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Sabyasachi ChatterjeeView profile

Associate Professor

• Nonparametric Signal Estimation
• Shape Constrained Estimation
• Statistical Information Theory
• +5 more

Sabyasachi Chatterjee is an Associate Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign, affiliated with the College of Liberal Arts & Sciences. He joined UIUC in 2017 after serving as a Kruskal Instructor at the University of Chicago. He earned his PhD in Statistics from Yale University (2014), advised by Andrew Barron. His research focuses on nonparametric signal estimation, shape-constrained estimation (monotonicity, convexity, unimodality), statistical information theory, and resampling methods like cross-validation. He also explores statistical learning theory, online learning, and applied probability. His recent work includes advancements in quantile regression via dyadic CART, adaptive estimation of piecewise polynomials, and spatially adaptive prediction algorithms. Key contributions involve risk bounds for trend filtering and cross-validation frameworks for signal denoising. His research is supported by NSF Grant DMS-1916375 on nonparametric estimation under shape/norm constraints. Chatterjee collaborates on grants and has advised multiple students (though specific names are not listed in available texts). His lab’s work bridges theoretical statistics with practical applications in data science and signal processing.

Laura SanchezView profile

Associate Professor

• Mass Spectrometry
• Natural Products Discovery
• Metabolomics
• +4 more

Laura Sanchez is an Associate Professor in the Department of Chemistry and Biochemistry at the University of California, Santa Cruz (UCSC). She leads a research lab focused on imaging mass spectrometry and natural products discovery, with a particular emphasis on microbial interactions, metabolomics, and applications in women’s health. Previously, she was at the University of Illinois at Chicago (UIC) before relocating to UCSC in 2021. Education: B.A. in Chemistry from Whitman College (Walla Walla, WA) followed by a Ph.D. in Chemistry at UCSC under Professor Phil Crews. Postdoctoral training included work under Professors Roger Linington (UCSC) and Pieter Dorrestein (UC San Diego as an NIH IRACDA Fellow). Her research integrates advanced mass spectrometry techniques with biological systems to study small molecule communication in microbial communities and cancer metabolism. Research Interests : Specialization in imaging mass spectrometry (MALDI-TIMS-MS2), microbial metabolomics, and development of novel techniques like SICRIT (Soft Ionization by Chemical Reaction in-Transfer). Her lab investigates how microbial interactions influence metabolite production and explores metabolomic signatures in ovarian cancer progression. Recent work includes spatial quantification of signaling molecules (e.g., c-di-GMP in biofilms) and the role of neurotransmitters like norepinephrine in cancer cell survival. Awards : K12 BIRCWH Scholar (2016–2017) 2019 UIC Rising Star in the Life Sciences 2022 ACS Infectious Diseases Young Investigator Award 2022 American Society for Pharmacognosy Matt Suffness Young Investigator Award Grants & Collaboration : Developed the Natural Products Atlas (open-access knowledge base) and contributed to workflows like TIMSCONVERT. Collaborations span microbiology, oncology, and bioinformatics, with a focus on translational research in women’s health. Current projects include analyzing fallopian tube-ovary cross-talk in high-grade serous ovarian cancer and optimizing mass spectrometry for high-throughput screening. Labs & Teams : Directs an interdisciplinary lab at UCSC, emphasizing open-source method development and collaborative microbiome research. Her team includes graduate students and postdocs working on microbial communication, cancer metabolomics, and imaging technology innovation.

• Social Physics
• Graph Convolutional Neural Networks
• Large Language Models (LLMs)
• +4 more

Dr. Alexander Mantzaris is an Associate Professor in the Department of Statistics & Data Science at the University of Central Florida, College of Sciences. His research bridges physics and sociology through Social Physics frameworks, focusing on statistical mechanics and thermodynamic analogies to model social phenomena. Current research explores criticality points in social systems Developing computational tools for NLP and big data Former work on Graph Convolutional Networks in social analysis Specializes in entropy-based modeling of polarization and segregation His publications emphasize interdisciplinary approaches combining network science, computational modeling, and sociological dynamics. Recent articles address thermodynamic formulations of political cycles, energy states in Schelling models, and memory-efficient data processing algorithms. Dr. Mantzaris teaches graduate courses in big data analytics and statistical learning theory. He maintains active research in computational social science with applications to political dynamics, media influence, and complex systems analysis.

