Find Academic Supervisors

• Computer Vision
• Data Science
• Data Visualization
• +5 more

Michael D. Grossberg is an Assistant Professor in the Department of Computer Science at the City College of New York (CUNY). He is affiliated with the NOAA-CREST Institute and the Computational Science of Data Intensive Remote Sensing (CS:dir$) group. His research spans interdisciplinary fields including computer vision, data science, remote sensing, climate science, and medical imaging. Grossberg holds a BA in Mathematics from the University of Pennsylvania, a PhD in Mathematics from MIT, and has held academic positions at Columbia University before joining CCNY. Education: Bachelor of Arts in Mathematics, University of Pennsylvania Doctor of Philosophy in Mathematics, Massachusetts Institute of Technology Postdoctoral Fellow at Hebrew University (Jerusalem) Assistant Professor of Mathematics, Columbia University Instructor and Research Scientist in Computer Science, Columbia University Research Interests: Grossberg's work focuses on applying computational methods to solve challenges in science and engineering. Key areas include: Quantitative image restoration for satellite data (e.g., MODIS, VIIRS) Machine learning applications in climate modeling and sea ice classification Semi-automatic medical image segmentation for radiation treatment planning Data visualization tools for climate and remote sensing data Lossless compression algorithms for Earth science imagers His research is supported by grants from NIH, NOAA, NASA, and the Office of Naval Research. Grants & Collaborations: NIH P20 grant for medical image segmentation NOAA-funded projects on satellite data restoration and compression Climate research collaborations with Columbia University and Memorial Sloan Kettering Cancer Center Labs & Teams: Grossberg leads the CCNY research lab focusing on interdisciplinary projects. Collaborators include Irina Gladkova (NOAA-CREST), Gikas Mageras (MSKCC), and climate scientist Niko Krakauer. His lab supports student projects in web development, data science, and medical imaging through programs like the NOAA-CREST HIRES initiative.

Bulent YenerView profile

Professor

• Applied Machine Learning
• Bioinformatics
• Medical Informatics
• +4 more

Bulent Yener is a Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI) with a courtesy appointment in Electrical, Computer, and Systems Engineering. He serves as the founding Director of the Data Science Research Center and Associate Director of the Institute for Data Exploration and Applications (IDEA). He holds a Ph.D. (1994) and M.S. (1987) in Computer Science from Columbia University and previously worked at Bell Laboratories. Research Focus: Dr. Yener's work spans interdisciplinary applications of machine learning and data science, with core domains including: Bioinformatics/medical informatics: Tuberculosis pathogenesis modeling, computational pathology, and biomarker discovery Cyber security: Adversarial machine learning, causal inference attacks, and malware analysis Clinical applications: EEG-based seizure prediction and neurological disorder diagnostics Theoretical foundations: Graph neural networks, trustworthy AI, and combinatorial optimization Research Leadership: He directs multidisciplinary initiatives through: Data Science Research Center (petabyte-scale analytics combining cloud/HPC) IDEA Institute (data-driven scientific exploration) Affiliations with Cognitive and Immersive Systems Lab (CISL) and Center for Computational Innovations (CCI) Honors: IEEE Fellow for contributions to applied machine learning ACM Senior Member recognizing computing advancements

• Machine Learning
• Communication Theory
• Data Storage
• +3 more

Elnaz Banan Sadeghian is an Assistant Professor at Stevens Institute of Technology, affiliated with the Charles V. Schaefer, Jr. School of Engineering and Science and the Department of Electrical and Computer Engineering. She holds a PhD in Electrical and Computer Engineering from Georgia Institute of Technology (2017), alongside MS degrees from Georgia Tech (2015) and AmirKabir University (2008), and a BS from Shahid Beheshti University (2005). Her research focuses on Communication Theory, Machine Learning, Data Storage (particularly Magnetic Recording), Assistive Technology, and Brain-Computer Interfaces (BCI). Recent work includes neural network-based approaches for multitrack detection in magnetic recording and low-latency BCI systems leveraging time-series prediction and self-attention mechanisms. She also explores subject-independent BCI frameworks using supervised autoencoders. Dr. Sadeghian has received notable grants including an NSF CAREER Award and NSF CRII funding for machine learning applications in magnetic storage. Her contributions span over 20 peer-reviewed publications since 2007, addressing topics like asynchronous track detection algorithms, timing error correction in read channels, and fractal analysis of EEG signals for BCI. She is an ASTC Fellow and active member of IEEE and EMBS societies. Currently, she advises PhD candidates with expertise in machine learning and communication theory.

