Find Academic Supervisors

Anna-Karin GustavssonView profile

Assistant Professor

• Single-Molecule Imaging
• Super-Resolution Microscopy
• Cellular Dynamics
• +4 more

Anna-Karin Gustavsson is an Assistant Professor of Chemistry at Rice University, holding the Norman Hackerman-Welch Young Investigator Chair. Her research focuses on developing 3D single-molecule tracking and super-resolution imaging techniques to study cellular nanoscale structures and dynamics, with applications to aging and cancer. She leads the Gustavsson Lab, which integrates innovative microscopy tools and analysis algorithms to advance biomedical and biophysical understanding. Dr. Gustavsson earned her PhD in Physics from the University of Gothenburg (2015), followed by postdoctoral work in Nobel Laureate W.E. Moerner’s lab at Stanford University, where she pioneered light-sheet-based super-resolution imaging. Her honors include the FEBS Richard Perham Prize (2012), NIH K99/R00 Award (2019), and CPRIT Recruitment Award (2020). Research Interests: 3D Single-Molecule Super-Resolution Microscopy Cellular Nanoscale Architecture Primary Cilia Biogenesis FLASH Radiation Therapy Mechanisms Genomic Stability and Epigenetic Regulation Grants & Awards: CPRIT Recruitment of First-Time Tenure-Track Faculty (2020) NIH K99/R00 Pathway to Independence (2019) PicoQuant Young Investigator Award (2018) Lab Focus: The Gustavsson Lab develops microscopy platforms to investigate molecular mechanisms in health and disease, emphasizing translational applications in cancer and laminopathies. Their work bridges physics, chemistry, and biology through cutting-edge imaging innovations.

• Statistical Signal Processing
• Bayesian Methods
• Multi-Target Tracking
• +4 more

Professor Ba Tuong Vo is a faculty member in the Department of Electrical and Computer Engineering at Curtin University, Perth, Australia. He holds the academic rank of Professor within the School of Electrical Engineering, Computing, and Mathematical Sciences. His primary research interests lie in statistical signal processing, Bayesian methods, multi-target tracking, and random finite set theory, with applications to sensor fusion, data-driven decision making, and stochastic control systems. He has contributed significantly to developing advanced filtering techniques such as the Generalized Labeled Multi-Bernoulli (GLMB) filter and multi-sensor fusion algorithms for large-scale tracking problems. His work bridges theoretical advancements in random finite set theory and practical implementations for real-world applications like autonomous robotics, medical imaging, and environmental monitoring. Notably, he has pioneered methodologies for handling unknown clutter rates, sensor management, and occlusion challenges in multi-object systems. Professor Vo’s research also extends to multi-modal data integration (e.g., audio-visual tracking) and has been applied to diverse domains including space situational awareness and cell lineage tracking. His academic profile includes over 150 peer-reviewed publications, with a focus on IEEE Transactions journals and leading conferences in signal processing and robotics. Beyond technical contributions, he actively mentors students and collaborates internationally on projects funded by industry and government grants. A key focus is fostering innovation in scalable multi-target tracking solutions for emerging technologies like autonomous systems and smart infrastructure.

• Digital Health
• Artificial Intelligence
• Machine Learning
• +6 more

Professor Dorothy Monekosso holds a position at Durham University as Professor of Computer Science and serves as Chief Technical Officer (0.5FTE) at More Life UK Ltd, a health services company. Her academic career spans over two decades, with a PhD in Spacecraft Engineering (2000) from Surrey Space Centre, a Master's in Satellite Engineering, and a Bachelor's in Electronic Engineering. Her research focuses on assistive technologies, digital health innovations, and applications of machine learning in healthcare. Notable contributions include the Virtual Physiotherapist project (funded by MRC) and the Health-5G mixed reality system for emergency medical assistance. She has pioneered work on ear recognition biometrics and VR-based rehabilitation frameworks like Rehab-Immersive. Professional roles include leadership as PGR Director at Durham and membership in prestigious committees such as the Royal Society Diversity Committee and World Economic Forum initiatives. Awards include the Royal Academy of Engineering Foresight Award (2000) and BCS Honorary Fellowship (2020). Her work integrates robotics, AI, and sensor networks into solutions for independent living and healthcare delivery. Current projects explore digital twins in healthcare and smart environments to support aging populations.

