without using heuristics.
2019-2020 Princeton University, Princeton, NJ Research Assistant Supervisor: Miklos Racz, Yacine Ait-Sahalia and Prateek Mittal Research broadly focused on sequential decision problems in healthcare and in finance, and research focused on developing privacy preserving machine learning methods.
2018-2019 Oxford University, Oxford, United Kingdom Research Intern Supervisor: Aleksandr Sahakyan Research focused on developing novel combinatorial optimization techniques to solve specific problem of interest in computational biology.
2018 Montreal Institute of Learning Algorithms, Montreal, Quebec Research Intern Supervisor: Yoshua Bengio Research focused on reproducing and expanding upon state of the art ResNet results for Computer Vision.
Honors and Awards
2020 INFORMS Undergraduate Operations Research Prize Finalist “Sequential Stochastic Network Structure Optimization With Applications To Addressing Canada's Obesity Epidemic” The award honors a group of students who conducted significant applied or theoretical work in operations research as undergraduate students.
2020 The James Hayes-Edgar Prize in Engineering Awarded by Princeton University to the engineering student who has best manifested excellent scholarship, capacity for leadership and the promise of achievement in engineering.
2020 The Frank Castellana Prize Established in 1999, the prize is awarded by the Princeton Operations Research and Financial Engineering department to a senior for outstanding scholarship and academic achievement.
2020 Richard D. Challener '44 Senior Thesis Prize in Canadian Studies Established in 2000 in honor of Professor Richard D. Challener, Princeton Class of 1944, the Challener prize is awarded by the Faculty Committee on Canadian Studies to an undergraduate senior in any department or program who's senior thesis is of outstanding quality on a topic having substantial relevance to Canadian culture, themes, experiences or issues.
2019 Rhodes Scholarship Finalist Selected as one of 12 finalists for the 2020 Quebec Rhodes Scholarship.
2019 The Class of 1939 Princeton Scholar Award Awarded each year by Princeton University to the undergraduate who, at the end of junior year, has achieved the highest academic standing for all preceding college work at the University 2019 Dr. Angela E. Grant Best Modelling Poster Award “Optimus: A General Purpose Monte Carlo Optimisation Engine in R” Awarded at the 2019 Conference for African American Research in the Mathematical Sciences.
Skills and Activities
Programming Languages: Java, Python, C, C++, R, Julia, Matlab, SQL Optimization Software: JuMP, CPLEX, MOSEK Languages: English (native), French (fluent) Extracurriculars: Chess, Basketball, Fitness
Citizenship Citizen of Canada, Bahamas Lea Kapelevich
Operations Research Center Massachusetts Institute of Technology 77 Massachusetts Avenue, E40-103 Cambridge, MA 02139 OFFICE TEL. 617-955-2086 Email: [email protected]
Education Massachusetts Institute of Technology, Cambridge, MA Candidate for PhD in Operations Research; expected completion, February 2022. GPA: 4.9/5.0 Advisor: Juan Pablo Vielma
University of Auckland, Auckland, New Zealand Bachelor of Engineering with Honors, 2016.
Work Experience
2016 Fisher and Paykel Healthcare, Auckland, New Zealand (Summer) Assistant Product Development Engineer Implemented PDE models for human ventilation to guide development of medical devices.
2015 EROAD, Auckland, New Zealand (Summer) Analytics Intern Applied analytics tools to build reports and models for monitoring system quality and reliability.
Research Experience
2018–Present Massachusetts Institute of Technology, Cambridge, MA Research Assistant Advisor: Professor Juan Pablo Vielma Developing computational tools for challenging optimization problems. My group is currently working on an open source conic optimization solver.
2017-2018 Massachusetts Institute of Technology, Cambridge, MA Research Assistant Supervisor: Professor Dimitris Bertsimas Developed an integer-programming based algorithm for sparse regression with clustered data.
2016-2017 University of Auckland , Auckland, New Zealand Summer Scholarship Student Supervisor: Professor Andy Philpott Applied a distributionally robust optimization approach to a multistage stochastic problem in the New Zealand electricity system.
2016-2016 University of Auckland, Auckland, New Zealand Undergraduate Thesis Supervisor: Professor Andy Philpott and Dr. Ziming Guan Built a new software implementation of a model for the New Zealand hydrothermal scheduling problem. Teaching Experience
2020 Massachusetts Institute of Technology, Cambridge, MA (IAP) Teaching Assistant for Computing for Optimization and Statistics 15.S60 Taught session on Introduction to Julia and JuMP.
2018 Massachusetts Institute of Technology, Cambridge, MA (Fall) Teaching Assistant for Data, Models, and Decisions 15.060 Organized tutorials and office hours, and graded exercises.
Publications
”Low dimensional cones for sum of squares optimization”, with Chris Coey and Juan Pablo Vielma, Working paper, 2020.
”Towards practical generic conic optimization”, with Chris Coey and Juan Pablo Vielma, submitted to Siam Journal on Optimization, 2020.
”Sparse regression over clusters: SparClur”, with Dimitris Bertsimas, Jack Dunn, Rebecca Zhang, submitted to Operations Research Letters, 2020.
”SDDP.jl: a Julia Package for Stochastic Dual Dynamic Programming”, with Oscar Dowson, INFORMS Journal on Computing, 2020.
” Distributionally robust SDDP”, with Andy Philpott, Vitor de Matos, Computational Management Science, 2018.
