Find Academic Supervisors

• Circadian Rhythm
• Dynamics
• Neuroscience
• +3 more

Casey Diekman is a Professor in the Department of Mathematical Sciences at New Jersey Institute of Technology (NJIT). His research focuses on mathematical and computational modeling of circadian rhythms, neural dynamics, and physiological systems. He has led NSF-funded projects, including studies on circadian clock mechanisms, neuronal data assimilation, and hybrid modeling of Alzheimer’s disease. His work integrates biophysical models with machine learning to study systems like cardiac electrophysiology and respiratory control. Roles: Professor, PI of multiple NSF grants Key Collaborations: Projects with researchers in neuroscience, cardiology, and computational biology Education & Background: While specific educational details are not provided, his academic position implies advanced training in applied mathematics or systems biology. Research Interests: Circadian rhythms, entrainment dynamics, computational modeling of biological systems, and applications in health (e.g., arrhythmias, neurodegenerative diseases). His work bridges mathematical theory and experimental data to address clinical problems like drug timing and respiratory failure. Grant Highlights: NSF GOALI (2022–2026): Integrates deep learning and mechanistic models for circadian clock neurons and Alzheimer’s disease NSF Career (2016–2021): Developed data assimilation tools for circadian rhythm studies NSF (2014–2017): Modeled circadian clock mechanisms from synapse to gene Recent Research Trends: Publications since 2022 focus on circadian impacts on drug safety, respiratory control during SARS-CoV-2 infection, and Alzheimer’s disease modeling using deep learning. His work often emphasizes interdisciplinary approaches to understand biological timing and its disruptions. Awards & Recognition: While specific prizes are not listed, his extensive grant portfolio and citation count (792) reflect scholarly impact. Advising & Grants: Oversees research teams in circadian biology and computational neuroscience. Media coverage highlights breakthroughs like explaining ‘happy hypoxia’ in COVID-19 patients and antiarrhythmic drug timing risks. Labs/Teams: Engaged in collaborative projects with institutions like NIH and other universities, focusing on systems-level biological modeling.

David TowersView profile

Professor

• Optical Sensing Technologies
• Fluid Mechanics
• Structural Assessment
• +6 more

Professor David Towers is the Head of the School of Engineering at the University of Warwick and holds the position of Professor of Mechanical Engineering. With over 30 years of combined academic and industry experience, he leads a dynamic research group focused on optical sensing technologies, fluid mechanics, and structural assessment. His work bridges engineering, biology, and clinical practice, with significant contributions to malaria control strategies through mosquito behavior analysis. Education: BSc (1st Class) in Mechanical Engineering Science from the University of Warwick PhD in Optical Engineering from the University of Warwick Royal Society Fellowship at ETH Zürich Research Interests: Professor Towers’ research spans three core areas: fluid mechanics (turbulent flows, sprays, multi-phase mixing), structural assessment (optical interferometry for deformation and stress measurement), and clinical optical systems (mosquito tracking for malaria intervention). His multi-disciplinary approach emphasizes collaboration with end-users, particularly in developing novel optical instruments for industrial and healthcare applications. Grants & Projects: AI for Mosquito Trajectory Understanding (£X, 2022–Present) Bill & Melinda Gates Foundation: Next-Gen LLIN Development (£X, 2019–2023) MRC: Indoor Mosquito Behavior Mapping (£X, 2019–2022) Awards: Athena SWAN Silver Award (2021) for promoting gender equality in STEM. Labs & Teams: Leads the Applied Optics Laboratory and collaborates with the Vector Control Research Group, focusing on AI-driven solutions for vector-borne disease control.