Nelly ElsayedView profile

Assistant Professor

• Applied AI
• Machine Learning
• Healthcare Informatics
• +7 more

Dr. Nelly Elsayed is an Assistant Professor at the School of Information Technology, University of Cincinnati. She leads the Applied Machine Learning and Intelligence (AMLI) Lab, focusing on AI, cybersecurity, healthcare informatics, and smart technologies. Her research emphasizes practical applications in IoT security, neural networks, and predictive modeling. Her work includes grants from the National Science Foundation, Procter & Gamble, and federal agencies, totaling over $600k. Notable projects include IoT security frameworks, mental health monitoring systems, and cyberbullying detection. Dr. Elsayed has received multiple recognitions, including the Golden Apple Award for Teaching and UCAADA Advising Excellence. She actively serves on technical committees for IEEE and ACM conferences, contributing to academic service and outreach. Research Interests: Applied AI & Machine Learning Cybersecurity for IoT/Healthcare Deep Learning Models (LSTM, CNN) Data Science & Pattern Recognition Smart Technologies for Accessibility Recent Research Trends: Her publications emphasize hybrid models (e.g., LSTM-CNN) for intrusion detection, cybersecurity frameworks, and AI-driven solutions for mental health and elderly care.

Nedret BillorView profile

Professor

• Robust Multivariate Data Analysis
• Robust Functional Data Analysis
• Outlier Detection
• +3 more

Nedret Billor is a Professor of Statistics in the Department of Mathematics and Statistics at Auburn University, within the College of Sciences and Mathematics. She holds a Ph.D. in Statistics from the University of Sheffield, UK. Her research focuses on robust multivariate data analysis, outlier detection, and functional data analysis, with applications in healthcare, environmental science, and astrophysics. She has published extensively and received notable awards, including an Elected Membership in the International Statistical Institute (ISI) since 2012 and a Fullbright Specialist Scholar designation in 2016. Dr. Billor has advised numerous graduate students and served on interdisciplinary committees. Her work emphasizes practical solutions to real-world problems, leveraging statistical methodologies to address challenges in STEM fields. She has contributed to medical data analysis, machine learning, and environmental modeling, while also advocating for women in statistics through her role in the ISI Committee on Women in Statistics (CWIS). Her teaching excellence has been recognized through awards for Outstanding Teacher of Graduate Students and Excellence in Teaching. Her research spans diverse domains, including robust statistical modeling for biomedical data, functional regression techniques, and outlier detection in high-dimensional datasets. Recent articles highlight advancements in Gaussian process modeling, image classification in astronomy, and post-surgical cardiac function analysis using machine learning. She also explores the impact of public health policies on disease transmission through data-driven epidemiological models. Dr. Billor’s career reflects a commitment to bridging statistical theory with practical applications, contributing to advancements in science, healthcare, and environmental sustainability.

Elisa KallioniemiView profile

Assistant Professor

• Neural Engineering
• Brain Stimulation
• Neurological Disorders
• +2 more

Elisa Kallioniemi is an Assistant Professor in the Department of Biomedical Engineering at NJIT and Director of the Brain Stimulation Lab. Her research uses brain stimulation to study neural mechanisms and develop therapies for neurological and psychiatric disorders. She holds a Ph.D. in Applied Physics from the University of Eastern Finland and M.S./B.S. in Electronics and Electrical Engineering from Aalto University. Dr. Kallioniemi's work integrates neural engineering, neuroimaging (TMS-EEG/EMG), and computational approaches to map cortical excitability, understand neuroplasticity, and develop diagnostic biomarkers. She leads collaborative initiatives through the ENIGMA-Neuromodulation working group and investigates the effects of interventions like hypnosis combined with neurostimulation. Awards include IEEE Senior Membership, ACNP Travel Award, and Cornell Rising Star recognition. Her lab focuses on translating neuroengineering innovations to clinical applications.

Sarah PowerView profile

Associate Professor

• Biomedical Engineering
• Physiological Signal Processing
• Brain-Computer Interfacing
• +1 more

Sarah Power is Associate Professor jointly appointed in the Faculty of Engineering and Applied Science (Electrical and Computer Engineering) and Faculty of Medicine at Memorial University. She holds a B.Eng. from Memorial (2006), M.A.Sc. from Toronto, and PhD in Biomedical Engineering from Toronto. Her research applies signal processing and pattern recognition to medical/rehabilitation challenges, focusing on mental state classification using EEG and physiological signals. This includes workload assessment, stress detection, and brain-computer interfaces. Power's publications demonstrate expertise in EEG signal classification, fNIRS-BCI development, and real-world neurotechnology applications. Recent work advances hierarchical classification systems and addresses validation challenges in passive BCI designs.

