Ana Fiszbein is an Assistant Professor of Biology at Boston University, specializing in gene regulation and RNA processing. Her lab employs high-throughput genomics, bioinformatics, and molecular approaches to study co-transcriptional gene regulation in mammalian systems, with a focus on cancer genomics and therapeutic strategies. She earned her PhD from the University of Buenos Aires and leads research on how gene architecture influences transcriptional programs. Her research interests include understanding the interplay between transcription and splicing, particularly through phenomena like Exon-Mediated Activation of Transcription Starts (EMATS). The lab develops computational tools like evopython to predict gene regulatory networks and designs strategies to manipulate gene expression for therapeutic applications. Current projects explore promoter-driven RNA processing decisions, transcriptional interference between promoters, and the connection between transcript initiation and termination. The lab actively recruits undergraduate, graduate, and postdoctoral researchers. Key findings include the discovery of hybrid exons and splicing-dependent transcriptional control mechanisms.
Dr. Biniam Kebede is an Assistant Professor in the Department of Food Science at the University of Guelph. His research focuses on integrating bioprocessing, food analytics, and data science to enhance food functionality and sustainability. He leads the Food Bioprocessing and Data Science Research Group, which explores fermentation, enzymatic treatments, and machine learning for food innovation. Academic History: B.Sc. in Food and Biochemical Technology, Bahir Dar University (Ethiopia) M.Sc. in Food Technology, Ghent University (Belgium) Ph.D. in Bioscience Engineering, KU Leuven (Belgium) Research Interests: Dr. Kebede's work addresses sustainable food systems through: Optimizing fermentation to unlock bioactive compounds from plant-based sources Mitigating off-flavors in pulses via value-chain analysis Engineering food structures to improve nutrient bioaccessibility Developing portable machine learning tools for food authenticity Valuing indigenous food ingredients and traditional processing methods His approach combines multi-omics, imaging, and AI to create predictive models for food innovation. Awards & Affiliations: 2022 IUFoST Young Scientist Award 2014 EFFoST PhD Student of the Year Award Member of IFT, CIFST, and IUFoST Honorary affiliation with University of Otago (New Zealand) Training Philosophy: Dr. Kebede emphasizes personalized mentoring through Individual Development Plans (IDPs), biweekly lab meetings, and interdisciplinary collaboration. Students engage in all research phases, including conference presentations and industry partnerships to build technical and professional skills.
Mone Zaidi is an endowed Mount Sinai Professor of Clinical Medicine and Director of the Center for Translational Medicine at the Icahn School of Medicine at Mount Sinai, New York. With a career spanning over three decades, he has held leadership roles such as Chief of Endocrinology and Associate Dean at multiple institutions, including the Medical College of Pennsylvania and Veterans Affairs Hospitals in New York and Philadelphia. Education: MD and PhD from the University of London (Hammersmith Hospital). His research focuses on endocrine regulation of skeletal homeostasis , pituitary-bone axis , and FSH inhibition as a therapeutic target for osteoporosis, obesity, and neurodegeneration. His groundbreaking work on FSH blocking antibodies and hormonal crosstalk between bone, fat, and brain has been recognized in Nature , Cell , and PNAS , with over 450 publications and continuous funding from the NIH and UK’s MRC. Recent articles highlight therapeutic strategies for osteoporosis , neurodegenerative diseases , and metabolic disorders through hormonal modulation. His work has been featured in Nature Medicine , New York Times , and Newsweek , including selection as one of the 8 'Notable Advances' in biomedicine for 2017. Scientific Awards Doctor of Science Honoris Causa (University of Connecticut, King Georges Medical College, Sanjay Gandhi Postgraduate Institute, Amity University, University of Bari) Master of the American College of Physicians (2017) Elected Fellow of the U.S. National Academy of Inventors (2022), AAAS (2019), and Royal Society of Biology (1996) Austrian International Research Prize (2022) Harrington Scholar–Innovator Award (2017)
Rhema Linder is a Teaching Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. His academic journey includes a BS in Computer Science and Mathematics from LeTourneau University (2009) and a PhD in Computer Science from Texas A&M University (2019). Prior to his current role, he served as a postdoctoral researcher at the PAIRS lab at UT Knoxville. His research focuses on Human-Computer Interaction (HCI), AI Art, Information Visualization, and Creative Cognition. He explores how AI and software systems can enhance creative productivity, particularly in collaborative online environments. His work integrates theories from creative cognition, social science, and HCI to design tools for engineering, design, and scholarship. Rhema has held internships at Adobe Research and Microsoft Research, contributing to projects in data science and human-computer interaction. His GitHub repositories include open-source projects like kivy-games, demonstrating his engagement with software development and educational tools. His research interests emphasize multidisciplinary approaches, blending art, technology, and social science to innovate in creative online spaces. He is actively involved in the PAIRS lab, focusing on advancing understanding of human-AI collaboration and information management systems.
