Find Academic Supervisors

Yuki MiuraView profile

Assistant Professor

• Climate Risk Assessment and Management
• Urban Resilience and Adaptation
• Engineering, Social Science, and Economics
• +3 more

Dr. Yuki Miura serves as Assistant Professor at New York University's Tandon School of Engineering in the Department of Mechanical and Aerospace Engineering and Center for Urban Science and Progress (CUSP), with additional affiliations at NYU Stern's Volatility and Risk Institute and the New York City Panel on Climate Change (NPCC5). Her academic credentials include a Ph.D. (2022), M.Phil. (2021), and M.S. (2017) in Civil Engineering and Engineering Mechanics from Columbia University, complemented by a B.Eng. in System Design Engineering from Keio University (2015). Dr. Miura's research integrates engineering, climate science, finance, and social sciences to develop actionable resilience solutions. Her work focuses on climate risk quantification , urban adaptation strategies , and socioeconomic impact modeling through advanced data analytics. She pioneers methodologies that couple physical climate modeling with socioeconomic vulnerability assessments to deliver implementable risk mitigation frameworks for public and private institutions. Her publication portfolio demonstrates consistent advancement in climate risk analytics, with recent work addressing urban flooding dynamics, precipitation extremes, and infrastructure protection. These studies reveal a distinct trajectory toward integrated risk modeling that bridges climate physics with financial and social dimensions, increasingly leveraging AI-driven approaches for compound hazard assessment. Recognition for her contributions includes the Mindlin Scholar award from Columbia University (2022), with research featured in The New York Times , The New Yorker , and The Nikkei . As director of the Climate, Energy, and Risk Analytics Lab (CERA), Dr. Miura mentors graduate students in developing data-driven solutions for climate resilience. Her industry experience at Morgan Stanley (2021-2024) in climate risk management directly informs her applied research approach, fostering strong connections between academic innovation and real-world implementation. CERA operates as an interdisciplinary hub developing AI-driven methodologies for urban flood modeling, compound climate risk assessment, and socioeconomic impact analysis. The lab maintains active collaborations with the National Center for Atmospheric Research and New York City/State governments to translate research into actionable climate adaptation policies.

Thalia Dominguez BucioView profile

Research Fellow

• Integrated Photonics
• Silicon Nitride Photonics
• Optoelectronics
• +4 more

Dr. Thalia Dominguez Bucio is a Senior Research Fellow at the Optoelectronics Research Centre (ORC), University of Southampton, within the Faculty of Engineering and Physical Sciences. She is a key member of the Silicon Photonics research group and actively contributes to the Horizon Europe PIXEurope project, advancing integrated photonic technologies. Her work focuses on silicon nitride as a material platform for next-generation photonic integrated circuits. Her research interests center on integrated photonics , silicon nitride platforms , nonlinear optics , and CMOS-compatible fabrication . She investigates low-loss photonic devices, efficient coupling mechanisms, and high-speed electro-optic modulators, with applications in optical communications and signal processing. Recent publications (2024) demonstrate a strong trend toward high-efficiency grating couplers , broadband wavelength conversion via intermodal four-wave mixing, and monolithic integration for low-power operation. These works reflect a cohesive research direction focused on enabling scalable, high-performance photonic integrated circuits using advanced silicon nitride technology. Speaker, Advanced Silicon Nitride Integration for CMOS Photonic Circuits, 2023 She currently supervises PhD students Qian Zhang and Lifeng Bao , and is accepting new PhD applicants. Her research is supported by major EU-funded initiatives like Horizon Europe. She is part of the Photonic Systems, Circuits and Sensors Group and the broader Silicon Photonics team at the ORC, fostering interdisciplinary collaboration in photonics research and development.