Mojgan A. JadidiView profile

Associate Professor

• Geospatial Visual Analytics
• Building Information Modeling (BIM)
• 3D GIS Integration
• +5 more

Dr. Mojgan A. Jadidi serves as Associate Professor in the Teaching Stream and Director of Common Engineering & BSc Science within the Department of Civil Engineering at York University's Lassonde School of Engineering. A Professional Engineer (P.Eng) and founder of the GeoVA Lab, she leads research at the intersection of geospatial analytics, digital infrastructure, and innovative engineering education aligned with UN Sustainable Development Goals. Education: PhD in Geomatics, Université Laval (2014) MSc in Earthquake and Seismology Engineering, ROSE Center (Italy) & Université Joseph Fourier (France) BSc in Civil-Survey Engineering, Iranian University of Science and Technology Research Focus: Her pioneering work in Geospatial Visual Analytics spans 2D/3D environments, Building Information Modeling (BIM) and 3D GIS integration, and Spatial Quantum Computing applications for smart cities. She develops Infrastructure Digital Twins using sensor data fusion while revolutionizing engineering education through gamification and augmented/virtual reality pedagogies that transform complex spatial concepts into immersive learning experiences. Research Trends: Recent publications (2021-2023) demonstrate convergent innovation across three domains: (1) Building energy optimization through BIM-graph analytics, (2) Transportation safety via AI-driven situational awareness, and (3) Educational technology using VR sandboxes and visual-verbal comics. These works consistently integrate quantum computing principles and UN SDG frameworks to solve urban sustainability challenges. Scientific Recognition: ASEE Zone III Best Paper Award (2023) ASEE Saint Lawrence Best Research Paper & Poster (2022) 3D GeoInfo Conference Best Paper (2018) NSERC Postdoctoral Fellowship (2016) ESRI Student Award (2011) Erasmus Mundus Scholarship (2006) Research Leadership: As Associate Director of York's ESRI Center of Excellence, she manages multi-source funding from NSERC, Mitacs, and York University internal grants. Her professional service spans global organizations including ISPRS Commission IV (Secretary), IEEE Women in Engineering (Member), PEO Etobicoke (Chair), and buildingSMART Canada (Committee Member), driving standards for BIM and digital twin implementation in urban infrastructure. Lab Innovation: The GeoVA Lab develops cutting-edge tools including the TopoSurvey Game for immersive surveying education, PAN-Lassonde XR Sandbox for virtual lab experiences, and quantum computing frameworks for bike-sharing optimization, establishing new paradigms in spatial data interaction and engineering pedagogy.

Erik SchemeView profile

Associate Professor

• Biomedical Engineering
• Machine Learning
• Neuroprosthetics
• +5 more

Erik Scheme is an Associate Professor in the Department of Electrical and Computer Engineering at the University of New Brunswick (UNB), and serves as Associate Director of the Institute of Biomedical Engineering (IBME). He holds a PhD and is a Professional Engineer (PEng). His roles include advising the Dr. J. Herbert Smith Centre for Technology Management and Entrepreneurship, emphasizing innovation in biomedical technologies and healthcare systems. His research focuses on advanced human-machine interaction through biomedical engineering, with a strong emphasis on myoelectric prosthetics, wearable sensors, and machine learning applications. Key areas include improving neuroprosthetic control via incremental learning, gait analysis using underfoot pressure sensors, and developing robust EMG-based gesture recognition systems. His work bridges clinical needs with technological innovation, addressing challenges in rehabilitation, activity monitoring, and user-centric design. Recent publications highlight advancements in adaptive control systems, sensor fusion, and ethical data practices in healthcare. His contributions span both theoretical frameworks (e.g., self-supervised learning models) and applied technologies (e.g., gold-plated 3D-printed electrodes). Dr. Scheme collaborates across disciplines, integrating robotics, signal processing, and clinical validation to create impactful solutions. His lab, affiliated with IBME, actively explores emerging areas like exhaled breath analysis for disease detection and federated learning in healthcare data analytics.

