Hongzhe Li is a Professor of Biostatistics and Director of the Laboratory of Statistical Genetics and Genomics. His research spans methodological and collaborative work in bioinformatics, biostatistics, computational biology, statistical genetics, genomics, and metagenomics, with applications to complex human diseases. His primary research focuses on developing statistical methodologies for multi-omics data integration, particularly in microbiome-host interactions. Key areas include robust high-dimensional modeling, transfer learning, mediation analysis with complex mediators, and phylogenetic methods. His work addresses critical challenges in analyzing compositional data, longitudinal microbiome dynamics, and multi-tissue genetic studies, with significant applications in inflammatory bowel disease, kidney disease progression, and rare disorders like Castleman disease. Recent publications (2023-2025) demonstrate a cohesive trajectory in advancing statistical frameworks for microbiome and multi-omics research. Dominant themes include transfer learning for high-dimensional models, mediation analysis with distributional mediators, and robust covariance estimation. These methods are consistently applied to translational problems in gastroenterology, nephrology, and rare hematologic diseases, emphasizing clinical utility through prospective cohort studies and multi-omics integration. Scientific Awards: No awards mentioned in provided text. Advising and Grants: Student advising details and grant information absent from source material. Research appears driven by methodological innovation with strong clinical collaborations, particularly in IBD and kidney disease consortia. Laboratory: Leads the Laboratory of Statistical Genetics and Genomics, which develops computational frameworks for genetic and microbiome data analysis. The lab specializes in creating open-source statistical tools for high-dimensional biological data, with emphasis on reproducibility and clinical translation through partnerships with medical researchers.
Evangelos Ioannidis is an Associate Professor at the Department of Statistics, School of Informatics and Statistics, Athens University of Economics and Business. Born in 1962, he holds a Mathematics PhD from the University of Heidelberg (1993) and has served in his current department since 1999, progressing from Lecturer (1999) to Assistant Professor (2007) and Associate Professor (2023). His expertise spans spectral analysis of time series , cointegration methods , and bootstrap applications in economic data analysis, with additional focus on Official Statistics and sampling techniques . University of Heidelberg: MMath (1987), PhD (1993) Researcher, University of Heidelberg (1987-1991) Visiting Researcher, University of Orsay, Paris Sud (1992-1993) OECD, Paris (1994-1998) National Institute of Labour (1999) His scientific contributions focus on time series econometrics, VAR model spectra, and R&D expenditure analysis. Recent work includes non-parametric spectral estimation and risk-based sampling methodology. He has collaborated with Eurostat on statistical projects (2012-2014). Current affiliations include the Athens University of Economics and Business , where he teaches and conducts research on economic time series analysis and statistical methods.
Hyungsik Roger Moon is Professor of Economics in the Department of Economics at the University of Southern California's Dornsife College of Letters, Arts and Sciences, where he has served since 2000 after beginning his career at UC Santa Barbara. His academic trajectory progressed from Assistant Professor (2000) to Associate Professor (2004) and full Professor (2008), reflecting sustained contributions to econometric methodology. His educational foundation includes: Ph.D. in Economics, Yale University, 1998 M.A. in Economics, Yale University, 1995 B.A. in Economics, Seoul National University, 1989 Moon's research centers on econometric theory development and applied methodology, with particular expertise in panel data analysis, dynamic modeling, and high-dimensional estimation. His theoretical innovations address complex challenges in interactive fixed effects, unit root testing, and heterogeneity modeling, while applied work spans labor economics (income dynamics), health economics (pancreatic cancer trials), and macroeconomics (Covid-19 forecasting). This dual focus bridges rigorous mathematical frameworks with real-world policy applications across multiple economic subfields. Analysis of recent publications reveals an intensifying focus on robust estimation techniques for dyadic data, Bayesian approaches to sparse heterogeneity, and methodological innovations in forecasting with censored panel data. His work increasingly integrates machine learning concepts with traditional econometrics, particularly in high-dimensional seemingly unrelated regression systems and network-based peer effect modeling. His distinguished scientific contributions have been recognized through: Fellow of the Econometric Society (2023) Fellow of the Journal of Econometrics (2019) RK Cho Economics Award (2018) Maekyung/KAEA Economist Award (2012) Econometric Theory Multa Scripsit Award (2006-2007) Korea-America Economic Association Young Scholar Award (2005) Moon has secured significant research funding including an NSF grant of $180,675 for 'Forecasting with Dynamic Panel Data Models' (2016-2020) and $68,000 for 'Asymptotic Analysis of Panel Regression Models' (2009-2010). His academic leadership extends to editorial roles at the Journal of Business and Economic Statistics, Econometric Theory, and Journal of Econometrics, plus administrative service as Director of Graduate Studies for USC's Economics Ph.D. program (2018-2021) and Associate Director of USC Dornsife INET (2015-2017). Through his position at USC Dornsife INET and graduate program leadership, Moon actively shapes research directions in new economic thinking while mentoring future econometricians through advanced courses like Big Data Econometrics.
