Dipesh Niraula is an applied research scientist working in Dr. Issam El Naqa’s research group. He received his Ph.D. and M.S. in Theoretical and Computational Device Physics from the University of Toledo. He worked in Dr. El Naqa’s lab as a post-doctoral researcher in the Department of Radiation Oncology at the University of Michigan and then in the Department of Machine Learning at Moffitt Cancer Center. During his post-doctoral research, he developed ARCliDS, a clinical decision support software for AI-assisted decision-making in adaptive RT and developed a hybrid quantum deep reinforcement learning framework for decision-making in adaptive RT. ARCliDS is capable of recognizing dose-response, predicting RT outcome, and recommending optimal dose adaptation. Currently he is engaged in a Human-AI interaction type experiment for evaluating ARCliDS.
Eduardo is an experienced computer engineer with a background in research and development, software development, and data analytics. Previously, he worked as a Senior Applications Programmer Analyst in The Quality & Performance Department at UTHealth Neurosciences, where he developed robust software and analytical solutions. His primary area of research is the applications of Natural Language Processing to oncology. He is responsible for developing Machine Learning infrastructure that integrates Electric Health Records and molecular data into machine learning tools and serves as the liaison between the Collaborative Data Science Core and the Machine Learning department.
Eduardo Carranza has published a paper entitled:
Eduardo is pleased to be working with his research trainee Yasmin Saeed.
Naveena’s primary area of research interest is Computer Vision. She holds master’s degree in Computer Engineering from Southern Illinois University Edwardsville and worked as Data Scientist at bioMerieux for a year. At Moffitt she will be responsible for developing ML infrastructure and integrate the PACS systems to data servers. She is working on developing an API which acts an interface, amalgamating the developed/ongoing machine learning models, visualization, interpretability and explainability techniques. This API would not only help doctors to elucidate various model results but also for researchers, data scientists in flexible prototyping. Other projects include - Liver classification for HCC, pathology data analysis.
Naveena is pleased to be working with her Research Database Coordinator (Mohammad Adam Kazmouz) and research student trainee (Duong Tung Mai).
Ruwani recently graduated from the University of South Florida (USF), where she earned a Ph.D. in Mathematics with concentration in Statistics. She received her B.Sc. from the University of Colombo, Sri Lanka, and M.A in Statistics from the University of South Florida. Her research interests focus on Probabilistic Deep Learning, Medical Computer Vision and Machine Learning. Her dissertation research is centered on Uncertainty Quantification in Deep and Statistical learning with applications in Bio-medical Image Analysis. She has held internships at the Florida Center for Cybersecurity located at USF, and Citigroup, Tampa in its quantitative research division. She has also served in the American Statistical Association (ASA), USF student chapter as the President.
Muhammad received his BSc. and Ph.D. from Dublin City University (DCU).
His research for his doctoral studies was laser induced breakdown spectroscopy applied to pharmaceuticals using machine learning.
His diverse background has included work as a Medical Physicist at St. James Hospital and The Mater Misericordiae University Hospital, holding a Quant position at Ireland's most prestigious bank, Allied Irish Banks p.l.c. and a machine learning research internship at IBM.
He is currently engaged in research collaborations with IBM, DCU, University College Dublin and the University of Michigan.
His research is a mix of digital twins and AI for real time dose tracking via ionizing radiation acoustic imaging.
Another vein of research is running the machine learning optics lab for investigations surrounding Cherenkov radiation and how to leverage insights to inform cancer treatment.
Denis Dudas recently graduated from the Czech Technical University in Prague, where he earned a Ph.D. in medical physics. He also received his M.S. from the same university. During his Ph.D. he focused on the development and application of new radiation tolerant hybrid and monolithic pixel detectors for radiation oncology. He has held an internship in CERN working on the LHCb detector. As a research medical physicist in the Motol University Hospital in Prague, he was responsible for a workflow automation using the Eclipse Scripting API and he also worked on implementing new radiation detectors in the clinics. Over the last four years, he took part in several foreign missions in developing and low-income countries where he participated on building and starting complex radiotherapy oncology centers. At Moffitt, Denis is focusing on development of deep learning models for radiotherapy treatment outcome predictions.