Zhengtian XuView profile

Assistant Professor

• Urban Transportation Systems
• Mathematical Modeling
• Operations Research
• +3 more

Zhengtian Xu is an Assistant Professor in the Department of Civil & Environmental Engineering at George Washington University , affiliated with the School of Engineering & Applied Science . His research focuses on optimizing urban transportation systems through mathematical modeling and data-driven analysis, emphasizing emerging mobility services and vehicle technologies. Education: B.S., Tsinghua University (2014) M.S., University of Florida (2016) Ph.D., University of Michigan (2020) Research interests include urban transportation systems, mathematical modeling, operations research, and sustainable mobility solutions. His work addresses challenges such as electrification of ride-hailing services, driver labor dynamics, and last-mile delivery optimization. Recent publications explore topics like drone delivery networks and platform competition in autonomous vehicle markets. No academic awards or student advisees are listed. His articles highlight trends in electrification strategies, behavioral driver dynamics, and urban logistics optimization. He has not disclosed grants or lab affiliations in the provided information.

Han WangView profile

Assistant Professor

• Computer Science
• Data Privacy
• Cybersecurity
• +2 more

Han Wang is an Assistant Professor in the Electrical Engineering and Computer Science department at the University of Kansas School of Engineering. His research develops privacy-preserving frameworks for distributed systems, machine learning, and data analytics. Core research areas include differential privacy implementations for federated learning environments, privacy-preserving outsourcing of anomaly detection, and adversarial attack mitigation. Recent work focuses on developing staircase randomized response mechanisms that enhance privacy without compromising data utility in location services and video analytics. Publications demonstrate consistent innovation in privacy engineering with applications spanning vehicle trajectory protection, energy trading systems, and video recognition security. Methodological approaches combine theoretical privacy guarantees with practical system implementations.

Stephen PankavichView profile

Professor

• Partial Differential Equations
• Kinetic Theory
• Mathematical Biology
• +4 more

Stephen Pankavich is a Professor and Department Head in the Department of Applied Mathematics and Statistics at the Colorado School of Mines. He holds a PhD in Mathematical Sciences from Carnegie Mellon University, with research focused on partial differential equations, kinetic theory, and mathematical biology. His work bridges theoretical analysis and computational methods, addressing challenges in plasma dynamics, epidemiological modeling, and multiscale systems. Education: PhD, Mathematical Sciences, Carnegie Mellon University (2005) MS, Mathematical Sciences, Carnegie Mellon University (2001) BS, Mathematical Sciences, Carnegie Mellon University (2000) Research interests include the analytical and numerical study of collisionless plasmas, HIV dynamics, and epidemiological models. He has received awards such as the W.M. Keck Mentorship Award and the Colorado School of Mines Alumni Teaching Award. His articles explore topics like plasma decay rates, HIV therapy models, and particle-tracking algorithms. He has advised over 20 graduate and undergraduate students, contributing to impactful research in applied mathematics and computational science.

• Photonics
• Microresonators
• Nonlinear Optics
• +3 more

Manuel Crespo-Ballesteros is a Research Fellow at the Aston Institute of Photonic Technologies (AiPT) within the College of Engineering and Physical Sciences at Aston University. He holds a BEng in Electronics and Automation Engineering from the Technical University of Cartagena (2007), an MSc in Theoretical Physics from the University of Murcia (2012), and a PhD in Radar Systems from the University of Birmingham (2016). His research focuses on Surface Nanoscale Axial Photonics (SNAP) technology, particularly microresonators, nonlinear optics, and optical frequency combs. He has contributed to advancements in microresonator fabrication, resonant tunneling phenomena, light-by-light transportation, and parametrically modulated frequency combs. His work bridges quantum mechanics and photonics, with applications in sensing, communications, and optical computing. Education: BEng in Electronics and Automation Engineering, Technical University of Cartagena, Spain (2007) MSc in Theoretical Physics, University of Murcia, Spain (2012) PhD in Radar Systems, University of Birmingham, UK (2016) Research Interests: Crespo-Ballesteros investigates microresonators created via SNAP technology, emphasizing their picometer precision and quantum-mechanical analogies. Key areas include resonant tunneling, nonlinear soliton-based light transport, and parametric frequency comb generation. His work explores applications ranging from high-speed communications to quantum physics simulations, such as black hole analogues using time-dependent potentials. Grants and Labs: Active in AiPT’s advanced photonics research, his lab develops cutting-edge SNAP microresonators with applications in precision spectroscopy and sensing. Collaborations include projects on fiber-optic bending-induced tunability and soliton-based transportation systems.