Anne-Marie BrouwerView profile

Professor

• Psychophysiology
• Cognitive Neuroscience
• Human-Computer Interaction
• +3 more

Anne-Marie Brouwer is a Professor (part-time) at Radboud University’s Donders Institute for Brain, Cognition and Behaviour, holding the chair in ‘Mental State Monitoring’. She concurrently serves as Senior Scientist at TNO Human Factors (Soesterberg, Netherlands). Her work focuses on extracting cognitive and emotional states from physiological signals (EEG, skin conductance, eye tracking) and applying these insights to improve human performance and well-being in real-world contexts. Education: MSc in Experimental Psychology (Radboud University, 2002) Her research emphasizes multi-modal signal integration, leveraging wearables and lab equipment to predict behavior and mental states. Key themes include attention monitoring, interpersonal physiological synchrony, and ethical applications of biometric data. Her interdisciplinary approach bridges neurocognitive science with human factors engineering. Her recent work explores how physiological synchrony reflects shared attention in groups and develops robust measures of attentional engagement using wearable sensors. While no awards are explicitly listed, her contributions to mental state monitoring are recognized through her academic and industry roles. Advising and grants: No formal advisees listed, but her research labs at TNO and Radboud collaborate on applied projects. Current focus includes food experience, man-machine interaction, and real-time cognitive assessment systems. Labs/teams: Active in Donders Institute’s interdisciplinary teams and TNO’s Human Performance division.

• Human-Computer Interaction (HCI)
• Mobile Computing
• Wearable Computing
• +5 more

Quentin Roy is an Assistant Professor at Université Grenoble Alpes (UFR SHS) with extensive academic experience across multiple institutions including University of Waterloo, National University of Singapore, and Télécom ParisTech. His research focuses on Human-Computer Interaction (HCI) with specialties in conversational agents, wearable computing, mobile interfaces, and gesture-based interaction. He has authored over 20 peer-reviewed publications in top venues like CHI, AVI, and TVCG, receiving two Best Paper Honorable Mentions. His work explores topics ranging from tactile feedback systems to AR medical guidance. Quentin has taught courses such as Interaction Humain-Machine, Programming, and Mobile Technologies across various universities. His research interests include: Design of conversational agents considering user personality and task context Optimization of touch-based and gesture interaction techniques Development of wearable computing interfaces for physical activity scenarios Error reduction strategies in word suggestion systems Multi-touch interaction modalities for medical visualization Key contributions include studies on: Spacial anchoring in AR surgery guidance (CHI '25) Cognitive load effects on vibrotactile pattern recognition (AVI '18) Path creation using interactive videos on smartglasses (CHI '17) He has collaborated widely with institutions like Singapore Management University, University of Waterloo, and Télécom ParisTech. Current research explores adaptive interfaces for emerging device form factors and multimodal interaction systems.

Loren W. BurgerView profile

Professor

• Conservation Biology
• Wildlife Ecology
• Agricultural Land Management
• +4 more

Dr. Loren W. Burger is Dean and Director at the College of Forest Resources, Mississippi State University. He holds a PhD in Wildlife & Fisheries from the University of Missouri and has extensive expertise in conservation biology and wildlife ecology. His research focuses on measuring environmental benefits of conservation practices in agricultural and forested landscapes, strategic conservation planning, and avian population dynamics. Research interests include evaluating conservation buffers, climate impacts on migratory birds, and optimizing conservation practices for water quality and biodiversity. He has authored over 200 peer-reviewed publications, with recent work emphasizing climate-driven population dynamics and Bayesian modeling approaches. Awards include the William L. Giles Distinguished Professorship and the National Quail Symposium Award of Excellence. His work integrates precision agriculture and geospatial tools to enhance conservation outcomes in agricultural systems. He advises on wildlife management policies and collaborates with stakeholders to translate research into actionable conservation strategies. Key projects include the Catalpa Creek watershed study and the CP33 Habitat Buffers initiative. He leads teams in the Forest and Wildlife Research Center, emphasizing interdisciplinary approaches to ecological challenges.