Dr. Carrie Weidner is a Senior Lecturer at the University of Bristol, affiliated with both the School of Physics and the School of Electrical, Electronic and Mechanical Engineering. Her research spans quantum control, atom interferometry, and quantum technology education, with a focus on robust control techniques in optical lattices and spin networks. Principal Investigator for Quantum Positioning, Navigation, and Timing Hub (2024-2029) Lead on EPSRC-funded project EP/Y004728/1 for trapped ultracold atom interferometry (2023-2025) Her recent work includes energy landscape shaping for quantum systems, deterministic generation of squeezed states, and innovative educational tools like the Quantum Composer. Publications analyze robustness metrics, control algorithms, and quantum-classical system comparisons. Collaborations span international institutions in quantum physics and engineering domains. She contributes to quantum outreach through gamification and interactive platforms, targeting improved education and community inclusivity. Current research trends emphasize precision measurement, error mitigation, and AI integration in quantum control systems.
Skrobotov Anton Andreevich is a Professor at the Faculty of Economic Sciences and Director of the Center for Big Data in Economics and Finance at the National Research University Higher School of Economics (HSE). With 15 years of scientific and teaching experience, he joined HSE in 2024 and focuses on econometrics, financial econometrics, and non-stationary time series analysis. His research emphasizes robust statistical methods. Education: Doctor of Economics (2024) Candidate of Economic Sciences (2018), Saint Petersburg University Master's degree in Economics (2013), Russian Presidential Academy of National Economy and Public Administration (RANEPA) Skrobotov specializes in econometrics, time series analysis, and robust testing. His recent publications address financial bubbles, volatility clustering, and structural shifts in economic data. Scientific incentives: High Professional Potential Group (HSE Personnel Reserve) Category 'New Teachers' (2025) He has led courses in Econometrics at RANEPA and HSE, and secured multiple grants from the Russian Science Foundation and Russian Foundation for Basic Research. His work involves collaborations with institutions like the Gaidar Institute and Saint Petersburg State University.
Seung Yeoun Lee is a Professor in the Department of Mathematics and Statistics at Sejong University, where he has been faculty since 1993. His research focuses on applying advanced statistical methodologies to biomedical problems, particularly in cancer research and survival analysis. He serves as Vice President of the Korean Statistical Society and previously served as President of the International Biometric Society Korean Region from 2016-2017. Education: Ph.D. in Statistics, University of Michigan (1990) M.S. in Statistics, Seoul National University (1986) B.S. in Statistics, Seoul National University (1984) Professor Lee's research primarily centers on survival analysis methodologies and their applications in biomedical research, with a particular emphasis on gene-gene interactions and clinical trial statistics. He has pioneered innovative approaches including the Cox model based unified MDR method for gene-gene interaction analysis for survival phenotypes. His work bridges mathematical statistics with practical medical applications, particularly in pancreatic cancer diagnostics and prognostics. The fingerprint analysis of his work shows strong connections to Dimensionality Reduction (94%), Pancreas Cancer research (89%), and Gene Interaction studies (64%). His recent publications demonstrate a clear trend toward interdisciplinary research combining traditional biostatistical methods with modern machine learning approaches. Many papers focus on survival prediction models, dimensionality reduction techniques, and applications to pancreatic cancer research. His work consistently involves international collaborations across statistics, oncology, and computational biology fields, reflecting the highly interdisciplinary nature of modern biomedical research. Professor Lee has made substantial contributions to biostatistical methodology with an h-index of 22, reflecting the impact of his 89 research publications. His work contributes to UN Sustainable Development Goals related to good health and well-being through advanced statistical approaches to medical research. His research group focuses on developing and applying advanced statistical methods to solve complex biomedical problems, with particular emphasis on cancer research and survival analysis. The interdisciplinary nature of his work suggests extensive collaborations with medical researchers, oncologists, and computational biologists across multiple institutions.
