جست‌وجوی استاد و پژوهشگر

V. Ashley Villarمشاهده پروفایل

استادیار

• Stellar Astrophysics
• Machine Learning in Astronomy
• Supernova Studies
• +۳ مورد دیگر

Dr. V. Ashley Villar is an Assistant Professor of Astronomy at Harvard University, specializing in using machine learning and data-driven methods to study stellar explosions, mergers, and transient phenomena. She holds a PhD in Astronomy & Astrophysics from Harvard (2020) and a BS in Physics from MIT (2014). Her research focuses on supernova light curves, circumstellar interactions, and binary progenitor systems. She previously held positions at Columbia University as a Simons Junior Fellow and at Penn State as the inaugural Mercedes Richards Career Development Professor. Her work leverages large-scale surveys like LSST and JWST to analyze transients, develop classification tools (e.g., SPLASH, Superphot+), and model explosion mechanisms. Awards include NSF and Ford Foundation fellowships. She collaborates on projects such as the Young Supernova Experiment (YSE) and contributes to multimessenger astronomy efforts linking gravitational waves to electromagnetic counterparts. Key Projects: YSE Data Release 1 (1,975 supernova classifications), extrabol bolometric light curve estimator, SBI++ inference framework Grants: NSF, Simons Foundation, LIGO collaborations Labs/Teams: Harvard CfA Transient Research Group, LIGO HET Response Collaboration Her recent work includes analyzing GRB 221009A's supernova emission, kilonova light-curve interpolation, and binary merger signatures in double-peaked supernovae. She actively develops machine learning tools for transient classification and anomaly detection in big astronomical datasets.

• Data Analysis
• Statistical Modeling
• Demand Modeling
• +۳ مورد دیگر

Lidia Montero Mercade is a Professor at the Polytechnic University of Catalonia (UPC) within the Department of Statistics and Operations Research and affiliated with the Barcelona School of Informatics (FIB). She leads the IMP - Information Modeling and Processing research group and is a core member of inLab FIB. Her academic work spans data science, statistical modeling, demand forecasting, and simulation for sustainable urban mobility. Her research focuses on transport analytics , integrating spatiotemporal sequence analysis gender equality in mobility patterns dynamic origin-destination matrices simulation-optimization techniques smart city policy evaluation ICT data fusion for transport . Recent publications emphasize fragmented mobility, urban sustainability, and data-driven equity assessments. Key trends in her 15 most recent articles include gender-specific travel behavior , activity-based sequence modeling , and smart mobility policy evaluation using simulation tools. Awards highlight social inclusion (Premi UPC) and excellence in transport research (Premi Abertis). She contributes to academic governance as a scientific committee member of conferences like Euro Working Group on Transportation and SEFI.

Silvia Paracchiniمشاهده پروفایل

استاد

• Neurogenetics
• Genomics
• Human genetics
• +۱۱ مورد دیگر

Silvia Paracchini is Professor of Neurogenetics and Genomics at the University of St Andrews, School of Medicine , where she leads the Neurogenetics group within the Cellular Medicine Division. She is affiliated with several interdisciplinary units including the Centre for Biophotonics, Biomedical Sciences Research Complex, Institute of Behavioural and Neural Sciences, and the St Andrews Bioinformatics Unit. Her research focuses on the genetic basis of complex cognitive and behavioral phenotypes , particularly dyslexia, handedness, language disorders, and neurodevelopmental conditions . She employs multidisciplinary approaches such as genome-wide association studies (GWAS), whole exome sequencing, polygenic scoring, and functional genomics to uncover the molecular mechanisms underlying these traits. Her work contributes to UN Sustainable Development Goals in health and well-being. Recent publications reveal strong trends in human genetics and neurodevelopmental genomics , with studies on handedness, language acquisition in bilingual children, grip strength in youth, and machine learning applications in mental health . Her research increasingly integrates public and patient involvement , as seen in work shaping UK research priorities through lived experience of learning difficulties. Fellow of the Royal Society of Biology (2018) Fellow of the Royal Society of Edinburgh (2019) Royal Society University Research Fellowship (2011) Member of the RSE Young Academy of Scotland (2013, 2014) She actively mentors postgraduate researchers and leads significant funded projects, including the Specific Learning Difficulties Network funded by the Medical Research Council. Her lab produces open datasets and software, promoting transparency and reproducibility. She is an active participant in international conferences and public engagement, including media appearances on Left Handers Day . Her research group, the Neurogenetics group , collaborates widely across the UK and internationally, with recent work involving institutions in Hong Kong, Germany, Spain, and the US. She has served as Principal Investigator on grants from the Medical Research Council, The Royal Society of Edinburgh, and Scottish Funding Council .

