جست‌وجوی استاد و پژوهشگر

Jeho Parkمشاهده پروفایل

استادیار مهمان

• Applied Mathematics
• Computer Science
• Data Science
• +۳ مورد دیگر

Jeho Park, Ph.D., is a Visiting Assistant Professor and Director of the Murty Sunak Quantitative and Computing Lab at Harvey Mudd College (HMC). He holds a Ph.D. in Applied Mathematics and Computer Science from Claremont Graduate University (2009). His roles include directing computational resources and services for students and faculty, teaching courses in parallel computing, data science, and computer science education, and co-founding SoDAVi, a non-profit aiding the Korean-American community with data analytics. He served as President of the Korean-American Computer Scientists and Engineers Association (KOCSEA) and actively participates in the Korean-American Scientists and Engineers Association (KSEA). Education: Ph.D. in Applied Mathematics and Computer Science, Claremont Graduate University, 2009 Research Interests: High Performance Computing (HPC), data science, AI/Machine Learning, and advancing computational education. He designs workshops in R, MATLAB, Big Data, and Tableau, emphasizing practical applications in academia and industry. Labs/Teams: Leads the Murty Sunak Quantitative and Computing Lab, focusing on computational tools and training for interdisciplinary research.

Steven Wilkinsمشاهده پروفایل

استادیار

• Electric Vehicle Systems
• Powertrain Engineering
• Battery Management Systems
• +۳ مورد دیگر

Dr. Steven Wilkins is an Assistant Professor in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e), specializing in Electromechanics and Power Electronics. He concurrently serves as a Senior Research Scientist at TNO, focusing on powertrains and electric vehicle systems. His research emphasizes battery management, electric drive systems, and sustainable transportation. Education: PhD in Mechanical Engineering from Imperial College London (postdoc until 2007) Research Interests: Wilkins' work centers on hybrid/electric vehicle systems, powertrain modeling, and battery management systems. He actively contributes to projects like TRANSFORMERS, CONVENIENT, and the UN Sustainable Development Goals related to clean energy and sustainable mobility. Collaborations: He collaborates with organizations such as EARPA, EGVIA, and TNO, and has been involved in EU-funded initiatives. His expertise spans R&D, MATLAB modeling, and homologation processes. Labs/Teams: Affiliated with the Electromechanics Lab at TU/e and the EIRES Research group. His work integrates academic research with industrial applications in electric mobility.

Stephen Tobinمشاهده پروفایل

پژوهشگر ارشد

• Phonetics
• Speech Production
• Speech Perception
• +۳ مورد دیگر

Stephen Tobin is a Research Fellow in the Department of Linguistics at the University of Michigan, joining in August 2018. He holds a BA in Modern Languages and Linguistics from the University of Cambridge, an MA from the University of Southern California, and a PhD from the University of Connecticut (2015). Prior to UMich, he was a Research Fellow at Universität Potsdam (Germany) from 2014 to 2018. His research focuses on phonetics, including speech production, perception, and their interrelation, particularly through studies of phonetic accommodation. He employs advanced methodologies such as Electromagnetic Articulography (EMA), real-time MRI, eye-tracking, and data analysis using MATLAB and R. Stephen contributes to the Linguistics department’s research initiatives and is affiliated with 440 Lorch Hall, Ann Arbor.

• Machine Learning
• Causal Inference
• High-Dimensional Statistics
• +۲ مورد دیگر

Malte Kurz is a researcher affiliated with the University of Hamburg Business School, specifically within the Department of Statistics. His primary role was as a Researcher under the Professorship of Statistics with Applications in Business Administration. He holds a PhD in Statistics from Ludwig-Maximilians-Universität München (2018), an M.Sc. in Statistics from LMU Munich (2013), and a B.Sc. in Mathematical Finance from Universität Konstanz (2011). His research interests focus on Machine Learning , Causal Inference , High-Dimensional Statistics , Financial Econometrics , and Dependence Modelling & Copulas . He has contributed to advancements in vine copula theory, distributed machine learning frameworks, and state space models. Key publications include works on vine copula simplifications (2019), low-dimensional Kalman smoothers (2018), and distributed double machine learning architectures (2021). He has developed influential software tools like the pacotest R package for copula hypothesis testing and the DoubleML framework for Python/R. Kurz has also contributed to open-source projects such as the VineCopulaMatlab toolbox and the SSMwLS MATLAB package for state space modeling. His work bridges statistical theory and computational implementation, with applications in finance and econometrics.

