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About Us

We are an interdisciplinary group of researchers who are eager to solve fundamental problems with high technological and societal impact. We have a very open, collaborative research team. The theme that connects all our research areas is dynamics at the nanoscale. We both develop new methods (both experimental (e.g., microscopy) and computational (e.g., AI/ML)) and apply existing and newly developed methods to problems that have interesting questions on the nanoscale, ranging from the properties of nanomaterials with energy applications to how biological molecules function. The goal of our research is to uncover the underlying physical principles that govern the dynamics, statistics, and self-organization of nanostructured materials. Using a combination of experiment, theory, and computation such as in-situ liquid phase transmission electron microscopy (LPTEM), fluorescence optical microscopy, nanoscience and colloidal chemistry, stochastic thermodynamics, and machine learning, we work on the following projects in our group:

  • Dynamics of nanoparticles in interaction with heterogeneous environments
  • Phase behavior and dynamics of biological macromolecules in their native liquid environment at the nanoscale
  • AI for finding the hidden physical rules in nanoscale systems
  • Team

  • Principle Investigator

  • Vida Jamali (CV)
    Email: vida@gatech.edu

    Vida is an assistant professor of Chemical and Biomolecular Engineering at GeorgiaTech since Aug 2022. She is also a program faculty of Machine Learning and Bioengineering PhD programs. She is also an affiliated faculty member of the Institute for Matter and Systems (IMS), Institute for Bioengineering and Biosciences (IBB), and Institute for Data Engineering and Sciences (IDEaS). Vida's work has been recognized by the NSF CAREER Award (2024), ACS PRF Doctoral New Investigator Award (2023), and she has been named a Scialog fellow in Automating Chemical Labs (2024), 30 voices of Chemistry of Materials (2023), and a rising star in soft and biological matter by the University of Chicago MRSEC (2021). Vida is currently one of the initiative leads of IDEaS for AI in Chemical and Materials Discovery at Georgia Tech. Before joining Georgia Tech, Vida was a postdoctoral researcher at the University of California, Berkeley and Kavli Energy Nanoscience Institute working with Paul Alivisatos where she also collaborated closely with Kranthi Mandadapu. She received her PhD in Chemical and Biomolecular Engineering from Rice University advised by Matteo Pasquali in 2017. Prior to that, Vida did her BS in Chemical Engineering at Sharif University of Technology in 2011.


  • Graduate Students

  • Zain Shabeeb
    BEng Chemical Engineering, Newcastle University, 2018
    MSc Advanced Process Integration and Design, The University of Manchester, 2019
    Email: zshabeeb3@gatech.edu

    Zain was born and raised in Lahore, Pakistan and lived in Switzerland for four years during middle school. Zain completed his BEng in Chemical Engineering and MSc in Advanced Process Integration and Design from the UK. During his undergraduate studies at Newcastle University, Zain started developing an interest in mathematical modeling and computer programming. For this reason, he pursued a Master’s degree in Process Integration and Design, and in his free time, learned Python programming, Machine Learning and Data Science. Before starting his Ph.D., Zain worked in Fatima Group in Pakistan as a Supply Chain management associate, where he used statistical time-series forecasting methods to forecast the prices of important commodities. Zain is an avid fan of cricket, and in his free time loves to work out, play table tennis and pool.



    Isabel Panicker
    BS Chemical Engineering, Purdue University, 2023
    Email: ipanicker@gatech.edu

    Isabel was born and raised in Suburban Chicago, Illinois. She earned her Bachelor’s degree in Chemical Engineering from Purdue University in 2023. There, she worked with Dr. Rakesh Agrawal in his Solar Energy Group where her research focused on solution based synthesis of Multinary Sulfides for Photovoltaics. She has also spent time as a Quality Engineering Intern at Tesla working on the continuous improvement of the Drive Unit Manufacturing Line. Isabel joined the Jamali Lab in 2023 and is excited to be working with nanoparticles and studying their non-equilibrium behavior utilizing Liquid TEM. In her free time, she likes watching films, drawing and painting.


    Brian Chettle
    BS Chemical Engineering, Pennsylvania State University, 2024
    Email: bchettle3@gatech.edu

    Brian was born and raised in Villanova, PA. Brian earned his bachelor's in chemical engineering with Honors in Chemistry from Penn State in 2024. In undergrad, he worked with Dr. Andrew Zydney in characterizing fouling effects of proteins during viral filtration, a step in the downstream processing of biotherapeutic development. He also performed physical chemistry research with Dr. Bratoljub Milosavljevic to elucidate solvation effects on bimolecular reactions in ethanol-water solutions. Brian joined the lab in 2024 and is excited to study the behavior of biomolecules at the nanoscale using LPTEM. In his free time, Brian enjoys watching films, reading, and crossword puzzles.

