Find Academic Supervisors

• Big Data
• Cloud Computing
• Data Management
• +7 more

Dr. George Yu is an Associate Professor of Computer Science and Information Systems at Youngstown State University (YSU), Ohio. He holds a Ph.D. in Computer Science from Southern Illinois University (2013), an M.S. in Pure Mathematics from Shandong University (2008), and a B.S. in Information and Computation Science from Northeastern University (2005). As the Campus Champion of NSF XSEDE at YSU, he facilitates access to national supercomputing resources and organizes workshops on High-Performance Computing (HPC). His research focuses on Database Systems , Approximate Query Processing (AQP) , Big Data Analytics , Cloud Computing , and Bioinformatics . Notable contributions include the AQPrius framework for error-aware AQP and collaborative work on genomic data analysis in plants like Aspergillus niger and tomato. Dr. Yu has received multiple recognitions, including the 2020 Distinguished Professor in Scholarship at YSU and the 2019 Best Paper Award at the International Conference on Software Engineering and Data Engineering. He serves as an associate editor for journals like Transactions on Large-Scale Data and Knowledge-Centered Systems and actively contributes to conference program committees. His educational initiatives include developing YSU's Data Analytics (DATX) Certificate Program and teaching courses on databases, cloud computing, and blockchain. He leads the YSU Data Lab, which operates high-performance research clouds like Sarah Cloud and YSU STEM Cloud, and mentors students in NSF-funded research projects.

Rachel AmbagtsheerView profile

Research Fellow

• Gerontology
• Public Health
• Healthcare Systems
• +3 more

Rachel Ambagtsheer is a Research Fellow and Senior Lecturer in Health at Torrens University Australia, based in the Centre for Public Health, Equity and Human Flourishing (PHEHF). With over 20 years of experience in health research, policy, and consulting, she specializes in frailty, aging, and healthcare system interactions. Her work focuses on improving older adults' participation in policy discussions and optimizing healthcare interventions for aging populations. Education: PhD in Public Health (2020) Master of Information Studies (Librarianship) Bachelor of Arts (Honours) Her research interests include: Frailty screening and diagnostic accuracy in primary care Health policy engagement for older adults Cross-cultural adaptation of healthcare tools Rachel leads the MRFF-funded IMPAACT Project, enhancing older Australians' involvement in policy decisions about aging-related conditions. She previously contributed to the NHMRC CRE for Frailty and Healthy Ageing, co-authoring a landmark study on frailty screening in general practice. Her scientific achievements include Distinguished Membership of the Australian Association of Gerontology. Key projects involve frailty interventions in residential care and technology-based solutions for social isolation reduction. Research collaborations focus on: Frailty screening instruments in Sub-Saharan Africa Heart failure management in primary care Human-centered design of health resources Rachel has produced over 29 peer-reviewed outputs, emphasizing evidence-based practices and participatory research methodologies.

• Virtual Reality
• Augmented Reality
• Educational Technology
• +7 more

Roghayeh Barmaki is a researcher with extensive publications in virtual reality, augmented reality, and educational technology. Her work spans rehabilitation systems, autism therapy, and neuroimaging analysis using fNIRS. She collaborates with institutions including Georgia Institute of Technology and University of Florida. Key Collaborators : Anjana Bhat, Jicheng Li, Shayla Sharmin, Vuthea Chheang Research Themes : Wearable sensors, LSL framework, movement synchrony, multimodal learning analytics Research Interests She focuses on immersive technologies for education and therapy, with expertise in: Functional Near-Infrared Spectroscopy (fNIRS) for cognitive engagement Wearable sensor integration in rehabilitation Gender perspectives in AR/VR education Multimodal affect analysis for autism Privacy-preserving behavioral analytics 3D visualization for anatomy training Publication Trends Recent work emphasizes fNIRS applications in gaming environments and hybrid deep learning models for cognitive analysis. Earlier studies focused on gesture recognition, movement synchrony estimation, and autism intervention datasets. Impactful Projects She developed the MMASD dataset for autism research and contributed to VR therapy systems. Her collaborations include: Georgia Tech's Autism Technology Research University of Florida's TeachLivE virtual classroom Germany's AIxVR conference contributions International collaborations on sensor systems

