Assoc. Professor Mariano Rodrigo is affiliated with the University of Wollongong. His primary research focuses on Biological mathematics , Financial mathematics , Numerical solution of differential and integral equations , and Dynamical systems in applications . His work bridges applied mathematics with real-world problems in tourism economics and physical sciences. Research Funding: Weather, climate & geological risks: derivative pricing & risk management (2024–2026, ARC Discovery Project) Modelling the post-stagnation stage of a tourism area life cycle (2023, UOW Internal Grant) Teaching & Supervision: Current PhD supervision topics include Derivative Pricing for Geological Risks , Mathematical Models for Time of Death Estimation , and Weather/Climate Risk Modelling .
Amit Sachdeva is an Associate Professor of Bio-Organic Chemistry at the University of East Anglia (UEA), where he leads the School of Chemistry, Pharmacy and Pharmacology's Department of Chemistry. He serves as Director of Postgraduate Research in Chemistry, overseeing doctoral training and academic strategy. His research focuses on Chemical Biology and Synthetic Biology, with a specific emphasis on expanding genetic code capabilities to engineer novel proteins for biomedical applications. Dr. Sachdeva completed his PhD at the University of Illinois at Urbana-Champaign, investigating DNA-based enzymes, followed by postdoctoral work at the MRC Laboratory of Molecular Biology in Cambridge. His current work includes developing light-responsive antibodies for targeted therapies and ultrafast viral diagnostics. Notable achievements include pioneering photoactive antibody fragments and securing patents (e.g., WO-2020193981-A1) for light-controlled antigen binding. Key Projects: Designing cancer biotherapeutics (Leverhulme Trust), fluorescent switches for SARS-CoV-2 detection (Royal Society of Chemistry), and biomolecular wire development (Engineering and Physical Sciences Research Council). Grants: Over £5M from institutions like The Big C Appeal and Wellcome Trust. Research Interests: Genetic code expansion, protein engineering, non-natural amino acids, and their applications in diagnostics/therapeutics. His work contributes to UN Sustainable Development Goals, particularly in health and innovation. Publications: Over 25 peer-reviewed articles in top journals like Nature Chemical Biology , Angewandte Chemie , and Nature Reviews Chemistry , with several highly cited contributions (e.g., 99th percentile in 2023).
Wei Ding is a Professor in the Department of Computer Science at the University of Massachusetts Boston (UMass Boston). She earned her Ph.D. in Computer Science from the University of Houston in 2008. From 2019 to 2023, she served as a Program Director at the National Science Foundation's Division of Information and Intelligent Systems (IIS), overseeing programs in Information Integration, Smart Health, Deep Learning Foundations, and Scalable Systems. Her research integrates knowledge discovery, data mining, and machine learning with applications spanning health sciences, astronomy, geosciences, and environmental sciences. She employs advanced techniques like spatio-temporal modeling, deep neural networks, and semantic analysis to address complex real-world problems such as disease subtyping, physical activity prediction, and environmental forecasting. Her work emphasizes interdisciplinary collaboration and societal impact. Analysis of her recent publications reveals a focus on AI-driven healthcare solutions (e.g., neuroimaging biomarkers, disorder diagnosis), fundamental ML advancements (e.g., generalization, GAN stability), and cross-domain applications (e.g., climate forecasting, animal behavior analysis). Recurring themes include low-data learning, interpretability, and scalable algorithms. Awards & Honors: IEEE Fellow (2023) NSF Director's Award (2022) WISAY Distinguished Woman in Science Award, Yale University (2019) AI for Earth Award (2018) Best Paper Awards (ICTAI 2011, ICCI 2010) Advising & Grants: She mentors PhD and Master’s students in the Knowledge Discovery Lab (KDLab), with alumni at institutions like Facebook, Google, and McKinsey. Her research is funded by NSF, NIH, NASA, and DOE, including: NIH R01: Predicting youth physical activity (2016) NSF EAGER: Machine learning for cancer subtyping (2017) NIH R01: Accelerometer/gyroscope data for activity estimation (2022) Leadership: She directs the KDLab and co-founded the Women in Sciences Club (WINS). She serves as Associate Editor for ACM TKDD, TIST, and KAIS journals.