Lorenzo CavallaroView profile

Professor

• Trustworthy AI for Systems Security
• Adversarial Machine Learning
• Malware Detection
• +6 more

Lorenzo Cavallaro is a Full Professor of Computer Science at University College London (UCL), specializing in Trustworthy AI for Systems Security. His research focuses on developing learning-based methods that are robust against adversaries by understanding the interplay between program analysis, representations, and machine learning models. His research interests span multiple critical areas in cybersecurity, including adversarial machine learning, malware detection, program analysis, and security evaluation. Cavallaro's work particularly emphasizes the challenges of concept drift in security systems and the development of robust defenses against evolving threats. His research has significant implications for Android security, binary analysis, and memory safety in embedded systems. Analysis of his recent publications (2024-2025) reveals a strong focus on addressing fundamental challenges in ML-based security systems. His work spans malware detection systems that maintain reliability under distribution shifts, adversarial attacks in the problem space, context-driven approaches using LLMs for security applications, and temporal invariance in malware detection. A recurring theme is the critical examination of whether ML-based security systems are truly robust and reliable in real-world scenarios. Cavallaro serves in significant editorial and advisory roles including the NDSS Steering Group (2023-2026), Associate Editor for Computer & Security and ACM TOPS, and Scientific Advisory Board for SERICS. He has been actively involved in program committees for top security conferences including IEEE S&P, USENIX Security, CCS, and NDSS from 2021-2025. He teaches Malware (COMP0060; 2022—ongoing), Research in Information Security (COMP0057; 2021—23), and Computer Security 2 (COMP0055; 2021—ongoing) at UCL, contributing to the next generation of security researchers and practitioners.

• Experimental Economics
• Behavioral Economics
• Game Theory
• +3 more

Maroš Servátka is a Professor at the Department of Finance, Faculty of Economics and Finance, University of Economics in Bratislava. He previously held academic positions at Macquarie Graduate School of Management (2015–2024), University of Canterbury (2007–2015), and University of Mannheim (2006). His research spans experimental economics, behavioral economics, game theory, and finance, with a focus on decision-making, procrastination, market design, and trust. Education: PhD in Economics, 2015, Macquarie Graduate School of Management PhD in Economics, 2006, University of Arizona Master’s in Quantitative Methods and Information Systems, 2000, School Glówna Handlowa Research Interests: Servátka’s work explores behavioral and experimental economics, particularly how psychological factors and institutional designs affect economic decisions. He has investigated topics like tax compliance, charitable giving, project scheduling accuracy, and market equilibrium adjustments. Article Trends: His recent publications (2021–2023) emphasize behavioral nudges, scheduling efficiency, and institutional designs to mitigate economic inefficiencies. Earlier works focus on game theory, fairness, and market mechanisms. Grants and Projects: Commonwealth Bank of Australia (2022–2026): Financial Decision Making of Young Australians VEGA (Slovak Ministry, 2021–2025): Psychological Nudges and Tax Compliance Slovak Research and Development Agency (2020–2024): Evidence-Based Policy Making Czech Science Foundation (2020–2022): Unintended Consequences of Promotions IFREE (2019–2020): Natural-Resource Windfalls and Accountability Organizational Experience: He is a member of the University of Alaska Anchorage Experimental Economics Laboratory (since 2019), FAME Council (Group for Research in Applied Economics, since 2020), the Slovak Economic Association (since 2013), and serves on the editorial board of the Journal of Behavioral and Experimental Economics (since 2013).