Guowei WeiView profile

Professor

• Machine Learning
• Topological Data Analysis
• Biochemistry
• +4 more

Guowei Wei is a MSU Research Foundation Professor in the Department of Mathematics and the Department of Biochemistry & Molecular Biology at Michigan State University. He holds a dual appointment in the BioMolecular Science Gateway and is affiliated with the Wei Lab. His research focuses on integrating topological data analysis, machine learning, and computational methods to address complex problems in biochemistry, biophysics, and materials science. Dr. Wei’s work spans multiple disciplines, including topological machine learning for protein-ligand interactions, persistent homology applications in molecular flexibility analysis, and AI-driven drug discovery. He develops novel algorithms combining algebraic topology with deep learning to model biological systems and material properties. His recent contributions include advancements in predicting protein binding affinities, analyzing viral mutations, and optimizing drug candidates for opioid use disorder and addiction treatments. He teaches advanced graduate courses (MTH 890/990) and maintains an active research lab. His methodologies bridge mathematical theory with practical applications in computational biology and healthcare, leveraging tools like the Poisson-Boltzmann equation and transformer networks for molecular analysis. Dr. Wei’s work emphasizes interdisciplinary approaches to solve challenges in drug design, pathogen evolution, and systems biology. Key contributions include topological deep learning frameworks for biomedical data, persistent Laplacian techniques for scarcely labeled data classification, and AI models for predicting mutation impacts on protein function. His research has significant implications for personalized medicine, pandemic preparedness, and understanding complex biological networks.

Laurent ServaisView profile

Professor

• Pediatric Neuromuscular Diseases
• Spinal Muscular Atrophy
• Duchenne Muscular Dystrophy
• +3 more

Laurent Servais is Professor of Paediatric Neuromuscular Diseases at the University of Oxford and Invited Professor at the University of Liège. He leads research on neuromuscular disorders including spinal muscular atrophy, Duchenne muscular dystrophy, and myotubular myopathy. His work focuses on newborn screening, gene therapy, and digital outcome measures. Servais directs the UK's first pediatric clinical research facility and founded Belgium's SMA newborn screening program. His clinical trials expertise spans antisense oligonucleotides and AAV gene therapies. Research integrates wearable sensors for real-world motor function assessment. Recent publications address gene therapy guidelines, newborn screening implementation, caregiver burden in rare diseases, and therapeutic strategies for neuromuscular conditions. He consults internationally to improve care in resource-limited settings.

• Artificial Intelligence
• Advanced Analytics
• Operations Research
• +5 more

Dr. Su Nguyen is a Senior Lecturer in AI and Analytics at RMIT University's Department of Accounting, Information Systems & Supply Chain, located at the City Campus in Australia. His research focuses on integrating artificial intelligence, analytics, and operations research to address challenges in sustainability, safety, and critical domains like healthcare and logistics. Key areas include transparent AI systems, simulation models for dynamic environments, and optimization algorithms for energy and transportation. His work emphasizes making AI systems accountable to mitigate risks such as discrimination and ensures fairness in autonomous decision-making. Collaborations span Australia, New Zealand, and the Asia Pacific region, promoting AI applications in industry. Supervised projects include topics like personalized care plans, workforce rostering optimization, and AI-driven solutions for supply chains and cyber-risk evaluation. Dr. Nguyen's research bridges theoretical advancements with practical industry engagement, aiming to enhance decision-making through advanced analytics and business optimization frameworks.

• Cloud Computing
• Networked Systems
• Cybersecurity
• +7 more

Dr. Qiang Fu is a Senior Lecturer at RMIT University's School of Computing Technologies, specializing in Cloud, Networked Systems, and Security. He holds a PhD from The University of Queensland and is actively involved in industry collaborations. His research focuses on Internet and Cloud-based systems, including Content Delivery Networks (CDNs), data centre design, Cyber-Physical Systems (CPS)/IoT, virtualization, and SDN/NFV. Recent work emphasizes network telemetry, fault detection, and IoT workflow optimization. He has published extensively in top-tier journals and conferences like IEEE Transactions and IFIP NOMS. Dr. Fu supervises PhD/Master students in areas such as network security, cloud computing, and IoT. His projects are often industry-funded and address real-world challenges like network scalability and blockchain integration. He is open to supervising students in these domains through RMIT's scholarship programs.