• Data Quality
• Software Quality and Measurement
• Business Intelligence
• +۶ مورد دیگر

Manuel A. Serrano is a Professor at the School of Computer Science, University of Castilla-La Mancha, Spain. With over 25 years of professional experience including industry roles as programmer/web designer and secondary school teacher, he has been a full-time researcher in the Alarcos group since 2000. Currently coordinates professional practice programs in Computer Engineering. Education : PhD in Computer Engineering (2004, UCLM, cum laude) Research Interests : Data quality, quantum software engineering, business intelligence, Big Data security, IoT, and AI ethics His research spans two major domains: Quantum Computing : Pioneering quantum software engineering, developing testing methodologies, assertion frameworks, and hybrid system guidelines Cybersecurity : Creating security reference architectures for Big Data and automotive systems, with focus on quantum security integration Recent publications demonstrate these interests through: Quantum algorithm verification techniques Security frameworks for automotive systems Ethical AI methodology development Big Data risk analysis patterns Scientific achievements include: 24 JCR-indexed journal articles Over 50 SCI-category conference papers 3 research six-year periods Best Scientific Article Award at JNIC 2018 Research leadership: Coordinated regional project with University of Alicante Principal investigator for national project QUASIMODO Contributor to 20+ national/regional and 3 international projects (SDGear, DQIoT, Di4SPDS) Industrial collaborations with INDRA on ORIGIN and LPS-BIGGER projects Additional qualifications: Professional Scrum Manager certification Cambridge C1 English certification Spin-off participation in AQCLabs and DQTeam

Sergei Maslovمشاهده پروفایل

استاد

• Microbial Ecology
• Epidemiology Modeling
• Systems Biology
• +۳ مورد دیگر

Sergei Maslov is a Full Professor and Bliss Faculty Scholar in the Department of Bioengineering at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Grainger College of Engineering. He also holds a non-active appointment in the Department of Physics (0%). His research integrates theoretical physics, computational biology, and microbiology to study microbial ecosystems, epidemic dynamics, and systems-level biological processes. Education: Ph.D. in Theoretical Statistical Physics from Stony Brook University (1996). Academic leadership includes founding roles in UIUC's and Illinois Governor's COVID-19 modeling teams, advising on mitigation strategies during the pandemic. He leads interdisciplinary initiatives, such as the 2024 Strategic Research Initiative on spatial transcriptomics funded by Grainger College of Engineering. Research Interests: Microbial community dynamics, computational epidemiology, origins of life, and systems biology. Key projects include modeling gut microbiome behavior, predicting epidemic waves, and developing tools like FUN-PROSE and DR-BERT for biological data analysis. Awards: 2021 University of Illinois Presidential Medallion, AIMBE Fellowship (2021), APS Fellow (2020), and PECASE (2002). His work bridges theoretical frameworks with real-world applications, such as SHIELD testing protocols and community-level ecological modeling. Grants and Service: Principal Investigator (PI) on multi-million-dollar projects, including SRI grants. Served on campus committees like the Math Bio Faculty Search Committee (2022). Lab collaborations span departments, with co-PI roles in electrical engineering, computer science, and statistics. Labs/Teams: Maslov Lab focuses on computational and systems biology. Recent work includes a Nature Communications paper on microbial resource preferences and eLife publications on pandemic modeling. The lab actively engages in international conferences and hosts visiting scholars, such as Griffin Chure from Stanford.