Stanislav Anatolyev serves as Full Professor of Economics at the New Economic School (NES) since 2009 and holds an Associate Professor position at CERGE-EI in Prague. Affiliated with NES since 2000, he teaches advanced econometrics courses including Econometrics 3, Applied Time Series Econometrics, and Selected Chapters in Econometrics. Education PhD in Economics, University of Wisconsin-Madison (2000) MSc in Economics, New Economic School (1995) Specialist Diploma in Applied Mathematics, Moscow Institute of Physics and Technology (1992) Research Focus : Professor Anatolyev's work centers on econometric theory with expertise in method of moments, time series modeling, and high-dimensional data analysis. His contributions span theoretical developments in factor models, volatility estimation, and instrumental variables methods, alongside practical applications in financial econometrics and portfolio optimization. He maintains active research collaborations across international institutions. Publication Trends : Recent work demonstrates increasing emphasis on ultra-high-dimensional econometrics, with significant contributions to copula-based portfolio allocation, many-instrument regressions, and financial market belief updating mechanisms. His publications bridge theoretical rigor with empirical applications, frequently appearing in top econometrics journals including Journal of Econometrics and Econometric Theory. Awards Econometric Theory Multa Scripsit Award (2022) for exceptional scholarly output Academic Leadership : As founding Editor-in-Chief of the Russian-language journal Quantile since 2006, he has fostered econometric research dissemination in Eastern Europe. His co-authored textbook Methods for Estimation and Inference in Modern Econometrics serves as a key reference in graduate econometrics education. Professional Activities : Regularly presents at international conferences and serves as referee for leading econometrics journals, maintaining active engagement with the global econometrics community through seminar presentations and collaborative research projects.
Tormod Rogne, MD, PhD, is an Assistant Professor Adjunct in Chronic Disease Epidemiology at the Yale School of Public Health. His research focuses on perinatal epidemiology, with particular interest in how climate change affects pregnancy, modifiable risk factors on reproductive health, and the long-term health consequences of being born preterm. He applies modern epidemiological methods including Mendelian randomization, negative controls, and genetic epidemiological approaches to address clinically relevant questions using high-quality population-based data. MD from Norwegian University of Science and Technology, NTNU (2015) PhD from Norwegian University of Science and Technology, NTNU (2016) Residency at Akershus University Hospital and Ski Municipality (2018) Fulbright Scholar at Yale School of Public Health (2019) Postdoctoral Scholar at Norwegian University of Science and Technology, NTNU (2021) Dr. Rogne's research spans several key areas of perinatal and environmental epidemiology. He investigates how environmental factors like climate change and temperature extremes affect pregnancy outcomes and child health. His work also explores the genetic and environmental determinants of reproductive health and adverse pregnancy outcomes. Of particular note is his research on how being born preterm affects long-term risk of cardiovascular and infectious diseases. Dr. Rogne emphasizes the use of high-quality data from population-based cohorts and national registries, applying sophisticated methods like negative controls, inverse-probability weighting, and genome-wide association analyses to ensure robust findings. His recent publications reveal a strong methodological focus on Mendelian randomization techniques to establish causal relationships in perinatal health. His work spans cardiovascular epidemiology, infectious disease epidemiology, and environmental health, with consistent themes including climate change impacts on pregnancy, socioeconomic determinants of health outcomes, and the developmental origins of disease. The geographic scope of his research includes both high-income settings like Norway and global health contexts in Africa. Fulbright Scholarship (2017) Tom Wilhelmsen Foundation's Research Stipend (2013) Dr. Rogne maintains active collaborations with multiple researchers including Andrew DeWan, Xiaomei Ma, Joshua Warren, Kai Chen, Rong Wang, and Zeyan Liew. His work is supported by various research initiatives including the Fulbright Program and the Tom Wilhelmsen Foundation. He is affiliated with the Yale Center for Perinatal, Pediatric and Environmental Epidemiology, where he contributes to advancing research on women's and children's health through epidemiologic studies investigating environmental, genetic, and clinical factors. As part of the Yale Center for Perinatal, Pediatric and Environmental Epidemiology, Dr. Rogne works within a collaborative team focused on promoting women's and children's health through rigorous epidemiologic research. The Center, originally founded in 1979 as the Yale Perinatal Epidemiology Unit, continues to be a leader in investigating how environmental, genetic, and clinical factors impact pregnancy, birth, and childhood development.