Carlos SotoView profile

Assistant Professor

• Differential Privacy
• Bioinformatics
• Statistical Analysis
• +3 more

Carlos Soto is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. His research focuses on the intersection of differential privacy, bioinformatics, and statistical analysis, with an emphasis on genomic data, geometric modeling, and privacy-preserving techniques. He is recognized for award-winning teaching and has contributed to interdisciplinary projects involving chromosome structure analysis and Bayesian statistical methods. His research interests include developing mathematical frameworks for privacy in data analysis, analyzing three-dimensional chromosome conformations, and applying geometric and algebraic methods to biological problems. His work often bridges theoretical statistics with computational biology, addressing challenges in genomic data interpretation and structural bioinformatics. Key trends in his publications include advancements in differential privacy for healthcare and genomic data, Bayesian approaches to topological analysis of genomes, and statistical methods for comparing chromosomal shape populations. He has also explored applications of shape analysis in power systems fault detection and incremental learning. Award-winning teaching in mathematics and statistics Advising & Grants: No advising or grant details explicitly provided in the text. Labs/Teams: No specific lab or collaborative team affiliations mentioned.

• Computer-Aided Drug Design
• Molecular Dynamics Simulations
• Structural Biology
• +3 more

Jacob Durrant is an Associate Professor in the Department of Biological Sciences at the University of Pittsburgh. His research focuses on computer-aided drug design (CADD), molecular dynamics simulations, and structural biology. He leads the Durrant Lab, which develops innovative computational tools for drug discovery, such as AutoGrow4 and ProteinVR. The lab investigates infectious diseases, cancer, and neurological disorders, emphasizing small-molecule ligand identification and molecular visualization. Key affiliations include the Molecular Biophysics and Structural Biology Graduate Program, the Department of Computational & Systems Biology, and the Joint CMU-Pitt PhD Program in Computational Biology. Durrant collaborates on projects like the RECOVERY trial, exploring treatments for severe COVID-19. His work integrates machine learning, cloud computing, and GPU acceleration to advance drug discovery. Research highlights include developing open-source tools (e.g., MolModa, FPocketWeb) and studying protein dynamics via molecular simulations. The lab’s ProteinVR project enables immersive virtual reality visualization of molecular structures for educational and collaborative purposes.

• Treatment-Resistant Depression
• Psychopharmacology
• Electroconvulsive Therapy
• +7 more

Sina Nikayin, MD, is an Assistant Professor Adjunct in Psychiatry at Yale School of Medicine and a psychiatrist at the Yale Interventional Psychiatry Service (IPS). He specializes in advanced modalities for treatment-resistant depression (TRD), including ketamine, esketamine, electroconvulsive therapy (ECT), and transcranial magnetic stimulation (TMS). His research focuses on the efficacy and safety of these interventions, particularly in bipolar and Parkinson's disease contexts. Education: MD from Tehran University of Medical Sciences. He is affiliated with the Center for Brain & Mind Health and contributes to clinical trials like the Ketamine Parkinson’s Disease (PD) trial. His research interests include catatonia, delirium, and patient-reported outcomes. Recent work explores ketamine vs ECT efficacy, psychedelic therapy expectations, and genetic factors in severe depression. Publications span 20+ peer-reviewed articles, emphasizing clinical trials and interventional psychiatry advancements. Collaborations include Gerard Sanacora and Samuel Wilkinson. No specific awards are listed, though his work reflects significant contributions to mental health innovation.