Christopher J. Kiely is the Harold B. Chambers Senior Professor of Materials Science and Chemical Engineering at Lehigh University (USA) and, since 2017, Professor of Electron Microscopy and Catalysis in the School of Chemistry at Cardiff University (UK). He also serves as Co-Director of the Cardiff Catalysis Institute and Director of the Materials Characterisation Facility at Lehigh. Education Ph.D., Microstructural Physics, Bristol University, 1986 B.Sc. (1st Class Honours), Chemical Physics, Bristol University, 1983 Research Focus Professor Kiely is internationally recognised for applying aberration-corrected analytical electron microscopy (AC-AEM), scanning transmission electron microscopy (STEM) XEDS/EELS spectrum imaging, and electron diffraction to the study of nanoscale features in particulate materials and interfaces. His work spans catalyst design, nanoparticle self-assembly, quantum dots, carbonaceous materials, and heteroepitaxial interfaces, with a strong emphasis on elucidating structure–activity relationships in supported gold, gold-palladium, and other bimetallic nanocatalysts. Scientific Awards & Distinctions Member of Academia Europaea (2019) Fellow of the Microscopy Society of America (2017) Fellow of the Learned Society of Wales (2015) Harold B. Chambers Senior Professorship (2010) Honorary Visiting Professor, Cardiff University (2009–2016) Innovator Award, NanoTECH Briefs (2005) Personal Chair in Materials Chemistry, University of Liverpool (1999) Leadership & Outreach Dr Kiely has directed the Lehigh Microscopy Summer Schools for two decades (2004–2024), sits on the Council of the Microscopy Society of America, and is a founding member and grant-holder of the UK’s SuperSTEM facility at Daresbury. He has published >350 journal papers and delivered numerous invited lectures across Europe and the United States.
Jennifer Jie Zhang is the Daniel Himarios Endowed Chair Professor of Information Systems in the College of Business at the University of Texas at Arlington , within the Department of Information Systems and Operations Management . She earned her Ph.D. in Computer Information Systems from the University of Rochester in 2003, preceded by an M.S. in Management Science from the same university and a B.E. in Engineering Economics from Tianjin University . Her research interests lie at the intersection of information systems, digital marketing, and AI applications , focusing on: Social Media & Networks : Impact on business and consumer behavior. AI in Business & Healthcare : Analytical and empirical studies. Digital Marketing : Online channel strategies, pricing, and advertising. Crowdsourcing & Innovation : Tournament design and team dynamics. Privacy & Ethics : Consumer data protection and market mechanisms. Her recent publications (2021-2024) emphasize real-time analytics in meal delivery, AI-driven insights for fitness apps, and social media dynamics affecting box office performance. These works highlight her expertise in leveraging big data for operational and strategic decision-making. Awards & Honors: Daniel Himarios Endowed Chair Professorship (2021-present). Best Paper Awards/Runner-ups at ICIS, WITS, HICSS, and AMCIS. Distinguished Research Publication Awards from UTA and College of Business. NSF-funded grants including "Convergence Accelerator Pilot: Credible Open Knowledge Network" ($999,870). Advising & Grants: PhD Chair/Co-Chair for 6+ students (e.g., Siddhi Nair, Jiang Hu). Committee Member for 10+ doctoral candidates. Undergraduate/Master’s Advisor for capstone and thesis projects. Principal/Co-Investigator on federal grants (NSF, totaling ~$1M). Labs & Teams: Leads research initiatives in Web Analytics and Digital Enterprise Management , collaborating with interdisciplinary teams across UTA’s College of Business and external partners (e.g., industry sponsors, international workshops).