Skills and Activities
Programming languages: Julia, MATLAB, C, R, VBA, SQL Languages: English, Russian
Citizenship Citizen of New Zealand and Israel Michael Lingzhi Li
Operations Research Center 580 Washington St, Unit 712 Massachusetts Institute of Technology Boston, 02111 77 Massachusetts Avenue, E40-130 857-998-9610 Cambridge, MA 02139 Email: [email protected]
Education Massachusetts Institute of Technology, Cambridge, MA Candidate for PhD in Operations Research; expected completion: Summer 2021. GPA: 5.0/5.0 Advisor: Professor Dimitris Bertsimas
Massachusetts Institute of Technology, Cambridge, MA Master’s in Business Analytics, June 2018. GPA: 5.0/5.0 Focus: Integer Optimization, Statistics, Applications of Machine Learning
University of Cambridge, Cambridge, UK Bachelor of Arts (Hons.) in Mathematics, June 2017. 1st Class Honors (Wrangler); Top 10% of Class
Work and Project Experience
2020–Present CovidAnalytics (COVID-19 Research Effort), Cambridge, MA Team Lead (Epidemic Projections & Policy Evaluations) ⋅ Lead design of DELPHI Epidemiology model for COVID-19; Achieved <10% 1-month out of sample MAPE ⋅ DELPHI part of core CDC models; In use with 5+ hospitals and Janssen Pharmaceuticals (Johnson & Johnson) for trial planning and execution of its leading candidate Ad26.Cov.S
2019–Present Lineage Logistics, San Francisco, CA Machine Learning Scientist ⋅ Led development of Lineage Logistics’ first machine learning system to predict duration-of-stay of shipments; Patent Granted (USPTO Patent Number: 10,796,278) ⋅ Resulting algorithm in all major RDCs of Lineage Logistics; Paper accepted by ICLR 2020
2018 StubHub (Ebay), San Francisco, CA Machine Learning & Quantitative Analyst ⋅ Led development of StubHub’s first machine learning system to predict ticket pricing ⋅ Designed new algorithm for heterogeneous treatment detection that exceeds current state-of- the-art; Paper accepted by Manufacturing & Service Operations Management
2017 Boston Consulting Group, London, UK Summer Associate (Received Full-Time Offer) ⋅ Led data analytics effort in a 9-person team for a $50 million operational transformation case ⋅ Employed Alteryx and Tableau to redesign key KPIs and provide insights into over 25M rows of CSV data; constructed interactive dashboard for global monitoring ⋅ Recommended 10+ actionable business moves to senior management using analytics, creating impact of over $10 million 2016-17 Royal Dutch Shell, London, UK Remote Quantitative Analyst (Received Full-Time Offer) ⋅ Analyzed financial market data using PCA and Support Vector Machines ⋅ Created Deep Learning architecture for predicting supply chain movements in European energy markets using Recurrent Neural Networks and Residual Connections
2016 J.P. Morgan Chase, London, UK Structuring Intern (Received Full-Time Offer) ⋅ Helped team in structuring multiple >$100 million strategic loans and building financial models ⋅ Revamped Python deal monitoring script to reduce compile time by 90% ⋅ 2nd Place in firm-wide trading competition; Best Communication Presentation Award
Research Experience
2017–Present Massachusetts Institute of Technology, Cambridge, MA Research Assistant Advisor: Dimitris Bertsimas My primary research interests fall under two fields: (1) Scalable machine learning algorithms with an interpretability focus, and (2) Application of machine learning and optimization in healthcare and logistics. My past work includes using ideas from discrete optimization to develop new algorithms for regression and matrix completion. Currently, we are focusing on precision healthcare and supply chain automation.
Teaching Experience
2020 Fall Massachusetts Institute of Technology, Cambridge, MA Head Teaching Assistant for Machine Learning under a Modern Optimization Lens (15.095) Leads a group of 4 TAs to design and prepare an online/in-person hybrid teaching class. Conducted weekly recitations on advanced machine learning/optimization topics for 110+ students. Created midterm, final and responsible for mentoring final project. Holds weekly office hours to further engage with students.
2019 Fall Massachusetts Institute of Technology, Cambridge, MA Teaching Assistant for Machine Learning under a Modern Optimization Lens (15.095) Conducted weekly recitations on advanced machine learning/optimization topics for 90 students. Created 6 problem sets, midterm, and responsible for mentoring final project. Holds weekly office hours to further engage with students. Student Rating: 6.2/7.0
2019 Spring Massachusetts Institute of Technology, Cambridge, MA Teaching Assistant for The Analytics Capstone (15.089) Mentored two students in Master of Business Analytics program on “Predicting Disease from Longitudinal Laboratory Data” with Quest Diagnostics
Publications
“Stochastic Cutting Planes for Data-Driven Optimization”, with D. Bertsimas. 2020. Submitted to Mathematical Programming.
“Optimizing Vaccine Allocation to Combat the COVID-19 Pandemic”, with D. Bertsimas, J. Ivanhoe, A. Jacquillat, A. Previero, O. Skali Lami, H. Tazi Bouardi. 2020. Working Paper.
“Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S.”, with E. Ray, N. Wattanachit, J. Niemi, A. Kanji, K. House, E. Cramer, J. Bracher, et al. 2020. Revision at PNAS (Proceedings of the National Academy of Sciences of the United States of America).
“From predictions to prescriptions: A data-driven response to COVID-19”, with D. Bertsimas, L. Boussioux, R. Cory Wright, A. Delarue, V. Digalakis, A. Jacquillat, et al. 2020. Submitted to Health Care Management Science.
“Forecasting covid-19 and Analyzing the Effect of Government Interventions”, with H. Tazi Bouardi, O. Skali Lami, T. Trikalinos, N. Trichakis, and D. Bertsimas, 2020. Major Revision at Operations Research.
”Prescriptive analytics for reducing 30-day hospital readmissions after general surgery”, with D. Bertsimas, I. Paschalidis, and T.Wang. 2020. PLOS One.
“Scalable Holistic Linear Regression”, with D. Bertsimas, 2020. Operations Research Letters.
