Find Academic Supervisors

• Experimental Psychology
• Affective Science
• EEG Research
• +5 more

Richard Naar is a Junior Lecturer in Quantitative Methods and Senior Specialist in EEG Research at the University of Tartu's Faculty of Social Sciences, Institute of Psychology. His academic career spans over a decade at the university, progressing from laboratory assistant positions to his current dual appointment beginning in 2025, reflecting his growing expertise in experimental psychology and neuroscience methodologies. Education: PhD in Psychology (Interrupted, 2016-2024), University of Tartu MSc in Psychology (2015), University of Tartu BSc in Psychology (2013), University of Tartu Naar's research focuses on experimental psychology , particularly the intersection of affective science and cognitive processes . His work examines how emotional states influence time perception, attention mechanisms, and physiological responses. He specializes in EEG methodology and quantitative analysis of psychological phenomena, with significant contributions to understanding emotion regulation through cognitive reappraisal techniques. His doctoral research explored outcome-associated predictions in relation to affective states, feelings of control, and personality. His recent publications demonstrate a strong focus on affective neuroscience , with multiple 2024 papers examining emotion regulation using multimodal approaches including EEG, EMG, and self-report measures. His work bridges experimental psychology with practical applications in emotional well-being, particularly through the Estonian Center of Excellence of Well-Being Sciences project. Awards: Diploma from the Estonian Research Council's Master's Thesis Competition in Social Sciences (2015) Naar is actively involved in major research projects including the Estonian Center of Excellence of Well-Being Sciences (2024-2030), building on his previous work with the 'Psychological mechanisms of emotion regulation with reappraisal' project (2020-2024). His research has received substantial funding from Estonian Research Council and other national bodies, with cumulative project funding exceeding one million euros. As a Senior Specialist in EEG Research, Naar contributes to the university's neuroscience infrastructure and methodology development, working with advanced electrophysiological techniques to investigate affective attention and cognitive processes. His dual role as Junior Lecturer in Quantitative Methods highlights his expertise in both experimental design and statistical analysis within psychological research.

• Digital Marketing
• Consumer Behavior
• Influencer Marketing
• +5 more

Andreas Bayerl is an Assistant Professor of Marketing at the Erasmus School of Economics, Erasmus University Rotterdam. He holds a PhD in Quantitative Marketing from the University of Mannheim and conducts interdisciplinary research at the intersection of digital behavior, consumer psychology, and data science. His research focuses on how individuals generate, process, and are influenced by digital information, particularly in the context of online reviews and influencer marketing. Using a mixed-method approach—combining large-scale observational data, text and image analysis, and field and laboratory experiments—Andreas investigates behavioral patterns in digital ecosystems. His work has been published in leading journals including Journal of Marketing , Harvard Business Review , MIT Sloan Management Review , and Nature Human Behavior . The most recent publications reveal a consistent focus on digital influence, credibility, and consumer decision-making. Key themes include the effectiveness of micro-influencers, the psychological impact of fake reviews, multimodal analysis of visual and textual content, and experimental validations of marketing strategies across platforms. His methodological rigor and real-world relevance are evident across these works. Andreas has received significant recognition for his contributions, including: H. Paul Root Award for groundbreaking research in influencer marketing He actively teaches in the area of data science and marketing analytics, contributing to the next generation of analytically skilled marketers. While no formal advisees are listed, his role as an assistant professor suggests involvement in student supervision and academic mentorship. His research program appears to be supported by empirical rigor and industry relevance, though specific grants or lab affiliations are not mentioned in the provided text.

