Members
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Elham Azizi
Principal Investigator Elham is the Herbert and Florence Irving Associate Professor of Cancer Data Research (in the Irving Institute for Cancer Dynamics) and Associate Professor of Biomedical Engineering at Columbia University. She is also affiliated with the Department of Computer Science, Data Science Institute, and the Herbert Irving Comprehensive Cancer Center. Elham holds a BSc in Electrical Engineering from Sharif University of Technology, an MSc in Electrical Engineering and a PhD in Bioinformatics from Boston University. She was a postdoctoral fellow in the Dana Pe'er Lab at Columbia University and Memorial Sloan Kettering Cancer Center. Her multidisciplinary research utilizes novel machine learning techniques and single-cell genomic and imaging technologies to study the dynamics and circuitry of interacting cells in the tumor microenvironment. She is a recipient of the Vilcek Prize for Creative Promise in Biomedical Science, Takeda/NYAS Early-Career Innovator in Science Award, Allen Distinguished Investigator Award, Chan Zuckerberg Initiative SDL Award, NSF CAREER Award, Tri-Institutional Breakout Prize for Junior Investigators, NIH NCI Pathway to Independence Award, American Cancer Society Postdoctoral Fellowship, and IBM Best Paper Award at the New England Statistics Symposium. Curriculum Vitae. Teaching: BMCS 4480 Statistical Machine Learning in Genomics (Fall Semester) BMCS 4575 High Dimensional Statistics for Biomedical Data (Spring Semester) |
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Lingting Shi
NCI T32 Postdoctoral Fellow (co-mentored with José McFaline-Figueroa) Lingting is a postdoctoral research scientist at the Herbert and Florence Irving Institute for Cancer Dynamics. She received a BS in biomedical engineering at Rutgers University, where she worked on developing an in vitro approach to identifying skin sensitizers with machine learning tools in Dr. Martin Yarmush’s Lab. Then for her Ph.D. degree, she joined Dr. Lance Kam’s lab at Columbia BME to study the mechanosensing of regulatory T cell induction for the generation of Tregs to treat autoimmune diseases. Now, Lingting is very excited to study cancer with computational approaches and explore the interaction between the immune system and cancer in the Azizi lab! Outside of the lab, Lingting enjoys cycling, camping, traveling, and exploring NYC. |
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Aaron Zweig
Damon Runyon Quantitative Biology Postdoctoral Fellow (co-mentored with David Knowles) Aaron is a postdoctoral research scientist at the Herbert and Florence Irving Institute for Cancer Dynamics, also jointly affiliated with the New York Genome Center. Previously, he studied mathematics for his bachelor’s degree at Stanford University, followed by a master’s degree at Stanford in Stefano Ermon’s lab doing research on graph neural networks. Aaron completed his PhD in Joan Bruna’s lab at NYU, where his research concerned approximation bounds and learnability guarantees of permutation-invariant networks. He is very keen to apply his computational background in the Azizi lab, utilizing geometric deep learning tools for causal inference of gene regulatory networks and spatial transcriptomics. |
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Yinuo Jin MS/PhD Student, Columbia Presidential Fellow Yinuo is currently pursuing his PhD in BME after completing his BS in Computer Science at Columbia. He is interested in applying computational and statistical methods to sequence analysis, especially processing single-cell and long-read sequencing data. |
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Ioana (Lia) Lia MS/PhD Student (co-advised with José McFaline-Figueroa) Ioana is currently pursuing her PhD in the Biomedical Engineering department at Columbia University. She completed her undergraduate degrees in Applied Mathematics and Biomedical Engineering at Columbia, where she became interested in better understanding cancer and its possible solutions. After working on engineering bacteria to treat cancer in the Danino lab, Ioana is hoping to use single cell genomics and machine learning methods to gain more insight into the disease progression and treatment response in cancer; and she is very excited to do so under the guidance of Prof. Azizi and Prof. McFaline-Figueroa. |
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Justin Hong PhD Student Justin is a Columbia PhD student in the CS department. He completed his undergrad and Master’s at UC Berkeley in 2020 where he studied Computer Science & Molecular and Cellular Biology with an emphasis in Immunology. Justin has experience developing methods for single-cell data analysis in both the Song Lab and the Yosef Lab at UC Berkeley. He hopes to continue developing computational methods to uncover valuable biological insights in the Azizi Lab! |
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Kevin Hoffer-Hawlik
MS/PhD Student (co-advised with José McFaline-Figueroa), NSF Graduate Research Fellow and Blavatnik Fellow Kevin graduated from Dartmouth College with a BA in Biomedical Engineering with High Honors and is an entering MS/PhD student in the Biomedical Engineering Department at Columbia. As an undergraduate, he conducted research using data science and machine learning to improve emerging biomedical imaging modalities such as fluorescence-guided surgery and photoacoustic imaging in breast cancer. After graduating, Kevin joined ClearView Healthcare Partners as a strategy consultant to advise biotechnology and pharmaceutical companies. He is interested in advancing novel therapeutics in cancer and other intractable diseases. He joins the Azizi and McFaline-Figueroa labs to further their work in machine learning and cancer biology dynamics. |
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Mingxuan Zhang
PhD Student (co-advised with Andrea Califano) Ming is a Ph.D. student in the Molecular Therapeutics and Systems Biology Departments at Columbia University Medical Center. He completed his BS in Computer Science and Mathematics from UC San Diego in 2019 and his MS in Computational Biology from Cornell University in 2022. Mingxuan has experience developing deep learning and statistical models for cancer genomics data at Memorial Sloan Kettering Cancer Center. His research focuses on developing novel probabilistic learning models for multi-omics data that decode cancer dynamics under molecular therapies with interaction networks and graphs. He is excited to create interpretable machine learning models that offer quantitative perspectives on systems onco-pharmacology in the Azizi and Califano labs. |
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Khushi Desai PhD Student Khushi is currently pursuing an PhD in Computer Science. She previously received her MS in CS at Columbia and majored in CS at the University of California, Berkeley. Previously, her research focused on deep learning and computer vision architectures. At the Azizi lab, she hopes to develop her interest in developing these models for biomedical advancements. In her free time, Khushi enjoys painting, running, and scuba diving! |