Samuel Shueمشاهده پروفایل

استادیار

• Robotics
• Embedded Systems
• Wireless Sensor Networks

Samuel Shue is a Teaching Assistant Professor in the Department of Electrical and Computer Engineering at the University of North Carolina Charlotte. His research focuses on Robotics, Embedded Systems, and Wireless Sensor Networks, with a particular emphasis on RSSI localization techniques and wireless communication frameworks. Ph.D., University of North Carolina Charlotte, 2017 M.Sc., University of North Carolina Charlotte, 2015 B.Sc., University of North Carolina Charlotte, 2014 Shue's research explores the intersection of robotics and wireless sensor networks, including: Signal processing for localization accuracy XBee-based communication protocols Portable embedded system frameworks Procedural simulation environments His publications (2013-2017) demonstrate expertise in Kalman filter applications, wireless mesh networking, and open-source development platforms. Key collaborations include James M. Conrad and Andrew R. Willis. For direct contact, email slshue@charlotte.edu or call 704-687-5885.

• Finance
• Energy Finance
• Commodity Finance
• +۶ مورد دیگر

Dr. Thomas Walther is an Associate Professor of Finance at the Utrecht School of Economics, Utrecht University. He previously served as Assistant Professor for Energy Finance at the University of St. Gallen and holds a Research Fellow position at the Faculty of Business and Economics, Technische Universität Dresden. Dr. Walther received his B.Sc. (2010) and M.Sc. (2013) in Industrial Engineering and Management and a PhD (2017) in Business & Economics from Technische Universität Dresden. His academic journey includes visiting researcher positions at International University HCMC and John von Neumann Institute. His research spans energy & commodity finance, climate finance, financial econometrics, and sustainable finance . Dr. Walther employs advanced quantitative methods including time series analysis and machine learning techniques to address pressing issues in risk management and market dynamics. His work integrates traditional financial theory with modern computational approaches to analyze complex market phenomena. His recent publications demonstrate a strong focus on sustainable finance applications, with increasing attention to climate risk, clean energy technologies, and explainable artificial intelligence in financial decision-making. The research shows a clear trajectory toward addressing sustainability challenges through rigorous financial analysis and innovative methodological approaches. AMFET Best Paper Award (2016) Best PhD Paper in Finance (2021) Campbell Watkins Energy Journal Best Paper Award (2022) Certificate of Appreciation (2016) Dr. Feldbausch-Förderpreis (2018) Dr. Walther serves as Associate Editor for Finance Research Letters since 2019 and was ranked among the top scientists in business administration under 40 years in Germany, Austria, and Switzerland in 2024. He initiated the European Sustainable Finance PhD Workshop in 2023 and co-founded the Dutch Sustainable Finance Network in 2024, demonstrating strong commitment to building academic communities around sustainable finance research. His technical expertise includes advanced programming skills in Matlab, R, and Python, which he applies to complex financial data analysis and modeling problems. His research network spans multiple countries and institutions, reflecting the international nature of his scholarly contributions.

Karishma Kumariمشاهده پروفایل

پژوهشگر

• Precision Agriculture
• Plant Science
• Crop Disease Detection
• +۳ مورد دیگر

Karishma Kumari is a Ph.D. student and Graduate Research Assistant at the Raven Precision Agriculture Center, South Dakota State University, affiliated with the College of Agriculture, Food and Environmental Sciences and the Department of Agronomy, Horticulture and Plant Science. Her research focuses on precision agriculture, plant science, and sensor technology, with recent publications exploring AI-driven crop disease detection, climate-smart agriculture, and deep learning applications for seed classification. Education: Ph.D. in Plant Science (current, South Dakota State University), Master of Technology in Agricultural Engineering (ICAR-Indian Agricultural Research Institute, New Delhi), Bachelor of Technology in Agricultural Engineering (Dr. R.P.C.A.U, Pusa). Her work integrates sensor development, remote sensing, and machine learning for sustainable agricultural solutions, including real-time disease monitoring and IoT-enabled farming systems. She has received recognition for her academic excellence, securing 8th all India ranks in two competitive examinations. Scientific Awards: 8th all India rank in ICAR AIEEE PG-Junior Research Fellow Examination 2020 8th rank in AICE-Ph.D. Senior Research Fellow Examination 2022 (EWS category) Her recent publications emphasize trends in AI, machine learning, and sensor-based technologies to enhance agricultural productivity and sustainability. She is trained in programming tools like MATLAB, R, and hardware platforms such as Raspberry Pi, Arduino, CATIA, and ANSYS.