Rachel MelamedView profile

Assistant Professor

• Computational Biology
• Cancer Genomics
• Data Science
• +3 more

Rachel Melamed is an Assistant Professor of Biology at the University of Massachusetts Lowell, affiliated with the Kennedy College of Sciences and the Center of Biomedical and Health Research in Data Sciences (CHORDS). Her research focuses on linking health and molecular data to understand disease mechanisms and identify interventions for conditions like cancer. She employs computational methods from computer science and epidemiology, integrating diverse datasets including health records, biobanks, and drug experiments. Education: BA in Computer Science (Brown University), PhD in Biomedical Informatics (Columbia University), Postdoctoral training in Biomedical Data Science (University of Chicago). Research Interests: Disease biology & genomics, data science, epidemiology, causal inference. Projects include studying drug impacts on cancer pathways, genetic overlaps between diseases, and leveraging big data for personalized medicine. She leads a lab with graduate and undergraduate researchers exploring computational biology, bioinformatics, and pharmacology. Lab Team: Supervises students specializing in computational biology, statistics, and machine learning. Current members include Anugrah Vaishnav, Jianfeng Ke, Panagiotis Lalagkas, and others. Past researchers include Aylinh Eng and Bishoy Sargius. Key Focus Areas: Cancer etiology, drug repurposing, genetic risk stratification, and translational bioinformatics. Her work bridges computational methods with clinical and biological datasets to uncover actionable insights in disease prevention and treatment.

Sean HumbertView profile

Professor

• Flight Dynamics and Control
• Bio-inspired Perception and Estimation
• Autonomous Robotics
• +5 more

J. Sean Humbert is a Professor at the University of Colorado Boulder, holding a courtesy appointment in the College of Engineering and Applied Science (AES). He serves as Director of the Robotics Program and Faculty Director for the Aerospace and Defense Western Colorado University Partnership Program. His research focuses on bio-inspired robotics, autonomous systems, and advanced control methodologies. Key research areas include flight dynamics, bio-inspired perception, micro-robotics, and soft robotics. He leads the Robotics and Systems Design group, affiliated with the Hypersonic Vehicles IRT. His work integrates bio-mimetic principles with engineering challenges, emphasizing robust control in unstructured environments. Recent publications span topics like soft robotic actuators, distributed sensing, and neural dynamics in robotics. His lab develops cutting-edge technologies for subterranean exploration, UAV navigation, and bio-inspired sensor systems. Collaborations include industry partnerships and interdisciplinary projects at the intersection of robotics, biology, and control theory. Awards and recognitions are not explicitly mentioned in the provided texts. Sean Humbert’s lab is located at ECES 1B14, with an office in ECES 146. His academic contributions bridge theory and application, addressing real-world challenges in autonomous systems and robotics innovation.

• Biomedical Signal Processing
• Computational Neuroscience
• Brain-Computer Interfaces (BCI)
• +7 more

David B. Grayden is a Professor at The University of Melbourne, affiliated with the Melbourne School of Engineering and the Department of Electrical and Electronic Engineering . His work spans Biomedical Signal Processing , Computational Neuroscience , and Brain-Computer Interfaces (BCI) , focusing on applications in Epilepsy Research and Cochlear Implants . Melbourne Neural Engineering Laboratory member Collaborator in multidisciplinary biomedical research Key research interests include: Developing Seizure Prediction Algorithms using long-term EEG/iEEG data Neural mass modeling for Epilepsy and Inhibitory Network Behavior Optimizing Cochlear Implants via computational models Advancing Endovascular BCI Systems and Neural Stimulation Recent publications highlight trends in Machine Learning , Path Signatures , and Multi-Frequency Stimulation for SSVEP-based BCIs . His work integrates Computational Modeling with Biomedical Engineering to address clinical challenges in neuroprosthetics and sensory processing. Grayden leads projects on Neural Network Dynamics , Biomedical Signal Analysis , and Neurostimulation , often collaborating with institutions like Monash University and Royal Melbourne Hospital .

