Dewang Agarwal
Dewang is a first-year doctoral student. His interest lies in leveraging modern tools to create data-backed solutions to impact health. In the past, he has worked across industries ranging from consulting, to finance, to supply chain and logistics. In his free time, he loves watching movies and writing poetry. He graduated from Georgia Tech with a Bachelors in Industrial Engineering and a Masters in Operations Research.
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Judith Brugman
Judith is a third-year PhD student at Tilburg University (The Netherlands), currently advancing her research during a visit at MIT. Her work primarily focuses on robust optimization, with a recent specialization in machine learning, and has a special interest in healthcare. Judith completed her Master’s degree in Business Analytics and Operations Research at Tilburg University, where she developed a strong foundation in advanced analytical techniques and decision-making methodologies.
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Bernard Burman
Bernard has been immersed in Machine Learning in healthcare for the past two years. His focus is in using computer vision to detect different health conditions. While taking classes at MIT, Bernard joined HAIM to continue his work within the healthcare industry and gain experience designing multi-modal systems. He grew up in southern Spain and the South Florida area, where he attended undergrad at the University of Miami.
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Kimberly Villalobos Carballo
An MIT instructor and an incoming Assistant Professor at NYU, Kimberly’s research integrates optimization and machine learning tools to enhance the performance of conventional algorithms. She is passionate about healthcare applications, and much of her research has been inspired by collaborations with hospitals (including work with Hartford Hospital on predictive machine learning models for length of stay reduction and identification of life-threatening patient events) that aim to improve the quality of their services and operations.
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Ikram Chairi
Ikram Chairi is an Assistant Professor in the College of Computing at UM6P and participates in the HAIM project as an affiliate scholar at the Sloan School of Management at MIT, under the supervision of Prof. Bertsimas. Her research interests focus on Data Analysis and Machine Learning, particularly in the development of new training algorithms that address limitations in data quality and quantity. She earned an engineering degree in Statistics and Data Warehousing in 2010, followed by a PhD in Machine Learning and Data Analysis in 2014 from Abdelmalek Essaâdi University. In 2015, she was one of two individuals from North Africa selected for an Erasmus-Mundus Postdoctoral Research fellowship at the GIAA (Laboratory of Applied Artificial Intelligence) at Carlos III University of Madrid. Since joining UM6P, Ikram has been especially interested in applying AI in various fields, particularly in medicine.
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Yubing Cui
Yubing is a second-year doctoral student at MIT’s Operations Research Center. She earned her bachelor’s degree in Honors Mathematics and Computer Science from the University of Michigan, Ann Arbor. She has engaged in Machine Learning research since her undergraduate studies, covering theoretical foundations, algorithm development, and real-world applications, and has expanded into optimization and generative AI as new focus areas in her PhD work. She is passionate about applying these techniques in healthcare to make a positive impact in the real world.
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Lisa Everest
Lisa is a third-year doctoral student and her research integrates optimization and machine learning with applications in healthcare and special interest in prescription. She previously worked for three years as a quantitative analyst in Goldman Sachs' Special Situations Group in private asset management. She received bachelor’s degrees in mathematics and management from MIT. In her free time, she enjoys snowboarding, figure skating, and baking.
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Carol Gao
Carol is a first-year graduate student. She has collaborated with Hartford Healthcare on projects including liver injury prediction, infections prediction, and optimal surgery scheduling. She received her bachelor's degree in mathematics and economics from Smith College.
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Jiayi Gu
Jiayi is a third-year doctoral student with research interests that center around the synergy of machine learning and optimization in healthcare applications. She holds a bachelor's degree in industrial engineering and global health studies from Northwestern University, and is particularly interested in using holistic, data-driven approaches to address public health challenges.
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Emily Hahn
Emily completed her Master of Business Analytics degree at the MIT Sloan School of Management in August 2024. She is interested in applying data analytics and machine learning to improve healthcare efficiency and effectiveness. Before attending MIT, she earned a Bachelor of Science in Operations Research from Cornell University.
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Joanna Kondylis
Joanna is a fourth-year undergraduate student at MIT, majoring in electrical engineering and computer science. She has previous research experience in computational biology, including a project in Harvard’s Systems Biology Department, where she studied the effects of specific kinases on cancer metastasis. Before joining Professor Bertsimas's lab, Joanna also interned at the National Institutes of Mental Health, analyzing large fMRI datasets to predict the onset of adolescent depression. Currently, in Professor Bertsimas’s group, Joanna is collaborating with the Hartford Hospital system on a project aimed at predicting mental illnesses from MRI data.
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Rohan Kumar
Rohan is a fourth-year undergraduate at Boston University studying computer engineering. His academic experience has been in machine learning, data structures, and applied mathematics. He is currently working on projects to apply machine learning to healthcare and cloud computing.