Xin-She YangView profile

Associate Professor

• Nature-inspired computing
• Mathematical modelling
• Engineering simulation
• +3 more

Xin-She Yang is an Associate Professor in Simulation & Modelling at Middlesex University, London. He holds a DPhil in Applied Mathematics from the University of Oxford and has over 25 years of experience in research and teaching. His academic roles include serving as an Editor-in-Chief, editorial board member for multiple journals, and chair of IEEE CIS Task Force on BIKM (2015-2020). He is a Fellow of the Institute of Mathematics and its Applications (2021), Asian Computational Intelligence Society (2023), and Soft Computing Research Society (2025). Research interests span nature-inspired computing, mathematical modelling, engineering simulation, AI, algorithms, and optimization. Notable achievements include being listed as a top 0.1% scientist in Web of Science since 2016 and receiving multiple Leader Awards in Computer Science (2023–2025). He supervises PhD students in areas like parameter tuning, nature-inspired computing, and optimization. Teaching includes courses on engineering simulation, mathematical optimization, and partial differential equations. He has authored books like Optimization Techniques and Applications with Examples and contributed to Springer's Nature-Inspired Computing and Optimization series. Education: DPhil in Applied Mathematics, University of Oxford (1998) Affiliations: IEEE, IMA, Asian CIS Grants: UKRI Doctoral Training Centres, National Research Councils (Iceland, Ireland, etc.)

Peyman MirtaheriView profile

Professor

• Biomedical Engineering
• Neurotechnology
• Sensor Technology
• +4 more

Peyman Mirtaheri is a full Professor of Biomedical Engineering at Oslo Metropolitan University's Faculty of Technology, Art and Design, leading the NIRS/optical lab established in 2009. He holds an adjunct professorship at Michigan Technological University's Biomedical Engineering department. His research focuses on developing novel sensor technologies for brain activity detection and applying fNIRS/EEG to study motion and balance in the cerebral cortex. As PI, he oversees major projects like PACER (RCN-funded), MgSafe (Horizon 2020), and Future Running Shoes (IP-N collaboration with Gaitline AS). He teaches medical sensors and actuators in the ACIT master's program and leads the ADEPT research platform integrating health intelligence and brain-inspired technologies across multiple faculties. Education details are not explicitly listed in the provided texts, but his academic roles imply advanced credentials in biomedical engineering. His research spans interdisciplinary domains including neurotechnology, wearable sensors, and clinical applications of brain monitoring. Over 70 publications demonstrate expertise in fNIRS signal processing, neuromorphic computing, and user-centered biomedical design. Professional networks include active Twitter (@PMirtaheri) and LinkedIn profiles. Current projects emphasize translational research and ethical design considerations in neurotechnology. His work bridges engineering, health sciences, and arts through collaborative platforms like ADEPT, addressing both technical innovation and societal impact.

Sara HanrahanView profile

Senior Lecturer

• Biomedical Engineering
• Neural Engineering
• Neurophysiology
• +3 more

Sara Hanrahan serves as a Senior Lecturer in the Mechanical, Aerospace and Biomedical Engineering (MABE) department at the University of Tennessee, Knoxville since August 2023, following her role as BME Lecturer from January 2018 to July 2023. Her academic career builds upon prior research positions at the Colorado Neurological Institute Neuromodulation Lab and the University of Utah Neural Engineering Lab. She holds a PhD in Biomedical Engineering with a Neural Interfaces specialization from the University of Utah (2013) and a BS in Biomedical Engineering with a Chemistry minor from the University of Pittsburgh (2008). Dr. Hanrahan's research focuses on neural signal analysis in movement disorders, particularly deep brain stimulation applications for Parkinson's disease. Her work integrates clinical neurophysiology with engineering solutions for neurological conditions, emphasizing subthalamic nucleus dynamics and neural interface development. Current investigations include therapeutic optimization for movement disorders and neural decoding of motor functions. Her publication history demonstrates consistent contributions to neural engineering, with recent work examining long-term neural dynamics in Parkinson's disease, STN-based behavior classification, and multisensory neural processing. Key thematic areas include surgical targeting algorithms, neural decoding for brain-computer interfaces, and anesthetic effects on neural signaling. Dr. Hanrahan has received significant recognition for her teaching excellence: 2019 MABE Outstanding Faculty Initiative and Leadership Award 2021 Pi Tau Sigma Excellence in Teaching Award 2022 Leon & Nancy Cole Teaching Fellow Award 2023 Toby Boulet Outstanding Faculty Service Award She provides extensive academic service as an ABET Program Evaluator, ASEE Reviewer, and faculty advisor for the Engineers in Medicine Society. Her committee work includes chairing the MABE Teaching Discussion Lunch and serving on the Curriculum and Assessment Committee while supporting the Interdisciplinary Graduate Minor in Neuroscience. Her research trajectory spans clinical neurophysiology at the Colorado Neurological Institute, neural engineering at the University of Utah, and rehabilitation engineering at the University of Pittsburgh, establishing a foundation for translational neuroscience applications in academic medicine.