• Medical Image Analysis
• Computer Vision
• Machine Learning
• +7 more

Tolga Tasdizen is a Professor in the Department of Electrical & Computer Engineering and an Adjunct Professor at the School of Computing, University of Utah. His research bridges computer vision, machine learning, and interdisciplinary applications in medical imaging, nuclear forensics, and urban health. Primary affiliation: Department of Electrical & Computer Engineering, University of Utah Secondary affiliation: School of Computing, University of Utah Research Interests Developing novel deep learning frameworks for histopathological image analysis and medical signal processing Applying computer vision to characterize built environments and examine public health outcomes Advancing nuclear forensic techniques through material morphology and machine learning Exploring biases in AI models for clinical and epidemiological applications Creating explainable AI workflows for medical diagnostics Analyzing urban infrastructure impacts on traffic safety and chronic disease prevalence Article Trends demonstrate expertise in: Medical imaging (histopathology, ECG analysis, chest X-rays) with applications in Alzheimer's disease and cancer diagnostics Urban health studies using Google Street View data to assess built environments' impacts on obesity, diabetes, and traffic injuries Nuclear forensics through SEM image analysis of uranium oxides and actinide materials Robust AI training techniques (contrastive learning, domain adaptation) for clinical and environmental datasets Collaborative Networks span radiology, cardiology, epidemiology, and nuclear engineering disciplines. His work often involves multi-institutional teams and emphasizes scalable data collection methods like eye-tracking and computer vision.

• Medical Imaging
• PET/CT Imaging
• Quantitative Image Analysis
• +5 more

Drew A. Torigian is a Professor of Radiology at the Perelman School of Medicine, University of Pennsylvania, with extensive contributions to medical imaging research. His work focuses on developing advanced methodologies for PET/CT image analysis, disease quantification, and anatomical segmentation through innovative applications of deep learning and computer vision techniques. Dr. Torigian's research interests span multiple critical areas in medical imaging including quantitative image analysis, PET/CT applications, deep learning in medicine, radiation therapy planning, and anatomical segmentation. His work has significantly advanced the field of medical image analysis through the development of novel algorithms for disease quantification without explicit object delineation, standardized anatomic space frameworks, and attention-based neural networks for medical image interpretation. His recent publications demonstrate a strong trend toward integrating artificial intelligence with medical imaging, particularly in developing gaze-guided neural networks, geographical attention mechanisms, and hybrid transformer-convolutional architectures for improved medical image analysis. These works consistently address critical challenges in radiology including disease quantification, anatomical segmentation, and the development of clinically interpretable AI models. Dr. Torigian has received recognition through his substantial publication record in top-tier medical imaging venues including Medical Image Analysis, IEEE Transactions on Biomedical Engineering, and leading medical imaging conferences. His research has been consistently funded through various mechanisms supporting innovation in medical imaging technology. Through his mentorship and collaborative research, Dr. Torigian has contributed to advancing the field of medical imaging with practical applications in radiation therapy planning, disease quantification, and the development of standardized methodologies for image analysis. His work bridges the gap between computer science innovation and clinical radiology applications.

• Neuroimaging
• Neuroinformatics
• MRI-based morphometric analysis
• +7 more

David N. Kennedy is a Professor at UMass Chan Medical School, affiliated with the T.H. Chan School of Medicine. He holds appointments in the Departments of Psychiatry and Behavioral Sciences, Radiology, NeuroNexus Institute, and Eunice Kennedy Shriver Center. He also contributes to the MD/PhD Program and Neuroscience Department at the Morningside Graduate School of Biomedical Sciences. PhD in Nuclear Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States Dr. Kennedy is a leading expert in neuroimaging and neuroinformatics, with foundational contributions to MRI-based morphometric analysis, functional MRI, and diffusion tensor imaging. His research focuses on developing neuroinformatics tools and resources, including the Internet Brain Volumetric Database and the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC). He co-founded the Center for Morphometric Analysis at Massachusetts General Hospital and has been instrumental in advancing data sharing, reproducibility, and standardization in neuroscience. His recent publications emphasize FAIR data principles, neuroimaging data models, open-source tools like DataLad and HeuDiConv, and collaborative frameworks for neuroscience. These works reflect a strong trajectory toward enhancing data interoperability, transparency, and community-driven standards in neuroimaging research. Community liaison for the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) Co-PI of the morphometry Biomedical Informatics Research Network (mBIRN) Founding editor of the journal Neuroinformatics Dr. Kennedy has not listed specific grants or advising roles in the provided text but plays a central role in major neuroinformatics initiatives. He has no listed scientific awards in the provided materials. He is a key figure in the development and dissemination of neuroimaging informatics resources, contributing to national and international efforts to standardize and share neuroscience data through platforms like NITRC and mBIRN.