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Yu Ma
A final-year doctoral candidate, Yu has been collaborating with Hartford Hospital on early warning index and cardiovascular surgery. She has authored and co-authored research articles in Nature npj Digital Medicine, JAMA Surgery, Lancet Oncology, International Journal of Radiation Oncology and JCO Clinical Cancer Informatics. She was part of the original HAIM team that earned the Cognex prize at MIT’s Machine Intelligence for Manufacturing and Operations Symposium, and was the graduate student representative on the MIT committee for the use of humans as experimental subjects.
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Muhammad Maaz
Muhammad Maaz is an MD/PhD student at the University of Toronto. He is interested in optimization and machine learning for healthcare applications.
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Georgios (George) Margaritis
George is a fourth-year doctoral student, with research interests centered around AI for Healthcare and AI for Optimization. He finished his undergraduate degree in Electrical & Computer Engineering at the Technical University of Crete, achieving the highest grade in the school’s 30-year history. George has also considerable experience in developing and scaling AI tools for real-world applications, which was gained through roles in both startups and major companies like Netflix.
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Nidhish Nerur
Nidhish is pursuing a Master's of Business Analytics Degree at the MIT Sloan School of Management. He is passionate about leveraging data science and machine learning to improve patient outcomes and healthcare operations. He is interested in researching further applications of predictive modeling in the healthcare sector. Prior to MIT, he received a bachelor's degree in mathematics and business analytics from the University of Texas at Austin.
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Catherine Ning
Catherine is a second-year Ph.D. student at the Operations Research Center, MIT. She graduated in June 2023 from the University of Oxford with a four-year integrated M.Eng. in Engineering Science. Born and raised in Luxembourg, Catherine has always been driven by curiosity, pursuing a broad range of interests and academic subjects, and she is fascinated by interdisciplinary fields. Recently, her research has focused on optimization and enhancing diagnostic and survival models in biomedicine and healthcare. She has led the HAIM project on the prediction of LVEF for the past year, which has inspired her to contribute to a more inclusive and universal healthcare system through data analysis, the multi-faceted capabilities of AI models, and shared knowledge.
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Phevos Paschalidis
Phevos is a senior at Harvard University studying computer science. He has been working with Hartford Hospital for more than a year to apply interpretable machine learning techniques to prescribe the optimal valve type to minimize post-operative pacemaker risk during Transcatheter Aortic Valve Replacement (TAVR) procedures. His other research interests include theoretical computer science and reinforcement learning.
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Periklis Petridis
Periklis Petridis is a fourth-year PhD candidate at MIT's Operations Research Center, where he is advised by Prof. Dimitris Bertsimas. In a few words, his research primarily focuses on: 1) Applications of machine learning and natural language processing to tackle real-life challenges in healthcare. 2) Large-scale network design, capacity expansion, and investment planning for power systems with renewable energy, with a focus on robustness against uncertainty. Prior to MIT, his undergraduate studies were in Electrical & Computer Engineering in Aristotle University of Thessaloniki. There, he was member of Aristurtle, the first Formula Student Driverless team in Greece, where he worked on Computer Vision and Optimal Control for Path Tracking.
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Matthew Peroni
Matthew is a third-year doctoral student. His research is focused on building interpretable, multi-modal, and robust machine learning systems for high-stakes decision making, especially in the healthcare domain. Prior to graduate school, he worked in industry as a machine learning engineer for a healthcare AI start-up. He received his bachelor’s in mathematics and computer science from Cornell University.
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Konstantina Rasvani
Konstantina is a fourth-year undergraduate student at MIT studying business analytics and mathematics. Her research interests include optimization and machine learning. She is particularly interested in applying data analytics in healthcare. Outside of academics, she loves to travel.
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Vasiliki “Vassilina” Stoumpou
A third-year doctoral student, Vassilina grew up in Greece. She finished her five-year undergrad studies at the National Technical University of Athens in electrical and computer engineering, with a major in computer science. She worked at Tesla as an associate electrical design engineer before coming to MIT, where she is currently working on projects that focus on leveraging machine learning techniques in healthcare.
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Cindy Wang
Cindy is a third-year doctoral student with research interests centered around machine learning and optimization, particularly in healthcare applications. She holds a bachelor’s degree in applied mathematics from Columbia University. She enjoys biking, playing the piano, and hiking in her free time.
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Karl Zhu
Karl is a second-year doctoral student specializing in operations research. Before joining MIT, he was a graduate research assistant in the Department of Engineering Science at the University of Auckland, New Zealand, where he gained substantial experience in applying statistical and optimization models. His work earned him the Best Presentation of the Young Practitioner’s Prize at the 2022 Operations Research Society of New Zealand conference. Karl has also developed multiple machine learning and optimization software tools used in both academic and industry research. He holds a Bachelor of Engineering degree in Engineering Science from the University of Auckland.
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