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Neeha Kothapalli MD-PhD Student (co-advised with Ben Izar) Neeha is an MD-PhD student at Columbia. Originally from Kansas, she graduated from Yale University with a double major in Molecular Biophysics & Biochemistry and Statistics & Data Science. As an undergrad, she discovered her interests in computational biology while working in the Kaminski lab studying chronic lung diseases. Under the guidance of Profs. Azizi and Izar, she is excited to develop computational tools to address complex questions in genomics and cancer biology! |
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Joshua Myers PhD student starting in Fall 2025 Josh completed his BS in Computer Science at Rensselaer Polytechnic Institute in 2022 with a focus on machine learning applications and received his MS degree in the Biomedical Engineering Department at Columbia. Josh has experience in developing deep learning models combining clinical data and imaging techniques for clinical usage such as surgical navigations. He is currently interested in researching interpretable machine learning models that contribute to identification of biological systems for the Azizi lab. |
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Sopho Kevlishvili PhD student starting in Fall 2025 Sopho comleted her MS in Computer Science at Columbia. As an undergrad, she studied biomedical engineering and computer science also at Columbia University. She is interested in machine learning, artificial intelligence, and their applications to medicine and computational biology. She aspires to work on developing novel machine learning methods to answer research questions regarding diseases and treatment methods. In her free time she enjoys visiting museums and exploring New York. |
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Nicolas Beltran-Velez
Collaborating PhD Student Nicolas is a Computer Science Ph.D. student at Columbia University interested in Bayesian Statistics, Reinforcement Learning and Probabilistic Programming. Previously, he worked at Fero Labs where he focused on developing new machine learning algorithms for improving process efficiency in industrial settings with the aim of reducing CO2 emissions. More generally he is interested in applications of AI and Machine Learning to high-impact domains that contribute positively to society. Nicolas holds a B.A in Computer Science, Mathematics, and Statistics from Columbia University. |
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Bar Rozenman MS Student Bar is an MS student in Biomedical Engineering specializing in Bioinformatics and Machine Learning. In the Azizi Lab, he applies computational approaches to analyze cellular interactions and multi-omics data in single-cell genomics and spatial transcriptomics. Drawing from his experience in various AI startups, he is excited to apply deep learning to research problems in the world of single-cell biology. He also holds a Bachelor's degree in BME and Biology from Tel Aviv University. |
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Achsah Aruva MS Student |
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Nicholas Djedjos Undergraduate Student Nicholas is a senior studying Computer Science at Columbia’s School of Engineering and Applied Science. He is looking forward to tackling tough single cell genomics problems to help drive clinical breakthroughs. In his free time, he likes playing soccer and practicing Taekwondo. |
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Sharanya Chatterjee
Undergraduate Student Sharanya is an undergraduate in Columbia Engineering studying Computer Science and Applied Mathematics. She was recognized as a US Presidential Scholar by the US Department of Education. She is passionate about multidisciplinary applications in biomedical research, with interest in developing innovative computational methods to improve disease treatment strategies and clinical outcomes. Sharanya has conducted research at the Azizi Lab since the start of her freshman year, applying machine learning and statistical techniques to tackle complex challenges in cancer biology. Before joining the Azizi Lab, she contributed to the development of digital twin models to investigate cell signaling under hypoxic and ischemic conditions. In her free time, Sharanya loves to read and play the flute, and had the honor of performing a flute solo at Carnegie Hall. |
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Ella Yee
Undergraduate Student Ella Yee is an undergraduate student at Columbia University studying Biomedical Engineering and Computer Science. She is passionate about leveraging computational and data-driven approaches to address challenges in immunology and cancer research. Before joining the Azizi lab, Ella contributed to projects on NK cell-based cancer immunotherapy and prostate cancer bone metastasis at Stanford Cancer Institute and Columbia University Irving Medical Center. Outside the lab, she is a board member of the Columbia Society of Women Engineers and enjoys dancing, listening to music, and exploring the city with friends. |
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Harjaisal Brar
Undergraduate Student Harjaisal is an undergraduate studying Biomedical Engineering at Columbia University. He is interested in applying modern machine learning paradigms to improve our understanding of tumor-immune interactions and cancer biology. In the past, he has worked on assaying the ATP usage rate of kinesin in various conformational states to characterize biological power management systems, developing green synthesis methods for silver nanoparticles, and designing and fabricating novel low-cost ventilators, wound care dressings, and other medical devices. In his free time, he enjoys working on mechatronics projects in the Makerspace, listening to audiobooks, and spending time with his friends. |
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Marina Milea
Research Technician Marina is an undergraduate Biology honors student at The City College of New York. She joined the Azizi and McFaline-Figueora Labs through the IICD’s Summer Research Program and is continuing to develop a high throughput T cell perturbation study using CRISPR. She is excited to combine the lab’s computational frameworks for single-cell transcriptomic data to uncover immune cell dynamics and guide future immunotherapy strategies. Marina aspires to pursue a PhD to investigate the molecular mechanisms of disease and chronic pain, with the goal of advancing women’s health research and inspiring future scientists through teaching. |
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Pistachio Minute taker / Zoom meeting disruptor. |
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Udon Note taker / Mood enhancer. |
Alumni:
Xueer Chen, Postdoctoral Research Scientist (2021-2022); Next position: Senior scientist at Bristol Myers Squibb.
