Mohan M. Email & Phone Number

Boston, MA, US

Princeton, New Jersey, United States

Leading a team of 8 - Researching, Developing, and Deploying CV models to better diagnose Esophageal Cancers from Endoscopic Images.Primary researcher on the VR User Testing study; Designed and conducted an experiment testing medical students performance in thoracic surgery before & after training with our custom built AR operating room environment.

Boston, Massachusetts, United States

Primarily interned as a Deep Learning Engineer under Dr. Sandeep Manjana - a collaborating researcher at HMS/MGHAssisted building a RNN vascular recognition Computer Vision RAS model, with the goal of optimizing back propagation memory consumption.Intern on the MosaicML team working on creating surgical LLMs; meant to identify surgical images through.

Boston, Massachusetts, United States

• Originally a member of the Surgical Artificial Intelligence Innovation Laboratory (SAIL) at HMS/MGH under Dr. Lana Schumacher-Beal & Dr. Danny Yang. Engaged in research concerning Transformer CV models in robotic.
• Primary worked on running various deep learning surgical AI models and documenting their performance on test datasets (ex: EndoVis2017) and then conducting a data analysis of their Dice and mIOU scores respectively.
• Collaborated on numerous other research manuscripts on the topic of Transformers in robotic-assisted thoracic surgery.

Boston, Massachusetts, United States

Position Moved to Tufts Medical Center

Boston, Massachusetts, United States

• Machine Learning Engineering Intern in the Loddenkemper Lab for Pediatric Epilepsy Research spring 2024 cohort, developing ML models to take in EEG data and predict the onset of epileptic episodes.
• Built the ‘E4’ data pipeline with an iterative algorithm, taking Avro file data from a cloud source and converting them into readable CSV files with patient timestamp data ready to be through a ML model.
• Cleaned and prepared extracted data, visualizing and normalizing it to account for any outliers.
• Worked on backend development, database management, building data pipelines with real-time data processing algorithms.
• Algorithms built using neural networks (pytorch)

• Assisted with data analysis for Lipid-shell project; Lipid Shells are used as vesicles to transport bio particles into the body. Worked on finding optimized frequency, volume, and amplitude of ultrasound waves required.
• Trained reinforcement learning model on Lipid shell data to optimize waves.

• Student Instructor as part of UTD Medical Engineering Association; teaching pre-medical and research students how to code in Python.
• Taught basics in Python, OOP, Data Structures& Algorithms, Statisitical Analysis, and ML

Dallas-Fort Worth Metroplex

Member of Advisory Board for UT Dallas chapter of Society for Neuroscience

• Undergraduate Cancer Researcher focused on Central Nervous System Cancers (CNS) and Non-Small Cell Lung Cancer Metastases (NSCLC).
• Utilized R statistical programming to analyze clinical data and establish high quality data representation models such as graphs, scatter charts, and progression curves to use in our studies primarily focused on.
• Independently conducted research on cancer cell signaling biology in spinal cancers and their relation to metastasized non-CNS cancers; reviewed the literature and conducted independent meta- analysis.
• Conducted research concerning malignant lung cancer migration into the CNS, and assisted in radiotherapy data analysis to determine its effectiveness.
• Worked on, and was the primary collaborator for, an unpublished paper in association with the Department of Radiation Oncology; researched the efficacy of systemic therapy on NSCLC manifested CNS metastases.
• Built a large database from ground up, the “melanoma database”, for an additional study; cataloging data and studies that reported metastasized melanomas in the CNS and patient data associated with those cases.

• Observed a variety of neuro-surgical procedures from skull base tumor removals, malignant tumor removal, etc.
• Gained a thorough understanding of human brain anatomy at a clinical and surgical level and relate different procedures to different parts of the brain.
• Received valuable insight into the surgical aspects of cancer care and was able to learn a lot of surgical research and how it differs from diagnostic research.
• Shadowed multiple Neurosurgeons and reviewed their differing specialties.

• Assisted in the conduction of psychometrics research on the decaying motor functions of aging patients.
• Gathered relevant data and cataloged the data of over 15+ patients with motor issues
• Worked heavily on studying neuroimaging and packaged a Python program to help identify key areas of degradation in affected patients.
• Utilized R for statistical analysis on collected psychometric data and produced visuals for data representation on research papers.

Dallas-Fort Worth Metroplex

• Mainly worked on the ‘Radiation’ Project where I was responsible for aiding graduate researchers in finding the optimal rate of X-ray to photon balance in order to treat deep-seated brain/head and neck cancers with.
• Optimization involved measuring differing doses of radiation and photon combinations and testing their effectiveness on aggressive vs non-aggressive cancer cell types.
• Cultured and maintained metastasized cancer cells in wet-labs to be used in treatment testing and other studies.
• Worked with different radiotherapies, primarily reduced oxygen free radicals to be fired at cancerous cells to test cell resistance to treatment.
• Received general training and experience in working in wet-lab settings such as, piperting, cell culturing, record keeping. manipulating glassware, and operating complex lab equitment.
• Worked with radiation and cell cultures more generally and gained experience in radiotherapy research.

Dallas-Fort Worth Metroplex

• Bioengineering Research Intern shadowing in the Obaid Molecular Imaging and Optic Therapeutics Lab; maintained lab, cell cultures, and treated test cells with different contrast solutions.
• Assisted in finding optimal radiation dosage for PDT by running a comparative statistical analysis and finding optimal dosages in Python.
• Assisted grad students in the development of the unstable free-radical oxygenation particles, and studied their physics, to find their applications in cancer cell apoptosis

Houston, Texas, United States

• Assisted engineers in building a “syntax-standardization” natural language processing ML algorithm in python that was trained on a dataset of 800 clinical trials with the goal of standardizing clinical language to.
• Used R statistical programming to analyze clinical data, establish high quality data representation models such as graphs, scatter charts, and progression curves.
• Assisted research measuring clinical syntax comprehension by comparing different editions of AJCC guidelines for clinical trial verbiage.
• Made contributions to the Pain Staging Study, where we measured the difference in patient reported pain for different staging methods of head and neck cancers caused by HPV.
• Produced and presented a poster presentation of our findings for the Clinical Syntax Study at MD Anderson's annual Radiation Oncology Summer Symposium.

• Primarily shadowed a radiation oncologist and gained valuable insight into the treatment of cancer patients, cancer diagnosis, and standard and novel cancer treatment.
• Shadowed other Radiation Oncologists, Neuroradiologists, and Neurosurgeons throughout the MD Anderson departments.

Postgraduate Study, Computer Science & Mathematics

Currently working towards a Masters in CS CS1301 CS1331 CS1332

B.S Neuroscience, Biomedical/Clinical Research

Activities and Societies: Medical Engineering Association School of Behavioral Brain Sciences Department of Bioengineering