Seth Kaplan is a Professor in the Department of Psychology at George Mason University. His research focuses on employee well-being, team effectiveness, virtual work, and occupational health through projects like NSF-funded emotion regulation interventions and Army Research Institute collaborations on affective forecasting. He directs the KA-Lab, studying team resilience, metaperceptions, and statistical methodologies. Recent publications analyze job boredom, cognitive reappraisal interventions, and personality measurement innovations. His book Crisis-Ready Teams (2024) synthesizes data from high-risk environments. Courses taught include Occupational Health, Organizational Change, Psychometrics, and Multivariate Statistics. Grants: National Science Foundation (Co-PI): Just-in-Time Adaptive Interventions for Emotion Regulation Army Research Institute (PI): Affective Forecasting Errors Recent Presentations: Personality and generative AI use at work (SIOP 2025) Work situation identification via NLP (SIOP 2025) Helicopter helping in teams (SIOP 2025)
Weiwen Jiang is a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University (GMU), affiliated with the College of Engineering and Computing (CEC). He leads the JQub lab, focusing on hardware/software co-design for computing systems, spanning classical (FPGAs, ASICs) and quantum computing applications in AI-driven fields like medical imaging and geophysics. Prior to GMU, he held a postdoctoral position at the University of Notre Dame and earned his PhD in Computer Science from Chongqing University with a joint PhD in Electrical and Computer Engineering from the University of Pittsburgh. His research emphasizes quantum computing, AI accelerators, and domain-specific computing. Notable achievements include the 2025 NSF CAREER Award, ACM Sigda Meritorious Service Award (2024), and IEEE QuantumWeek Best Paper Award (2023). His work is funded by NSF, DoE, ARO, Meta, and Leidos. He co-chaired IEEE QuantumWeek (2023–2025) and created workshops like StableQ at ESWEEK 2023. Key contributions include developing frameworks like QuPAD for quantum learning and JQub's AI-driven geophysical and medical imaging tools. His lab graduated Dr. Yi Sheng (now at University of South Florida) and Dr. Zhepeng Wang (Amazon Applied Scientist). Current research explores quantum machine learning, noise mitigation, and fairness in AI for edge devices.
Amit Morey is an Associate Professor in the Department of Poultry Science at Auburn University's College of Agriculture. His research focuses on food safety, poultry meat quality, and advanced sensing technologies. He leads projects involving biosensor development, microbial pathogen detection, and spoilage prediction using machine learning and spectral imaging. His work bridges laboratory innovations with industry applications, addressing challenges in poultry processing, packaging, and supply chain management. Education details are not explicitly listed, but his extensive publications suggest advanced training in food science, microbiology, and engineering. Research interests include antimicrobial biopolymer films, texture analysis of catfish and chicken fillets, and the application of functional ice in seafood preservation. He has pioneered methods for rapid Salmonella detection using microfluidics and fiber optics-based SERS sensors. Notable contributions include developing predictive models for spoilage using near-infrared spectroscopy and exploring cyclic temperature abuse impacts on poultry safety. His interdisciplinary approach integrates artificial intelligence with traditional food science techniques to enhance food safety and reduce waste. While no specific grants or awards are listed, his active publication record (over 100 papers from 2005–2025) indicates sustained research funding. He collaborates on projects addressing global food safety inequities, such as sensor-enabled decision support systems (SENS-D) for vulnerable communities. Lab activities include the Auburn Poultry Science Lab, focusing on meat quality assessment, microbial interventions, and smart packaging solutions. His work has direct industry impact, with applications in poultry processing plants and retail cold chain management.