• High-Performance Computing
• Distributed Systems
• Graph Algorithms
• +3 more

Tahsin Reza is an Assistant Professor at the University of Waterloo, affiliated with the Faculty as a full-time member. His research focuses on high-performance computing, distributed systems, and large-scale graph processing. His work emphasizes algorithmic optimization for irregular parallelism, distributed approximation algorithms, and efficient handling of massive graphs with billions of edges. Key research interests include developing frameworks like YGM for HPC, HyGN for NUMA architectures, and tools such as PruneJuice for graph pruning. His contributions span graph algorithms for Steiner trees, temporal graphs, and metadata-driven pattern matching. He has extensively explored GPU and hybrid CPU-GPU systems to accelerate graph processing tasks in domains like InSAR data analysis and VANET tracking. No scientific awards or grants are explicitly mentioned in the provided materials. His work has been published in top venues, consistently addressing challenges in scalability, efficiency, and real-world applicability of graph-based solutions.

Cranos WilliamsView profile

Professor

• Agricultural Analytics
• Machine Learning
• Artificial Intelligence
• +3 more

Dr. Cranos Williams is the Goodnight Distinguished Professor of Agricultural Analytics at North Carolina State University, holding primary and secondary appointments in the Department of Electrical and Computer Engineering and the Department of Plant and Microbial Biology, respectively. He serves as Platform Director of the NC Plant Sciences Initiative and leads the EnBiSys Research Laboratory. His research integrates electrical engineering methodologies with plant biology to address challenges in biofuel production, environmental adaptability, and stress resilience in plants. Dr. Williams holds a Ph.D. in Electrical Engineering from NC State (2008), an M.S. from the same institution (2002), and a B.S. from NC A&T State University (2001). His work focuses on applying machine learning, signal processing, and systems analysis to quantify genetic and environmental impacts on plant responses. His research highlights include advancing sweetpotato quality assessment via AI, studying gene expression dynamics in Drosophila, and developing high-throughput phenotyping tools. His articles span topics like plant microbiome analysis, freezing tolerance mechanisms, and diversity in STEM education. Dr. Williams has received notable awards, including the University Faculty Scholar (2023), Alumni Association Outstanding Research Award (2021), and Alcoa Foundation Engineering Research Achievement Award (2019). He advocates for broadening participation in STEM and leads initiatives like the GRAD-AID for Ag program to train AI-driven plant science researchers. As head of EnBiSys Lab, Dr. Williams fosters interdisciplinary innovation at the intersection of engineering, data science, and agriculture. His contributions bridge computational tools and biological systems to address global agricultural challenges.

• Neuroscience
• Neuroimaging
• Brain Connectivity
• +4 more

Kangjoo Lee is an Associate Research Scientist in Psychiatry at Yale School of Medicine, specializing in neuroimaging and computational neuroscience. Their research focuses on understanding brain dynamics, particularly in mental health conditions like schizophrenia and mood disorders, using advanced neuroimaging techniques and AI-driven analysis. Education: PhD in Neuroscience from McGill University (2019), Postdoctoral Research Associate at Yale University (2023). Research interests include brain connectivity, sleep deprivation effects, and developing AI tools for neuroscience. Lee has contributed to studies on ketamine's neural mechanisms, sleep recovery networks, and large language models' predictive capabilities in neuroscience. Notable achievements include the Merit Abstract Award (2021) and leadership roles in the Organization for Human Brain Mapping's Diversity Committee. Their work bridges clinical neuroscience with computational methods, emphasizing reproducibility and inclusivity in scientific practices. Current roles include Guest Editor for Biological Psychiatry and committee memberships in neuroimaging and diversity initiatives. Lee collaborates across disciplines, advancing translational research in mental health biomarkers and neurotechnology.

