Find Academic Supervisors

• Digital Design Education
• Low-Power Electronics
• Hardware-Software Co-Design
• +3 more

Dr. Mark Johnson is a Senior Lecturer at the Elmore Family School of Electrical and Computer Engineering, Purdue University, located in West Lafayette, Indiana. His role focuses on advancing educational methodologies and curriculum development in electrical and computer engineering. He is affiliated with Purdue’s Brown Family Hall and maintains an active research agenda centered on digital design education, low-power electronics, and hardware-software co-design. Research interests include integrating industry standards (e.g., ABET) into teaching, FPGA applications, and fostering student success through innovative pedagogical frameworks. His work emphasizes practical tools like SOC prototyping and embedded systems design, alongside active learning strategies and technology integration in classrooms. Publications highlight a consistent focus on educational innovations, such as tablet-based documentation, dual-core prototyping challenges, and FPGA performance analysis. Despite no listed formal awards, his contributions to curriculum design and student project resources have significantly impacted undergraduate education in electrical engineering. No formal advisees or grants are listed, but his teaching portfolio includes senior project courses on x86 motherboards and RTL code structures. His work reflects a commitment to bridging academic theory with hands-on engineering practice.

Olga KostopoulouView profile

Professor

• Medical Decision Making
• Cognitive Psychology
• Clinical Judgment
• +6 more

Olga Kostopoulou is a Reader in Medical Decision Making at Imperial College London's Department of Surgery & Cancer, Faculty of Medicine. She specializes in applying cognitive psychology to healthcare decisions, focusing on clinical judgment in primary care, surgery, and diagnosis. Her research addresses diagnostic errors, decision support systems, and the impact of algorithms on clinical decisions. She has secured significant funding from Cancer Research UK and the EU, leading projects like the TRANSFoRm initiative. She is an Associate Editor of Medical Decision Making and Frontiers in Cognition , and leads the Decision Psychology Research Group. Olga's education includes a PhD and MSc from Cardiff University, and a Bachelor's from the National and Kapodistrian University of Athens. She has held academic positions at King's College London and the University of Birmingham. Her work spans over 20 years, with notable grants such as the CRUK project on cancer risk algorithms (2020–2024) and the EU-funded TRANSFoRm program (2010–2015). She has supervised three PhD students and examined numerous others across UK and international institutions. Her research interests include diagnostic support systems, predecisional information distortion, and the role of cognitive factors in medical decisions. She is affiliated with the Institute of Global Health Innovation, the Patient Safety Translational Research Centre, and the Centre for eXplainable Artificial Intelligence (XAI). Her recent articles explore AI in healthcare, antibiotic prescribing behavior, and collective intelligence in diagnostics. Olga has served as President of the Society for Medical Decision Making (2021–2022) and held leadership roles in international committees. She advocates for integrating behavioral science into healthcare policy, particularly in combating antimicrobial resistance and improving learning health systems.

Timo GerkmannView profile

Professor

• Statistical Signal Processing
• Machine Learning
• Speech and Audio Processing
• +3 more

Prof. Timo Gerkmann is a Professor at the University of Hamburg's Department of Informatics, leading the Signal Processing Research Group. His research focuses on statistical signal processing and machine learning for speech and audio applications, including communication devices, hearing aids, audiovisual media, and human-machine interfaces. He previously held roles at Technicolor Research & Innovation, KTH Royal Institute of Technology, and Siemens Corporate Research. His work emphasizes generative models, diffusion-based approaches, and acoustic signal enhancement. He currently serves as Senior Area Editor of the IEEE/ACM Transactions on Audio, Speech, and Language Processing. Research Interests: Statistical Signal Processing for Speech and Audio Machine Learning Applications in Acoustic Environments Diffusion Models for Audio Restoration Audio-Visual Speech Enhancement Human-Machine Interaction Systems Acoustic Scene Analysis Publications Highlight Trends: Recent works focus on diffusion models for speech enhancement, generative approaches to dereverberation, and audiovisual multimodal analysis. He has pioneered frameworks like ReverbFX datasets and FlowDec codecs, emphasizing perceptual quality and unsupervised domain adaptation. Advising & Grants: While no specific students or grants are listed, his research group actively publishes in top venues, indicating sustained academic contributions. His work bridges theoretical signal processing with applied systems engineering. Labs/Teams: Leads the Signal Processing (SP) Research Group at UHH, specializing in cutting-edge audio technologies and human-centric signal processing solutions.