M. Tariq Iqbal is a Professor in the Department of Electrical and Computer Engineering at Memorial University of Newfoundland. He holds a B.Sc. from UET Lahore, M.Sc. from QAU Islamabad, and PhD from Imperial College London. His research develops renewable energy solutions including hybrid power systems, solar applications, and IoT-based monitoring. Projects focus on off-grid communities, industrial applications, and energy-efficient electronics. Specific interests include microgrid design, solar water pumping, and power consumption analysis. Recent publications emphasize techno-economic modeling of microgrids, IoT-enabled SCADA systems, and energy efficiency in computing. Work demonstrates increasing focus on practical implementations in remote locations. No scientific awards are documented. Iqbal advises graduate students on projects across 20+ countries. Current research includes solar-powered oil pumps, electric vehicle charging, and community microgrids. He directs multiple projects through the faculty's engineering design initiative.
Ibrahim RADWAN is an Associate Professor in Machine Learning/AI and Robotics at the University of Canberra. His research focuses on advancing AI techniques in areas such as human pose estimation, affective computing, and healthcare technology. He leads projects addressing challenges in robotics, autonomous systems, and human behavior analysis. RADWAN’s work bridges theory and application, contributing to fields like sports science, medical diagnostics, and security through innovative machine learning approaches. Research Projects: Assistive Technologies for Young People Safety on Two-Wheelers AI-Based Methods for Driver Sentiment and Mood Prediction Robotics Applications in Organic Waste Management Research Interests: RADWAN’s expertise spans human pose reconstruction , nonverbal behavior analysis , and EEG-based healthcare diagnostics . He pioneers methods for real-world applications such as: 6G Extended Reality systems using wearable sensors Multimodal deception detection via motion analysis Affective computing for mood and emotion inference Publications: His recent work emphasizes trends in spatiotemporal data analysis, few-shot learning, and synthetic data applications in healthcare and robotics. Key contributions include novel architectures like CrossFormer for 3D pose estimation and Resanet for dense prediction tasks. Advising & Grants: RADWAN supervises PhD students and has secured grants for projects integrating AI with robotics and medical technology. His team collaborates on interdisciplinary challenges, including railway safety and surgical instrument tracking. Labs/Teams: Part of the AI and Robotics research group at the University of Canberra, contributing to cutting-edge solutions in autonomous systems and human-centered AI.
Dr. Yang Wang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on computer vision and machine learning, with a particular emphasis on domain adaptation, meta-learning, and privacy-preserving techniques. He actively advises prospective graduate students through a dedicated webpage outlining application procedures. Research interests include few-shot learning, test-time adaptation, and cross-modal applications such as handwritten text recognition and gaze estimation. His work explores how models can adapt dynamically to new domains using limited labeled data, with applications in crowd counting, medical data analysis, and cybersecurity. He also investigates privacy-preserving methods for deep learning models to protect user attributes and sensitive information. Recent publications highlight advancements in meta-auxiliary learning frameworks and efficient user adaptation techniques. His contributions span journals and conferences, showcasing innovations in both foundational machine learning methodologies and real-world applications.
Douglas Nychka is a Professor in the Department of Applied Mathematics and Statistics at Colorado School of Mines since 2018. He holds an emeritus position at the National Center for Atmospheric Research (NCAR), where he previously directed the Institute for Mathematics Applied to Geosciences (IMAGe) from 2004 to 2017. Nychka earned his PhD in Statistics from the University of Wisconsin-Madison and a BA in Mathematics (with Physics emphasis) from Duke University. His research focuses on spatial statistics, nonparametric regression, and computational methods for large datasets, particularly applied to environmental and geophysical problems. He has developed influential R packages like fields and LatticeKrig , which are widely used for spatial data analysis. Nychka received prestigious awards including the Jerry Sacks Award for Multidisciplinary Research (2004) and recognition as a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics. His work bridges statistical theory, computational innovation, and real-world applications in climate science and renewable energy. His academic career includes 14 years as a faculty member at North Carolina State University and roles at the National Institute of Statistical Sciences. Nychka's research emphasizes spatial statistics for climate data, statistical downscaling, and uncertainty quantification, with contributions to solar radiation modeling and extreme event analysis. He actively engages in interdisciplinary projects, collaborating with experts in climatology, environmental science, and data science. Professional service includes roles on committees for the National Research Council and leadership in statistical societies. His teaching focuses on modernizing curricula to integrate data science with applied statistics. Nychka’s work is characterized by a commitment to open-source software and reproducible research, exemplified by his R package contributions.