  • Undergraduate Students



  • Risha Goel
    Email: rgoel63@gatech.edu


    Risha is a fourth-year Chemical Engineering student at Georgia Tech from Aurora, CO. She is interested in exploring the applications of machine learning on nanoparticle research. She enjoys playing tennis, crocheting, and trying new coffee shops.




    Laurence Lines
    Email: llines3@gatech.edu


    Laurence is a fourth-year Chemical Engineering major at Georgia Tech from Marietta, GA. He joined the Jamali Lab to learn more about nanoparticles and heterogeneous fluid environments. In his free time he enjoys cooking, hiking, and reading about language and history.




    Cecilia Reed
    Email: creed62@gatech.edu


    Cecilia is a fourth-year Chemical Engineering major at Georgia Tech from Atlanta, Georgia. She joined the Lab to learn more about data analytics and X-Ray Photoelectron Spectroscopy. She enjoys watching movies of all kinds, reading, and cooking.





    Lauren Caldwell
    Email: lcaldwell30@gatech.edu


    Lauren is a third-year Chemical Engineering major at Georgia Tech from Fishers, Indiana. Within the Jamali Lab, she is interested in nanoparticle fluid analysis via both laboratory techniques and machine learning developments. Outside of school, she enjoys playing soccer, traveling, baking, and exploring Atlanta.





    Matthew Abernathy
    Email: mabernathy8@gatech.edu


    Matthew is a fourth year Chemical & Biomolecular Engineering major at Georgia Tech from Columbus Georgia. He became interested in the Jamali Lab based on the group's involvement in characterization of the Nanoscale using techniques like liquid phase TEM. Outside of research and academics, he is interested in the German language and culture, Classical Music, and abstract strategy games, primarily Chess and Go.





    Isabella Faciano
    Email: ifaciano3@gatech.edu


    Isabella is a fourth year chemical engineering major born and raised in Brooklyn, NY. She joined the Jamali lab to build upon her understanding of the synthesis and the applications of nanoparticles, more specifically their computational and experimental methods. In her free time, she enjoys working out, exploring Atlanta with friends, and watching TV shows (usually the same 5 over and over again)!



  • Group Alumni
  • Naisargi Goyal (2022-2024), CHBE undergrad, Current Position: PhD student at Princeton
    Gabriel Joaquim Sampaio De Almeida (2023), CHBE undergrad
    Arko Roy (2023), CHBE undergrad

    Want to join our team?

    Prospective graduate students should apply directly to the School of Chemical and Biomolecular Engineering at GeorgiaTech, and Interdisciplinary Bioengineering and Machine learning PhD programs . We always look for motivated students interested in areas including nanoparticle synthesis and characterization, chemical and biological soft materials (colloids, polymer, proteins), active matter, liquid phase transmission electron microscopy, and machine leaning for physics and chemistry. ​ Admitted graduate students, please email Vida directly to schedule a meeting to chat.


    Undergraduate students interested in research position, please fill out this application form: https://forms.gle/dyeFPu4yERx9EHCo9 .