Joy Linyue Fan, PhD student (2020-2025). Next position: Postdoctoral Fellow, Genentech.
Cameron Park, PhD student (2020-2025). Next position: Consultant, McKinsey.
Achille Nazaret, PhD student co-advised with Dave Blei (2020-2025). Next position: Research Scientist, Apple.
Siyu He, PhD student co-advised with Kam Leong (2020-2023). Next position: Postdoctoral fellow, Stanford.
Michael Pressler, MS Student (2022-2024). Next position: PhD student in Statistics at Columbia.
Jia Yi (Ady) Zhang, MS Student (2023-2024). Next position: research scientist at NYGC.
Xumin Shen, MS Student (2022-2023). Next position: research technician at MSKCC.
Crystal Shin, MS Student (2022-2023). Next position: bioinformatician at Mount Sinai.
Shouvik Mani, MS Student (2021-2023). Next position: CS PhD student at Stanford.
Lauren Friend, MS Student (2020); Next Position: R&D Engineer at NASA Ames Research Center.
Alyssa King, Undergraduate Student (2024-2025).
Mathias Perez, IICD Alliance Intern (Summer 2025).
Jessie Huang, Undergraduate Student (2023-2024).
Kaylee Wanlu Fang, Undergraduate Student (2022-2024). Next position: postbac at Stanford.
Abdullah Naqvi, Research Technician and IICD Intern (2024-2025).
Emma Losonczy, Undergraduate Student (2024).
Lea Bohbot, IICD Alliance Program Summer Intern, Ecole Polytechnique (France) (Summer 2024).
Marc Chevriere, IICD Alliance Program Summer Intern, Ecole Polytechnique (France) (Summer 2024).
Danielle Maydan, Undergraduate Student (2023-2024).
Hannah Khanshali (CCNY), IICD Research Intern (2023-2024). Next position: postbac at NYGC.
William O'Brien, Undergraduate Student (2023).
Tu Duyen Nguyen, IICD Alliance Program Summer Intern, Ecole Polytechnique (France) (Summer 2023).
Joshua Fuller, Research Assistant (2020-2023). Next position: MD student at Columbia.
Anabel Ojeda, IICD Summer Intern (Summer 2023).
Siddhant Sanghi, Undergraduate Student (2022-2023). Next position: PhD student at UC Davis.
Noa Kalfus, Undergraduate Student (2022-2023).
David Carrera, Undergraduate Student (2022-2022). Next position: Software engineer at Palantir.
Isha Arora, IICD Program Intern, Biomedical Engineering/Computer Science, Cornell University (2022).
Sopho Kevlishvili, Undergraduate Student (2021-2022).
Pranik Chainani, Visiting undergraduate from Statistics and Data Science, Yale University (Summer 2022).
Tamjeed Azad, Undergraduate Student (2020-2022); Next position: CS PhD student at Princeton U.
Ruxandra Tonea, Undergraduate Student (2021-2022); Next position: BME PhD student at U Chicago.
James Wang, Undergraduate Student (2021-2022); Next position: Associate Machine Learning Scientist at Atlassian.
Alex Toberoff, Undergraduate Student (2020-2021); Next position: Researcher at Jump Trading.
Max David Gupta, Undergraduate Student (2021).
Jose Pomarino Nima, Undergraduate Student (2020-2021).
Debra Duval, Research Assistant (2021).
Veronica Woldehanna, Undergraduate Student (2019-2020); Next Position: Software Engineer, Microsoft.
Kaleem Mehdi, Bioforce high school summer internship program (Summer 2022).
Madeline Rohde, High school Intern (2021-2022).
Princess Della Tsivor, High School Summer Student (2021); Next position: undergrad at Brown U.
Rachel Africk, High School Summer Student (2020); Next position: undergrad at U Penn SEAS.
