Honors & Awards

  • Damon Runyon Postdoctoral Fellowship, Damon Runyon Cancer Research Foundation (07/2022)
  • AACR-AstraZeneca Immuno-oncology Postdoctoral Fellowship, American Association for Cancer Research (07/2022)
  • Dean's Postdoctoral Fellowship Award, School of Medicine, Stanford University (06/2022)
  • NIH-NHGRI T32 Postdoctoral Trainee, Stanford Genome Training Program (10/2021)
  • Paper of the Year Award, Boston University's Biomedical Engineering Department (1/2021)
  • Distinguished Biomedical Engineering Fellowship, Boston University (09/2015)

Boards, Advisory Committees, Professional Organizations

  • Associate Member, American Association for Cancer Research (AACR) (2021 - Present)
  • Member, American Association for the Advancement of Science (2019 - Present)

Professional Education

  • PhD, Boston University, Biomedical Engineering (2021)
  • MSc, University of Tehran, Biomedical Engineering (2015)
  • BSc, Ferdowsi University of Mashhad, Mechanical Engineering (2013)

Lab Affiliations

All Publications

  • Machine learning for microfluidic design and control. Lab on a chip McIntyre, D., Lashkaripour, A., Fordyce, P., Densmore, D. 2022


    Microfluidics has developed into a mature field with applications across science and engineering, having particular commercial success in molecular diagnostics, next-generation sequencing, and bench-top analysis. Despite its ubiquity, the complexity of designing and controlling custom microfluidic devices present major barriers to adoption, requiring intuitive knowledge gained from years of experience. If these barriers were overcome, microfluidics could miniaturize biological and chemical research for non-experts through fully-automated platform development and operation. The intuition of microfluidic experts can be captured through machine learning, where complex statistical models are trained for pattern recognition and subsequently used for event prediction. Integration of machine learning with microfluidics could significantly expand its adoption and impact. Here, we present the current state of machine learning for the design and control of microfluidic devices, its possible applications, and current limitations.

    View details for DOI 10.1039/d2lc00254j

    View details for PubMedID 35904162

  • Machine learning enables design automation of microfluidic flow-focusing droplet generation NATURE COMMUNICATIONS Lashkaripour, A., Rodriguez, C., Mehdipour, N., Mardian, R., McIntyre, D., Ortiz, L., Campbell, J., Densmore, D. 2021; 12 (1): 25


    Droplet-based microfluidic devices hold immense potential in becoming inexpensive alternatives to existing screening platforms across life science applications, such as enzyme discovery and early cancer detection. However, the lack of a predictive understanding of droplet generation makes engineering a droplet-based platform an iterative and resource-intensive process. We present a web-based tool, DAFD, that predicts the performance and enables design automation of flow-focusing droplet generators. We capitalize on machine learning algorithms to predict the droplet diameter and rate with a mean absolute error of less than 10 μm and 20 Hz. This tool delivers a user-specified performance within 4.2% and 11.5% of the desired diameter and rate. We demonstrate that DAFD can be extended by the community to support additional fluid combinations, without requiring extensive machine learning knowledge or large-scale data-sets. This tool will reduce the need for microfluidic expertise and design iterations and facilitate adoption of microfluidics in life sciences.

    View details for DOI 10.1038/s41467-020-20284-z

    View details for Web of Science ID 000665627200001

    View details for PubMedID 33397940

    View details for PubMedCentralID PMC7782806

  • Rapid and inexpensive microfluidic electrode integration with conductive ink LAB ON A CHIP McIntyre, D., Lashkaripour, A., Densmore, D. 2020; 20 (20): 3690-3695


    Electrode integration significantly increases the versatility of droplet microfluidics, enabling label-free sensing and manipulation at a single-droplet (single-cell) resolution. However, common fabrication techniques for integrating electronics into microfluidics are expensive, time-consuming, and can require cleanroom facilities. Here, we present a simple and cost-effective method for integrating electrodes into thermoplastic microfluidic chips using an off-the-shelf conductive ink. The developed conductive ink electrodes cost less than $10 for an entire chip, have been shown here in channel geometries as small as 75 μm by 50 μm, and can go from fabrication to testing within a day without a cleanroom. The geometric fabrication limits of this technique were explored over time, and proof-of-concept microfluidic devices for capacitance sensing, droplet merging, and droplet sorting were developed. This novel method complements existing rapid prototyping systems for microfluidics such as micromilling, laser cutting, and 3D printing, enabling their wider use and application.

    View details for DOI 10.1039/d0lc00763c

    View details for Web of Science ID 000577744000002

    View details for PubMedID 32895672

  • Performance tuning of microfluidic flow-focusing droplet generators LAB ON A CHIP Lashkaripour, A., Rodriguez, C., Ortiz, L., Densmore, D. 2019; 19 (6): 1041-1053


    The required step in all droplet-based devices is droplet formation. A droplet generator must deliver an application-specific performance that includes a prescribed droplet size and generation frequency while producing monodisperse droplets. The desired performance is usually reached through several cost- and time-inefficient design iterations. To address this, we take advantage of a low-cost rapid prototyping method and provide a framework that enables researchers to make informed decisions on how to change geometric parameters and flow conditions to tune the performance of a microfluidic flow-focusing droplet generator. We present the primary and secondary parameters necessary for fine-tuning droplet formation over a wide range of capillary numbers and flow rate ratios. Once the key parameters are identified, we demonstrate the effect of geometric parameters and flow conditions on droplet size, generation rate, polydispersity, and generation regime. Using this framework, a wide range of droplet diameters (i.e., 30-400 μm) and generation rates (i.e., 0.5-800 Hz) was achieved.

    View details for DOI 10.1039/c8lc01253a

    View details for Web of Science ID 000462666200009

    View details for PubMedID 30762047

  • Multi-criteria optimization of curved and baffle-embedded micromixers for bio-applications CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION Rasouli, M., Mehrizi, A., Goharimanesh, M., Lashkaripour, A., Bazaz, S. 2018; 132: 175-186
  • An adaptive neural-fuzzy approach for microfluidic droplet size prediction MICROELECTRONICS JOURNAL Lashkaripour, A., Goharimanesh, M., Mehrizi, A., Densmore, D. 2018; 78: 73-80
  • Desktop micromilled microfluidics MICROFLUIDICS AND NANOFLUIDICS Lashkaripour, A., Silva, R., Densmore, D. 2018; 22 (3)
  • Numerical Study of Droplet Generation Process in a Microfluidic Flow Focusing JOURNAL OF COMPUTATIONAL APPLIED MECHANICS Lashkaripour, A., Mehrizi, A., Rasouli, M., Goharimanesh, M. 2015; 46 (2): 167-175
  • Fractional Order PID Controller for Diabetes Patients JOURNAL OF COMPUTATIONAL APPLIED MECHANICS Goharimanesh, M., Lashkaripour, A., Mehrizi, A. 2015; 46 (1): 69-76
  • Diabetic Control Using Genetic Fuzzy-PI Controller INTERNATIONAL JOURNAL OF FUZZY SYSTEMS Goharimanesh, M., Lashkaripour, A., Shariatnia, S., Akbari, A. 2014; 16 (2): 133-139