Shoji Makino is a Professor at Waseda University's Graduate School of Information, Production and Systems. He has held academic and research positions at institutions such as the University of Tsukuba and NTT Communication Science Laboratories. His work spans acoustic signal processing, blind source separation, and adaptive filtering. Education: Ph.D., Tohoku University (1993.03) Mechanical Engineering, Tohoku University Graduate School of Engineering (1979.04–1981.03) Engineering, Tohoku University Faculty of Engineering (1975.04–1979.03) Research Interests: His research focuses on acoustic signal processing for speech and audio, including blind source separation (BSS) , beamforming , and adaptive filtering . He pioneered methods for solving permutation alignment in frequency-domain BSS and developed geometrically constrained ICA techniques. Scientific Awards: Hoko Award (2018.10, Hattori Hokokai Foundation) Outstanding Contribution Award of the Institute of Electronics, Information, and Communication Engineers (2018.06) IEEE Signal Processing Society Best Paper Award (2014.01) IEEE Fellow (2004.01) IEICE Achievement Award (1997.05) Committee Memberships: He has served as Chair of the IEEE CAS Society's Blind Signal Processing TC, General Chair of IEEE WASPAA2007, and Associate Editor of IEEE Trans. SAP. He is actively involved in EURASIP, APSIPA, and the Acoustical Society of Japan.
Vinh Nguyen is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Michigan Technological University, where he directs the Michigan Tech Center for AI and coordinates the NIST-PREP program. His research focuses on advanced manufacturing through Industry 4.0, human-robot-machine interaction, and physics-based/data-driven modeling. He has developed solutions for machining, additive manufacturing, metal forming, and robotic assembly to promote smart and sustainable manufacturing. Prior to joining Michigan Tech in 2022, he was a National Research Council Postdoctoral Fellow at NIST (2020–2022). Dr. Nguyen earned his PhD (2020), MS in Mechanical Engineering (2017), and MS in Electrical & Computer Engineering (2017) from Georgia Institute of Technology. He received dual bachelor’s degrees in Electrical and Mechanical Engineering from Rensselaer Polytechnic Institute (2014). His research portfolio spans Advanced Manufacturing Industry 4.0 and 5.0 Human-Robot Interaction Physics-Based/Data-Driven Modeling Industrial Automation based on his lab’s interdisciplinary focus on human-centric, resilient solutions. His recent publications address trends in Machine Learning for Manufacturing Autonomous Vehicle Sensors Hybrid Additive/Subtractive Manufacturing Augmented/Mixed Reality Interfaces Industrial Robot Diagnostics Material-Specific Machining with keywords spanning Robotics, Data Science, and Industrial Engineering.
Mehtaab Sawhney is a Clay Research Fellow and a tenure-track assistant professor at Columbia University specializing in combinatorics, probability, analytic number theory, and theoretical computer science. His academic journey began at the University of Pennsylvania where he enrolled in a Bachelor of Engineering in Computer Science (2016-2017), then continued at MIT where he earned a Bachelor of Science in Mathematics with Minor in Computer Science (2017-2020), followed by a Doctor of Philosophy in Mathematics (2020-2024) under the advisorship of Yufei Zhao. His research spans probabilistic combinatorics, random matrix theory, additive number theory, and theoretical computer science. Sawhney's work bridges theoretical mathematics with computational applications, focusing on random structures, additive combinatorics, and spectral properties of discrete objects. His publications demonstrate a strong interdisciplinary approach that connects number theory with probabilistic methods to solve complex combinatorial problems. The analysis of his publication record reveals a consistent focus on foundational mathematical structures with applications across multiple domains. His work on random graphs, additive bases, and arithmetic progressions has established him as a leading researcher in modern combinatorics, often collaborating with prominent mathematicians including Ashwin Sah, Yufei Zhao, and Vishesh Jain. His research output shows remarkable depth and breadth, with contributions to both pure mathematics and theoretical computer science. 2024 Clay Research Fellow 2021 Frank and Brennie Morgan Prize for Outstanding Research in Mathematics by an Undergraduate Student (joint with Ashwin Sah) Churchill Scholar 2020 Best Student Paper STOC 2021 (Joint with Ryan Alweiss, Yang Liu) Best Student Paper ITCS 2022 (Joint with Yang Liu, Ashwin Sah) 2023 Hartley Rogers Jr. Prize 2022 Charles W. and Jennifer C. Johnson Prize (joint with Ashwin Sah) NSF Graduate Fellowship Sawhney has established a robust research program with significant contributions across multiple mathematical disciplines. His frequent collaborations with top researchers worldwide indicate an active and influential research network. While specific advisees aren't listed in available information, his extensive publication record with numerous co-authors suggests active mentorship of junior researchers through collaborative projects.