• Categorical Data Analysis
• Group Testing
• Bayesian Estimation
• +3 more

Christopher McMahan is a Full Professor at Clemson University's School of Mathematical and Statistical Sciences within the College of Science. His academic roles include serving as Associate Director for Graduate Studies and co-leading the Clemson-MUSC AI Hub. McMahan holds a Ph.D. in Statistics from the University of South Carolina (2012), M.S. in Mathematics from Western Kentucky University (2008), and B.S. in Mathematics from Austin Peay State University (2006). His research focuses on developing statistical methodologies for complex data problems, with interdisciplinary applications spanning epidemiology, agriculture, and biomedical sciences. Core research areas include Bayesian modeling, group testing designs, high-dimensional regression, and spatio-temporal analysis. Current projects involve genomic prediction in crop science, infectious disease modeling, and machine learning applications for public health surveillance. McMahan's publication record demonstrates consistent contributions to statistical theory and applied interdisciplinary research. Recent work exhibits strong emphasis on: Bayesian computational methods for large-scale problems Epidemiological modeling of COVID-19 transmission Genomic prediction of agricultural phenotypes Statistical learning approaches for public health data Biomedical applications of group testing designs Environmental risk assessment methodologies His scholarly recognition includes: Fellow of the American Statistical Association (2022) Multiple ASA Outstanding Statistical Application Awards ENAR Distinguished Student Paper Awards College of Science Dean's Associate Professor (2021-2023) McMahan maintains an active research program with substantial grant funding, including NIH R01 awards totaling over $2 million. He currently advises four postdoctoral scholars and has mentored eight doctoral graduates. His collaborative work involves interdisciplinary teams across computational biology, plant genomics, and public health epidemiology. Laboratory and team leadership includes directing graduate research through the Clemson-MUSC AI Hub and coordinating multi-institutional consortia like the NCAA-DoD CARE Consortium. Future research directions emphasize developing computationally efficient statistical methods for high-dimensional biomedical data and expanding applications in precision agriculture.

• Laser Spectroscopy
• Nanomaterials
• Biological Imaging
• +5 more

Jason McNeill is a Professor in the Department of Chemistry at Clemson University's College of Science. He obtained his Ph.D. in Chemical Physics from the University of California at Berkeley in 1999 and joined Clemson in 2002. His research focuses on laser spectroscopy, nanomaterials, and biological imaging, particularly using fluorescent nanoparticles for sensing and charge transport studies in organic semiconductors. Educational Background A.S., Chemistry, Sauk Valley Community College B.S., Chemistry, Northern Illinois University Ph.D., Chemical Physics, University of California at Berkeley His work spans super-resolution microscopy, single-molecule spectroscopy, and energy transfer dynamics in polymer nanoparticles. Recent publications highlight advancements in dual-mode imaging, charge carrier tracking, and biofunctionalized polymer dots. He received the NSF CAREER Award in 2006. Notable courses taught include Physical Chemistry I/II, Atomic and Molecular Structure, Quantum Chemistry, and Modern Laser Spectroscopy.