Scott LindermanView profile

Assistant Professor

• Machine Learning
• Computational Neuroscience
• State Space Models
• +4 more

Scott W. Linderman is an Assistant Professor of Statistics at Stanford University and a Faculty Scholar at the Wu Tsai Neurosciences Institute. He holds courtesy appointments in Computer Science and is affiliated with Stanford Bio-X and the Stanford AI Lab. His research focuses on developing probabilistic models and statistical methods to analyze neural data, bridging computational neuroscience and machine learning. Linderman earned his PhD in Computer Science from Harvard University, with postdoctoral training at Columbia University under Liam Paninski and David Blei. He previously worked as a software engineer at Microsoft and holds an undergraduate degree in Electrical and Computer Engineering from Cornell University. Research Interests : Machine learning, computational neuroscience, state space models, neural data analysis, and probabilistic modeling. His lab develops tools like the SSM and Dynamax packages, applying methods to problems such as neural decoding, behavioral tracking, and understanding latent neural dynamics. Awards : 2023 McKnight Scholar Award, 2022 Sloan Research Fellowship, Leonard J. Savage Award (2016). Linderman has advised over 20 PhD students and postdocs, contributing to breakthroughs in neuroscience and machine learning. His work includes collaborations with experimental neuroscientists like David Anderson and Sebastian Seung. Labs & Teams : Linderman Lab focuses on advancing statistical methods for neuroscience. Key projects include state space models (e.g., rSLDS, Gaussian Process SLDS) and behavioral analysis tools like Keypoint MoSeq. The lab emphasizes open-source software and interdisciplinary collaboration.

Nooshin AhmadiView profile

Senior Lecturer

• Human-Centered Design
• Architectural Design
• Design Research
• +3 more

Nooshin Ahmadi serves as a Senior Lecturer in the Department of Human Centered Design within Cornell University's College of Human Ecology, where she applies research-driven methodologies to align spatial design with human behavioral needs and capabilities. Her academic credentials include: Master of Science in Architecture from Texas A&M University, College Station Professional Bachelor of Architecture from Art University of Isfahan, Iran Dr. Ahmadi's research synthesizes architectural theory with human behavioral science to develop environments that enhance usability and well-being, emphasizing evidence-based approaches to object and space optimization. She actively promotes design advocacy and innovative pedagogy to cultivate student problem-solving skills for real-world societal challenges. No scientific awards or honors were documented in the source material. Her advising philosophy centers on fostering lifelong learning through engaging educational experiences, though specific grant funding or formal mentorship records remain unreported in available biographical information.

Robert XiaoView profile

Assistant Professor

• Human-Computer Interaction
• Virtual Reality
• Augmented Reality
• +4 more

Robert Xiao is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Designing for People research cluster. He holds a Ph.D. from Carnegie Mellon University and a BMath from the University of Waterloo. His research focuses on interactive technologies, including VR/AR interfaces, sensing systems, and cybersecurity. Notable contributions include Lumitrack (tracking system), TouchTools (touch interaction), and CVE-2023-37271 (Python sandbox exploit). Education: Ph.D., Human-Computer Interaction Institute, Carnegie Mellon University; BMath, Computer Science & Combinatorics, University of Waterloo Affiliations: Core member of UBC's Designing for People cluster Research interests span novel input modalities, mixed-reality systems, and security challenges. He actively competes in DEF CON CTF (multiple 1st places) and publishes in top venues like CHI, UIST, and ISMAR. Recent work explores VR decision-making, low-latency tracking, and collaborative AR/VR environments. Awards: SIGCHI Outstanding Dissertation Award, CHI Honorable Mention, DIS Honourable Mention Teaching includes courses on computer systems (CPSC 213), human-computer interaction (CPSC 554X), and cybersecurity (CPSC 436S). His lab develops tools like SurfShare (surface sharing) and VirtualNexus (collaborative AR).