Kannan Srinivasan is the H.J. Heinz II Professor of Management, Marketing and Business Technology at Carnegie Mellon University's Tepper School of Business, a position he has held since 1999. Prior to joining CMU, he taught at the business schools of the University of Chicago and Stanford University. His academic career spans over three decades with significant contributions to marketing science and data analytics. His educational background includes: Ph.D. in Management from University of California Los Angeles (1986) MBA in Marketing/Finance from Xavier School of Management, Jamshedpur, India (1980) BA in Engineering from University of Madras, Chennai, India (1978) Srinivasan's research focuses on advanced data analytics models applied to marketing problems, with particular expertise in internet-generated large-scale data analysis. His work bridges the gap between theoretical marketing models and practical business applications, especially in the areas of algorithmic pricing, consumer behavior analysis, and AI-driven marketing strategies. He has pioneered research in dynamic pricing systems, location-aware marketing technologies, and the economic implications of AI in consumer markets. Analysis of his recent publications reveals a strong trend toward examining the intersection of artificial intelligence, consumer welfare, and market dynamics. His work increasingly focuses on ethical implications of AI in marketing, algorithmic bias, and the socioeconomic impacts of digital platforms across various sectors including real estate, social media, and e-commerce. His scientific achievements include: Elected Fellow of the Informs Society of Marketing Science (2013) for lifetime contribution to the field Served as President of the Informs Society of Marketing Science Holds multiple patents related to time and location aware dynamic push content, dynamic pricing, and online advertising Srinivasan has advised numerous doctoral students whose careers have led them to faculty positions at top institutions including Duke, Harvard, Columbia, Yale, University of Chicago, Wharton, University of Michigan, and Indian Institute of Management Bangalore. He has extensive consulting experience with large firms and startups, translating academic research into practical business applications. His professional service includes editorial roles at prestigious journals including Management Science, Marketing Science, and Quantitative Marketing and Economics, as well as significant committee service within CMU including the Elliott D. Smith Award Committee and various Dean's Advisory committees. His research is organized around several key initiatives focused on applying advanced analytics to solve complex marketing problems, with particular emphasis on developing interpretable AI models that balance business objectives with consumer welfare considerations.
Bernadette Byrne is a Professor and Associate Dean (Equality, Diversity and Inclusion) at the Faculty of Natural Sciences, Imperial College London, within the Department of Life Sciences. She leads research on membrane protein structure and function, focusing on transporters, receptors, and advanced vibrational spectroscopy techniques for antibody behavior analysis in collaboration with Prof. Sergei Kazarian. Her work is funded by BBSRC, EPSRC, MRC, and EU programs. Affiliations include the Agri Futures Lab, Bacterial Pathogenesis Group, Centre for Structural Biology, and Industrial Biotechnology Hub. She has served as an external examiner at the University of Cambridge and University of Edinburgh, and chairs the BBSRC Committee D. Research interests span membrane biology, biochemistry, structural biology, and biophysics. Recent studies emphasize detergent engineering for protein stabilization and industrial bioprocessing challenges. Her publications highlight innovations in membrane mimetic systems, GPCR signaling, and protein aggregation analysis. She actively contributes to interdisciplinary initiatives such as the Chemical Biology CDT and the Membrane Receptor Network.