Arron LaceyView profile

Senior Lecturer

• Health Data Science
• Machine Learning
• Natural Language Processing
• +4 more

Dr Arron Lacey is a Senior Lecturer in Health Data Science and Natural Language Processing at Swansea University Medical School. He has been based at the SAIL Databank since 2011. His academic background includes a BSc in Physics, MSc in Computer Science, and a PhD in Healthcare Studies from Swansea University. Education: BSc Physics, Swansea University MSc Computer Science, Swansea University PhD Healthcare Studies, Swansea University Research Interests: Focuses on applying health data science and natural language processing (NLP) to healthcare challenges. Key areas include clinical data extraction from unstructured records, cardiovascular epidemiology, epilepsy outcomes analysis, and genomic data integration. His work emphasizes population-level data linkage and translational research. Recent Research Trends: Recent publications highlight NLP applications in extracting structured data from clinical letters, cardiovascular disease management post-intervention, and epilepsy mortality during the pandemic. His work bridges computational methods with real-world healthcare data to improve diagnosis, treatment monitoring, and public health policy. Grants & Collaborations: Led grants including HDR UK-funded NLP implementation projects (2017). Collaborates with multidisciplinary teams on initiatives like MedGATE for scalable NLP healthcare frameworks. Labs/Teams: Core member of the SAIL Databank, part of Swansea University's health data science ecosystem. Involved in the Center for Doctoral Training in Computation.

Sumit SarkarView profile

Professor

• Personalization and Recommendation Systems
• Crowdsourcing
• Sharing Economy
• +6 more

Sumit Sarkar is the Charles and Nancy Davidson Chair Professor of Information Systems and Director of PhD Programs at the Jindal School of Management, University of Texas at Dallas. He also serves as the Academic Director of the International Center for Decision and Risk Analysis. His research focuses on personalization systems, crowdsourcing, data privacy, and software project management. He holds a PhD from the University of Rochester, an MBA from the Indian Institute of Management Calcutta, and a BTech from the Indian Institute of Technology New Delhi. Professor Sarkar’s research interests span multiple domains including recommendation systems, knowledge management, and the sharing economy. His work emphasizes practical applications such as privacy-preserving data sharing, optimal contest design for crowdsourcing platforms, and strategic pricing in digital markets. Notable contributions include frameworks for hiding sensitive association rules and methodologies for evaluating online review credibility. Education: PhD, University of Rochester, 1991 MBA, Indian Institute of Management Calcutta, 1982 BTech, Indian Institute of Technology New Delhi, 1979 His awards include the AIS Fellowship (2022), INFORMS President’s Service Award (2021), and the Davidson Chair appointment (2011–present). He has held leadership roles in professional organizations such as the Workshop on Information Technology and Systems and served on editorial boards of journals like MIS Quarterly and Management Science . Professor Sarkar’s work bridges theory and practice, addressing challenges in information systems through interdisciplinary approaches. His current projects explore AI-driven counterfactual explanations, competitive dynamics in search advertising, and the ethical implications of recommendation systems.

Malgorzata O'ReillyView profile

Associate Professor

• Applied Probability
• Stochastic Modelling
• Operations Research
• +4 more

Malgorzata O'Reilly is an Associate Professor in Mathematics at the University of Tasmania's School of Natural Sciences. Her expertise lies in applied probability, stochastic modelling, and matrix-analytic methods (MAMs), with applications in healthcare systems optimization, evolutionary biology, and operations research. She leads the multidisciplinary team 'Designs for a Better Patient Journey' focused on healthcare system modeling. Her career includes roles from Lecturer (2005–2014) to Senior Lecturer (2014–2019) before her current position since 2019. Education: MSc in Mathematics and Education, Wroclaw University (1987) PhD in Applied Probability, University of Adelaide (2002) Research Interests Her research integrates theoretical and applied aspects of stochastic processes, including Markov-modulated models, healthcare system efficiency, and evolutionary gene duplication dynamics. She collaborates internationally on projects like MAM10 conference leadership and healthcare optimization with Tasmania's Department of Health. Key Contributions O'Reilly has pioneered work on two-dimensional and time-varying Markov-modulated models, contributing to algorithms for transient/stationary performance measures and numerical methods. She chairs MAM conferences and edits related proceedings, emphasizing interdisciplinary applications. Grants & Collaborations Her funding includes ARC Discovery and Linkage grants. Collaborations span healthcare modeling (e.g., emergency department wait times) and evolutionary genomics. She co-authored influential studies on GP co-payments' impact on hospital wait times and stochastic niche-based ecological models. Labs & Teams Leads the 'Designs for a Better Patient Journey' team, focusing on healthcare system modeling. Active in the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS).