Jessica MarView profile

Professor

• Bioinformatics
• Single-cell Biology
• Stem Cell Biology
• +3 more

Professor Jessica Mar is a Professorial Research Fellow at the Australian Institute for Bioengineering and Nanotechnology (AIBN) at the University of Queensland. Her research focuses on developing bioinformatics methods to understand regulatory processes in human diseases, particularly leveraging single-cell RNA-sequencing to analyze cellular heterogeneity in contexts like cancer, aging, and stem cell biology. Key areas include modeling gene expression variability, analyzing transcriptomic data, and developing statistical frameworks for omics integration. She has led projects funded by grants such as the Georgina Sweet Award and ARC Future Fellowships. Her work spans computational approaches to cancer heterogeneity, senescence mechanisms in stem cells, and the impact of senolytic therapies on aging. Recent studies include applications of single-cell technologies to brain organoids and breast cancer metastasis. Awards include recognition for contributions to quantitative biomedical science and stem cell research. She collaborates widely, mentoring students in computational biology, stem cell biology, and systems biology. Publications highlight her contributions to understanding tumor heterogeneity, epigenetic dynamics, and the interplay between mechanics and cell reprogramming. Her lab’s work integrates machine learning with experimental biology, addressing challenges in data analysis for single-cell and spatial omics. Current projects include biomarker discovery for hypoxic-ischemic encephalopathy and developing optimal transport models for cancer progression analysis.

Liisa HirvonenView profile

Senior Lecturer

• Optical Microscopy
• Fluorescence Microscopy
• Fluorescence Lifetime Imaging
• +6 more

Liisa Hirvonen is a Senior Lecturer in Optical Microscopy at the Centre for Microscopy, Characterisation & Analysis (CMCA) at The University of Western Australia. She serves as Platform Leader for the Optical Microscopy facility and represents Western Australia in both Light Microscopy Australia and the Australia and New Zealand Society for Cell and Developmental Biology. Physics, PhD, King's College London (2008) Physics with Computer Science, BSc, King's College London (2005) Her research focuses on specialized fluorescence microscopy techniques including super-resolution, lightsheet microscopy, and fluorescence lifetime imaging (FLIM). With expertise spanning from instrument development to practical applications, she applies these techniques across biotechnology, bioengineering, medicine, agriculture, and materials science. Her work enables correlative and multimodal imaging by combining optical microscopy with electron microscopy, μCT, and nanoSIMs. Analysis of her recent publications reveals a strong focus on advancing time-correlated single photon counting (TCSPC) techniques, improving FLIM applications, and developing correlative microscopy workflows. Her research consistently bridges physics, engineering, and biological applications, with particular emphasis on quantitative imaging solutions for complex biological problems. Dr. Hirvonen actively leads multiple research projects including an ARC-funded WA lightsheet microscopy facility, development of human microvasculature models, and high-throughput histopathology scanning. She provides extensive training through online programs and practical courses for microscopy users across Western Australia, with a special focus on supporting HDR students in their research imaging needs. As Platform Leader of the CMCA Optical Microscopy facility, she oversees a comprehensive suite of advanced equipment including multiphoton confocal microscopes, Lionheart FX High Content Imagers, and specialized FLIM systems, enabling cutting-edge research across multiple scientific disciplines.

Gleb PogudinView profile

Assistant Professor

• Symbolic computation
• Algorithms for differential equations
• Algorithms for difference equations
• +5 more

Gleb Pogudin is an Assistant Professor at École Polytechnique, Institute Polytechnique de Paris, where he is a member of the MAX team within the Laboratoire d'informatique. His research focuses on the intersection of symbolic computation, differential equations, and algebraic methods with applications across multiple scientific domains. Dr. Pogudin's primary research interests span several interconnected areas: Symbolic computation and computer algebra algorithms Theory and applications of differential and difference equations Nonlinear algebra and polynomial systems Structural identifiability of dynamical models Model reduction techniques for complex systems His recent publications (2024-2025) demonstrate a strong focus on developing theoretical foundations for differential elimination, structural identifiability analysis, and model reduction. These works span applications in systems biology, epidemiology, pharmacology, and optics. Notably, his research bridges pure mathematical theory with practical computational implementations, creating tools that address real-world scientific challenges. Dr. Pogudin has developed several significant software tools that implement his theoretical advances: StructuralIdentifiability.jl: A Julia package for assessing structural identifiability CLUE: Software for exact model reduction of ODE models via constrained lumping SIAN: Software for structural identifiability analysis of ODE models His GitHub repositories contain numerous implementations of algorithms from his papers on differential elimination and related topics, demonstrating his commitment to making theoretical advances practically accessible to researchers across disciplines.