Professor Danilo Mandic is a leading academic in Machine Intelligence and Signal Processing at Imperial College London's Department of Electrical and Electronic Engineering. He holds roles including President of the International Neural Network Society and Distinguished Lecturer for IEEE Computational Intelligence and Signal Processing Societies. His research spans Statistical Learning, Wearable Sensing (Hearables), Financial Signal Processing, and Tensor Networks for Big Data. Key contributions include pioneering in-ear physiological sensing and developing quaternion-based adaptive filters. He has authored over 600 publications, including seminal monographs on neural networks and complex-valued signal processing. Education: PhD in Nonlinear Adaptive Signal Processing from Imperial College (1999). Professional accolades include the 2019 Dennis Gabor Award and multiple IEEE Best Paper Awards. His labs include the Financial Signal Processing & Machine Learning Lab and collaborations with the Centre for Neurotechnology. He advises numerous students and leads projects on AI ethics, graph signal processing, and biomedical applications. His work emphasizes translating research into educational curricula via participatory sensor-based learning.
Sarah Masud Preum is an Assistant Professor of Computer Science at Dartmouth College, with adjunct roles in the Department of Biomedical Data Science at Geisel School of Medicine and as Faculty Affiliate at the Center for Technology and Behavioral Health (CTBH). She also serves as Technical Associate Director of the Dartmouth Center for Precision Health and Artificial Intelligence. Her work focuses on machine learning for computational health, including natural language processing, temporal modeling, and human-AI interaction to develop personalized decision support systems in healthcare. Education includes a B.Sc. from Bangladesh University of Engineering and Technology, followed by M.Sc. and Ph.D. degrees from the University of Virginia. Previously, she was a postdoctoral research scholar at Carnegie Mellon University's School of Computer Science, recognized as a Rising Stars in EECS (2020) for her academic excellence and contributions to equity in STEM. Her research interests span Human-AI Interaction, Natural Language Processing, Mobile Health, and Cyber-Physical Systems. Over 2020–2023, her publications emphasize AI-driven solutions for healthcare challenges like conflict detection in health information and cognitive assistants for emergency response. Earlier work includes behavioral prediction models (MAPer) and spatial database optimizations (Maximum Visibility Queries). Awards: Rising Stars in EECS (2020) In teaching, she offers courses like Transforming Healthcare through Machine Learning and Machine Learning and Statistical Data Analysis. Her affiliations with multidisciplinary centers reflect her commitment to bridging technology and healthcare.
Carlos Silvera Batista is an Assistant Professor of Chemical and Biomolecular Engineering at Vanderbilt University’s School of Engineering. His research focuses on manipulating colloidal systems to design functional materials, emphasizing nanoscale interactions and electrokinetic phenomena. He holds a Ph.D. from the University of Florida and a B.E. from City College of New York. Education: Ph.D., Chemical Engineering, University of Florida B.E., Chemical Engineering, City College of New York Research Interests: Dr. Batista investigates the forces and flows governing colloidal assembly, with applications in nanomedicine, energy, and materials science. Key areas include solvation forces in nanoscale systems, electrokinetic transport of anisotropic colloids, and the directed assembly of reconfigurable materials. Techniques such as analytical ultracentrifugation and confocal microscopy are central to his work. Articles Trends: Recent work emphasizes electrodiffusiophoresis-driven colloidal dynamics, long-range transport of charged particles, and applications in CRISPR delivery and structural materials. His studies bridge fundamental physics with practical applications in biomedicine and nanotechnology. Awards: None explicitly listed. Advising & Grants: No advisees listed. Received the NSF CAREER Award (2023) for research on colloidal dynamics under electrodiffusiophoresis. Labs & Teams: Leads the Colloids & Interfacial Phenomena lab, part of Vanderbilt’s Nano Science and Technology intellectual neighborhood. Focus areas include colloidal dispersions, nanomaterials, and interfacial phenomena.