Rebecca Muenich is an Associate Professor in the Department of Biological & Agricultural Engineering at the University of Arkansas. Her research bridges watershed modeling, agricultural ecosystems, and the food-energy-water nexus, with a focus on surface hydrology, water quality, and climate impacts. She holds a Ph.D. (2015) and M.S. (2011) in Agricultural & Biological Engineering from Purdue University, and a B.S. in Biological Engineering (2009) from the University of Arkansas. Her research explores watershed and environmental modeling, agricultural management, urban agriculture, and climate impacts on water resources. Key interests include mitigating nutrient pollution, enhancing ecosystem services, and developing sustainable land-use strategies. Recent publications emphasize machine learning applications in environmental monitoring, phosphorus circularity, and climate-resilient water management. Muenich leads significant grants including a USDA NRCS project on PFAS in agriculture (2023-2027), NSF-STC’s Science and Technology for Phosphorus Sustainability (2021-2031), and DISES research on cyanobacterial blooms (2022-2025). She mentors students in her research group (Muenich Lab) and collaborates on interdisciplinary projects. Lori Libbert New Faculty Commendation (2024) Early Career Alumni Award, UA Engineering (2022) New Face of ASABE (2020) National Science Foundation Graduate Research Fellowship (2009)
Dr. Łukasz Baran is an Assistant Professor at the Department of Theoretical Chemistry , Maria Curie-Skłodowska University (UMCS), Lublin, Poland. He earned his PhD in Chemical Sciences (2022) with a dissertation on computer simulations of molecular self-assembly processes on solid surfaces. His current research focuses on interfacial behavior of water in liquid/crystalline forms and the influence of confinement curvature on patchy particle systems, combining molecular dynamics and advanced Monte Carlo simulations. Current Affiliation: Maria Curie-Skłodowska University (UMCS), Lublin, Poland Additional Affiliation: Complutense University of Madrid, Spain (PostDoc, 2024-) His research spans self-assembly mechanisms , ice friction phenomena , and 2D supramolecular networks , with applications in nanoscience and materials chemistry. Recent work includes studies on colloidal diamond formation, ice premelting layers, and confinement effects on Janus particles. His publications have appeared in high-impact journals like Nanoscale , PNAS , and Journal of Chemical Physics . Scientific Awards Ministry Scholarship for Young Researchers (2022) START Scholarship by Foundation for Polish Science (2020) Best PhD Thesis Award, UMCS Faculty of Chemistry (2022) Dr. Baran has secured significant grants including NCN PRELUDIUM 20 (2022-2025) and the "Diamentowy Grant" (Diamond Grant) (2018-2021). His work has been featured in Polish media (TVP Lublin) and international outlets like phys.org . He collaborates with researchers across Europe, including teams in Madrid and Lublin.