Marco Rianiمشاهده پروفایل

استاد

• Robust Statistics
• Regression Analysis
• Time Series Analysis
• +۵ مورد دیگر

Marco Riani is Full Professor of Statistics at the Department of Economic and Business Sciences, Faculty of Economics, University of Parma since 2006. He directs the Interdepartmental Center Ro.S.A. (Robust Statistics Academy) and has held academic positions including Post Doctoral Fellow (1996-1998), Lecturer (1998-1999), and Associate Professor (1999-2006) at the same institution. Education: Bachelor of Science in Economics (cum laude), University of Parma (1986-1990) PhD in Statistics, University of Florence (1992-1995) His research centers on robust statistics with methodological focus on robust regression , time series , and multivariate analysis . Applied work spans risk management , quality evaluation , international trade monitoring , and medical statistics (neonatal health parameters). He pioneers outlier detection without arbitrary thresholds and robust machine learning. Recent publications highlight robust transformations for regression, explainable AI, and applications in environmental/medical fields. Key trends include automated fraud detection in trade data, soil moisture estimation through time series, and biomarker analysis for birth anomalies. Scientific awards: Best Italian PhD thesis in Statistics (1997) Best contribution at MATLAB expo 2016 (Milan) As advisor, he has supervised five PhD theses and over 100 degree theses. His grant leadership includes Horizon 2020 PrimeFish (2014-2019) and multiple Italian Ministry projects on robust methods. External expertise spans EU Automated Monitoring Tool projects (AMT3-AMT5) for trade fraud detection. Principal Investigator: 11 national/EU projects (2000-2019) Steering roles: ICORS, SIS CLADAG, Ro.S.A. center He co-developed the FSDA toolbox (MATLAB/R) with the European Commission, enabling robust multivariate analysis for institutions like the Joint Research Centre. The Ro.S.A. center drives his collaborative work on outlier detection and data mining.

Prashant Srivastavaمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Image Analysis
• +۵ مورد دیگر

Prashant Srivastava is a Researcher at the Department of Systems Biology and Bioinformatics under the Faculty of Computer Science and Electrical Engineering at the University of Rostock, Germany. His work bridges machine learning with bioinformatics and financial risk modeling. Education: MSc Data Science and Analytics (2018-2020) from Royal Holloway, University of London Integrated B.S.-M.S. in Physics (2013-2018) from Indian Institute of Science Education and Research, Kolkata Master's Thesis in Computer Science (2020) on statistical risk methods in financial markets Master's Thesis in Physics (2017-2020) on extraordinary transmission in silicon-based micro-meter wire grid polarizers Research Focus: Prashant specializes in machine learning and deep learning applications. His work spans bioinformatics (bitter peptide prediction), data science (ConvGeN algorithm for imbalanced datasets), and financial risk modeling using VaR and Expected Shortfall frameworks. Recent Publications: His research trends include graph neural networks for peptide analysis, convex space learning for generative oversampling, and self-attention mechanisms for molecular interaction extraction from biological texts. Technical Expertise: Proficient in Python (numpy, scipy, tensorflow), R, C++, MATLAB, cloud computing (Azure), and database systems (SQL, NoSQL). He develops libraries like mgarch for GARCH modeling and applies advanced ML techniques (CNN, RNN, GAN, NLP) across domains. Labs & Teams: Active member of the Systems Biology and Bioinformatics group at the University of Rostock, contributing to interdisciplinary research at the intersection of computer science and life sciences.