Silvia MarconiView profile

Researcher

• Applied Mathematics
• Biomedical Modeling
• Computational Biology
• +3 more

Silvia Marconi is a Researcher at the Department of Methods and Models for Economy, Territory, and Finance at Sapienza University of Rome. She teaches the Basic Mathematics Course for Economics and Finance students and coordinates doctoral tutoring services. Her academic background includes a PhD in Mathematical Models and Methods for Technology and Society from Sapienza University. Her research spans applied mathematics with focus areas in: Biomedical modeling of cardiovascular and pulmonary systems Computational approaches to stem cell therapy and tissue regeneration Image processing techniques for digital forensics Signal processing and frequency analysis Marconi's recent publications demonstrate strong interdisciplinary collaboration, particularly in developing computational models for medical applications. Her work frequently combines mathematical theory with clinical problems, especially in cardiology and medical device simulation. The research outputs show consistent focus on developing numerical solutions for physiological systems. She has held multiple research positions including at the National Research Council (CNR) and has collaborated on projects investigating cardiovascular modeling, stem cell behavior, and image processing algorithms. Her teaching experience includes courses in Mathematical Analysis for Engineering programs at both undergraduate and doctoral levels.

• Clastic Sedimentology
• Fluvial Sedimentology
• Paleoenvironmental Reconstruction
• +12 more

Peter P. Flaig is a Research Associate Professor at the Bureau of Economic Geology's State of Texas Advanced Resource Recovery (STARR) program within the Jackson School of Geosciences at The University of Texas at Austin. He also serves as an Affiliate Faculty member at the University of Alaska Fairbanks, Department of Geology. His expertise spans clastic sedimentology, paleoenvironmental reconstruction, and high-latitude depositional systems, with specialized knowledge in dinosaur-bearing strata and advanced imaging techniques for geological analysis. Dr. Flaig's educational background includes: Ph.D. in Geology (2010) from the University of Alaska-Fairbanks M.S. in Geology (2005) from the University of Wisconsin-Milwaukee B.S. in Geology with a Geography Minor (2002) from the University of Wisconsin-Milwaukee His research encompasses multiple dimensions of sedimentology and paleoenvironmental interpretation. Dr. Flaig has developed significant expertise in fluvial sedimentology, examining ancient river systems and their deposits to reconstruct past hydrological conditions. His work on paleoenvironmental reconstruction spans continental, deltaic, shoreface, and shallow-marine systems, providing holistic interpretations of ancient environments. A distinctive aspect of his research focuses on high-latitude clastic depositional sequences, particularly from Arctic regions, which offers critical insights into Earth's climate history during greenhouse periods. His investigations of dinosaur-bearing clastic successions connect sedimentological evidence with paleontological findings to understand extinction events and ecosystem dynamics. Dr. Flaig's innovative use of high-resolution imaging techniques, including photography and LiDAR, has advanced field methodologies in geology. His work in paleopedology (the study of ancient soils) and expertise in remote logistics demonstrate his comprehensive approach to geological research in challenging environments. Dr. Flaig's publication record reveals consistent contributions across sedimentology, paleoenvironmental science, and Arctic geology. His recent work demonstrates increasing interdisciplinary integration, combining sedimentological analysis with paleontological, climatological, and geochemical approaches. Many publications focus on Cretaceous systems, particularly in Arctic Alaska, providing valuable data about ancient greenhouse climates. His research typically involves extensive field work in remote locations followed by sophisticated laboratory analysis, reflecting both logistical expertise and analytical rigor. The collaborative nature of his work is evident in diverse author lists spanning multiple institutions. Dr. Flaig has actively mentored students and early-career researchers through his academic positions. His extensive publication record suggests successful acquisition of research funding, particularly for complex field operations in remote Arctic locations. His dual affiliation with UT Austin and University of Alaska Fairbanks indicates strong collaborative networks across institutions and geographical boundaries. As part of the Bureau of Economic Geology and the STARR program at UT Austin, Dr. Flaig contributes to a research ecosystem focused on both fundamental geological understanding and practical applications in resource recovery and environmental management. His affiliation with the Quantitative Clastics Laboratory suggests involvement in advanced analytical techniques for sedimentary systems, bridging traditional field geology with modern quantitative methods.