    Publications

    * denotes equal contribution, † denotes corresponding author

    • Learning the Diffusion of Nanoparticles in Liquid Phase TEM via a Physics-informed Attention Network,
      Zain Shabeeb, Naisargi Goyal, Pagnaa Attah Nantogmah, Vida Jamali†, Under Review, (2024).
    • Learning the Physics of Liquid Phase TEM Nanoparticle Trajectories Using Physics-Informed Generative AI .
      Zain Shabeeb, Naisargi Goyal, Pagnaa Attah Nantogmah, Vida Jamali†, Microscopy and Microanalysis , (2024), 30, S1, 1724 .
    • Determining Diffusion Characteristics of Nanoparticles in Liquid Phase TEM Using Deep Learning .
      Zain Shabeeb, Naisargi Goyal, Vida Jamali†, Microscopy and Microanalysis , (2024), 30, S1, 2074 .
    • Studying diffusion of colloidal nanoparticles in solution using liquid phase TEM and machine learning .
      Vida Jamali†, A. Paul Alivisatos†, Microscopy and Microanalysis , (2022), 28, S1 .
    • Observation of an orientational glass in a superlattice of elliptically-faceted CdSe nanocrystals .
      Abdullah S. Abbas, Emma Vargo, Vida Jamali, Peter Ercius, Priscilla F. Pieters, Rafaela M. Brinn, Assaf Ben-Moshe, Min Gee Cho, Ting Xu, A. Paul Alivisatos, ACS Nano, (2022), 16, 6 .
    • Recent advances in the study of colloidal nanocrystals enabled by in situ liquid phase transmission electron microscopy .
      Ivan Moreno-Hernandez*, Michelle Crook*, Vida Jamali*, A. Paul Alivisatos, MRS Bulletin, (2022), 47 .
    • Anomalous nanoparticle surface diffusion in liquid cell TEM is revealed by deep learning-assisted analysis .
      Vida Jamali, Cory Hargus, Assaf Ben Moshe, Amirali Aghazadeh, Hyun D. Ha, Kranthi K. Mandadapu, A. Paul Alivisatos, PNAS, (2021), 18, 10 .
    • Enhanced ordering in length-polydisperse carbon nanotube solutions at high concentrations as revealed by small angle X-ray scattering.
      Vida Jamali, Francesca Mirri, Evan G. Biggers, Robert Pinnick, Lucy Liberman, Yachin Cohen, Yeshayahu Talmon, Fred C. MacKintosh, Paul van der Schoot, Matteo Pasquali , Soft Matter, (2021), 17, 5122-5130 (Featured on the front cover of Soft Matter).
    • In situ quantification of interactions between charged nanorods in a predefined potential energy landscape.
      Hoduk Cho, Ivan A. Moreno-Hernanzdez, Vida Jamali, Myoung H. Oh, A. Paul Alivisatos, Nano Letters, (2021), 21, 1, 628-633 .
    • Perovskite-carbon nanotube fibers for light emitting fibers.
      Vida Jamali*, Farnaz Niroui*, Lauren W. Taylor, Oliver S. Dewey, Brent A. Koscher, Matteo Pasquali, A. Paul Alivisatos, Nano Letters, (2020), 20, 5, 3178-3184.
    • The effect of carbon nanotube diameter and stiffness on their phase behavior in crowded solutions.
      Lucy Liberman, Vida Jamali, Matteo Pasquali, Yeshayahu Talmon, Langmuir, (2020), 36, 242-249 .
    • Fluid phase ordering of charge-stabilized carbon nanotube solutions.
      Francesca Mirri*, Rana Ashkar*, Vida Jamali, Lucy Liberman, Robert A Pinnick, Paul van der Schoot, Yeshayahu Talmon, Paul D Butler, Matteo Pasquali, Macromolecules, (2018), 51, 17, 6892-6900.
    • Flexible and conductive fibers made from highly concentrated aqueous dispersions of carbon nanotubes.
      Laurent Maillaud, Robert J. Headrick, Vida Jamali, Julien Maillaud, Dmitri E. Tsentalovich, Wilfrid Neri, E. Amram Bengio, Francesca Mirri, Olga Kleinerman, , Yeshayahu Talmon, Philippe Poulin, Matteo Pasquali, Industrial and Engineering Chemistry Research, (2018), 57, 10, 3554-3560.
    • Purification and dissolution of carbon nanotube fibers spun from the floating catalyst method.
      Thang Q Tran, Robert J Headrick, E Amram Bengio, Sandar Myo Myint, Hamed Khoshnevis, Vida Jamali, Hai M Duong, Matteo Pasquali, ACS applied materials & interfaces, (2017), 9, 42, 37112-37119.
    • Line tension of twist-free nematic liquid crystal microdroplets on flat solid surfaces.
      Vida Jamali, Evan G Biggers, Paul van der Schoot, Matteo Pasquali, Langmuir (2017), 33, 36, 9115-9121.
    • Increased solubility and fiber spinning of graphenide dispersions aided by crown-ethers.
      Chengmin Jiang, Zhiwei Peng, Carlos de los Reyes, Colin C Young, Dmitri E Tsentalovich, Vida Jamali, Pulickel M Ajayan, James M Tour, Matteo Pasquali, Angel A Martí. Chemical Communications, (2017), 53(9), 1498-1501.
    • Experimental realization of crossover in shape and director field of nematic tactoids.
      Vida Jamali*, Natnael Behabtu*, Bohdan Senyuk, J Alex Lee, Ivan I Smalyukh, Paul van der Schoot, Matteo Pasquali, Physical Review E, (2015) 91,4, 042507.