Amy MuellerView profile

Associate Professor

• Biogeochemistry
• Environmental Engineering
• Sensor Technology
• +3 more

Amy Mueller is an Associate Professor in Civil and Environmental Engineering and Marine and Environmental Sciences at Northeastern University, with affiliations in Electrical and Computer Engineering and membership in The Plastics Center. She holds dual academic appointments and leads research in biogeochemistry, sensor technology, and environmental sustainability. Education: PhD in Environmental Chemistry (MIT, 2012), MEng in Electrical Engineering and Computer Science (MIT, 2003). Awards include the NSF Ocean Sciences Postdoctoral Fellowship (2015–2016), NSF Graduate Research Fellowship (2004–2007), and Northeastern’s CEE Excellence in Teaching Award (2021). Research focuses on in-situ sensors for environmental monitoring, remediation strategies in coastal environments, and machine learning applications. Projects include the Common SENSES initiative for climate resilience, DOE-funded methane sensing, and sensor-driven resource optimization in engineered systems. Collaborations span disciplines such as robotics (e.g., robotic aquaculture systems) and community engagement in Boston’s air quality initiatives. Key grants include NSF, DOE, Schmidt Marine Technology Partners, and the Woodard & Curren consortium. She directs the Environmental Sensors Lab and contributes to institutes like iSUPER Impact Engine and the Global Resilience Institute. Recent work emphasizes equitable, community-led environmental solutions and hyperlocal pollution monitoring in urban areas.

Bart De MoorView profile

Professor

• Numerical Linear Algebra
• Optimization
• Algebraic Geometry
• +11 more

Bart De Moor is a Full Professor at the Department of Electrical Engineering, KU Leuven, Belgium, and a guest professor at the University of Siena. He leads the STADIUS research group and has supervised 85 PhD students. His roles include chairman of Health House (2016–present), member of the Board of VIB (Biotech Institute), and former Vice-Rector for International Policy (2009–2013). Education: Master Degree in Electrical Engineering (1983), KU Leuven PhD in Engineering (1988), KU Leuven Research Interests: His work spans numerical linear algebra, optimization, algebraic geometry, systems and control theory, data-driven AI, machine learning, and applications in process industry and biomedical big data. He has contributed to subspace identification, tensor decomposition, bioinformatics, and quantum computing. Publications Trends: His publications highlight subspace identification methods, tensor decomposition, bioinformatics, and biomedical data analysis. These reflect interdisciplinary advancements in control theory, quantum physics, and mathematical engineering, with applications in industrial and healthcare domains. Scientific Awards and Honors: Leslie Fox Prize (1989) Laureate of the Belgian Royal Academy of Sciences (1992) Bi-annual Siemens Award (1994) Fellow of IEEE (since 2004) Member of the Royal Academy of Belgium for Science and Arts (since 2000) Fellow of IFAC (since 2022) Commander in the Order of King Leopold I (2020) Fellow of SIAM (since 2017) FWO Excellence Award (2010) Advising and Grants: He has led a research group of 20 PhD students and postdocs, co-founded 8 spinoff companies, and secured the ERC Advanced Grant ‘Back to the roots’ (2020–2025). He also co-holds the KU Leuven Chair on healthcare systems (2018–present). Labs and Organizations: Active in the STADIUS research group (KU Leuven), he has served on boards of the Flemish Interuniversity Institute for Biotechnology (VIB), the Alamire Foundation, and the Health Tech Experience Center Health House. His spinoffs include Trendminer, Cartagenia, and Ugentec.

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

Dr. Peng Xu is a Research Fellow in the Department of Engineering Science at the University of Oxford, affiliated with the Institute of Biomedical Engineering. His roles include advancing Healthcare AI, Deep Learning, and Machine Learning applications in biomedical research. Previously, he held positions at Nanyang Technological University (postdoctoral research fellow) and earned his Ph.D. from Beijing University of Posts and Telecommunications. Dr. Xu’s research focuses on integrating computational methods with clinical data to address respiratory diseases, particularly asthma and COPD. His work emphasizes novel lung function assessment techniques like Computed Cardiopulmonography (CCP), which evaluates ventilation-inhomogeneity indices and small-airway abnormalities. He also investigates therapeutic interventions, including biologics and corticosteroids, and their effects on Type-2 inflammation biomarkers. His recent studies explore post-COVID-19 pulmonary physiology and the role of gut microbiota in cardiovascular diseases linked to diabetes. His publications highlight contributions to lung function technology (e.g., the TREETOP Study) and the mechanisms of asthma treatment. Collaborations span clinical, engineering, and computational domains, aiming to bridge AI-driven diagnostics and precision medicine. Dr. Xu has contributed to interdisciplinary initiatives such as the Bellhouse BioEngenuity Keynote Lecture at IBME and projects promoting underrepresented students in engineering. His work underscores a commitment to advancing medical technology for improved patient outcomes.