”Interpretable Matrix Completion: A Discrete Optimization Approach”, with D. Bertsimas, 2019. Major Revision at Operations Research.
”Fast Exact Matrix Completion: A Unifying Optimization Framework”, with D. Bertsimas, 2019. Accepted with Minor Revision at Journal of Machine Learning Research.
”Selecting children with VUR who are most likely to benefit from Antibiotic Prophylaxis: Application of Machine Learning to RIVUR”, with D. Bertsimas and Advanced Analytics Group of Pediatric Urology, 2019. Accepted at Journal of Urology.
”Targeted Workup after Initial Febrile Urinary Tract Infection: Using a Novel Machine Learning Model to Identify Children Most Likely to Benefit from Voiding Cystourethrogram”, with D. Bertsimas, J. Dunn, D. Zhuo, and Advanced Analytics Group of Pediatric Urology, 2019. Journal of Urology, 202(1), 144-152.
”Experimental Evaluation of Individualized Treatment Rules”, with K. Imai, 2019. Submitted to Journal of American Statistical Society.
“Pricing for heterogeneous products: Analytics for ticket reselling”, with M. Alley, M. Biggs, R. Hariss, C. Hermann, G. Perakis, 2019. Accepted at MSOM (with Revision).
“Duration-of-Stay Storage Assignment under Uncertainty”, with E. Wolf, D. Wintz, 2019. Accepted at ICLR 2020.
Honors and Awards
2019 Finalist in 2019 MSOM Practice-Based Paper Competition
2015, 2016 Christine and Hermann Bondi Prize for Mathematics (Top of College)
2015 Finalist in Mathematical Competition in Modeling (Top 0.2%)
2014 Longmeng Scholarship (Surpassing All-time High School Academic Record)
Professional Qualifications and Activities
Associate Member of the Institute and Faculty of Actuaries Programming: Python, Julia, R, Matlab, SQL Optimization/Machine learning: Gurobi, Tensorflow, Pytorch, CPLEX Languages: Mandarin, English (Native), Japanese (Basic to Intermediate) Interests: Piano (ABRSM Grade 8), Swimming, Diving, Mountain Biking
Citizenship Citizen of Canada Zhen Lin
Operations Research Center 235 Albany Street, Ashdown House Massachusetts Institute of Technology Cambridge, MA 02139 77 Massachusetts Avenue, E40-103 617-909-7881 Cambridge, MA 02139 Email: [email protected]
Education Massachusetts Institute of Technology, Cambridge, MA Candidate for PhD in Operations Research; expected completion, June 2024. GPA: 5.0/5.0 Advisor: Prof. Dimitris Bertsimas
The Chinese University of Hong Kong, Shenzhen, Shenzhen, China BS in Statistics, May 2019.
Research Experience
2019–Present Massachusetts Institute of Technology, Cambridge, MA Research Assistant Advisor: Professor Dimitris Bertsimas Optimization and Machine Learning.
2017-2019 Shenzhen Research Institute of Big Data, Shenzhen, China Research Intern Supervisor: Professor Zhi-Quan Luo Design, analysis and applications of optimization algorithms.
Teaching Experience
2020 Massachusetts Institute of Technology, Cambridge, MA (Fall) Teaching Assistant for Machine Learning under a Modern Optimization Lens (15.095) (Graduate level) TA duties: Holding weekly recitations on advanced optimization and machine learning topics for PhD students and masters; Mentoring final project; Creating and grading problem sets, midterm exam, final exam; Holding weekly office hours to assist students.
2019 The Chinese University of Hong Kong, Shenzhen, Shenzhen, China (Spring) Teaching Assistant for Linear Algebra (MAT2040) (Language of instruction: English) Gave weekly tutorials (in English), held weekly office hours, answered students’ questions online.
2018 The Chinese University of Hong Kong, Shenzhen, Shenzhen, China (Fall) Teaching Assistant for Linear Algebra (MAT2040) (Language of instruction: English) Gave weekly tutorials (in English), held weekly office hours, answered students’ questions online.
2017 The Chinese University of Hong Kong, Shenzhen, Shenzhen, China (Fall) Teaching Assistant for Calculus I (MAT1001) (Language of instruction: English) Gave weekly tutorials (in English), held weekly office hours, answered students’ questions online.
2017 The Chinese University of Hong Kong, Shenzhen, Shenzhen, China (Spring) Teaching Assistant for Probability and Statistics I (STA2001) (Language of instruction: English) Gave weekly tutorials (in English), held weekly office hours, answered students’ questions online.
2016 The Chinese University of Hong Kong, Shenzhen, Shenzhen, China (Fall) Teaching Assistant for Calculus I (MAT1001) (Language of instruction: English) Gave weekly tutorials (in English), held weekly office hours, answered students’ questions online.
Publications
”Minimax Design of Constant Modulus MIMO Waveforms for Active Sensing”, with Wenqiang Pu, and Zhi-Quan Luo, published in IEEE Signal Processing Letters, October, 2019.
”Minimax Design of Constant Modulus MIMO Waveforms”, with Wenqiang Pu, and Zhi-Quan Luo, published in The 52nd Asilomar Conference on Signals, Systems, and Computers, October, 2018.