Ioannis D SchizasView profile

Associate Professor

• Signal Processing
• Machine Learning
• Data Mining
• +9 more

Ioannis D. Schizas is an Associate Professor in the Department of Electrical and Computer Engineering at The University of Texas at Arlington (UTA), with a primary appointment in the Nedderman College of Engineering. He joined UTA in 2011, progressing from Assistant to Associate Professor before being promoted to Full Professor (effective September 2024). His research focuses on machine learning, signal processing, distributed systems, and data mining, with applications in sensor networks, remote sensing, and graph-based learning. Education: He holds a Diploma in Computer Engineering and Informatics (University of Patras, Greece, 2004), an M.Sc. (2007), and a Ph.D. (2011) in Electrical and Computer Engineering from the University of Minnesota. Research Interests: Schizas' work spans machine learning algorithms, statistical signal processing, and distributed optimization. Key areas include clustering techniques, kernel methods, dimensionality reduction, and applications in wireless sensor networks, hyperspectral imaging, and multi-target tracking. Publications: His recent work emphasizes scalable machine learning (e.g., kernel-based clustering, neural network pruning for IoT), distributed sensor data processing, and graph filtering for data reduction. Notable trends include fusion of modalities (e.g., hyperspectral unmixing) and real-time systems (e.g., video tracking). Awards: He received the Outstanding Advisor Award (2024) and Advisor Excellence Award (2023) at UTA. His research has been supported by grants from the Army Research Office, AFOSR, and NSF, totaling over $1M in funding. Advising & Grants: Schizas has advised numerous PhD/Master’s students on topics like kernel learning, distributed tracking, and data-driven techniques. His grants include projects on dynamic data-driven systems (e.g., multi-threat tracking, DDROSSIL framework). Labs/Teams: His research group collaborates on projects involving distributed algorithms, sensor networks, and machine learning applications. Active collaborations exist with organizations like Adobe Systems and the Air Force Office of Scientific Research.

Qian LiaoView profile

Professor

• Environmental Fluid Mechanics
• Hydrodynamic-Biogeochemical Modeling
• Hydraulics and Hydrology
• +2 more

Qian Liao is a Professor in the Department of Civil and Environmental Engineering at the University of Wisconsin-Milwaukee's College of Engineering & Applied Science. Currently on sabbatical for the Fall 2024 semester, Dr. Liao specializes in environmental fluid mechanics and aquatic system dynamics. Dr. Liao holds a PhD in Civil and Environmental Engineering from Cornell University (2004), an MS in Urban and Environmental Science from Peking University (1999), and a BS in Hydraulic and Hydropower Engineering from Tsinghua University (1996). His research focuses on: Hydrodynamic-biogeochemical interactions in aquatic systems Turbulence modeling and mixing processes Advanced flow measurement techniques including imaging-based analysis Hydraulic systems and watershed management His recent publications demonstrate strong focus on signal processing applications in sensing systems, radar technologies, and wireless communications, with particular emphasis on multi-target localization, signal authentication, and efficient computational methods.

Mark TravassosView profile

Associate Professor

• Malaria Pathogenesis
• Malaria Epidemiology
• Cerebral Malaria
• +3 more

Dr. Mark Travassos is an Associate Professor of Pediatrics and Medicine at the University of Maryland School of Medicine. He serves as the Director of the Global Health Track for the Pediatrics Residency Program and co-directs the Immunoepidemiology and Pathogenesis Unit within the Center for Vaccine Development and Global Health's Malaria Research Program. His expertise spans malaria pathogenesis, epidemiology, and vaccine development, with a focus on cerebral malaria and protective immunity in malaria-endemic regions. Education: A.B. in Biology (Harvard University), MSc in Biology (University of Oxford), M.D. (Cornell University Medical College), pediatric residency at Rainbow Babies and Children's Hospital, and a fellowship in Pediatric Infectious Diseases and Tropical Pediatrics at the University of Maryland School of Medicine. Research Interests: Dr. Travassos investigates malaria parasite variant surface antigens, sickle cell trait protection mechanisms, and natural immunity development. His work includes serosurvey studies in Ethiopia and global health initiatives addressing vaccine coverage and migrant health. He collaborates on projects analyzing immune responses to malaria antigens and genomic epidemiology. Advising & Grants: Advises PhD students Emily Stucke and Albert Zhou. Group members include Noah Ventimiglia, Rafal Sobota, Rachel Bailey, and Bryan Cummings. His research has been supported by grants focusing on malaria pathogenesis, vaccine development, and global health interventions. Labs & Teams: Active in the Malaria Research Program and Center for Vaccine Development and Global Health. Collaborates with institutions in Mali and Ethiopia, focusing on field studies and clinical trials.