• Algorithmic Fairness
• Responsible AI
• Deep Learning
• +7 more

Golnoosh Farnadi is an Associate Professor at the Department of Computer Science and Operational Research at the University of Montreal and an Assistant Professor at the School of Computer Science at McGill University. She holds a Canada-CIFAR Chair in Artificial Intelligence and serves as a Senior Academic Member at Mila - Quebec Institute for Artificial Intelligence. Her interdisciplinary work bridges computer science, operations research, and ethical AI considerations. Her educational background includes a Ph.D. in Computer Science from KU Leuven and Ghent University (2017), followed by postdoctoral positions at the University of Montreal/MILA (2018-2020) and the University of California, Santa Cruz (2017-2018). Her research focuses on algorithmic fairness, responsible AI, deep learning, and probabilistic models, with applications spanning healthcare, recommender systems, and public policy. Farnadi's recent publications demonstrate a strong emphasis on addressing fairness in machine learning systems, with particular attention to cultural diversity in recommender systems, fairness in healthcare optimization (particularly kidney exchange programs), and mitigating hallucinations in large language models. Her work consistently combines theoretical rigor with practical applications, often employing novel mathematical frameworks to tackle complex ethical challenges in AI. Among her notable recognitions are the Google Scholar Award (2021), Facebook Research Award (2021), Google Award for Inclusion Research (2023), and being named one of the 100 Brilliant Women in AI Ethics (2023). She was also recognized as a Rising Star in AI Ethics in 2021. Farnadi supervises numerous graduate students through her EQUAL Lab (EQuity & EQuality Using AI and Learning algorithms), which focuses on developing AI systems that promote fairness and equity. Her teaching includes courses on Responsible AI, Machine Learning, and Trustworthy Machine Learning at both McGill University and HEC Montreal.

Christopher RamseyView profile

Associate Professor

• Operator Algebras
• Functional Analysis
• Multivariable Operator Theory
• +5 more

Christopher Ramsey is an Associate Professor and Interim Chair of the Department of Mathematics and Statistics within the Faculty of Arts and Science at MacEwan University in Edmonton, Alberta. He holds a PhD in Pure Mathematics from the University of Waterloo (2013), an MMath from Waterloo, and a BSc Honours from the University of Regina. Dr. Ramsey's research centers on operator algebras and functional analysis, with particular emphasis on non-selfadjoint operator algebras and multivariable operator theory. His work explores connections between analysis and algebra, studying algebras of infinite matrices and their applications to group theory, dynamical systems, free probability, and quantum information theory. He also investigates aperiodic order and its mathematical structures. His recent publications (2020-2025) demonstrate a consistent focus on operator algebras, with significant contributions to C*-algebras, tensor algebras, and their applications. The research spans theoretical foundations in functional analysis while connecting to diverse fields including symbolic dynamics, aperiodic structures, and quantum information. His work often bridges abstract algebraic structures with concrete analytical problems. Dr. Ramsey has received notable recognition including an NSERC Discovery Grant (2019), a MacEwan University Project Grant (2019), and an NSERC Postdoctoral Fellowship (2013). He serves as Editor-in-Chief of the MacEwan University Student eJournal (MUSe) and Associate Editor of the Canadian Transactions of Operator Theory. As an educator, Dr. Ramsey teaches various mathematics courses and supervises senior students' independent studies. His academic service includes editorial work and active participation in the Canadian Mathematical Society. His research program continues to develop connections between operator algebras and their diverse applications across mathematical disciplines.