Honors and Awards
2019 Presidential Award for Outstanding Students 2019 (Spring) This Award represents the highest honor the University can bestow on its graduates who have a proven track record of academic excellence and leadership over the period time of their undergraduate study at The Chinese University of Hong Kong, Shenzhen. (The Chinese University of Hong Kong, Shenzhen)
2016 Student Outstanding Performance and Leadership Award The award is to recognize students who have best demonstrated, and have been recognized for, their achievements or contributions in areas such as leadership, student activities, creative and performing arts, campus involvement, or career accomplishments. (The Chinese University of Hong Kong, Shenzhen)
2016-2017 Undergraduate Research Awards Awarded three times. Awarded RMB 10,000 in total. (The Chinese University of Hong Kong, Shenzhen)
2015, 2018 Academic Performance Scholarship – Class C Awarded twice. Awarded RMB 20,000. (The Chinese University of Hong Kong, Shenzhen)
2016, 2017 Academic Performance Scholarship – Class A Awarded twice. Awarded RMB 80,000 each time. (The Chinese University of Hong Kong, Shenzhen)
2015-2018 Dean’s List Awarded four times due to outstanding academic performance. (The Chinese University of Hong Kong, Shenzhen) 2017, 2018 Master’s List of Shaw College Awarded twice due to outstanding academic performance. Awarded RMB 1,000. (The Chinese University of Hong Kong, Shenzhen)
2014 Entrance Scholarship Awarded RMB 47,500. (The Chinese University of Hong Kong, Shenzhen)
Skills and Activities
Programming Skills: Julia, MATLAB, Python, R, C++, C. Optimization/Machine Learning: JuMP, Gurobi, MOSEK, CPLEX Software: Latex Editorial Team, Fall 2014 – Fall 2015, The Chinese University of Hong Kong, Shenzhen Student Helper for Orientation Activities, Fall 2014, Summer 2015, Fall 2015, The Chinese University of Hong Kong, Shenzhen Student Helper and Volunteer of Admission Office, Summer 2015, Fall 2016, The Chinese University of Hong Kong, Shenzhen
Citizenship Citizen of China Xinming Lily Liu
Operations Research Center 70 Pacific Street, Room 907 Massachusetts Institute of Technology Cambridge, MA 02139 77 Massachusetts Avenue, E40-103 607-262-6519 Cambridge, MA 02139 Email: [email protected]
Education Massachusetts Institute of Technology, Cambridge, MA Candidate for PhD in Operations Research; expected completion, June 2025. Advisors: Professor Karen Zheng and Professor Retsef Levi
Cornell University, Ithaca, NY BS in Operations Research and in Computer Science, May 2020. Academic distinctions: summa cum laude, with Honors
Work Experience
2019 Blackstone, New York, NY Summer Technology Analyst Completed a full-stack development in Java Script, C# and SQL for a private equity fund accounting tool.
2018 Amazon Robotics, North Reading, MA Data Engineer Coop Automated data validation and utilized lambda function in Amazon web Service to enable incremental updates in databases. Built Tableau dashboards used by 30 internal teams for production managers to monitor the performance metrics.
2017 ODH, Inc., Princeton, NJ Information Security Analyst Built dashboards to monitor and analyze online activities and user patterns for potential security concerns.
Research Experience
2020–Present Massachusetts Institute of Technology, Cambridge, MA Research Assistant Advisors: Professor Karen Zheng, Professor Retsef Levi Technology adoption within small-holder famers' social network: analyze social network data collected for 75 villages in India and formulate multi-layer leader-follower network structures; model the principal-agent problem for the central planner to schedule influencers to maximize technology adoption rate and for influencers to choose their effort level strategically.
2019-2020 Cornell University, Ithaca, NY Undergraduate Research Assistant Supervisor: Joe Halpern Human behavior modeling and bounded rationality: (1) formulated slot machines as a multi- armed bandit problem and used probabilistic finite automaton to model bounded rationality; designed human-like protocols and showed by simulations that they perform nearly-optimally with only finite states. (2) Formulated the ranger-poacher game as a two-player zero-sum game and proved the Nash equilibrium (NE) is unique; modeled the resource-bounded players as probabilistic finite automaton (PFAs) and showed that they play NE with large memory; designed and conducted human subject experiments on Amazon Mechanical Turk and showed by simulations that PFAs capture human-like behaviors as the number of memory states used decreases.
2017-2018 Cornell Computer Systems Laboratory, Ithaca, NY Undergraduate Research Assistant Supervisor: Christopher Studer Designed an optimized greedy feature selection algorithm, which reaches 95% accuracy using only 32 features out of 256 in total.
Teaching Experience
2020 Cornell University, Ithaca, NY (Spring) Teaching Assistant for Engineering Stochastic Processes (ORIE 3510) Designed the course project and prepared solution code, held weekly office hours, answer students' questions online.
2019 Cornell University, Ithaca, NY (Spring) Teaching Assistant for Optimization II (ORIE 3310) Held weekly office hours, gave weekly recitations, graded weekly assignments.
2018 Cornell University, Ithaca, NY (Spring) Teaching Assistant for Networks II: Market Design (CS 4852) Graded weekly assignments.
2018 Cornell University, Ithaca, NY (Spring) Teaching Assistant for Discrete Structure (CS 2800) Held weekly one-on-one tutoring sessions.
Publications
”Strategic Play by Resource-Bounded Agents in Security Games”, with Joe Halpern, submitted to International Joint Conference on Autonomous Agents and Multi-agent Systems (AAMAS), October, 2020.
”Bounded Rationality in Las Vegas: Probabilistic Finite Automata Play Multi-Armed Bandits”, with Joe Halpern, published in 36th Conference on Uncertainty in Artificial Intelligence (UAI), May, 2020.
”Analog-to-Feature (A2F) Conversion for Audio-Event Classification”, with Emre Gonultas, and Christoph Studer, published in 26th European Signal Processing Conference (EUSIPCO), May, 2018.
Honors and Awards
2019 Tau Beta Pi Scholarship
2019 Cornell Diversity Programming in Engineering Corporate Award The recognition of diversity and inclusion of Cornell engineering students.
2019 Omega Rho A scholastic honor society that recognizes academic achievement among students in the fields of operations research and management science.