Yoann Altmann is Professor in the School of Engineering & Physical Sciences at Heriot-Watt University and a member of the Institute of Sensors, Signals & Systems. Since 2024 he holds the Chair in Electrical, Electronic & Computer Engineering (EECE), directing a research programme that bridges statistical signal processing, computational imaging and quantum & neuromorphic sensing. Education & career: 2010 – Eng. degree (Electrical Engineering), ENSEEIHT, Toulouse, France 2010 – M.Sc. (Signal Processing), National Polytechnic Institute of Toulouse 2013 – Ph.D. (Signal & Communications), IRIT Laboratory, Toulouse 2014-2017 – Post-doctoral Research Fellow, Heriot-Watt University 2017 – Royal Academy of Engineering Research Fellow & Assistant Professor, HWU 2024 – promoted to Professor, School of Engineering & Physical Sciences, HWU Research interests: Prof. Altmann develops mathematical and algorithmic tools for Bayesian inverse problems, with emphasis on single-photon LiDAR, low-illumination imaging, neuromorphic computational sensing, variational inference and sparse reconstruction. His work combines principled statistical modelling with efficient computational schemes to enable imaging in extreme scenarios such as underwater scattering, photon-starved environments, quantum metrology and real-time 3-D scene reconstruction. Publication trends: Across 160 outputs (2011-2025) his recent articles reveal a clear trajectory toward integrating modern machine-learning paradigms—variational autoencoders, diffusion generative models, spiking neural networks—with rigorous physics-based forward models. Applications span quantum parameter estimation, multimode-fiber endoscopy, hyperspectral & Compton imaging, nuclear safeguards and cultural-heritage spectroscopy, demonstrating both methodological breadth and high-impact interdisciplinary deployment. Honours & recognition: Royal Academy of Engineering Research Fellowship – competitively awarded (2017) Grants & datasets: He has generated four open datasets supporting reproducible research in quantum sensing, variational autoencoders, underwater single-photon LiDAR and multispectral fluorescence imaging, reflecting sustained funding and commitment to open science. Continuous peer-review service for IEEE and Elsevier journals since 2013 underlines his standing within the signal-processing community. Labs & teams: He leads the Bayesian Imaging & Sensing Computing (BISC) group ( https://bisc.site.hw.ac.uk ) which hosts post-docs, PhD researchers and international visitors working on statistical machine-learning for imaging, sensing and quantum technologies.
Massi Pontil is a part-time Professor of Computational Statistics & Machine Learning in the Department of Computer Science at University College London (UCL). He joined UCL as a lecturer in 2003 and was promoted to Professor in 2010. Since 2016, his primary appointment has been at the Istituto Italiano di Tecnologia (IIT), where he leads the CSML research group. His work bridges theoretical machine learning with practical applications in physical sciences. His research interests span a wide range of topics in machine learning theory and algorithms: Machine Learning Theory and Statistical Learning Algorithmic Fairness and Ethical AI Kernel Methods and Reproducing Kernel Hilbert Spaces Transfer Learning, Multitask Learning, and Meta-Learning Operator Learning and Dynamical Systems Sparsity Regularization and Optimization Pontil's recent work focuses on the intersection of machine learning with numerical simulations of physical systems, particularly in molecular dynamics and climate science. His publications demonstrate a strong emphasis on theoretical foundations while addressing practical challenges in high-dimensional systems, symmetry-aware learning, and uncertainty quantification. Among his notable honors are: Best Paper Runner Up Award from ICML 2013 EPSRC Advanced Research Fellowship (2006-2011) Edoardo R. Caianiello Award for the Best Italian PhD Thesis on Connectionism (2002) Professor Pontil has served on program committees for major machine learning conferences (COLT, ICML, NeurIPS) and on editorial boards of prestigious journals including Machine Learning Journal, Statistics and Computing, and JMLR. He teaches Advanced Topics in Machine Learning at UCL, with a focus on convex optimization and statistical learning theory.