    News

  • Oct 2024- Vida will be presenting a talk at AIChE Fall meeting in San Diego, CA.
  • Oct 2024- Jamali Lab welcomes, Brian Chettle, a new PhD student! Welcome to the team Brian!
  • Aug 2024- Jamali Lab will have an open house for new class of graduate students on Thursday Aug 29th, 10 am-noon in ES&T 1229.
  • Aug 2024- Jamali Lab welcomes five new undergrads joining the group: Risha, Lauren, Matthew, Isabella, and Laurence!
  • Jul 2024- Zain will be presenting two posters at the upcoming M&M conference.
  • Jun 2024- Our lab will be at the ACS colloid and surface science symposium in Seattle, WA.
  • May 2024- Congratulations to Zain for passing his thesis proposal!
  • April 2024- Vida will be at the Scialog Conference on AI for Automating Chemical Labs.
  • April 2024- We will be hosting the Soft Matter Day Symposium for the Atlanta Area at Georgia Tech on April 5th in EBB Krone building.
  • Mar 2024- Vida will be giving a seminar at the Institute for Electronics and Nanotechnology (IEN) at Georgia Tech on 26th.
  • Mar 2024- Vida will be at APS to talk about our recent advancements in developing LPTEM as a single particle tracking method.
  • Jan 2024- We are awarded the NSF CAREER Award!
  • Jan 2023- Congratulations to Isabel for passing her qual!
  • Jan 2024- We are awarded the Institute for Electronics and Nanotechnology, IEN-1000x seed grant to develop a high resolution acquisition technique in collaboration with our ECE colleague Amirali Aghazadeh ( Amir group ).
  • Nov 2023- We are awarded the ACS PRF Doctoral New Investigator Award!
  • Oct 2023- Vida, Victor Fung, Pan Li, and Amirali Aghazadeh were awarded a seed grant to establish a new initiative at Georgia Tech on AI for Chemical and Materials Discovery. The seed grant is co-sponsored by the Institute for Data Engineering and Sciences and Institute for Materials.
  • Oct 2023- Vida was named one of the Scialog fellows in Automating Chemical Labs. She will participate in the first meeting of AUT meeting series in April 2024.
  • Oct 2023- Congratulations to Naisargi for winning the GT-AIChE chapter travel award which will sponsor her travel to AIChE fall meeting this year. She will be presenting her work on ML for electron microscopy.
  • Oct 2023- Isabel Panicker, first-year PhD student in CHBE joined Jamali Lab. Welcome Isabel!
  • Sep 2023- Vida will be presenting an invited talk at the International Microscopy Congress (IMC20) in Busan, Korea on applications of deep learning in LPTEM.
  • Jul 2023- Congratulations to Naisargi for winning the President's Undergraduate Research Award (PURA). We look forward to continuing working with her in Fall 2023.
  • Mar 2023- Vida will be speaking at the 2023 Symposium on Materials Innovation hosted by the Institute for Materials at Georgia Tech on March 31st, 2023.
  • Mar 2023- Vida will be the next seminar speaker of CCMST seminar series at Georgia Tech on Mar 14th to talk about deep learning and its application in LPTEM.
  • Mar 2023- Congratulations to Naisargi for being selected as the student rep of the GT-AICHE Chapter in 2023 AIChE Southeastern Regional Conference! she will present a poster on her research in our group.
  • Jan 2023- Congratulations to Zain for passing his qual!
  • Oct 2022- Vida will be giving an invited talk at the liquid phase TEM Gordon Research Conference in Ventura, CA.
  • Oct 2022- Jamali group welcomes new team members Zain and Naisargi.
  • Aug 2022- Vida will be giving an invited talk at the Microscopy and Microanalysis conference in Portland, OR in August.
  • May 2022- Vida will be joining the school of Chemical and Biomolecular Engineering at GeorgiaTech in Fall 2022. Our lab will integrate electron microscopy, statistical mechanics, and machine learning to investigate the dynamics of soft material systems at the nanoscale.
  • May 2022- Our paper on observation of orientational glass phase in superlattices of elliptically faceted nanocrystals is now published in ACS Nano.
  • Dec 2021- Vida will be presenting a talk on my recent works at the Kavli Energy Nanoscience Institute at UC Berkeley on Tuesday December 7th.
  • Nov 2021- Vida will be presenting a talk on my recent works on liquid phase TEM and anomalous diffusion at the AIChE conference in Boston, MA on Monday November 8th, 5:15 pm (203h).
  • Sep 2021- Vida has been selected as a Rising Star in Soft and Biological Matter by the University of Chicago MRSEC. I will present a summary of my research on Sep. 23rd in this symposium.
  • Sep 2021- Vida will be giving a talk in the Nano Seminar series at UC Berkeley on September 10, 2021.
  • May 2021- Our paper is featured on the front cover of the Soft Matter journal
  • March 2021- Our paper on deep learning-assisted study of nanoparticles' surface diffusion in liquid cell TEM is accepted to PNAS
  • March 2021- Our paper on studying high concentration solutions of carbon nanotubes using small angle X-ray scattering is accepted to Soft Matter
  • January 2021- Vida was awarded the Berkeley Postdoctoral Association Professional Development award
  • December 2020- Our paper on in-situ lift off of lithographed nanorods in liquid cell TEM is accepted to Nano Letters
  • November 2020- Vida will be giving a talk on using deep neural networks for studying anomalous surface diffusion of nanoparticles in liquid cell TEM at the AIChE fall meeting. The talk is publicly available on Youtube
  • October 2020- Vida was awarded the Women in Chemical Engineering (WIC) travel award to attend the AIChE fall meeting this November
  • September 2020- Vida was spotlighted by the UC Berkeley VSPA office in the national postdoctoral appreciation week
  • September 2020- Our new paper on deep-learning assisted liquid cell TEM is now available on ChemRxiv
  • July 2020- Alivisatos and Mandadapu's group are awarded a NSF EAGER grant to work on the surface diffusion of nanoparticles using in-situ liquid cell TEM
  • April 2020- Our paper on perovskite-carbon nanotube light emitting fibers is published in Nano Letters
  • November 2019- Vida will be presenting my work on light emitting fibers at the AIChE fall meeting, Orlando, FL
  • August 2019- Vida is selected as a participant in NSF-funded Future Faculty Workshop that will be held at Princeton University, NJ.
  • July 2019- Vida is selected to participate in ACS postdoc to faculty (P2F) workshop in Atlanta, GA
  • Resources