2018 Tau Beta Pi The oldest engineering honor society for engineering students in American universities who have shown a history of academic achievement as well as a commitment to personal and professional integrity.
2017 Cornell Early Career Research Scholarship
2017-2020 Cornell College of Engineering Dean's List
Skills and Activities
Programming Skills: Python, Julia, R, SQL, MATLAB, Java Softwares: Latex, Tableau, Word, Excel, PowerPoint Languages: English (proficient), Mandarin Chinese (native) Department Representative of Graduate Womxn at MIT, 2020-present President of INFORMS Cornell Chapter, 2019-2020 Professional Development Chair of Tau Beta Pi Cornell Chapter, 2019
Citizenship Citizen of China Nicholas Renegar
Operations Research Center 225 Chestnut St. Apt. 6 Massachusetts Institute of Technology Cambridge, MA 02139 77 Massachusetts Avenue, E40-103 206-518-0193 Cambridge, MA 02139 Email: [email protected]
Education Massachusetts Institute of Technology, Cambridge, MA Candidate for PhD in Operations Research; expected completion, June 2021. GPA: 5.0/5.0 Advisor: Retsef Levi
Cornell University, Ithaca, NY BSc, Operations Research & BA, Mathematics, May 2010. GPA: 3.8/4.0
Work Experience
2010-2015 Milliman, Inc., Seattle, WA Healthcare Consulting Actuarial Analyst • Predictive analytics to help healthcare organizations plan for the future • Commercial software development (e.g., GlobalRVUs) • Building revenue/funding models.
Research Experience
2019 Google, Inc., Mountain View, CA (Summer) Research Intern Mechanism Design for Google Search Ads
2016–Present Massachusetts Institute of Technology, Cambridge, MA Research Assistant Advisor: Retsef Levi Supply chain analytics, optimization, food safety, healthcare, and internet advertising.
Teaching Experience
2019 Massachusetts Institute of Technology, Cambridge, MA (Winter) Teaching Assistant for Risk Management (15.731) Assisting Students on Course Projects, Grading
2018 Massachusetts Institute of Technology, Cambridge, MA (Fall) Teaching Assistant for Healthcare Lab: Intro to Healthcare Delivery in the US (15.777) Teaching Background Seminars, Assisting Students on Action-Learning Projects, Grading
2018 Massachusetts Institute of Technology, Cambridge, MA (Spring) Teaching Assistant for Introduction to Operations Management (15.761) Teaching Weekly Seminars, Running Simulation-Based Projects, Grading
2010 Cornell University, Ithaca, NY (Spring) Teaching Assistant for Introduction to Game Theory (ORIE 4350) Teaching Weekly Seminars, Grading
Publications
”Testing at the Source: Analytics-Enabled Risk-Based Sampling of Food Supply Chains in China”, with Cangyu Jin , Qiao Liang, Retsef Levi, Stacy Springs, Jiehong Zhou, and Weihua Zhou. Forthcoming in Management Science.
”Supply Chain Network Analytics Guiding Food Regulatory Operational Policy”, with Retsef Levi, Stacy Springs and Tauhid Zaman. Submitted 2019.
”The Second-Price Knapsack Problem: Near-Optimal Real Time Bidding in Internet Advertisement”, with Jon Amar. Submitted 2019. MIT ORC Best Student Paper Award 2020
”The Link Between Food Safety and Zoonotic Disease Risks at Wholesale and Wet Markets in China”, with Qihua Gao, and Retsef Levi. Submitted 2020.
”Food Safety Inspections and the Adoption of Traceability: Evidence from Wholesale Market Surveys in China”, with Cangyu Jin , Qiao Liang, Retsef Levi, and Jiehong Zhou. Submitted 2019.
Skills and Activities
Programming: Python, R, JAVA, C#, Julia
Citizenship Citizen of the United States of America
Fransisca Susan
Operations Research Center 303 3rd St Massachusetts Institute of Technology Cambridge, MA 77 Massachusetts Avenue, E40-103 617-583-3599 Cambridge, MA 02139 Email: [email protected]
Education Massachusetts Institute of Technology, Cambridge, MA Candidate for PhD in Operations Research; expected completion, June 2023. GPA: 5.0/5.0 Advisor: Professor Negin Golrezaei
Massachusetts Institute of Technology, Cambridge, MA BS in Mathematics and Computer Science, minor in Economics, June 2018. GPA: 5.0/5.0
Work Experience
2019 ChatALIA, Cambridge, MA (Summer) Co-founder, Tech Advisor - Currently building Indonesia first AI-powered intelligent chat money assistant - Awarded Sandbox Innovation Fund
2018 Goldman Sachs, New York, NY (Summer) Securities Summer Analyst Ten-week rotational internship program involving five-week segments with two teams.
2017 Goldman Sachs, New York, NY (Summer) Strategist Summer Analyst - Ten-week rotational internship program involving five-week segments with two teams. - Implemented a more efficient order routing for FX within exchanges at multiple location and decreased latency by ~40%. - Automated client-tailored sector hedge fund VIP custom basket creation. Created a platform that allows clients to adjust various metrics such as risk to reward ratio and maximum draw down when customizing their custom baskets.
2017 Traveloka, Jakarta, Indonesia (Winter) Data Scientist Developed a recommendation system algorithm for the hotel business using various machine learning and statistics approach. Built a recommender system for the hotel business using various statistics and machine learning approach, increased customer conversion rate in hotel business by 1.7x.
2016 Twitter, Inc., San Franciscso, CA (Summer) Software Engineering Intern - Conducted feature experiments on “who to follow” modules for new users and resurrected users, implemented a follow recommendation system that increased new user retention by 56%. - Created a web re-onboarding flow to refine social graph of resurrected users that increased resurrected user retention rate by 12% over one-month period. - Implemented iOS Datalytics feature that measures the amount of data usage spent on Twitter and gives alerts when daily limit is reached.