Hudson SmithView profile

Assistant Professor

• Machine Learning
• Medical Imaging
• Quantum Physics
• +5 more

Hudson Smith is an Assistant Professor in the Department of Mathematical and Statistical Sciences at Clemson University, College of Science. His research focuses on integrating domain knowledge with machine learning to address data-constrained problems in healthcare, forensics, and physics. He holds a PhD in theoretical atomic physics from Ohio State University, combining first-principles approaches with data-driven methods. Key areas of research include medical imaging analysis (e.g., ultrasound quality assessment), forensic decomposition modeling (geoFOR database collaboration), and AI detection of coordinated disinformation campaigns. His work bridges theoretical physics (quantum systems, cold atoms) with applied machine learning in healthcare and social media analysis. Publications highlight contributions to AI in trauma care, forensic science, and social media disinformation detection. Active collaborations span forensic science, biomedical informatics, and quantum physics. GitHub repositories showcase contributions to visualization tools (e.g., Schrödinger equation solvers, Voronoi-based image stylization).

• Process Control
• System Dynamics
• Process Design Optimization
• +5 more

Nikolaos A. Diangelakis is an Assistant Professor at the Technical University of Crete (TUC), affiliated with the School of Chemical Engineering (ChEnvEng) and the Division II: Process Development, Analysis and Design. His research focuses on system dynamics, process control, and integrated process design optimization. He holds a PhD from Imperial College London (2017), an M.Sc. from the same institution (2012), and a Diploma in Chemical Engineering from the National Technical University of Athens (2011). He has conducted a Visiting Ph.D. research at Texas A&M University (2017). Diangelakis specializes in multi-parametric optimization and control strategies, with applications in chemical, environmental, and energy systems. He has contributed to software tools like PAROC and POP, advancing explicit model predictive control (MPC) and robust optimization frameworks. His work integrates process design, scheduling, and control to enhance operational efficiency in industries such as pharmaceuticals and energy systems. He has secured research funding through projects like 'PAROC' (National Science Foundation) and 'U Psi Psi' (EPSRC), and has been recognized with awards including the 2017 Excellence Award for Outstanding PhD Thesis in Computer-Aided Process Engineering (CAPE). His academic contributions span over 30 peer-reviewed publications, emphasizing topics like microgrid optimization, rotary tablet press control, and energy-efficient industrial processes. Diangelakis teaches Linear Algebra at TUC and actively engages in academic conferences, delivering invited lectures and presenting innovative research on MPC strategies and process operability. His research group collaborates internationally, advancing the frontiers of model-based systems engineering.

• Human Factors
• Human-Computer Interaction
• E-Health
• +6 more

John Paulin Hansen is a Professor at the Technical University of Denmark (DTU), affiliated with the Department of Technology, Management and Economics under the Technology and Business Studies school. His research focuses on human factors, human-computer interaction, and assistive technologies, particularly in gaze interaction and rehabilitation robotics. He holds a Ph.D. from Aarhus University (1992) and has led research groups at institutions like Risø National Laboratory and the IT University of Copenhagen. His work emphasizes improving quality of life for individuals with motor disabilities through innovations like The Eye Tribe and GazeIT lab initiatives. Research Interests include: Eye-tracking technology and gaze interaction systems Exoskeletons and AR interfaces for stroke rehabilitation Human digital twins in healthcare Telepresence robotics accessibility Brain-computer interfaces (BCI) Key Achievements: Founded EU’s COGAIN network, co-created The Eye Tribe startup (acquired by Facebook/Oculus), and leads the GazeIT lab supported by the Bevica Foundation. His projects address sustainable development goals through inclusive technology solutions. Awards: Vanførefondens Forskerpris 2018 Multiple Best Paper Awards (2019–2022) Reviewers' Favourite Best Paper Award at DESIGN2022 Advising & Grants: Mentor to startups like The Eye Tribe and Orbital (acquired by GN Audio). Current projects include EU Horizon 2020 Rehyb initiative for AR exoskeleton interfaces and Human Digital Twin applications in rehabilitation. Labs/Teams: GazeIT lab pioneers assistive tech innovations, collaborating with LEGO and Serious Games Interactive. Active in developing inclusive design pedagogy and digital twin methodologies for healthcare.