Robert L. Carrawayمشاهده پروفایل

استاد

• Data Analytics
• Decision Sciences
• Optimization
• +۴ مورد دیگر

Robert L. Carraway is a Professor at the University of Virginia's Darden School of Business, where he has been a faculty member since 1984. He currently serves as the Quantitative Analysis (QA) area head and previously held the position of Senior Associate Dean for MBA Programs from 2006-2011. Carraway teaches Data Analytics and Decision Sciences courses in Darden's MBA program, including First Year Decision Analysis, Managerial Quantitative Analysis, Optimization Models, and Darden Business Projects: Venturing. His global executive education work spans Europe, Asia, and Latin America. Education: B.S., East Carolina University M.B.A., East Carolina University Ph.D., Purdue University Carraway's research spans multiple areas of quantitative business analysis. His original work focused on Optimization, but he has expanded his focus to the broader role of analytics in decision making. He examines the relationship between intuition and rational analysis, exploring how these approaches complement one another in business contexts. His work investigates how analytical methods enhance innovation processes, making them more effective and efficient. A significant portion of his recent research addresses 'Big Data' applications in business. He has authored articles such as 'Big Data, Small Bets' and 'Meeting the Big Data Challenge: DON'T Be Objective,' which explore practical approaches to leveraging large datasets for competitive advantage. His work emphasizes combining analytical rigor with business judgment rather than relying solely on data-driven approaches. Carraway's publication record demonstrates a consistent focus on quantitative methods for business decision-making. His earlier work centered on optimization models and mathematical programming, while more recent publications address analytics, probability distributions, and risk analysis. A notable trend is his exploration of how decision-makers integrate quantitative analysis with intuitive judgment in complex, uncertain environments. Award Recognition: Multiple teaching honors at Darden School of Business Carraway has significantly influenced business education through his case method teaching, with numerous cases used in Darden classrooms worldwide. His global executive education work extends his impact to practicing managers across continents. While specific doctoral students aren't listed, his long tenure suggests substantial mentoring experience. As QA area head, he oversees faculty specializing in quantitative business analysis and collaborates with colleagues like Sam Bodily, Phillip Pfeifer, and Sherwood Frey on research teams focused on decision sciences.

Amirali Aghazadehمشاهده پروفایل

استادیار

• Machine Learning
• Bioinformatics
• Bioengineering
• +۱۲ مورد دیگر

Amirali Aghazadeh is an Assistant Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. He serves as program faculty for the Machine Learning, Bioinformatics, and Bioengineering Ph.D. programs and has affiliations with the Institute for Data Engineering and Science (IDEAS) and Institute for Bioengineering and Biosciences. Dr. Aghazadeh's research lies at the intersection of machine learning and AI, information and signal processing, and biological design and engineering. His work focuses on developing tools and algorithms that enable machines to learn, predict, scale, adapt, and be explained to solve challenging biological science and engineering problems, including: ML/AI-guided protein design & optimization Variant effect prediction Accelerated/guided sequence generation Origins of life and biosignature discovery AI for protein imaging His recent publications demonstrate a strong focus on applying machine learning and AI to biological problems, particularly in protein design and analysis, as well as developing novel algorithms for data processing. His work spans computational biology, astrobiology, and information theory, showing interdisciplinary breadth while maintaining technical depth. Dr. Aghazadeh's scientific achievements include winning the IDEaS GenAI for Science competition in July 2025, with support from Microsoft Azure to develop next-generation agentic AI tools for scientific discovery. His work on 'AI Scientists' for hypothesis generation about Origins of Life was featured in Nature. He advises several graduate students including Thomas Alan Walton, Darin Tsui, Lauren Fogel, Daniel Saeedi, Denise Buckner, and José Aponte, who have contributed to his research in protein design, AI for scientific discovery, and algorithm development. His lab appears to focus on the intersection of AI and biological sciences, with connections to NASA Goddard Space Flight Center as evidenced by collaborations on astrobiology research.