Research Experience
2019-Present Massachusetts Institute of Technology, Cambridge, MA Research Assistant Advisor: Professor Negin Golrezaei - Currently working on various assortment learning and product ranking problems using a data-driven optimization method and various modeling, machine learning, and statistical techniques. - Currently working on online variable selection methods for assortment optimization.
2018-2019 Lab of Financial Engineering, MIT Sloan School of Management, Cambridge, MA Research Assistant Advisor: Professor Andrew Lo - Developed a multi-party computation algorithm to share secretive financial cyber risk data securely across companies. - Simulated, formulated and created heuristics to solve large-scale pharmaceutical portfolio stochastic optimization.
2017-2018 Spoken Language Systems Group, MIT CSAIL, Cambridge, MA Undergraduate Research Assistant Supervisor: Professor James Glass, Yonatan Belinkov - Developed a classifier to investigate how well a Neural Machine Translation model infers multiple senses from homonyms, investigated the effects of the different layers, target languages, and model architecture on word disambiguation ability. - Improved Neural Machine Translator’s accuracy in translating English homonyms to French and German by 3%.
2015 Massachusetts Institute of Technology, Cambridge, MA Undergraduate Research Assistant Supervisor: Professor Jack Dennis Implemented parallel BFS Algorithm on Fresh Breeze Machine, a new multiprocessor chip architecture, built a benchmark for testing codes on Fresh Breeze Machine as a multi-core system.
Teaching Experience
2018 Massachusetts Institute of Technology, Cambridge, MA (Spring) Teaching Assistant for Introduction to Machine Learning, 6.036/6.862 Duties: holding office hours, making and grading homework and exams
Publications
”Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization”, with R. Niazadeh, N. Golrezaei, J. Wang, and Badanidiyuru, submitted to Management Science, July, 2020.
”SCRAM: A Platform for Securely Measuring Cyber Risk”, with L. de Castro, A.W. Lo, T. Reynolds, V. Vaikuntanathan, D. Weitzner, N. Zhang, submitted to Harvard Data Science Review, October, 2020.
Working Papers
”Active Learning for a Non-parametric Choice Model”, with N. Golrezaei and D. Kempe.
Honors and Awards
2019-2020 Tau Beta Pi Fellow
2017-2018 Keel Foundation Undergraduate Research and Innovation Scholars
2017 William Lowell Putnam Mathematical Competition Honorable Mention
2012-2014 International Mathematics Olympiad 1 Silver, 2 Bronze Medals
2014-2018 Presidential Scholarship
Skills and Activities
Languages: Indonesian (native), English, Malay, Chinese (basic) Programming: Python, Julia, Gurobi, Java, Scala, R, C++, Javascript, HTML, CSS INFORMS MIT Chapter, Treasurer, 2019 Association of Indonesian Student in New England, President, 2018-Present
Citizenship Citizen of Indonesia Prem Talwai
Operations Research Center 250 Kendall St # 1303 Massachusetts Institute of Technology Cambridge , MA 02142 77 Massachusetts Avenue, E40-103 916-833-2237 Cambridge, MA 02139 Email: [email protected]
Education Massachusetts Institute of Technology, Cambridge, MA Candidate for PhD in Operations Research; expected completion, May 2024. Advisor: David Simchi-Levi
Cornell University, Ithaca, NY B.A. Mathematics (summa cum laude), May 2019. Thesis title: Taylor-Based Approximate Dynamic Programming for Markov Decision Processes
University of Oxford, Oxford, England Visiting Student, Mathematics, 2017-2018. Relevant Coursework: Stochastic Analysis and PDEs, Continuous Martingales and Stochastic Calculus, Graphical Models, Probability and Statistics for Network Analysis
Research Experience
2020–Present Massachusetts Institute of Technology, Cambridge, MA Research Assistant Advisor: David Simchi-Levi Exploring bandit algorithms for contextual learning using kernel mean embeddings
2019-2020 Columbia Business School, New York, NY Research Associate Supervisor: Carri Chan, Paul Glasserman (1) Examined dynamic nurse staffing strategies for emergency departments through theoretical and computational analyses of a fluid queuing model (with Prof. Carri Chan); (2) Explored characterizations of optimal consideration sets for the Shannon model of rational inattention (with Prof. Paul Glasserman)
2018 Cornell Tech, New York, NY (Summer) Research Intern Supervisor: Itai Gurvich Developed novel policy/value iteration algorithms (with convergence guarantees) for discounted, infinite-horizon Markov decision processes.
2017-2018 Alan Turing Institute, London, UK Undergraduate Researcher Supervisor: Mihai Cucuringu Investigated spectral clustering algorithms for stochastic block graph models
2017 Institute for Pure and Applied Mathematics, Los Angeles, CA (Summer) Research Intern Supervisor: Shantanu Joshi Developed a novel metric learning technique to improve precision and recall in face verification, by applying a new stochastic proximal gradient approach
2016-2017 Cornell Center for Applied Math, Ithaca, NY Undergraduate Researcher Supervisor: Alex Vladimirsky Compared convergence properties of Semi-Lagrangian and Field D* algorithms for solving the Eikonal PDE
2016 Cornell University , Ithaca, NY (Summer) REU Student Supervisor: Robert Strichartz Derived a trace theorem for the Laplacian on the Sierpinski gasket
Publications
”Utilizing Partial Flexibility to Improve Emergency Department Flow: Theory and Implementation”, with Carri Chan, Vahid Sarhangian, and Kriti Gogia, July 2020 (submitted).
”A trace theorem for Sobolev spaces on the Sierpinski Gasket”, with Shiping Cao, Shuangping Li, Robert Strichartz, Communications on Pure and Applied Analysis, July 2020.