Erin C. Munro Krullمشاهده پروفایل

دانشیار

• Applied Mathematics
• Mathematical Modeling
• Statistics
• +۳ مورد دیگر

Erin C. Munro Krull is an Associate Professor of Mathematics and the Mark and Janice Franzen Professorship in Applied Mathematics at Ripon College. She joined the institution in 2019, specializing in applied mathematics with a focus on mathematical modeling, statistics, and data analysis. Her teaching emphasizes integrating modeling techniques into courses like calculus and guiding senior thesis projects through research and modeling. Education: Ph.D., Tufts University; M.A., Tufts University; B.A., Connecticut College Her research explores computational neuroscience topics such as action potential propagation across gap junctions (with epilepsy applications) and neocortical processing during sleep, combining mathematical modeling and data analysis. Beyond academia, she maintains interests in music and puzzles, having pursued opera singing and multilingual theater studies in her earlier years. Key tools she employs include MATLAB, Python, and R (currently being explored with students in data analysis courses).

Xin Zanمشاهده پروفایل

استادیار

• Weakly Supervised Learning
• Statistical Machine Learning
• Knowledge-Enhanced Deep Learning
• +۶ مورد دیگر

Xin Zan is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Iowa's College of Engineering, where he joined in Fall 2024. His research develops advanced data-driven analytics for complex systems in healthcare, manufacturing, and service sectors through novel weakly supervised learning methodologies. His educational background includes: PhD in Industrial and Systems Engineering from the University of Florida (2024) MS in Statistics from the University of Florida (2022) BS in Industrial Engineering from Shanghai Jiao Tong University, China (2019) Dr. Zan specializes in statistical and machine learning under weak supervision, knowledge-enhanced deep learning, spatio-temporal modeling, causal inference, and health informatics. His work bridges theoretical advances with practical healthcare applications including sleep disorder diagnostics, infectious disease surveillance, and medical system optimization. He actively develops frameworks for complex system monitoring where labeled data is scarce or imperfect. His publication portfolio reveals a concentrated focus on healthcare AI applications (60% of recent work), particularly sleep apnea monitoring and infectious disease tracking, while extending methodologies to recommender systems and big data streams. Core technical themes include Bayesian modeling, nonparametric statistics, and causal analysis across temporal and spatial dimensions. Dr. Zan is recruiting PhD students with engineering, statistics, or mathematics backgrounds and strong programming skills in Python, R, or Matlab. Prospective students should submit CVs, transcripts, and research statements to xin-zan@uiowa.edu for positions in applied statistics, data science, and AI-driven systems engineering.

• Chemometrics
• Spectroscopy
• Data Analysis
• +۳ مورد دیگر

Dragosav Mutavdžić serves as a Senior Research Associate at the Department of Living Systems Sciences, Institute for Multidisciplinary Research, University of Belgrade. His expertise lies in statistical processing of spectroscopic data from biological samples using chemometric techniques and advanced software platforms. His academic credentials include: PhD in Multidisciplinary Sciences - Chemometrics (2016), University of Belgrade Master of Science in Statistics (2010), Faculty of Economics, University of Belgrade Bachelor's in Chemistry (2003), Faculty of Chemistry, University of Belgrade Mutavdžić specializes in chemometrics applied to fluorescence, FTIR, and Raman spectroscopy data from biological systems. His research leverages SPSS, STATISTICA, The Unscrambler, R, Python, and Matlab for data visualization and analysis, with significant contributions to forensic science through nanomaterial-based fingerprint imaging techniques and food safety applications. Analysis of his 2015-2019 publications reveals dominant research themes in carbon-based nanomaterials for forensic imaging (quantum dots, nano-powders), multivariate analysis of spectroscopic data for contaminant detection, and biological system characterization using chemometric methods. Key application areas include forensic evidence analysis, food safety monitoring, and environmental sensing. He currently participates in COST Action CA16101 (MULTI-modal Imaging for Forensic Science Evidence) and previously contributed to project OI 173017 on plant cell wall engineering. His collaborative work spans multiple international research teams across Serbia, Spain, Portugal, and the UK, though no formal student supervision is documented.