Anne-Claude GavinView profile

Professor

• Lipid Metabolism
• APOE Lipidation
• Alzheimer's Disease Mechanisms
• +4 more

Anne-Claude Gavin is a Professor at the University of Geneva's Faculty of Medicine within the Department of Cell Physiology and Metabolism. Her research group, based at CMU office C05.1535.a, focuses on lipid metabolism with particular emphasis on APOE lipidation mechanisms and their implications in Alzheimer's disease. The laboratory actively recruits PhD candidates for high-level collaborative projects, with a recent opening targeting autumn 2025 start dates for research on APOE polymorphism and metabolic stressors. Her research interests center on lipid-protein interactions, particularly APOE homeostasis across isoforms and cell states, ceramide transport mechanisms, and the role of lipid dysregulation in neurodegenerative diseases. Recent work integrates structural biology, cellular imaging, and systems approaches to dissect molecular mechanisms of lipid transfer proteins like CERT and STARD4. Her group has made significant contributions to understanding how APOE variants differentially modulate cellular cholesterol metabolism and inflammatory responses in astrocytes. Analysis of recent publications (2023-2025) reveals strong thematic continuity in lipid-centric research, with increasing focus on Alzheimer's disease mechanisms through APOE studies, structural characterization of lipid transfer proteins, and cellular aging processes in hematopoietic systems. Key methodologies include molecular dynamics simulations, fluorescent biosensor development, and system-wide lipid transfer protein mapping. While no specific awards are listed in the provided materials, her research has garnered media attention including coverage in RTS's CQFD program (March 2022) discussing protein contributions to Alzheimer's disease. Her laboratory maintains active recruitment for doctoral positions, indicating ongoing grant funding for projects like the collaborative study on APOE lipidation mechanisms. Active recruitment for PhD positions in lipid metabolism Media features on Alzheimer's research Collaborative projects with clinical implications The laboratory operates within the Lipid Metabolism research group at the University of Geneva's Centre Médical Universitaire (CMU), utilizing advanced techniques including molecular dynamics, chemoproteomics, and genetically encoded fluorescent probes to investigate lipid dynamics in cellular systems. Current work emphasizes translational connections between fundamental lipid biochemistry and neurodegenerative disease pathways.

• Bioimage analysis
• Live cell imaging
• Quantitative cell biology
• +7 more

Joanna Pylvänäinen is a Project Researcher at the Faculty of Science and Engineering, Åbo Akademi University, specializing in Biochemistry and Cell Biology. She is affiliated with the InFLAMES flagship program (Innovation Ecosystem based on the Immune System) focusing on health-related solutions. Her research interests include: Bioimage analysis Live cell imaging Quantitative cell biology Software development for life science applications Flow dynamics Cancer cell biology Image analysis software Cell tracking Microfluidics 4D microscopy The research articles and publications cover a broad range of topics in image analysis, software development for life sciences, cell tracking, and microfluidics applications in biological research. The datasets she has contributed to focus on these same areas, showing a consistent research trajectory in computational biology and advanced imaging techniques. Joanna is active in developing tools and platforms for life scientists, with a particular focus on making bioimage analysis more accessible and on reducing technical challenges in live cell imaging. She is also interested in addressing phototoxicity issues and improving workflow efficiency in quantitative cell biology research. Her work includes contributions to educational tools like Ocul-AR for microscopy learning, Fast4DReg for microscopy data registration, and CellTracksColab for cell tracking data analysis. She is involved in the development of TrackMate 7 for integrating state-of-the-art segmentation algorithms into tracking pipelines.