• Cybersecurity
• Machine Learning
• Cryptography
• +3 more

Dr. Aqeel Sahi is a Lecturer in Computing at the University of Southern Queensland's School of Mathematics, Physics and Computing. He holds an MSc from Universiti Utara Malaysia and a PhD in Cybersecurity and Machine Learning from USQ. With over 15 years of teaching experience, Dr. Sahi instructs courses including Algorithms and Data Structures, Object-Oriented Programming, and Web Technologies. His research spans cybersecurity, machine learning, cryptography, and software engineering, with particular focus on developing advanced security algorithms and machine learning applications for network protection. Recent publications demonstrate strong emphasis on AI-driven security solutions, healthcare applications, and network defense mechanisms. Dr. Sahi maintains professional memberships with IEEE and ACS, bridging academic and industry expertise through his background as a senior software engineer.

Mirco RavanelliView profile

Assistant Professor

• Deep Learning
• Conversational AI
• Machine Learning
• +5 more

Mirco Ravanelli is an Assistant Professor at Concordia University (Gina Cody School of Engineering and Computer Science), specializing in deep learning for sequence processing with a focus on Conversational AI. He holds adjunct roles at Université de Montréal (DIRO) and is an Associate Member at Mila - Quebec AI Institute . He leads the SpeechBrain open-source project, a widely adopted toolkit for conversational AI. His academic journey includes a PhD (cum laude) from the University of Trento (2017) and a postdoc under Yoshua Bengio at Mila. He has published over 80 papers, focusing on self-supervised learning, cooperative deep learning, and speech/EEG signal processing. Key awards include the 2022 Amazon Research Award. Teaching involves machine learning courses for graduate and undergraduate students at Concordia. His research aims to bridge human-machine conversation through advanced neural models, emphasizing explainability and robustness in speech technologies. Key Projects: SpeechBrain, FocalCodec, Speech self-supervised benchmarking Labs: CRBLM Axis Leader (Center for Research on Brain, Language, and Music) Grants: Amazon Research Award, Others unlisted

• Medical Image Computing
• Image-Guided Surgery
• Human-Computer Interaction
• +9 more

Dr. Yiming Xiao is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University, holding the Research Chair in Intelligent & Intuitive Surgical Technology. His work focuses on medical image computing, image-guided surgery, and AI-driven surgical applications. He leads the HealthX Lab, advancing neurotechnology, explainable AI, and virtual/augmented reality systems for surgical planning. Research interests include medical image segmentation , deep learning , VR/AR in surgery , and collateral circulation analysis . Notable projects include PreVISE (VR SEEG planning), CABLD (brain landmark detection), and MedCLIP-SAM (text-driven medical segmentation). Publications emphasize few-shot learning , ultrasound-MRI co-registration , and neurological disorder diagnostics . He actively supervises PhD students in computer science, with a focus on interdisciplinary applications of AI in healthcare. His work bridges computational methods and clinical needs to improve surgical precision and patient outcomes.

Tomas WardView profile

Professor

Professor Tomas Ward is a Full Professor in the School of Computing at Dublin City University. His research develops neurotechnology and brain-computer interfaces for healthcare applications, including EEG-based art preference decoding, motor imagery classification, and Long COVID symptom monitoring. Recent work focuses on digital health interventions for clinical trials, athlete pain assessment frameworks, and self-powered wearable sensors. Publications demonstrate expertise in translating neuroengineering concepts into clinical and sports medicine applications.

• Machine Learning
• Medical Image Analysis
• Computer Vision
• +2 more

Claudia Mazo is Assistant Professor at Dublin City University's School of Computing, specializing in AI for medical imaging. Her research develops machine learning methods for brain tumor detection, stroke diagnosis, and surgical planning. Holder of a Marie Curie Fellowship and management committee member for a European brain cancer network. Publications focus on explainable AI in neuroimaging, transfer learning for pathology, and multimodal medical fusion. Dual Ph.D. from Universidad de León and Universidad del Valle.