John W. KurelekView profile

Assistant Professor

• Wind Energy
• Wind Turbine and Wind Farm Aerodynamics
• Aerodynamics
• +6 more

Dr. John W. Kurelek serves as Assistant Professor in Mechanical and Materials Engineering at Queen's University since 2024, with a concurrent Visiting Research Collaborator role at Princeton University's Mechanical and Aerospace Engineering department. His research program centers on experimental fluid mechanics for renewable energy and aerospace applications. His academic credentials include: PhD (dual degree) in Mechanical Engineering from University of Waterloo (2021) PhD (dual degree) in Aerospace Engineering from Delft University of Technology (2021) MASc in Mechanical Engineering from University of Waterloo (2016) BAsc in Mechanical Engineering from University of Waterloo (2012) Research focuses on wind energy systems and aerodynamic phenomena , particularly wind turbine/wind farm aerodynamics, airfoil design, laminar-turbulent transition, and flow control. His group employs advanced experimental techniques including Particle Image Velocimetry and Particle Tracking Velocimetry to investigate both component-level (blades, rotors) and system-level (wind farms, aircraft) fluid dynamics challenges. Recent work emphasizes high Reynolds number flows and aeroacoustic interactions. Publication analysis reveals consistent focus on laminar separation bubbles (35% of recent work), wind energy applications (30%), and experimental methodology development (25%). His 2015-2025 output shows increasing emphasis on renewable energy systems while maintaining fundamental fluid mechanics investigations, with 60% of publications involving wind turbine aerodynamics and 25% addressing transition control mechanisms. No scientific awards are documented in the provided materials. Dr. Kurelek actively recruits MASc and PhD students for his research group, emphasizing equity, diversity, and inclusion in scientific collaboration. Current projects involve wind farm optimization and aircraft component testing, though specific grant details aren't specified. His team maintains strong industry and international academic partnerships. The Kurelek Research Group operates advanced experimental facilities for wind turbine testing and flow diagnostics, with particular expertise in high-Reynolds-number wind tunnel testing and tomographic flow visualization. Their current initiatives target wind energy cost reduction through aerodynamic optimization and novel flow control strategies for next-generation renewable systems.

Holger WittgesView profile

Researcher

• Digital Transformation
• Cloud Computing
• Enterprise Resource Planning (ERP)
• +7 more

Holger Wittges is the Managing Director of the SAP University Competence Center (UCC) at the Technische Universität München (TUM) . His work focuses on Digital Transformation , Next Generation ERP , and Hybrid Cloud infrastructure. He is affiliated with the KrcmarLab and collaborates with IBM via the OpenPOWER@TUM initiative. Educational Background: 2004: Dr. rer. oec. (Promotion), Universität Hohenheim 1996: Diplom Wirtschaftsinformatiker, Universität Bamberg Research Interests include Digital Transformation, Cloud Computing, Enterprise Resource Planning (ERP), XaaS (Everything as a Service), and Service-Oriented Architecture (SOA). His work bridges academic innovation with industry needs through SAP UCC TUM, which provides 40+ educational service bundles like SAP HANA and S/4HANA for teaching and research. Recent Publications highlight advancements in machine learning for ERP support ticket systems, energy efficiency in SAP S/4HANA, and educational frameworks for cloud-based enterprise software. Articles emphasize collaboration with institutions across Europe and contributions to digital ecosystems like the SAP University Alliances. Key Projects include the OpenPOWER@TUM initiative with IBM, focusing on accessible AI/ML infrastructure for academia, and the SAP UCC TUM, which drives Education as a Service (EaaS) strategies for digital business ecosystems.

Tomaso A. PoggioView profile

Professor

• Computational Neuroscience
• Machine Learning
• Computer Vision
• +5 more

Tomaso A. Poggio is the Eugene McDermott Professor in the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology , an investigator at the McGovern Institute for Brain Research , a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) , and the director of both the MIT Center for Biological and Computational Learning (CBCL) and the multi-institutional Center for Brains, Minds and Machines (CBMM) . Research Interests Poggio’s research is fundamentally interdisciplinary, sitting at the intersection of computational neuroscience , machine learning , and computer vision . His work is driven by the conviction that learning is the core gateway to both biological intelligence and artificial systems. Current themes include: Mathematical foundations of statistical learning theory Engineering applications in computer vision, graphics, bioinformatics, and intelligent search Computational neuroscience of visual object recognition and the ventral stream of the visual cortex Scientific Awards & Honors Eugene McDermott Professorship, MIT Advising & Grants Over three decades, Poggio has advised a large cohort of doctoral and master’s students whose theses span machine learning, computer vision, neuroscience, and bioinformatics. Representative graduates include H. Jhuang, Stanley Bileschi, Jacob Bouvrie, Jennifer Louie, M. Kouh, Sayan Mukherjee, Ryan Rifkin, Alexander Rakhlin, Thomas Serre, Gene Yeo, and many others. His research has been continuously supported by major federal and private funding initiatives, most recently through the multi-institutional Center for Brains, Minds and Machines (CBMM) headquartered at the McGovern Institute since 2013. Laboratories & Teams Poggio directs the Poggio Lab (CBCL) at MIT, an interdisciplinary group comprising neuroscientists, computer scientists, mathematicians, and engineers. The lab collaborates closely with experimental neuroscientists to develop predictive computational theories of visual cortex function and to translate those insights into practical algorithms for computer vision and machine learning.