Suyong SongView profile

Associate Professor

• Econometrics
• Corporate Finance
• Empirical Industrial Organization
• +5 more

Suyong Song is an Associate Professor in both the Department of Economics and the Department of Finance at the Tippie College of Business, University of Iowa. He is also a Henry B. Tippie Research Fellow, recognizing his contributions to research in economics and finance. His academic appointments reflect a strong interdisciplinary profile bridging econometric theory and financial applications. Education: Ph.D. in Economics, University of California-San Diego (2010) M.A. in Economics, Korea University (2004) B.A. in Economics, Korea University (2002) Suyong Song's research lies at the intersection of econometrics, corporate finance, and machine learning. His work focuses on developing and applying advanced statistical methods to economic and financial problems, particularly in the areas of quantile regression, measurement error models, network analysis, and non-Euclidean data. He investigates how social and interfirm networks influence corporate decisions, and applies machine learning techniques to diverse domains such as body shape-income relationships and social media sentiment analysis. His methodological rigor is evident in his publications in top-tier econometrics and finance journals. The recent publications highlight a consistent trend toward integrating modern data science tools—especially machine learning and network analytics—into traditional economic frameworks. His work spans corporate governance, supply chain resilience, monetary policy evaluation, and labor economics, demonstrating broad applicability of his methodological innovations. The articles reflect a strong emphasis on causal inference, robust estimation, and the use of novel data sources such as social media and biometric data. Scientific Awards and Recognitions: Henry B. Tippie Research Fellow Suyong Song actively engages in research advising and has collaborated with numerous scholars across institutions. While specific grant details are not listed, his publication record in high-impact journals suggests successful funding and research leadership. His interdisciplinary collaborations indicate involvement in team-based research projects, particularly in econometrics and applied finance. There is no mention of formal student advising in the provided text, but his role as a research fellow and associate professor implies mentorship responsibilities. He is involved in the broader research ecosystem at the Tippie College of Business, contributing to seminars and research initiatives in business analytics and econometrics. His work with the Tippie Analytics Cooperative and participation in research seminars suggest engagement with data-driven research platforms and academic discourse. His research program appears poised to continue advancing econometric methods and their application to pressing economic and business challenges.

• Quantitative Finance
• Information Theory
• Econophysics
• +3 more

Aurelio F. Bariviera is an Associate Professor of Economics and Financial Mathematics at Universitat Rovira i Virgili, Spain, and a Visiting Professor at Universidad Nacional de La Plata, Argentina. He holds a PhD from Universitat Rovira i Virgili, following postgraduate studies at Università degli Studi di Padova and an undergraduate degree from Universidad Nacional de La Plata. His research focuses on quantitative finance, information theory, econophysics, and financial econometrics. Notably, two of his papers (2017) in Economics Letters and Physica A were recognized as Highly Cited Papers in Web of Science. Education: Bachelor’s Degree: Universidad Nacional de La Plata (Argentina) Postgraduate Diploma: Università degli Studi di Padova (Italy) PhD: Universitat Rovira i Virgili (Spain) Research Interests: Quantitative Finance Information Theory and Entropy Applications Econophysics and Financial Networks Cryptocurrency Market Dynamics Machine Learning in Finance Policy Uncertainty Analysis Recent Article Trends: Focus on cryptocurrency interconnections, market shocks, and regulatory frameworks Analysis of commodity and policy uncertainty impacts using wavelet and copula techniques Applications of clustering and machine learning in financial forecasting Awards: Highly Cited Paper in Web of Science (2017): Economics Letters Highly Cited Paper in Web of Science (2017): Physica A Advising & Grants: Consortium on Cloud Computing, Big Data & Emerging Topics Research projects on cryptocurrency regulation, trajectory clustering, and renewable energy forecasting Labs/Teams: Active in interdisciplinary teams applying data science to finance, transportation systems, and astronomy digitization.

Li BaiView profile

Professor

• Distributed Software Computing
• Wireless Sensor Networks
• System and Software Integration
• +7 more

Li Bai is a tenured Professor and Chair of the Department of Electrical and Computer Engineering at the College of Engineering, Temple University. His work bridges computer engineering and interdisciplinary medical imaging applications. His research interests include: Distributed Software Computing Wireless Sensor Networks System and Software Integration using Commercial-Off-the-Shelf (COTS) products Computer Network Security Robotic Operating System (ROS) Drone and Multi-Agent Systems Recent publications demonstrate a strong trend in applying deep learning to enhance biomedical imaging techniques, particularly in MRI-based perfusion and metabolic imaging. This reflects a growing interdisciplinary focus on medical data processing and AI-driven diagnostics. Dr. Bai leads the Computer Fusion Lab and has been involved in sponsored research projects involving portable wireless devices and 802.11 protocols. He was a core organizer of the 8th International Conference on Information Fusion (2005). He teaches undergraduate courses such as Digital Circuit Design, Robotic Control using ROS, and Processor Systems Laboratory. He has published over 60 peer-reviewed papers in international journals and conferences.