Mina Mirhosseini is a Research Fellow at the Faculty of Logistics, Molde University College, Norway. She holds a PhD in Computer Science from Shahid Beheshti University of Tehran, Iran, and has prior academic experience as a faculty member and lecturer in Iran and as a remote teaching assistant at the University of Hertfordshire, UK. Her primary research interests include Optimization Methods, Metaheuristics, Heuristics, Linear Integer Programming, Parallel Processing, Machine Learning, Artificial Intelligence, and Logistics. She has made significant contributions to solving complex computational problems such as the n-similarity problem and Mixed Integer Linear Programming (MILP) models using hybrid and parallel algorithms, particularly in the context of high-level synthesis and wireless sensor networks. The analysis of her recent publications reveals a strong focus on developing and applying advanced optimization techniques, especially quantum-inspired gravitational search algorithms and parallel genetic algorithms, to real-world engineering and computational challenges. Her work consistently emphasizes performance improvement, scalability, and load balancing in distributed and heterogeneous computing environments. Mina Mirhosseini has an extensive publication record in high-impact journals such as IEEE Transactions on Parallel and Distributed Systems, Journal of Parallel and Distributed Computing, Journal of Supercomputing, and Computers and Electrical Engineering. Her research has been published across a range of venues, reflecting interdisciplinary work at the intersection of computer science, electrical engineering, and applied optimization. She has actively contributed to the academic community through roles such as program committee member and executive committee member for conferences on fuzzy systems, swarm intelligence, and evolutionary computation. Her academic journey includes teaching and research roles in Iran, demonstrating a sustained commitment to higher education and scientific inquiry. Mina Mirhosseini is part of the research group focused on Planning, Optimization and Decision Support at Molde University College. Her current work continues to advance the state-of-the-art in parallel and metaheuristic optimization methods, with applications in logistics, synthesis, and sensor network design.
Stefan Leyk is a Professor of Geography at the University of Colorado Boulder within the Department of Geography in the College of Arts and Sciences. His research focuses on GIScience, spatial uncertainty modeling, and historical landscape analysis, with significant contributions to cartographic pattern recognition from historical maps and spatial dynamic modeling in public health. He holds a Ph.D. from the University of Zurich and the Federal Research Institute for Forest, Snow and Landscape (2005). His primary research interests include uncertainty in GIScience and spatial uncertainty modeling, land cover change modeling using historical spatial information, cartographic pattern recognition from historical maps, and spatial dynamic modeling approaches in public health. His work bridges historical geography with advanced computational methods, particularly in extracting settlement patterns from historical map archives and developing spatiotemporal datasets spanning centuries. Leyk's recent publications demonstrate strong trends in historical settlement analysis, with major projects like CHRONEX-US and HISDAC-US creating century-long datasets of urban infrastructure and settlement evolution. His work increasingly integrates machine learning with historical map processing, focusing on uncertainty quantification, built-up land validation, and environmental justice applications related to flood risk and coastal hazards. Key thematic areas include long-term urban growth patterns, rural poverty dynamics, and wildfire risk assessment at the wildland-urban interface. Leyk has received significant research funding through collaborative grants including 'HNDS-I: Building Long-term, National-scale Spatiotemporal Data Collections from Historical Map Archives' (2025) and 'HNDS-I: Data Infrastructure for Research on Historical Settlement and Population Growth in the United States' (2021). He actively mentors graduate students including Alek Berg, Caitlin McShane, and Yuying Ren, and teaches advanced GIS courses such as GEOG 4303/5303 GIS: Spatial Programming and GEOG 4103/5303 GIS: Spatial Analytics. His laboratory work centers on geospatial modeling of historical settlement and landscape analysis, with a focus on developing automated methods for processing historical map archives and creating linked spatiotemporal data. Current projects involve machine learning applications for feature extraction from historical maps, uncertainty prediction in built-up land layers, and the development of fine-grained datasets measuring 200 years of land development in the United States.
Prof. Yu Kang is a Professor of Precision Agriculture at the TUM School of Life Sciences, Technische Universität München (TUM). His research focuses on integrating imaging, sensing, and computational methods to study plant-environment interactions. He aims to enhance resource efficiency and reduce environmental impact through precision crop management. Prior to TUM, he held positions at China Agricultural University (CAU) and conducted postdoctoral research at ETH Zurich and KU Leuven. Prof. Yu's career includes roles such as Associate Professor of Crop Science at CAU and postdoctoral fellowships in physical geography. His educational background includes a doctoral degree from the University of Cologne (2014) and undergraduate studies at China Agricultural University. Key research areas include remote sensing for crop health monitoring, hyperspectral imaging for disease detection, and machine learning applications in agriculture. His work has led to innovations in crop nitrogen management and precision phenotyping. Awards include the Innovation Team Award (2019) from the Crop Science Society of China and the GSGS Fellowship (2014) from the University of Cologne.