Carsten Allefeldمشاهده پروفایل

مدرس

• Cognitive Neuroscience
• Statistical Modeling
• Neuroimaging
• +۷ مورد دیگر

Dr. Carsten Allefeld is a Lecturer at the Department of Psychology and Neuroscience, City St George's, University of London. With a background in physics (nonlinear dynamics) and philosophy, he develops statistical models and data analysis methods for neuroimaging (EEG, fMRI) and behavioral data. His work bridges concepts from physics, systems theory, and information theory with cognitive neuroscience and psychology, focusing on neural-mental state relationships. Education: Dr. rer. nat. in Physics (University of Potsdam, 2004), M.A. in Philosophy (Freie Universität Berlin) Employment: Lecturer at City St George's (2019–present), Research Associate at Bernstein Center for Computational Neuroscience Berlin (2009–2018) His research emphasizes multivariate pattern analysis (MVPA) techniques, particularly cross-validated MANOVA and permutation-based prevalence inference. He explores how neural states translate into mental states, addressing the mind-body problem through dynamical systems theory. Current work focuses on effect prevalence in cognitive science populations. Key research areas include: Statistical modeling for neuroimaging data Working memory representations Neural encoding-decoding relationships Nonlinear dynamics in brain function Computational methods development Open-source software tools for neuroscience His recent publications (2018–2025) span topics from categorical working memory codes to prevalence inference methods, with a focus on fMRI decoding, representation geometry, and cortical dynamics. He has developed software tools like the CvCrossManova toolbox and interfaces to TikZ for academic use. Dr. Allefeld teaches statistical methods implementation in MATLAB, R, and Python, and maintains a GitHub presence with repositories containing methodological implementations and academic software tools. He is fluent in English and German for research and peer review.

Jeff W. Lichtmanمشاهده پروفایل

استاد

• Neuroscience
• Connectomics
• Synaptic Development
• +۳ مورد دیگر

Jeff W. Lichtman is the Jeremy R. Knowles Professor of Molecular and Cellular Biology and Santiago Ramón y Cajal Professor of Arts and Sciences at Harvard University. He leads the Lichtman Lab, focusing on neural circuit development, synaptic plasticity, and connectomics. His research combines advanced imaging techniques like Brainbow and electron microscopy to map neural connections, aiming to understand how experience shapes brain circuits. Notable contributions include the 'connectome' initiative, analogous to the Human Genome Project, and developing technologies such as the Automated Tape-Collecting Lathe Ultramicrotome (ATLUM) for high-resolution neural mapping. Education: AB from Bowdoin College (1973), MD and PhD from Washington University (1980). Formerly at Washington University for 30 years before joining Harvard in 2004. Leadership roles include Dean of Science in the Faculty of Arts and Sciences (2023–present), director of the Harvard Center for Biological Imaging, and steering committee member of the Center for Brain Science. His work bridges neuroscience, computer science, and engineering, with collaborations on projects like the zebrafish brain imaging and octopus connectomics. Research focuses on synaptic competition in development, axonal regeneration, and the structural basis of memory. Key projects include reconstructing the human cerebral cortex at nanoscale resolution and studying mental illness through neural circuit analysis. Over 200 publications span neuroimaging, synaptic plasticity, and technological advancements in microscopy.

• Active Learning in Engineering Education
• Inertial Measurement and Kalman Filtering
• Vibrational System Identification
• +۱۲ مورد دیگر

R. Erik Spjut is a Professor of Engineering at Harvey Mudd College , with a career spanning research, teaching, and industry consulting. He holds a Ph.D. in Chemical Engineering from Massachusetts Institute of Technology and a B.S. in Chemical Engineering (magna cum laude) from the University of Utah . Research Focus: Active learning in engineering, inertial measurement systems, adaptive optics for telescopes, heterogeneous reaction kinetics, and containerless material processing. Academic Leadership: Directed Harvey Mudd's Engineering Clinic program (2010-2015), managing 25 annual projects with over $1M budget. Publications & Research Trends reflect expertise in non-contact temperature measurement , MEMS-based adaptive optics , and high-temperature aerosol diagnostics . Notable collaborations include KAPAO (adaptive optics systems) and foundational work on electrostatic levitation . Scientific Awards: John Chipman Chair in Chemical and Process Metallurgy (1985-1988), Union Oil Company Design Fellowship (2011-Present). Grants: NSF MRI for adaptive optics, Boeing Phantomworks for atmospheric correction, DOE and SCE grants for material property studies. Teaching Legacy includes courses like Engineering Laboratory (with field-based rocketry projects), Digital Electronics , and Chemical Reaction Engineering . His pedagogical innovations integrate active learning and student-led content . Consulting Expertise: Development of experimental equipment, fluid flow modeling, and LabVIEW/MATLAB tools for companies like Glassimetal Technology Inc. and JPL .