“Taylor-Based Approximate Dynamic Programming for Markov Decision Processes”, with Itai Gurvich, May 2019 (working paper).
Presentations
”Stochastic Proximal Gradient Methods for Metric Learning” (talk), presented at 2018 Joint Mathematics Meetings.
“A Trace Operator for the Laplacian on the Sierpinski Gasket”(poster), presented at the 2016 Young Mathematicians Conference.
“Model order reduction for cell signaling pathways: An investigation of G-protein coupled receptor signaling” (poster), presented at the 2015 AACR Special Conference: Computational and Systems Biology of Cancer.
“An investigation of the p53 ubiquitin-proteasome system using a novel non-steady-state enzyme kinetic model” (talk), presented at the 2013 Society for Mathematical Biology Annual Meeting.
Honors and Awards
2019 Kieval Prize Highest honor bestowed by Cornell’s math department on an undergraduate
2015 Semifinalist, Intel Science Talent Search
2014 4th Place Grand Award, Mathematics, Intel International Science and Engineering Fair
Skills and Activities
Programming: Python, Java, MATLAB, Mathematica
Citizenship Citizen of United States of America Holly Wiberg
Operations Research Center 14 Chandler St. #3 Massachusetts Institute of Technology Somerville, MA 02144 77 Massachusetts Avenue, E40-103 781-686-6849 Cambridge, MA 02139 Email: [email protected]
Education Massachusetts Institute of Technology, Cambridge, MA Candidate for PhD in Operations Research; expected completion, May 2022. GPA: 5.0/5.0 Research Interests: Machine Learning and Optimization in Medicine Advisor: Dimitris Bertsimas
Cornell University, College of Engineering, Ithaca, NY BS in Operations Research and Engineering, May 2016. Summa Cum Laud; Cumulative GPA 4.17/4.3, Major GPA 4.24/4.3
University of Edinburgh, Edinburgh, Scotland Semester Abroad, May 2015. Coursework in Mathematics and Statistics
Work Experience
2016-2017 athenahealth, Watertown, MA Member of Technical Staff, Data Science Served as an analytics liaison for a major product release. Built self-serve reporting tools to provide key stakeholders with high visibility into provider performance and migration progress for the project, enabling targeted support for clients and facilitating smooth completion of the migration.
2015 athenahealth, Watertown, MA (Summer) Intern, Data Engineering Developed a metric to quantify the productivity of healthcare providers, and established benchmarks for productivity based on identified key drivers. Delivered recommendations for application of the metric in both internal reporting and client-facing evaluation.
Research Experience
2017–Present Massachusetts Institute of Technology, Cambridge, MA Research Assistant Advisor: Dimitris Bertsimas Leveraging clinical and genomic data to develop better treatment response predictions and recommendations using optimization and machine learning techniques, particularly in oncology. Developing interpretable machine learning methods that allow for greater model transparency with a focus on clinical applications.
2015-2016 Cornell University, Ithaca, NY Research Assistant Supervisor: Davis Shmoys, Shane Henderson Collaborated with doctoral students in design of a simulation optimization model to improve the allocation of docks and bikes across stations in a New York City bike-sharing system using gradient-like heuristic methods. Improved model runtime and constructed a fluid model starting solution based on historical data.
Teaching Experience
2019-2020 Massachusetts Institute of Technology, Cambridge, MA (Spring) Teaching Assistant for 15.727 - The Analytics Edge TA for an Executive MBA course in Spring 2019 and Spring 2020. Led recitations and weekly office hours. Advised student teams on final projects and technical topics.
2020 Hartford HealthCare, Hartford, CT (January) Teaching Assistant for The Analytics Edge in Healthcare Co-developed an Executive Education course introducing analytics methods to 100 healthcare professionals (medical and administrative) at Hartford Hospital. Created course syllabus and lecture content, and worked on course administration. Co-wrote an accompanying textbook detailing analytics methods and case studies with a focus on healthcare applications.
2016 Cornell University, Ithaca, NY Teaching Assistant for ENGR 1101: Engineering Applications of Operations Research TA for an introductory undergraduate OR course. Led lab sessions and weekly office hours. Graded homework and exams.
Publications ”Machine Learning in Oncology: Methods, Applications, and Challenges”, with Dimitris Bertsimas. To appear in JCO Clinical Cancer Informatics.
”COVID-19 Mortality Risk Assessment: An International Multi-Center Study”, with Dimitris Bertsimas, Galit Lukin, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Bartolomeo Stellato, Sara Gonzalez-Garcia, Carlos Luis Parra-Calderón, The Hellenic COVID-19 Study Group, Kenneth Robinson, Michelle Schneider, Barry Stein, Alberto Estirado, Lia a Beccara, Rosario Canino, Martina Dal Bello, Federica Pezzetti, and Angelo Pan. Under review.
”From predictions to prescriptions: A data-driven response to COVID-19”, with Dimitris Bertsimas, Leonard Boussioux, Ryan Cory Wright, Arthur Delarue, Vassilis Digalakis Jr., Alexandre Jacquillat, Driss Lahlou Kitane, Galit Lukin, Michael L Li, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Theodore Papalexopolous, Ivan Paskov, Jean Pauphilet, Omar Skali Lami, Bartolomeo Stellato, Hamza Tazi Bouardi, Kimberly Villalobos Carballo, and Cynthia Zheng. Under review.
”Interpretable Clustering: An Optimization Approach”, with Dimitris Bertsimas and Agni Orfanoudaki. Machine Learning, July 2020.
"Prediction of cervical spine injury in young pediatric patients: an optimal trees artificial intelligence approach", with Dimitris Bertsimas, Peter Masiakos, and Konstantinos Mylonas, Journal of Pediatric Surgery, March 2019.