Davide Viviano is an Assistant Professor in the Department of Economics at Harvard University, affiliated with the Faculty of Arts and Sciences. He holds a Ph.D. in Economics from UC San Diego (2022) and a Master’s in Data Science from Pompeu Fabra University (2017). His postdoctoral research included fellowships at Harvard (2023–2024) and Stanford Graduate School of Business (2022–2023). His research focuses on econometrics, policy design, and causal inference, combining economics with data science to develop statistical methods for social science applications. Key areas include network interference, experimental design, and fair policy targeting. He has contributed to frameworks addressing generalizability of causal effects, remotely sensed outcomes, and synthetic control methodologies. Developed the DynBalancing R package for dynamic treatment effect estimation with high-dimensional covariates. Recipient of featured article status in the Review of Economic Studies (2024) for work on policy targeting under network interference. His advising emphasizes student involvement in research projects, with opportunities for part-time research assistantships. Collaborations include work with institutions like the AEA Registry for field implementations involving over 400,000 participants.
Marcos Caballero serves as Associate Professor at both the University of Oslo's Center for Computing in Science Education and Michigan State University. His work bridges physics education research with computational science instruction across educational levels from high school to graduate programs. His educational background includes: B.S. in Physics from University of Texas at Austin (2004) M.S. in Physics from Georgia Institute of Technology (opto-microfluidics research) Ph.D. in Physics Education Research from Georgia Tech (2011, first PER-focused doctorate there) Postdoctoral research at University of Colorado Boulder Caballero's research investigates how computational tools and science practices shape physics learning, employing both cognitive and sociocultural theoretical frameworks. Key projects examine measurement uncertainty assessment, computational literacy development, and equity in graduate admissions. His work spans micro-level analyses of student coding comprehension to macro-level studies of computing's impact across degree programs, with significant contributions to transforming upper-division physics curricula toward active learning environments. His recent publications (2021-2024) reveal concentrated focus on measurement uncertainty assessment instruments , computational thinking frameworks , and holistic graduate admissions reform . These works demonstrate increasing methodological sophistication through NLP applications and large-scale educational data analysis, while maintaining practical relevance for physics classroom transformation. Caballero co-founded Georgia Tech's Physics Education Research group and currently leads UiO's Center for Computing in Science Education and Michigan State's Physics Education Research Lab . His international partnership for computing in science education drives cross-institutional curriculum development, focusing particularly on integrating computational practices into core physics instruction while addressing equity challenges in STEM education.