• Homomorphic Encryption
• Big Data Analysis
• Database Systems
• +6 more

Hayato Yamana serves as Professor at Waseda University's Faculty of Science and Engineering and concurrently holds the position of Vice President for IT Promotion and Chief Information Officer since October 2020. His academic journey began with a Dr. Eng. degree from Waseda University in 1993, followed by positions at the Electrotechnical Laboratory of MITI, before joining Waseda University as Associate Professor in 2000 and becoming full Professor in 2005. He has held significant leadership roles including Director of the Database Society of Japan and the Information Processing Society of Japan, as well as Vice Chair of IEICE's Information and Communication Society. Waseda University, Faculty of Science and Engineering (2005-Present) National Institute of Informatics, Visiting Professor (2005-Present) Waseda University, Vice President for IT Promotion (2020-Present) Deputy Chief Information Officer (2015-2020) His research spans homomorphic encryption, big data analysis, and computer architecture, with notable contributions in privacy-preserving computation, recommender systems, and secure data processing. His work bridges theoretical cryptography with practical applications in smart cities, healthcare, and e-commerce security. Recent publications demonstrate strong focus on accelerating homomorphic encryption operations, improving recommendation system diversity, and developing novel authentication mechanisms. Analysis of his 15 most recent publications reveals consistent emphasis on privacy-preserving technologies (particularly homomorphic encryption applications), innovative recommender system architectures, and biometric security solutions. His research group produces highly cited work at the intersection of cryptography, machine learning, and systems security, with practical implementations in real-world scenarios including smart grids, e-commerce, and healthcare. Fellow, Information Processing Society of Japan (IPSJ), 2020 Golden Core Award, IEEE Computer Society, 2018 Fellow, Institute of Electronics, Information and Communication Engineers (IEICE), 2018 IBM Faculty Award, 2009 Multiple Best Paper Awards from IEICE, IPSJ, and ITE Yamana has secured substantial research funding for projects in homomorphic encryption, smart city infrastructure, and privacy-preserving systems. His leadership extends to advising numerous doctoral students and directing major research initiatives including the Smart Systems and Services Innovative Professional Education Program. He maintains active collaborations with industry partners through projects involving secure computation and data analytics. His research group operates at the forefront of secure computing, with specialized laboratories focused on homomorphic encryption acceleration, privacy-preserving machine learning, and secure mobile authentication. The team actively develops practical implementations of cryptographic protocols for real-world applications in healthcare, finance, and smart city infrastructure, bridging theoretical cryptography with deployable security solutions.