Håkan Fischer is a Professor of Human Biological Psychology at Stockholm University, where he has served as Head of the Department of Psychobiology and Epidemiology since 2011. He also holds an associate professor position at Karolinska Institutet, is affiliated with the Aging Research Center and Stockholm University Brain Imaging Centre, and is a faculty member at Digital Futures at the Royal Institute of Technology. Since September 2025, he has additionally served as a visiting professor at Linköping University. Fischer has established himself as a leading researcher in emotional and cognitive processing, with particular expertise in socio-emotional aspects across the lifespan. Fischer earned his PhD in psychology from Uppsala University in 1998, followed by postdoctoral research at Harvard Medical School (1999-2001). He then worked at the Aging Research Center at Karolinska Institutet before joining Stockholm University in 2011. His academic journey includes a sabbatical year (2021-2022) at the University of Florida's Department of Psychology. Fischer is actively involved in university governance as a member of the Swedish Research Council's Subject Council for Humanities and Social Sciences (2023-present) and represents Stockholm University in multiple international collaborations. Håkan Fischer's research primarily focuses on investigating intra- and interindividual differences in affective, cognitive, social and perceptual processing, with special emphasis on age-related differences in adults. His laboratory employs advanced neuroimaging techniques including fMRI, PET, and fNIRS to examine brain function, while also utilizing structural imaging methods like T1-weighted imaging, DTI, and perfusion imaging to study brain structure. Fischer advocates for single-subject small-N designs to better understand emotional and cognitive mechanisms. His current research lines include socio-emotional perception and recognition, oxytocin effects on socio-emotional processing across the lifespan, and AI development for interpersonal communication analysis. Analysis of Fischer's recent publications reveals a strong focus on emotion recognition across populations, neurobiological mechanisms of socio-emotional processing, and methodological innovations. His work consistently integrates behavioral testing, neuroimaging, and genetic analysis to provide comprehensive insights. The increasing incorporation of AI approaches demonstrates his adaptation to emerging technological advances in psychological research. Fischer has published 136 peer-reviewed articles with over 12,200 citations and a Google Scholar h-index of 53. Fischer has received consistent funding since 2002 from prestigious sources including the Swedish Research Council, Wallenberg Foundation, STINT, Riksbankens Jubileumsfond, and Konung Gustav V och Drottning Victorias stiftelse. He currently leads nine funded research projects (two as principal investigator totaling 6.9 million SEK, seven as co-applicant totaling 24.8 million SEK) spanning multiple international collaborations in Sweden, Germany, and the USA. As an educator, Fischer leads the basic course in Cognitive Neuroscience and the master's course in Emotion Psychology and Affective Neuroscience. He regularly teaches at both undergraduate and advanced levels, primarily in biological psychology, cognitive neuroscience, and emotion psychology. Fischer currently supervises six doctoral students (one as main supervisor, four as assistant supervisor) and has mentored graduate students since 2002. Håkan Fischer leads a dynamic research laboratory that investigates emotional, social, perceptual, and cognitive processing. The lab examines how intraindividual variations across stimuli and time, as well as interindividual differences in age, gender, genetics, personality, and sleep deprivation affect these processes. His lab maintains active national and international collaborations with researchers at Stockholm University, Uppsala University, Karolinska Institutet, University of Florida, and University of Gothenburg, creating a robust interdisciplinary research environment focused on translating basic neuroscience into practical applications.
Mohammad Rajabdorri is an Assistant Professor at the Institute for Research in Technology (IIT) of Comillas Pontifical University, holding a position since 2020 and currently serving as an Assistant Research Collaborator since 2024. He earned a BSc in Electrical Power Engineering from Shiraz University (2016), an MSc in Electrical Power Systems from Shiraz University of Technology (2018), and a PhD in Electrical Engineering from IIT (2023). His research focuses on isolated power systems, electricity markets, renewable energy dispatch, and power system operation. Key projects include modeling grid-forming converters for system stability, blackstart strategies for renewable plants, and UFLS optimization in island systems. He has collaborated with entities like Iberdrola and the European Commission. Rajabdorri’s work spans 9 journal articles and 8 conference papers, emphasizing data-driven approaches in power system optimization. He advises PhD student M. Sarvarizadeh Kouhpaye on resilience and reliability of island systems. His professional networks include international exchanges at Durham University and invited seminars at IIT.