• Dementia
• Parkinson's Disease
• Cognitive Neuroscience
• +5 more

Professor Simon Lewis is a Consultant Neurologist and Professor of Cognitive Neuroscience at the University of Sydney's Brain and Mind Centre within the Faculty of Medicine and Health. He serves as Clinical Director of the Ageing Brain Clinic and Director of the Parkinson's Disease Research Clinic, while also heading the NSW Movement Disorders Brain Donor program. With over 200 peer-reviewed publications, 2 books, and 8 book chapters to his name, Professor Lewis has secured more than $10 million in research funding from prestigious sources including the NHMRC, ARC, and Michael J Fox Foundation. Professor Lewis's research primarily focuses on dementia and Parkinson's disease, with particular expertise in cognitive neuroscience, movement disorders, and REM sleep behavior disorder. His work spans from basic neuroimaging studies to clinical trials investigating novel interventions for neurodegenerative conditions. He has pioneered research on freezing of gait in Parkinson's disease, visual hallucinations in Lewy body disorders, and the relationship between circadian rhythms and neurodegeneration. His laboratory employs a multidisciplinary approach combining clinical assessment, neuroimaging (structural and functional MRI), neurophysiological measurements, and innovative computational methods to understand disease mechanisms. The trajectory of Professor Lewis's recent publications reveals a growing emphasis on biomarker discovery for early diagnosis and disease progression, with increasing integration of artificial intelligence and machine learning approaches to analyze complex movement and sleep data. His work increasingly bridges basic neuroscience with clinical applications, particularly in developing targeted interventions for non-motor symptoms of Parkinson's disease. The collaborative nature of his research is evident in the international authorship patterns across his publications. Among his notable accolades is the Leonard Cox Award (2014) from ANZAN recognizing his significant contributions to neuroscience as an early career neurologist. Professor Lewis serves on multiple editorial boards including Movement Disorders, Translational Neurodegeneration, and Journal of Neurology, Parkinson's and Related Disorders. He actively contributes to international committees such as the Movement Disorders Society Asian Oceanian Section Executive Committee and the International REM Sleep Behaviour Study Group. Professor Lewis leads the world-leading Parkinson's and Related Diseases Research Group at the Brain and Mind Centre, which attracts PhD students interested in understanding disease mechanisms, developing biomarkers, and creating novel treatment modalities for Lewy body-related diseases. His international collaborations span institutions in the Netherlands (Radboud UMC), United Kingdom (King's College London, Cambridge, Newcastle), and other global centers of excellence in Parkinson's research. His work exemplifies a translational approach from bench to bedside, with particular focus on improving quality of life for patients with neurodegenerative conditions.

Fang LiuView profile

Professor

• Differential Privacy
• Statistical Machine Learning
• Bayesian Statistics
• +7 more