"Simulation Optimization for a Large-Scale Bike-Sharing System", with Nanjing Jian, Daniel Freund, and Shane Henderson, 2016 Winter Simulation Conference in Washington, D.C., December 2016. Honors and Awards
2019 National Science Foundation Graduate Student Research Fellowship
2017 Henry Gabbay Fellowship, MIT Sloan School of Management
2016 Byron W. Saunders Prize, Cornell University
2015 Omega Rho Honor Society, Cornell University ORIE Department
2014 Tau Beta Pi, Cornell University
Skills and Activities
Programming: Julia, R, Python, SQL. MIT INFORMS Chapter President, 2017. Graduate Student Council Representative, 2017.
Citizenship Citizen of the United States of America Qingyang Xu
Operations Research Center 20 Child Street, Apt. 1619 Massachusetts Institute of Technology Cambridge, MA 02141 77 Massachusetts Avenue, E40-103 650-804-3938 Cambridge, MA 02139 Email: [email protected]
Education Massachusetts Institute of Technology, Cambridge, MA Candidate for PhD in Operations Research; expected completion, June 2022. GPA: 5.0/5.0 Member of MIT Laboratory for Financial Engineering (LFE). Advisor: Andrew W. Lo
Stanford University, Stanford, CA BS in Physics (Honors) and in Mathematical and Computational Science, June 2017. University Distinction
Work Experience
2020 DAMO Academy, Alibaba Group, Seattle, WA (Summer) Research Intern Applied deep learning models for time series forecast and anomaly detection.
Research Experience
2018–Present Massachusetts Institute of Technology, Cambridge, MA Research Assistant Advisor: Andrew W. Lo Current research spans three areas: (1) apply machine learning and financial engineering to design a large portfolio of high-risk investments in biomedical clinical trials; (2) develop novel Bayesian framework to optimize clinical trials for severe diseases and highly infectious epidemic such as COVID-19; (3) analyze trader behavior from their real-time physiological characteristics. Program in Python/R/Matlab.
2017-2018 Cornell University, Ithaca, NY Research Assistant Supervisor: James Sethna Research in the intersection of theoretical physics and machine learning.
2016-2017 Stanford Institute for Theoretical Physics, Stanford, CA Research Assistant Supervisor: Peter Graham Investigated cosmological implications (such as axion dark matter density) of the Relaxion model as a viable solution to Electroweak Hiearchy Problem.
2014-2016 Enrich Xenon Observatory, Stanford, CA Research Assistant Supervisor: Giorgio Gratta Applied machine-learning algorithms to improve data analysis of the EXO-200 experiment to detect neutrinoless double-beta decay. Program in Python/C++.
Teaching Experience
2020 Massachusetts Institute of Technology, Cambridge, MA (Fall) Teaching Assistant for Healthcare Finance (15.482) Held weekly recitation, updated lecture notes and textbook, created assignment, developed novel Zoom class teaching technologies.
2018 Massachusetts Institute of Technology, Cambridge, MA (Fall) Teaching Assistant for Analytics Edge (15.071x) Taught data analytics in R and answered students' questions on online forum.
2018 Cornell University, Ithaca, NY (Spring) Teaching Assistant for Mechanics and Heat (PHYS 1112) Organized weekly discussion sessions and hands-on labs. Graded homework assignments and exams.
2015 Stanford University, Stanford, CA (Spring) Teaching Assistant for Practical Computing for Scientists (Physics 91SI) Taught scientific Python programming with emphasis on physics research skills.
Publications
"Bayesian Adaptive Clinical Trials for Anti-Infective Therapeutics during Epidemic Outbreaks", with S. Chaudhuri, D. Xiao, A. Lo, 2020. Harvard Data Science Review, Special Issue of COVID-19.
"Fair and responsible drug pricing: A cast study of Radius Health and abaloparatide", with A. Lo, 2020. Journal of Investment Management, 18(1), 90-98.
"Accelerating Therapeutic Innovation in Glioblastoma Treatments via NBTS Venture Fund", with K. Siah, K. Tanner, O. Futer, J. Frishkopf, A. Lo, 2020. Submitted.
"Visualizing probabilistic models in Minkowski space: an analytical coordinate embedding", with H. Teoh, K. Quinn, J. Kent-Dobias, C. Clement, J. Sethna, 2020. Physical Review Research, 2, 03321.
"Search for 2υββ decay of 136Xe to the 01+ excited state of 136Ba with the EXO-200 liquid xenon detector", with J. Albert and EXO collaboration, 2016. Physical Review C, 93, 035501.
Honors and Awards
2019 2nd Place, MIT FinTech Datathon (Spring) Designed novel quantitative trading strategy for fixed-income securities using Random Forest and Cox–Ingersoll–Ross model.
2017 Cornell Graduate Fellowship (Fall)
2017 University Distinction, Class of 2017 (Spring) Awarded to the top 15% of the graduating class based on cumulative GPA.
2016 David S. Levine Award (Spring) Presented annually in recognition of the top physics undergraduate student.
2016 Undergraduate Major Research Grant (Spring) "Cosmological Relaxation Solution to the Electroweak Hierarchy Problem" Presented to outstanding research proposals of undergraduate Honors Theses.
Skills and Activities
Reviewer: Harvard Data Science Review, Journal of Finance, IEEE International Conference on Data Mining (ICDM) 2020 Languages: Fluent in Chinese (native), English and Japanese Programming: Python, Julia, R, Matlab, C++, SQL Optimization/Machine Learning: Gurobi, PyTorch, TensorFlow President and Co-Founder, MIT Chinese Music Ensemble, 2018 to present Interests: Gu-zheng, Running, Reading, Chinese Art and Culture
Citizenship Citizen of People's Republic of China