Chris Irwin DavisView profile

Associate Professor

• Computational Linguistics
• Computational Semantics
• Natural Language Processing (NLP)
• +7 more

Chris Irwin Davis is an Associate Professor of Instruction in the Department of Computer Science at the University of Texas at Dallas (UTD), where he earned his Ph.D. under Dr. Dan Moldovan. He serves on the UTD Academic Senate (2025-2026). His expertise spans computational linguistics, computational semantics, and NLP for under-resourced languages, particularly Indo-Iranian languages like Farsi and Urdu. His research also explores machine ethics, computational creativity, and artificial general intelligence. Education: Ph.D. in Computer Science from UT Dallas (advisor: Dr. Dan Moldovan). Graduate research at the Human Language Technology Research Institute. Research Interests: Computational semantics and event representation NLP for languages with sparse resources Machine translation and transliteration systems Machine ethics and AGI Assistive technologies and personal robotics Publications focus on NLP applications for under-resourced languages, transliteration systems (e.g., Tajik-Farsi), and machine learning models for grammatical error detection. Recent work explores curriculum learning for infant-inspired AI models and multimodal semantic processing. Teaching includes foundational computer science courses (Discrete Math, Programming Paradigms) and advanced topics like NLP and AI. No formal advisees listed, but extensive teaching and curriculum development experience. Active in developing open-source tools like Phramer (statistical phrase-based translator) and frameworks for event semantics in Persian.

• Wireless Communications
• Millimeter-wave Technology
• Computer Architecture
• +1 more

Masoud Zarifneshat serves as a Lecturer in the Department of Computer Science at Renssela Polytechnic Institute (RPI), teaching computer architecture and hardware courses since 2022. He holds a PhD in Computer Science from Michigan State University and contributes to RCOS (Rensselaer Center for Open Source) as a supervisor for Fall 2025. His educational background includes: PhD in Computer Science, Michigan State University (2022) Dr. Zarifneshat's research centers on wireless communications and millimeter-wave technology, with emphasis on algorithmic solutions for reliable links. He investigates beam adaptation via out-of-band signals, multi-objective user association for load balancing, and machine learning-driven blockage prediction. His teaching expertise spans computer architecture and hardware, prioritizing student communication and tailored support. Publication trends show concentrated work on millimeter-wave communications (2021-2023) in top journals like IEEE Transactions on Mobile Computing, addressing blockage and reliability challenges. Earlier research (2013-2015) explored wireless sensor networks and mobility models, demonstrating consistent focus on network optimization. He actively mentors through RCOS supervision and teaches core courses including CSCI 4963 (Computer Architecture) and CSCI 2800 (CAOS). While specific grant funding isn't detailed, his publication record indicates ongoing research activity. His collaborative work with institutions like IEEE-affiliated journals highlights engagement with the broader academic community. Dr. Zarifneshat operates within RPI's Department of Computer Science without a named research lab, but his RCOS involvement provides student project leadership opportunities. His work bridges theoretical algorithm development and practical wireless network applications.

• 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.

Antonietta LanzaView profile

Assistant Professor

• Man-Machine Interaction
• Machine Learning
• Knowledge Acquisition
• +4 more

Antonietta Lanza is an Assistant Professor of Computer Science at the Department of Informatics, University of Bari, Italy. She earned her degree in Information Science from the same university in 1978 and has been a faculty member since 1984. Her research spans man-machine interaction, machine learning, and knowledge acquisition with applications in technical document processing and geospatial data interpretation. Education : Degree in Information Science (University of Bari, 1978) Affiliations : Department of Informatics, University of Bari Her research focuses on integrating machine learning with geospatial analysis, particularly for wind speed forecasting, topographic map interpretation, and cadastral data processing. She has developed algorithms for spatio-temporal modeling and spatial clustering of structured data. Recent publications primarily address wind power prediction, spatio-temporal lazy learning, and cluster-based time series modeling. Applications span renewable energy, environmental planning, and maritime AIS data analysis. She actively contributes to academic associations AI*IA (Italian Association for Artificial Intelligence) IAPR-IC (International Association of Pattern Recognition - Italian Chapter) and has created software like PS_package , a tool for line-drawing segmentation.