    Career Development

  • Advice by Andrea Armani for applying to PhD program and faculty position
  • Rising stars in soft and biological matter workshop by the University of Chicago MRSEC
  • FFW Diverse Leaders for the Future workshop by the University of Delaware
  • NextProf Nexus by GeorgiaTech, UC Berkeley, and University of Michigan
  • How to give a talk by Borroughs Wellcome Fund
  • You and your research by Richard Hamming
  • The Women of Color Project: Guidlines for grad school application
  • Research Fellowships

    teaching

    CHBE 4803/8803: AI for Experimental Chemical Engineers

    Course Description: This course has two primary objectives: (a) to introduce students to advanced deep learning and AI methods and (b) to teach students how to use such models on experimental data relevant to chemical engineers. This course links the topics of AI and data science to experimental chemical engineering applications. Chemical engineers work with experimental data of various modalities that span microscopy images, time series data, spectrophotometry data, and texts, to name a few. This course will first introduce students to the foundations of popular AI models developed in the last 10 years and their application in analyzing experimental data. In addition to covering various model architectures (e.g., CNN, VAE, LLM, etc.), we will cover foundational topics related to AI, including framing loss function, initialization, error backpropagation, gradient computation, and model fitting. The course will maintain a balance between neural network basics and application to experimental data at hand, such that the students obtain key knowledge on how to efficiently select the most suitable AI model and apply it to the data, know the mathematical principles of the model operation, test the model performance, and relevant statistical analysis. Overall, by the end of the course, the students will have a good understanding of how to apply AI models to experimental data relevant to chemical engineers and extract useful information.

    Prerequisites: Background in chemical engineering and Python programming
    Pre- and/or Co-Requisites:
    • 8803: Graduate students are encouraged to take ChBE 6745 and ChBE 6746 prior to taking this course. Students should be familiar with basic concepts in math, linear algebra, numerical methods, optimization, python programming, and data analytics to be successful in this course.
    • 4803: Prerequisites are: CHBE 3215 OR BMED 3310 OR MSE 3210
    For both undergraduate and graduate students taking this course, you do not need to have taken any Python course before, but be aware that we will use Python codes in this course. If you already know how to program in Python, great; if not, you will need to invest time to become literate in Python (all your knowledge from MATLAB can be easily transferred to Python). Module 0 of this course will offer resources that you can use to improve your Python programming.