• Data Science
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Marc Bravin is a Lecturer at Lucerne University of Applied Sciences and Arts in the Lucerne School of Computer Science and Information Technology, affiliated with the Digital Business Lab. He is also the Co-Founder & CTO of Gopf AG since 2023. Bravin holds a PhD in AI and Business from the University of Lucerne, alongside Master and Bachelor degrees in Computer Science from Lucerne University of Applied Sciences and Arts, with an apprenticeship in Computer Science at Pilatus Aircraft Ltd. PhD in AI and Business, University of Lucerne Master of Science in Engineering, Lucerne University of Applied Sciences and Arts Bachelor of Science in Computer Science, Lucerne University of Applied Sciences and Arts Apprenticeship in Computer Science, Berufsbildungszentrum Sursee His research focuses on Data Science, Machine Learning, Computer Vision, NLP, Self-Supervised Learning, and Digital Marketing . Key projects include CIRRNET, Health Innovation Transfer Study, Recommender Systems for Food and Telecom, and AI-based Employer Attractivity Benchmarking. He has also contributed to innovative ventures like AI Ice Cream and automated beer recipe generation. His publications span topics such as social media trend analysis, AI creativity, and machine learning applications. Notable works include studies on TikTok content prototypicality and minimal hand pose estimation for touchable systems. Best Poster Video Award (2022) for 'Minimal Hand Pose Estimation' Best Paper in AMA Conference Track (2022) for work on social media creativity Bravin’s career includes roles as a Data Scientist, Research Associate, and Software Developer in firms like Jaywalker Digital AG and Pilatus Aircraft Ltd. He collaborates with industry partners such as Prepress Media AG and Betty Bossi, applying AI to real-world challenges. He leads the Digital Business Lab’s initiatives in AI-driven innovation and maintains active involvement in academic-industry partnerships through projects like the Health Innovation Transfer Study.

Lilian Petersمشاهده پروفایل

استادیار

• Women's Health
• Clinical Epidemiology
• Electronic Health Record (EHR) Data
• +۷ مورد دیگر

Dr. Lilian Peters is an Assistant Professor/Clinical Epidemiologist at the University of Groningen’s Faculty of Medical Sciences, affiliated with the University Medical Center Groningen (UMCG) and Western Sydney University. Her roles include leading the Midwifery Science and Academic General Practice Living Lab. She specializes in using EHR data to improve healthcare outcomes, particularly in women’s health and maternal care. Peters also holds external positions at Amsterdam UMC and Western Sydney University. Her research focuses on data-driven innovations to address long-term maternal health, leveraging EHR data from national and international cohorts like the Lifelines Reproductive Origins of Adult Health and Disease. During the pandemic, her team applied AI techniques (e.g., NLP) to EHR data for monitoring care pathways and predicting adverse outcomes. She established the Women’s Health Data Research Infrastructure, linking GP networks with maternity registries. Peters has been awarded multiple prizes, including the EU COST Action Short Term Scientific Mission Award (2018) and SHARE Top Publication Award (2012). Her work aligns with UN Sustainable Development Goals related to health and well-being. She supervises students across medicine, biomedical sciences, and nursing programs at multiple institutions. Key projects include the Birth Experience Study and collaborations with international networks like ROAHD. Her publications span maternal health, AI-driven disease surveillance, and post-COVID-19 outcomes, emphasizing actionable insights from big data.

• Corporate Law
• Commercial Law
• Intellectual Property
• +۵ مورد دیگر

Silvia Scalzini is an Associate Professor at the Department of Economics and Business, University of Parma. She specializes in Corporate and Commercial Law, Intellectual Property, and EU Regulatory Frameworks, with a focus on blockchain technology, sustainability reporting, and digital markets. Her teaching includes courses on Italian and European Corporate Law and Marketing Law for Economics and Management programs. Corporate Sustainability Reporting Directive (CSRD) compliance Agri-food blockchain applications Competition law in digital markets Intellectual property and data-driven innovation EU copyright remedies and press publisher rights Her recent research explores the intersection of blockchain technology with intellectual property and supply chain transparency, alongside regulatory challenges in EU digital markets. She has contributed to debates on sustainability reporting, competition law, and data ethics in academic studies. Her publications span topics including economic dependence in digital markets, EU copyright misuse doctrines, and legal frameworks for emerging technologies. She has collaborated with researchers like Erica Adamo and Emanuela Maio on blockchain applications in agri-food sectors.