Glebys received her BS in Computer Science from Simon Bolivar University, Venezuela, and her Ph.D. from the School of Industrial Engineering at Purdue University. Her research focuses on Machine Learning applied to Medical Robotics. She has developed systems for workload recognition and touchless image manipulation in the OR, surgical teleoperation in the presence of delays, and automatic recognition and execution of surgical maneuvers. In addition, Glebys has collaborated with the Indiana University School of Medicine and developed grants for Intuitive Surgical, NIH, and the Department of Defense (Office of the Assistant Secretary of Defense for Health Affairs).
Elaheh Sobhani started in Issam El Naqa, M.A.,Ph.D, DABR, FAAPM, FIEEE, FAIMBE Lab on April 10, 2023, as an Applied Postdoctoral Fellow.
Elaheh Sobhani received her dual Ph.D. from the University of Grenoble Alpes (UGA), Grenoble, France, and Sharif University of Technology (SUT), Tehran, Iran in Electrical Engineering with a focus on Data Mining in March 2022. She received her B.Sc. from Shiraz University, Iran and M.Sc. from Sharif University of Technology (SUT), Tehran, Iran, both in Electrical Engineering. She was also a member of Iran's National Organization for the Development of Talented Talents (NODET) during her studies. Her research interests focus on applications of Tensor Decomposition in Machine Learning, Hidden Information Retrieval and Optimization. Her Ph.D. thesis focused on Tensor Decomposition Applications in data/text mining and data fusion. As a postdoctoral researcher at Moffitt, Elaheh is engaged in fusing relevant information by developing deep learning models so that treatment can be personalized for each patient.
Palak Dave started in Issam El Naqa, M.A.,Ph.D, DABR, FAAPM, FIEEE, FAIMBE Lab on February 6, 2023, as an Applied Postdoctoral Fellow.
Palak received her Ph.D. and M.S. in Computer Science from University of South Florida. Her research interests include Computer Vision, Machine Learning, and Pattern Recognition. Her current research focuses on deep learning for histopathology image analysis.
Mohammad Adam Kazmouz graduated from the University of Florida with a Biochemistry degree and is currently taking growth years to further challenge himself as he applies to medical school. During his undergrad studies, Mohammad was a chemistry tutor where he mastered the ability to break down complex information into digestible simple concepts while appealing to the unique learning styles of his students. He beams positivity and believes that our moods are transferable to others. This translates into Mohammad’s experience with Gulfside Hospice and Florida Cancer Foundation infusion center where he focuses on being a calm, compassionate, and fun companion as he comforts patients in stressful situations.
Mohammad volunteers and shadows at hospitals throughout Tampa Bay to gain diverse holistic clinical experiences to help prepare him for his future and maintain his humanity as a physician, Mohammad’s strong time management and self-motivation skills developed through UF’s rigorous academic course loads and his research experiences will give him the skills to succeed in the Machine Learning Lab. Mohammad hopes that through researching under the guidance of Naveena Gorre, MS, he will be able to make meaningful improvements in patient care and have valuable insight that will make him a better physician.
Katerina received her M.S. degree in Medical Physics from the Czech Technical University in Prague. After graduation, she did her residency at the Department of Nuclear Medicine at the Institute for Clinical and Experimental Medicine in Prague. Currently, Katerina is a Ph.D. student at the Czech Technical University in Prague. After two initial years of Ph.D. studies, Katerina proposed her own Ph.D. project based on her clinical experience. In her Ph.D. project, she applies generative models on SPECT brain images to reveal the individual normal uptake of 123I-ioflupane in brain. Beside her Ph.D. project, Katerina is supervising M.S. student thesis focused on SPECT planar image quantification using Monte Carlo simulation. At Moffitt, Katerina will focus on radiomics in nuclear medicine.
Kara originally started with Moffitt's Diversity team back in 2018. She holds a bachelor’s degree in Teaching and Training Technical Professionals from the University of Akron in Akron, Ohio. She has previously worked at Louis Stokes Cleveland VA Medical Center as a Pharmacy Administrative Support Clerk, as well as, SummaCare, Inc. as a Project Specialist in Medical Records and Data Management. She will be responsible for all administrative tasks within our department.