Chulsoon HwangView profile

Associate Professor

• Electromagnetic Compatibility (EMC)
• Signal Integrity
• Power Integrity
• +5 more

Dr. Chulsoon Hwang is an Associate Professor of Electrical Engineering at Missouri University of Science and Technology (Missouri S&T) , part of the College of Engineering . He leads the EMC Laboratory and focuses on RF desensitization, signal/power integrity, machine learning in hardware design, and electromagnetic interference (EMI). His work bridges theoretical electromagnetics with practical applications in high-speed digital systems and hardware security. Education: Ph.D., M.S., and B.S. in Electrical Engineering from KAIST (Korea Advanced Institute of Science and Technology), Daejeon, South Korea. Prior to joining Missouri S&T in 2015, he worked as a Senior Engineer at Samsung Electronics (2012–2015). Research Highlights: Developed machine learning-driven simulators for signal integrity (e.g., High-speed Channel Transformer). Pioneered physics-assisted genetic algorithms for PDN decoupling optimization. Explored intentional EMI attacks (e.g., inaudible voice command injection into smart speakers). Advanced understanding of power supply-induced jitter (PSIJ) and EMI mitigation strategies. Recent Trends in Publications: Focus on AI-driven hardware optimization (deep learning/RL for PDN design), EMI/RFI modeling, and security vulnerabilities in IoT devices. Over 150 IEEE publications and multiple patents (e.g., inaudible voice command injection, laser foil printing for resonators). Awards and Recognition: Best Paper Awards (7 papers, 4 student papers). Dean’s Scholar Award (2022–2023) and IEEE EMC Society Technical Achievement Award (2023). Google Faculty Research Award (2020) and APEMC Young Scientist Award (2018). Grants and Labs: Active in securing research grants for AI-driven hardware design and EMI mitigation. The EMC Lab collaborates on industry-relevant projects, such as acoustic noise analysis in power distribution networks. Labs/Teams: Director of the EMC Laboratory at Missouri S&T, fostering interdisciplinary research in electromagnetics and hardware security.

Jia WangView profile

Associate Professor

• VLSI Design Automation
• Embedded Systems
• Algorithm Design
• +4 more

Jia Wang is an Associate Professor in the Department of Electrical and Computer Engineering at Illinois Institute of Technology (IIT), affiliated with the Armour College of Engineering. He joined IIT in 2008 after completing his Ph.D. in EECS from Northwestern University. His research focuses on VLSI design automation, embedded systems, and algorithm design, with particular emphasis on power grid analysis, cloud computing security, and hardware-software co-design. Education: B.S., Electronic Engineering, Tsinghua University (2002) M.S., Electrical and Computer Engineering, Northwestern University (2005) Ph.D., Electrical Engineering and Computer Science, Northwestern University (2008) Research Interests: Dr. Wang’s work addresses challenges in VLSI design automation, including sequential optimization, floorplanning, and design for manufacturability. He explores synthesis techniques for embedded systems and distributed systems, and develops algorithms for secure cloud computing and energy-efficient systems. Key areas include power grid verification, secure outsourcing of computations, and scalable simulation methods. Courses Taught: ECE 429: Introduction to VLSI Design ECE 587: Hardware/Software Co-Design ECE 449: Object-Oriented Programming and Computer Simulation ECE 586: Fault Detection in Digital Circuits ECE 473: Cloud Computing and Cloud Native Systems Professional Contributions: He leads the ACG Floorplanner project , a widely referenced tool for floorplan optimization. His research has been published in top-tier journals and conferences such as IEEE Transactions on Computer-Aided Design, DAC, and ICCAD. He actively contributes to education through curricula development in VLSI, cybersecurity, and cloud computing.