• Household Finance
• Asset Pricing
• Financial Frictions
• +7 more

Alex Michaelides is a Professor of Finance at Imperial College Business School since 2013 and a Research Fellow at CEPR (International Macroeconomics and Financial Economics Programmes). He holds affiliations with Imperial's Energy Futures Lab and Language and Communication Intelligence initiatives. Previously, he served at the London School of Economics (LSE) as Lecturer (2001-2006) and Associate Professor (2006-2010), and at the University of Cyprus as Professor of Finance (2010-2014). He earned his PhD in Economics from Princeton University (1997) and BA in Economics from Harvard (1993). His research focuses on household finance (e.g., life-cycle portfolio choice), asset pricing with heterogeneous agents, housing markets, and macro-finance intersections. Notable work includes analyses of pension systems, sovereign debt crises (e.g., Cyprus), and banking system dynamics. He has held visiting roles at the Federal Reserve Bank of New York, ECB, and served on the Central Bank of Cyprus' board (2013-2014). Leadership roles include Head of Finance at Imperial (2014-2021) and Director of Postgraduate Studies (since 2023). His work bridges theoretical models with policy-relevant insights, addressing fiscal procyclicality, central bank independence, and climate regulatory impacts.

Patrick JennyView profile

Professor

• Computational Fluid Dynamics
• Turbulent Reactive Flows
• Multiphase Flow Modeling
• +3 more

Prof. Patrick Jenny is a Full Professor at the Department of Mechanical and Process Engineering and Head of the Institute of Fluid Dynamics at ETH Zurich. His research focuses on computational fluid dynamics (CFD), numerical methods for turbulent and multiphase flows, and reservoir simulation. He has held positions at ChevronTexaco and Cornell University, and received the National Latsis Prize 2005. PhD in CFD from ETH Zurich (1997) Postdoctoral work at Cornell University (1997–1999) Senior Researcher at ChevronTexaco (1999–2003) Research interests include: turbulent reactive flows, PDF modeling, multi-scale reservoir simulation, and data assimilation in engineering systems. He teaches courses on fluid dynamics, turbulence, and computational methods. Over 100 peer-reviewed publications span topics like fracture modeling, LES/RANS coupling, and particle-laden flows. His work bridges academia and industry, addressing challenges in energy systems, environmental engineering, and numerical algorithms. Winner: National Latsis Prize 2005 Led over 20 PhD projects and collaborates with institutions globally. His lab develops open-source tools for CFD and energy systems analysis.

• Machine Learning
• Data Mining
• Computer Vision
• +3 more

Professor Xue Li is a faculty member in the School of Electrical Engineering and Computer Science at the University of Queensland. His research focuses on machine learning, data mining, and their applications in healthcare, materials science, and computer vision. He has authored over 300 publications, including seminal works on knowledge graph completion, video quality enhancement, and alloy design using machine learning. His work bridges theoretical advancements with real-world applications, such as clinical diagnosis andTinyML systems. Key research interests include graph representation learning, medical informatics, and efficient algorithms for multimedia data. Notable contributions include developing commonsense-enhanced relation extraction models and frameworks for compressed video reconstruction. His research also addresses challenges in federated learning and privacy-preserving genomics. Prof. Li has collaborated extensively with industry and academia, contributing to projects in RFID systems, electronic nose pattern recognition, and cybersecurity. His work is published in top-tier venues like IEEE Transactions and ACM conferences. Despite no listed awards, his prolific output underscores academic impact.