Fu HuView profile

Professor

• Trauma Care
• Medical Informatics
• Critical Care Monitoring
• +5 more

Fu Hu, Ph.D., is a Professor in the Department of Anesthesiology at the University of Maryland School of Medicine. He holds secondary appointments in Epidemiology & Public Health and Surgery. His research focuses on trauma care, predictive analytics, telemedicine, and medical informatics, with an emphasis on leveraging continuous vital signs and machine learning to improve patient outcomes. Key areas include traumatic brain injury monitoring, blood transfusion prediction, and critical care systems design. Education: B.S. in Electrical Engineering (Shanghai University, China, 1984), M.S. and Ph.D. in Computer Science (University of Maryland Baltimore County, 1992 and 2013). Research interests span medical AI, surgical workflow optimization, and telemedicine platforms. His work integrates advanced computational methods with clinical data to address challenges in trauma and critical care. Recent efforts include developing algorithms for real-time patient monitoring and decision support systems. He has led/co-investigated multiple grants on topics like non-invasive monitoring, trauma patient outcomes, and telemedicine. His publications highlight innovations in predictive modeling, medical imaging analysis (e.g., retinal disease detection), and clinical informatics. Notable collaborations include studies on automated ICP monitoring and the ONPOINT project for trauma care.

• Digital Transformation
• Educational Technology
• Curriculum Analytics
• +1 more

Venky Shankararaman is Professor of Information Systems (Education) and Vice Provost (Education) at Singapore Management University. A PhD graduate from University of Strathclyde (1992), he holds the SMU Fortitude Fellow in Computing Education position and serves as President of Asia-Pacific Association for International Education. Research expertise spans digital transformation, educational analytics, and enterprise architecture. Primary focus areas include AI governance frameworks, curriculum analytics, and microservices design patterns. Publications emphasize practical applications in government AI systems (ACQAR framework), educational technology (ChatGPT mentoring), and enterprise architecture (microservices decision frameworks). Recent work explores explainable AI for public sector and project-based learning models. Major Awards: Public Administration Medal (Silver) - Singapore Government (2023) Distinguished Education Award - SMU (2023) AIS Best Conference Paper Award (2020) Public Administration Medal (Bronze) - Singapore Government (2017) SAP Outstanding Academic Award (2014) Advises PhD students Kevin TAUKOOR Heman and Ng Kok Leong. Leads university-wide education initiatives including the Data Access and Use Committee and Teaching Bank project. Pioneered SMU's competency-based curriculum analytics tools and digital transformation frameworks for banking. Serves on ACM Education Advisory Committee and developed national skills frameworks for Singapore's IT workforce.

Trevor CickovskiView profile

Assistant Professor

• Bioinformatics
• Microbiome Analysis
• GPU Computing
• +6 more

Trevor Cickovski is an Assistant Professor at Florida International University's Knight Foundation School of Computing and Information Sciences, with a focus on bioinformatics and computational biology. Holding Graduate Faculty status, he specializes in microbiome analysis, GPU computing, network analysis, and Unix systems. His academic journey began with a Ph.D. in Computer Science and Engineering from the University of Notre Dame (2008). Current roles: Associate Director, Assistant Teaching Professor Research affiliations: Bioinformatics Research Group (BioRG), ACM, IEEE Dr. Cickovski's research spans microbiome analysis, software engineering, and GPU-accelerated computing. His work explores the microbiome's role in neuropsychiatric disorders (ADHD), genetic conditions (A1AD), respiratory diseases (COPD), and environmental phenomena (red tides) through multi-omics integration. His publications in Journal of Medical Microbiology , Bioinformatics , and IEEE/ACM TCBB reflect strengths in plugin-based software frameworks (PluMA), network analysis, and computational pipeline optimization. Awards include multiple teaching excellence honors and industry funding from NVIDIA. 2020 Graduate Faculty Status 2019 FIU CAT Fellow 2019 FIU Faculty Teaching Award 2017 Teaching Excellence Award 2014 Promotion to Associate Professor As lead developer of PluMA, he enables cross-language plugin development for bioinformatics workflows. Funded by NIJ and NSF, his projects apply machine learning to epigenetics and computational approaches to vaccine discovery.