• Semiconductor thin film materials and devices
• Material nano-characterization
• Electromechanical coupling characterization
• +۳ مورد دیگر

Huang Boyuan serves as an Associate Professor and Doctoral Supervisor in the Department of Materials Science and Engineering at Southern University of Science and Technology (SUSTech). His research group operates within the Guangdong Provincial Key Laboratory of Information Functional Oxide Materials and Devices, utilizing advanced facilities including AFM, PLD, ALD, CVD, SHG, semiconductor analyzers, and cleanroom lithography equipment. Education: Ph.D. in Mechanical Engineering, University of Washington (2016-2020) B.S. in Physics, Nanjing University (2012-2016) Huang's research focuses on developing cutting-edge characterization techniques, particularly artificial intelligence atomic force microscopy (AI-AFM) and mechanically gated transistors (MGT). His work bridges materials science, nanotechnology, and artificial intelligence to investigate nanoscale multi-field coupling mechanisms in semiconductor materials. The research group actively explores two-dimensional thin film materials, experimental mechanics, and energy material interfaces, with particular emphasis on data-driven approaches to material characterization. Huang's publication portfolio demonstrates consistent high-impact output across materials science and nanotechnology domains, with increasing emphasis on AI-integrated characterization methods. His recent work shows strong interdisciplinary connections between materials physics, machine learning, and semiconductor device engineering, reflecting the growing importance of data science in advanced material characterization. Scientific Recognition: R&D 100 Awards Finalist (2020) Shenzhen Natural Science Award (2021) Shenzhen Overseas High-level Talent (2020) National Excellent Self-financed Overseas Student Scholarship (2020) Featured in MIT Technology Review Huang has successfully secured funding for eight research projects, including the NSFC Original Exploration Plan project and Major Research Plan Integration Project. His research group actively mentors graduate students and postdoctoral fellows, with several students receiving prestigious awards and scholarships. The team maintains strong industry connections, with two authorized patents achieving technology transfer. The research group operates within state-of-the-art facilities at SUSTech, maintaining collaborative relationships with national research initiatives including the National Major Scientific Research Instrument Development Projects. Current recruitment efforts focus on doctoral candidates and research assistants specializing in two-dimensional thin film materials, experimental mechanics, and energy material interface studies.

Chau-Wai Wongمشاهده پروفایل

دانشیار

• Machine Learning
• Multimedia Forensics
• Signal and Image Processing
• +۵ مورد دیگر

Chau-Wai Wong is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University, with affiliations to the Forensic Sciences Cluster and Secure Computing Institute. He previously served as a Data Scientist at Origin Wireless, Research Assistant at University of Maryland, and Research Associate at Hong Kong Polytechnic University. His work bridges machine learning with applications in multimedia forensics, signal processing, and computational social science. Ph.D. , Electrical Engineering, University of Maryland (2017) M.Phil. , Electronic and Information Engineering, Hong Kong Polytechnic University (2010) B.Eng. , Electronic and Information Engineering, Hong Kong Polytechnic University (2008) Research spans federated learning (security vulnerabilities, communication efficiency), physically unclonable features (PUF-based authentication), generative models (GANs for hardware modeling), and computational social science (AI chatbots for disaster communication, TikTok behavioral analysis). Recent work explores neural tangent kernels and multi-LLM agent collaboration . Key publications focus on decentralized AI systems (ICML'25), deepfake detection (under review), and social media health analytics (Telematics and Informatics'25). His federated learning research has appeared at ICLR, IEEE T-NNLS, and USENIX Security. NSF CAREER Award IEEE Signal Processing Cup Organizer (2016) Technical Program Committee Chair (IH&MMSec'25) Area Chair (ICME'21–'24) Senior Member, IEEE Advises students in AI security , physiological sensing , and social science automation . Collaborates with teams at University of Maryland, Hong Kong Polytechnic University, and industry partners. Current projects include identity-privacy protection for smart health and UAV-assisted network optimization (IEEE DySPAN'25).