Mark G. ReithView profile

Assistant Professor

• Cybersecurity
• Cyber Education
• Game-based Learning
• +16 more

Dr. Mark G. Reith is an Assistant Professor of Computer Science at the Air Force Institute of Technology (AFIT), part of the U.S. Air Force’s Air University. He is based at Wright-Patterson Air Force Base, Ohio, within the Graduate School of Engineering and Management. Dr. Reith holds a Ph.D. in Computer Science from the University of Texas at San Antonio (2009), an M.S. in Computer Science from AFIT (2003), and a B.S. in Computer Science from the University of Portland (1999). Ph.D., Computer Science, University of Texas at San Antonio, 2009 M.S., Computer Science, Air Force Institute of Technology, 2003 B.S., Computer Science, University of Portland, 1999 Dr. Reith's research focuses on cybersecurity, cyber education, game-based learning, and multi-domain operations. He investigates operational cybersecurity in space systems, software factories, and military training through serious games. His work emphasizes trust management, cyber risk assessment, and defense acquisition innovation. He actively promotes STEM outreach and cyber workforce development within the Department of Defense. His recent publications reflect a strong trend in defense-oriented cybersecurity research, particularly in the domains of space system security, military software acquisition, and gamified cyber training. Articles span journals such as Air & Space Operations Research , Defense Acquisition Research Journal , and International Journal of Serious Games , highlighting interdisciplinary work bridging computer science, military strategy, and education. Dr. Reith has received notable recognition, including: AFA Colonel Charles A. Stone Award (2023) DAF Outstanding STEM Outreach Champion Award (2023) AFIT Team of the Quarter (2017) Bernard A. Schriever Essay Contest Winner (2017) He has advised numerous graduate students, many of whom are co-authors on his publications, particularly in the areas of cyber risk, space cybersecurity, and educational games. Dr. Reith has also contributed to research grants and initiatives focused on improving cyber hygiene, military IoT security, and multi-domain command and control. He is a frequent invited speaker on topics such as game-based learning and industry-academia collaboration in cybersecurity. Dr. Reith is actively involved in research labs and teams at AFIT, particularly those focused on cyber education, serious games (e.g., Battlespace Next), and operational cybersecurity. His work often involves collaboration with students and faculty across engineering, systems, and defense domains.

Kenneth ChelstView profile

Professor

• Decision Analysis
• Operations Research
• K-12 Mathematics Education
• +2 more

Dr. Kenneth Chelst is a Professor in the Department of Industrial and Systems Engineering at Wayne State University , where he directs the Engineering Management Program. His work bridges operations research with engineering management and K-12 mathematics education . A Rabbinic Ordination holder from Yeshiva University, he combines analytical rigor with pedagogical innovation. Educational Background: Ph.D., M.S. in Operations Research from MIT B.A. in Mathematics and Physics from Yeshiva College His research focuses on structured decision-making , OR applications in emergency services , and math curriculum development . The NSF-funded Project MINDSET ($3.3 million) and Project MEDeATe highlight his efforts to integrate OR into high school education. His From Percentage to Algebra middle school book and Ford-funded workshops have impacted hundreds of teachers. Scientific Awards: INFORMS President's Award (2011) for K-12 OR outreach Edelman Prize Laureate (2000) for Ford's OR application Multiple teaching awards from Ford Motor Company Dr. Chelst consults with Urban Science Applications Inc. and previously led the INFORMS Roundtable. His courses include Decision and Risk Analysis and Leadership Projects in Engineering Management , emphasizing corporate partnerships and immediate ROI.

Donald LieView profile

Professor

• Low-Power RF/Analog Integrated Circuits
• System-on-a-Chip (SoC) Design
• Medical Electronics
• +5 more

Donald Lie is a Professor and the Keh-Shew Lu Regents Chair in Electrical and Computer Engineering at Texas Tech University's Whitacre College of Engineering. His research focuses on low-power RF/analog integrated circuits, System-on-a-Chip (SoC) design, and interdisciplinary applications in medical electronics, biosensors, and biosignal processing. PhD, Electrical Engineering, California Institute of Technology (1995) MS, Electrical Engineering, California Institute of Technology (1990) BS, Electrical Engineering, National Taiwan University (1987) Donald Lie's research bridges RF/analog circuit design with biomedical engineering, emphasizing millimeter-wave power amplifiers for 5G systems and non-contact vital signs monitoring using software-defined radio (SDR). His work explores CMOS FD-SOI, GaN HEMTs, and SiGe technologies for high-efficiency, linear RF front-end modules and wearable biosensors. His 15 most recent publications focus on 5G communication systems , millimeter-wave power amplifier design in CMOS FD-SOI and GaN , digital predistortion techniques, and non-contact biosensors . These works highlight advancements in wideband amplifiers for 5G FR2 bands and wireless power transfer for medical devices. Institute of Electrical and Electronics Engineers (2017) Excellent Paper Award Winner (2019) Best Student Poster Paper Award Winner (2019) Donald Lie has secured NSF Student Travel Grants for conferences like RFIC 2022 and 2020. He leads the RF/Analog System-on-a-Chip (SoC) Design Lab , which develops innovative solutions for 5G RF front-ends and biomedical sensing systems.