Fang Liu serves as Notre Dame Collegiate Professor and Associate Chair in the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame's College of Science. She holds a Ph.D. from the University of Michigan (2003), M.S. from Iowa State University (1999), and B.S. from Peking University (1997). Ph.D., University of Michigan, 2003 M.S., Iowa State University, 1999 B.S., Peking University, 1997 Her research spans five interconnected domains: 1) Development of modern data privacy protection approaches including differential privacy; 2) Statistical and machine learning methodologies with complex model regularization; 3) Bayesian frameworks for medical, biological and social science data analysis; 4) Advanced missing data analysis techniques; and 5) Biostatistical and epidemiological applications. She pioneered the integration of differential privacy concepts from computer science into statistical methodology, identifying numerous research opportunities at this interdisciplinary intersection. Dr. Liu leads the Trustworthy and Ethical Statistical Learning Lab (TESLLa), which focuses on differential privacy, trustworthy AI, synthetic data generation, and probabilistic machine learning. Her lab has produced significant output in privacy-preserving statistical inference and applications to pandemic response, vector-borne disease control, and social media emotional analysis. Key honors include: Elected Fellow of the American Statistical Association Elected Member of the International Statistical Institute (2024) As an advisor, she has mentored over 40 graduate students and researchers since joining Notre Dame in 2011, with alumni securing positions at institutions including Sandia National Laboratories, Los Alamos National Lab, JP Morgan Chase, TikTok, and academic faculty roles. Her research has received substantial funding from NSF, NIH, private foundations, and Notre Dame internal grants, including critical work on campus COVID-19 surveillance testing strategy and privacy-preserving pandemic data collection.

Ralf SalomonView profile

Professor

• Embedded Systems
• Localization and Positioning
• Time Interval Measurement
• +12 more

Ralf Salomon is a Professor at the Faculty of Computer Science and Electrical Engineering , University of Rostock , Germany. He holds the title of Prof. Dr.-Ing. habil. and is actively involved in research and teaching in embedded systems, localization, and signal processing. University: University of Rostock School: Faculty of Computer Science and Electrical Engineering Position: Full Professor Contact: Room 201, Tel: +49 381 498 7260 His research focuses on high-precision time measurement , ambient assisted living , industrial embedded systems , and localization technologies . He applies bio-inspired principles, particularly from the barn owl auditory system, to develop low-cost, high-accuracy systems. His work spans health monitoring (e.g., fall detection, cochlear implants), sports technology, and animal experimentation systems. The recent publications (2020–2025) show a strong trend in sensor systems , embedded vision , and real-time measurement , with applications in sports, healthcare, and automation. His work combines theoretical innovation with practical implementation, often using FPGAs and microcontrollers. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: He advises students such as Theo Gabloffsky , who co-authors multiple publications and assists in teaching. He has led multiple funded projects including Welisa (DFG Graduate College) , iHeal , CurlDat , and BOUNCE . Many of his projects focus on health, localization, and embedded intelligence. Labs and Teams: He collaborates closely with researchers like Ralf Joost , Matthias Hinkfoth , Gerald Bieber , and Marian Lüder . His team develops FPGA-based systems, embedded sensors, and intelligent algorithms for real-world applications in health, industry, and sports.

• Watershed Modeling
• Water Quality
• Agricultural Ecosystems
• +7 more

Rebecca Muenich is an Associate Professor in the Department of Biological & Agricultural Engineering at the University of Arkansas. Her research bridges watershed modeling, agricultural ecosystems, and the food-energy-water nexus, with a focus on surface hydrology, water quality, and climate impacts. She holds a Ph.D. (2015) and M.S. (2011) in Agricultural & Biological Engineering from Purdue University, and a B.S. in Biological Engineering (2009) from the University of Arkansas. Her research explores watershed and environmental modeling, agricultural management, urban agriculture, and climate impacts on water resources. Key interests include mitigating nutrient pollution, enhancing ecosystem services, and developing sustainable land-use strategies. Recent publications emphasize machine learning applications in environmental monitoring, phosphorus circularity, and climate-resilient water management. Muenich leads significant grants including a USDA NRCS project on PFAS in agriculture (2023-2027), NSF-STC’s Science and Technology for Phosphorus Sustainability (2021-2031), and DISES research on cyanobacterial blooms (2022-2025). She mentors students in her research group (Muenich Lab) and collaborates on interdisciplinary projects. Lori Libbert New Faculty Commendation (2024) Early Career Alumni Award, UA Engineering (2022) New Face of ASABE (2020) National Science Foundation Graduate Research Fellowship (2009)