Alex SavachkinView profile

Associate Professor

• Resilience Engineering
• Operations Research
• Public Health
• +5 more

Alex Savachkin is an Associate Professor and Graduate Director in the Department of Industrial & Management Systems Engineering at the University of South Florida, Tampa. His academic career is complemented by industry experience as a technology feasibility analyst at MBS Consulting and industrial engineer at Integral Inc., both in Minsk, Belarus. He holds a Ph.D. in Industrial Engineering from Texas A&M University (2005), an M.S. in Management of Technology from the University of Colorado (1998), and a B.S. in Industrial Engineering/Applied Math from Belarusian State University (1996). Savachkin's research focuses on applying applied stochastic processes to design resilient cyber-physical-social systems. Key research areas include stochastic measures of system resilience, assessment of resilience in physical and social systems, and multi-objective optimization models for resilience enhancement. His work bridges theoretical frameworks and practical applications in infrastructure and healthcare systems. His academic contributions are highlighted by teaching awards: the IIE Annual Award for Excellence in the Teaching of Operations Research (2013) and USF Outstanding Undergraduate Teaching Award (2012) . In advising, he has guided PhD students such as Daniel Romero (2018), Sandro Paz (2014), and co-advised Dayna Martinez (2012) and Diana Prieto (2012). Current advisee Weimar Ardilla is pursuing a Ph.D. His research team collaborates on projects spanning pandemic preparedness and infrastructure resilience. His research trends emphasize balancing costs/benefits in resilience decisions (2025), critical reviews in system resilience (2025), and pandemic influenza outbreak modeling (2024–2010). He specializes in resilient distribution networks, heuristic algorithms for optimization, and cross-regional resource allocation strategies.

Varadraj GurupurView profile

Associate Professor

• Health Informatics
• Decision Support Systems
• Data Semantics
• +3 more

Varadraj Gurupur is an Associate Professor at the University of Central Florida (UCF) in the School of Global Health Management and Informatics. He holds a Ph.D. and has over 15 years of teaching experience across two countries. His research focuses on software engineering decision support systems for healthcare and education, with a particular emphasis on health informatics, decision support systems, and data semantics. He has received numerous awards, including the AHIMA Triumph Award for Innovation (2021) and the Joseph M. Biedenbach Outstanding Engineering Educator Award (2023). Dr. Gurupur has authored over 100 peer-reviewed publications, including journal articles, conference papers, and book chapters. He has secured research grants and holds three patents in health information management. His work addresses challenges in electronic health records (EHRs), telehealth, and data quality, with applications in diabetes care, dental imaging, and frailty assessment in aging populations. He is active in IEEE, having chaired the Workgroup on Data Quality for EHRs and served as Chair of the IEEE Orlando Section (2021–2023). Key contributions include developing AI-driven decision support systems, improving EHR data completeness through neural network models, and advancing telemedicine frameworks. His interdisciplinary research bridges clinical needs with technological solutions, emphasizing global health equity and patient-centric care.

Hua DongView profile

Professor

• Inclusive Design
• User-Centered Design
• Human-Computer Interaction
• +6 more

Professor Hua Dong is a leading academic in inclusive design at Brunel University London , serving as Full Professor in Design, Director of Brunel Design Research Centre, and Inaugural Dean of Brunel Design School (2020-2024). She was elected Vice-Chair of the Design Research Society (DRS) in 2024, is a Fellow of DRS, and will chair QAA's Art and Design Subject Benchmarking Statement in 2025-26. Research Expertise: Dong pioneers inclusive design theory and application across industrial, engineering, architectural, and service design domains, with a focus on AI-powered products, mHealth for aging populations, and multisensory design. Her work bridges academia and industry through projects like the Design Bugs Out Challenge and collaborations with Ant Financial, Shanghai M&G Stationery, and UK-China initiatives. Scientific Contributions: She has secured major grants including an EPSRC grant and Newton Funds . Her publications span 2007-2024, covering AI-enhanced design ideation, service ontology for aging populations, and sustainable inclusion frameworks. She co-founded the Inclusive Design Research Centre in China and curated a special issue on inclusive design for Design magazine. Scientific Awards: Fellow of Design Research Society (2019) AHRC SEED Fellowship Member of Peer Review College of AHRC Teaching & Leadership: Dong holds a PGCert in Academic Practice and has shaped design education through co-design methodologies. She has supervised 14 PhD and 1 EngD student completions, organized the Cambridge Workshop for Universal Access since 2014, and served as External Examiner at Imperial College. Global Impact: As International Judge for SDGs Design Awards and advisor to UK charities (AgeUK), governments (Nanjing), and corporations (Reckitt Benkiser), she translates design research into tangible societal benefits. Her work has been exhibited at international venues including the Milan Triennale and Zurich Design Museum .