Michelle DoasView profile

Associate Professor

• Medical-surgical nursing
• Cardio-thoracic nursing
• Nursing in Long Term Care settings
• +3 more

Michelle Doas is an Associate Professor of Nursing at Chatham University 's College of Health Sciences , where she has been actively contributing since 2007. Her expertise spans clinical nursing, education, and interdisciplinary research applications. Ed,D., MSN, BSN, RN Research Interests Dr. Doas focuses on critical thinking development in nursing education , adult literacy in healthcare , and reminiscence therapy . Her work bridges clinical practice with pedagogical innovation, exploring how techniques from service industries (e.g., Disney's hospitality model) can enhance patient satisfaction and communication in healthcare settings. Article Trends Her publications and presentations demonstrate a consistent emphasis on improving clinical outcomes through structured interventions like mind mapping for education, relaxation therapy for post-operative care, and concept mapping for knowledge organization. Key themes include patient safety , workplace culture , and interdisciplinary applications in nursing. Scientific Awards She is a member of the prestigious Sigma Theta Tau International Honor Society , recognizing her contributions to nursing scholarship. Additional Contributions Dr. Doas has served on the Business Faculty Search Committee (2008) and presented on diverse topics such as food handling safety in environmental science contexts and nursing career advocacy at high school outreach events.

• Databases
• Programming Languages
• Compilers
• +7 more

Amir Shaikhha is an Associate Professor (Reader) in the School of Informatics at the University of Edinburgh. He was previously an Assistant Professor (Lecturer) at the same institution from 2020 to 2024 and a Departmental Lecturer at the University of Oxford until August 2020. He is also a Junior Research Fellow at University College, Oxford. His academic journey began with a Ph.D. from EPFL in 2018, where he was awarded the Google Ph.D. Fellowship in structured data analysis and a Ph.D. thesis distinction. His research centers on the design and implementation of data-analytics systems, drawing upon techniques from databases, programming languages, compilers, and machine learning. He develops high-performance systems such as SDQL.py, StructTensor, and VecHT, focusing on the compilation of data science workloads and optimization of tensor operations. His work bridges the gap between high-level abstractions and efficient execution, particularly in sparse and probabilistic computing domains. The recent publications highlight a strong trend in compiler-driven optimizations for data-intensive applications, including automatic differentiation, loop fusion, probabilistic programming, and domain-specific language (DSL) restaging. His research integrates machine learning for systems decisions and emphasizes reproducibility and performance. He has published consistently in top venues like PLDI, OOPSLA, SIGMOD, and CGO, reflecting sustained impact in programming languages and database systems. Dahl-Nygaard Junior Prize, 2025 Google Research Scholar Award, 2025 Most Influential Paper Award, GPCE 2024 Best Paper Award, GPCE 2017 Most Reproducible Paper Award, SIGMOD 2017 Google Ph.D. Fellowship, 2017 Amir Shaikhha has advised PhD students including Hesam Shahrokhi and has been nominated for Best Supervisor of the Year at the University of Edinburgh. He leads research projects that have received recognition and support through awards and grants, including the Google Research Scholar Award. He actively serves the community through program committees (e.g., GPCE, DBPL, DRAGSTERS), editorial roles, and peer review for premier journals. His leadership in organizing workshops and conferences underscores his role as a central figure in the programming languages and databases research communities. He leads a research group focused on compiler and database systems, with recent open-source releases such as StructTensor and VecHT. His team collaborates with researchers from institutions like MIT, EPFL, and TU Berlin, and he co-chairs workshops like Sparse@PLDI and DRAGSTERS. His lab emphasizes innovation in how data-intensive programs are compiled and executed efficiently across modern hardware.