Dr. Shunqiao Sun is an Assistant Professor in the Department of Electrical and Computer Engineering at The University of Alabama, College of Engineering. He joined the faculty in August 2019 as a tenure-track professor after working at Aptiv’s radar core team in Malibu, California. His research focuses on advanced signal processing, machine learning, and optimization for automotive and MIMO radar systems in autonomous vehicles. Ph.D. : Electrical and Computer Engineering, Rutgers University, 2016 M.S. : Electrical Engineering, Fudan University, 2011 B.S. : Electrical Engineering, Southern Yangtze University, 2004 Dr. Sun's research lies at the intersection of statistical and sparse signal processing , mathematical optimization , and machine learning , with applications in automotive radar , MIMO radar , and autonomous driving . His work emphasizes sparsity-oriented frameworks, AI-powered radar perception, and high-resolution 4D sensing. He leads a dynamic research group focused on next-generation radar technologies for intelligent transportation systems. His recent publications demonstrate a strong trend in deep learning for radar signal recovery , collaborative radar imaging , direction-of-arrival estimation with sparse arrays , and integrated sensing and communication . Several of his papers are among the most downloaded and cited in IEEE journals, including top articles in IEEE Signal Processing Magazine and IEEE Journal of Selected Topics in Signal Processing. Scientific Awards and Honors: NSF CAREER Award (2024) NSF CRII Award (2022) IEEE AESS Robert T. Hill Best Dissertation Award (2016) Best Student Paper Award at IEEE SAM Workshop (2020) Rutgers ECE Academic Achievement Award (2015–2016) University of Alabama Hewson Engineering Faculty Fellow (2025) Dr. Sun is actively involved in academic service and leadership. He is an Associate Editor for IEEE Signal Processing Letters and IEEE Open Journal of Signal Processing . He serves as Vice Chair of the IEEE Signal Processing Society’s Autonomous Systems Initiative and is an elected member of the IEEE Sensor Array and Multichannel (SAM) Technical Committee and the Integrated Sensing and Communication (ISAC) Technical Working Group. He has co-organized numerous workshops and special sessions at ICASSP, EUSIPCO, and IEEE Radar Conference. His lab has secured significant research funding from the National Science Foundation , NXP Semiconductors , MathWorks , and NOAA . He mentors multiple Ph.D. students, several of whom have interned at leading industry labs such as NXP and GM Cruise. He has co-organized the Workshop on Signal Processing for Autonomous Systems (SPAS) at ICASSP and EUSIPCO and delivered invited seminars at institutions including TU Delft, UC Davis, and Lehigh University.
Professor Ying Liu is a Professor and Chair in Intelligent Manufacturing at the School of Engineering, Cardiff University, UK, a position he has held since August 2021. He leads the High-value Manufacturing research group within the Department of Mechanical Engineering. Prior to this, he served as an Assistant Professor at the National University of Singapore (2010–2013) and the Hong Kong Polytechnic University (2006–2010). PhD, Innovation in Manufacturing Systems and Technology (IMST), Singapore-MIT Alliance (SMA), National University of Singapore (2006) MSc, Singapore-MIT Alliance (SMA), Nanyang Technological University (NTU) MEng & BEng, Mechanical Engineering, Chongqing University, China His research spans engineering informatics, digital and intelligent manufacturing, AI and machine learning in engineering design, and advanced ICT in manufacturing. He has published over 160 scholarly articles and contributed to major journals and conferences in the field. His recent work focuses on knowledge graphs, digital twins, human-robot collaboration, and energy modeling in smart manufacturing, often integrating large language models and advanced deep learning techniques. The most recent publications highlight a strong trend toward integrating AI, particularly large language models and knowledge graphs, into smart manufacturing systems. Themes include predictive maintenance, battery state estimation, human fatigue modeling, and sustainable manufacturing. His work increasingly emphasizes human-centric approaches aligned with Industry 5.0 principles. Best Paper Award 2022, CCF Transactions on Pervasive Computing and Interaction ESI Highly Cited Paper and Hot Paper, Research and Application of Machine Learning for Additive Manufacturing 2020 Reviewer of the Year, ASME Journal of Computing and Information Science in Engineering (JCISE) Professor Liu actively supervises postgraduate students and has advised several successful PhD candidates, including Dr. Chong Chen and Mr. Zhouyang Ding. His research is funded by major agencies such as EPSRC (UK), GRF (Hong Kong), MOE (Singapore), A*STAR, and NSF (China), as well as industrial partners. He serves as Associate Editor for ASME JCISE, IEEE T-ASE, and several other journals, and was recently appointed Senior Editor of the Journal of Engineering Design. He also leads special issues and topical collections on AI in engineering. He leads the High-value Manufacturing research group at Cardiff University, focusing on digital transformation in manufacturing. His team works on projects involving digital twins, knowledge graphs, and AI-driven design innovation, often in collaboration with international institutions.