• Astrophysics
• Cosmology
• Nuclear Astrophysics
• +۳ مورد دیگر

Brian Fields is a Professor in the Department of Physics at the University of Illinois Urbana-Champaign, affiliated with The Grainger College of Engineering. His research focuses on astrophysics, cosmology, and particle/nuclear astrophysics, particularly studying cosmic phenomena like supernovae, cosmic rays, and the origins of elements via nucleosynthesis. He teaches courses such as ASTR 350 (Big Bang, Black Holes, Universe) and ASTR 504 (Theoretical Stellar Physics). His work explores high-energy astrophysical processes, including the impact of nearby supernovae on Earth’s biosphere and interstellar medium. He contributes to interdisciplinary projects like the All-sky Medium Energy Gamma-ray Observatory mission concept. Education: Not explicitly listed in provided texts. Research Interests: Early universe cosmology, stellar explosions, cosmic ray interactions, and the interplay between particle physics and astrophysical observations. Fields was elected an APS Fellow, recognizing his contributions to advancing physics through groundbreaking research. His studies of radioactive isotopes in deep-sea sediments and lunar measurements aim to trace stellar explosion histories. Grants & Advising: No specific grants listed, but he advises students in astrophysics and nuclear astrophysics. His research group focuses on particle and nuclear astrophysics, exploring cosmic accelerators and element formation.

Andrew Harrisonمشاهده پروفایل

مدرس ارشد

• Bioinformatics
• Functional Genomics
• Biostatistics
• +۳ مورد دیگر

Dr. Andrew Harrison is a Senior Lecturer in the School of Mathematics, Statistics and Actuarial Science at the University of Essex. Originally an astrophysicist specializing in star formation, he transitioned to bioinformatics to explore life detection on exoplanets and apply mathematical-statistical methods to functional genomics. His work involves collaborating with biologists and medics to analyze data-rich experiments like microarrays. Qualifications include a PhD in Astrophysics and BSc in Physics with Astrophysics from the University of Manchester (1991). He has held his current position since 2004. Research interests span bioinformatics applications, computational genomics, and interdisciplinary collaborations in life sciences. Publications reflect expertise in gene selection methods, evolutionary cooperation models, and bioinformatics tools for genomic data analysis. He has contributed to rice stress-resistance databases and protein interaction studies. His work bridges astrophysics-derived analytical techniques with modern biological problems, emphasizing data-driven solutions. Grants and funding details are listed in institutional records, though specific projects are not detailed here. Teaching and supervision activities are integral to his role, though student names are not provided in available texts.

Barbara Bordalejoمشاهده پروفایل

استاد

• Digital Humanities
• Textual Scholarship
• Scholarly Editing
• +۴ مورد دیگر

Barbara Bordalejo is a Professor at the University of Lethbridge with dual appointments in Digital Humanities and English . She serves as President of the Canadian Society for Digital Humanities/Société canadienne des humanités numériques and has held leadership roles in Global Outlook:: Digital Humanities and the European Association of Digital Humanities . Her work focuses on: Digital Editions : Leading the CantApp project (2020) for mobile access to Chaucer’s Canterbury Tales , co-developing the Textual Communities platform with Peter Robinson, and editing Darwin’s Origin of Species . Textual Scholarship : Exploring phylogenetic methods for manuscript analysis, collation techniques, and the intersection of bioinformatics with textual criticism. Equity in Digital Humanities : Investigating gender and minority representation through publications like Minority Report (2018) and co-organizing ADHO diversity workshops (2017–2022). AI and Academic Integrity : Analyzing false positives in AI detection software (2025) and advocating for responsible AI policies in education. Her research projects include collaborations with Richard North and Peter Robinson on digital tools, work with Roopika Risam on intersectional DH, and contributions to global DH initiatives across disciplines and regions.