Maitreyee DeyView profile

Research Fellow

• Machine Learning
• Artificial Intelligence
• Low Carbon Technologies
• +9 more

Dr. Maitreyee Dey serves as a Visiting Fellow in Mechanical Engineering and Design and works as a Post-doctoral research fellow at London South Bank University's Department of Electrical and Electronics Engineering. She concurrently holds the position of Data Mining Lead at Neuville Grid Data Management Limited, where she contributes to significant energy research projects. Her research expertise spans machine learning/artificial intelligence and low carbon technologies, with additional interests in pattern recognition, big data analysis, deep learning, image processing, energy optimization in smart buildings, renewable energy technology, and Computer-aided Diagnosis for smart health applications. Her work directly contributes to UN Sustainable Development Goals related to clean energy and sustainable communities. Analysis of her recent publications reveals a strong research trajectory focusing on two primary domains: power grid analysis using machine learning techniques and microwave-based medical imaging for breast cancer detection. Her work demonstrates sophisticated applications of neural networks and data mining methods to solve complex problems in both energy systems and medical diagnostics. Best Paper Award at the CUE conference, Elsevier (2020) Endorsement by the Royal Academy of Engineering Dr. Dey serves as an Editorial board member for MDPI journals and as an Adhoc reviewer for high-impact publications including IEEE transactions and Nature Scientific Data. She is actively involved in the Access to Innovation (A2i) and Low Carbon London (LCLDN) projects funded by the EU commission through its regional development Fund, working in collaboration between London South Bank University and Neuville Grid Data Management Limited. Her professional affiliations include active membership in IEEE, CIGRE NGN, and the Energy Institute, demonstrating her engagement with key professional organizations in her fields of expertise.

Hong QinView profile

Professor

• Computer Graphics
• Geometric Modeling
• Physics-Based Modeling
• +5 more

Hong Qin is a Full Professor (with tenure) in the Department of Computer Science at Stony Brook University, where he is also a member of the Center for Visual Computing. He holds a B.S. and M.S. from Peking University, and a Ph.D. from the University of Toronto. His research focuses on computer graphics, geometric modeling, physics-based simulation, and scientific visualization. Education: B.S. (1986) and M.S. (1989) in Computer Science from Peking University; Ph.D. (1995) in Computer Science from the University of Toronto. Research interests include geometric modeling, computer-aided design, scientific visualization, medical imaging, and robotics. He has received notable awards like the NSF CAREER Award (1997), Alfred P. Sloan Fellowship (2001-2005), and multiple NSF grants. Qin has organized major conferences such as Computer Graphics International 2005 and chairs editorial boards for IEEE TVCG and The Visual Computer.

Tzu-Chieh Kurt HongView profile

Assistant Professor

• Shape Grammars
• Visual Computing
• Generative Modeling
• +4 more

Dr. Tzu-Chieh Kurt Hong is an Assistant Professor at the Department of Architecture within the School of Architecture and Design at the University of Kansas. He teaches design studios, algorithmic modeling, parametric design, and computer programming in design studies. He founded K9K Research & Design, focusing on innovative computational design methodologies. His research emphasizes shape grammars, visual computing, and generative modeling, particularly through the development of the Shape Machine—a computational tool integrating shape recognition and rewriting into CAD systems. Education: Ph.D. in Architecture (Design Computation), Georgia Institute of Technology (2021) M.S. in Architecture and Design, University of Michigan (2016) Master of Architecture, National Chiao Tung University (2008) M.S. and B.S. in Electronics Engineering, National Chiao Tung University (Taiwan) Research Interests: Shape embedding and rewriting in CAD systems Algorithmic design and generative systems Integration of traditional methods (e.g., Euclidean geometry) with computational tools Applications in architectural education and practice Awards: 2022 ARCC Dissertation Award for contributions to CAD systems through shape recognition research Professional Experience: Research Scientist at Georgia Tech's Shape Computation Lab (2016–2022) Contributions include the Shape Machine framework and the Smart 3D Atlanta visualization platform Labs/Teams: Leads the K9K Research & Design initiative, collaborating with academic and industry partners on computational design tools and generative workflows.