Algorithmic Studies Professors and Supervisors | ApplySharif

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Ketika GargView profile

Researcher

California Institute of Technology (Caltech)United States

Ketika Garg is a Postdoctoral Scholar Research Associate in the Division of the Humanities and Social Sciences at the California Institute of Technology (Caltech), with an office in the Broad Center for Biological Sciences. Her research focuses on the interplay between individual and social decisions, using experimental and computational methods to explore how social environments influence decision-making and collective behavior. She investigates contexts ranging from traditional foraging paradigms to modern social media landscapes, developing innovative experimental frameworks to study these dynamics. Her research interests span computational neuroscience, social media analysis, and collective behavior, with a particular emphasis on understanding exploration-exploitation trade-offs in both natural and digital environments. She has contributed to studies on hunter-gatherer foraging networks, online toxicity dynamics, and the evolution of search strategies in collective foraging systems. Her work bridges disciplines such as psychology, ecology, and computer science to address fundamental questions in decision-making and social interaction. Dr. Garg’s publications reflect her interdisciplinary approach, covering topics like synergy in collective problem-solving, the roots of online toxicity, and the application of Lévy walks in virtual foraging experiments. While no formal awards or grants are explicitly listed in the provided materials, her research trajectory demonstrates a commitment to advancing methodologies in computational social science. Contact: kgarg@caltech.edu Office: Broad Center for Biological Sciences (96) Phone: 626-395-1755

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Jan KnippersView profile

Professor

University of StuttgartGermany

Jan Knippers is a Professor and Institute Director at the University of Stuttgart's Faculty of Architecture and Urban Planning , leading the Institute of Building Structures and Structural Design (ITKE) . He co-founded Knippers Helbig Advanced Engineering and established Jan Knippers Ingenieure in 2018 to focus on innovative fiber composite and timber structures. Key roles: Deputy Executive Director of Cluster of Excellence IntCDC , former Vice-Rector for Research (2019-2021), and Dean of Faculty of Architecture (2021-2023) Technical focus: Hybrid FRP-Timber systems, computational design, and biomimetic principles His projects include the 2024 Hybrid Flax Pavilion , Urbach Tower (2024), and BUGA Fiber Pavilion (2019). He contributes to European standardization committees and serves as 2024 Leverhulme Visiting Professor at University College London.

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Benjamin SteinbergView profile

Professor

City University of New YorkUnited States

Benjamin Steinberg is a Professor in the Mathematics Department at the City College of New York (CCNY) and the CUNY Graduate Center. He holds a Ph.D. from the University of California, Berkeley (1998) under John Rhodes and has held positions at the University of Porto (Portugal) and Carleton University (Canada). His research focuses on algebra, including semigroups, geometric group theory, algebraic combinatorics, representation theory, and automata theory, with notable work on etale groupoids, inverse semigroups, and ring theory. He is the author of several books, including *The q-theory of Finite Semigroups* and *Representation Theory of Finite Monoids*. Steinberg serves as Managing Editor of the *International Journal of Algebra and Computation* and has organized conferences such as the International Conference on Semigroups and Groups in Honor of John Rhodes. Research interests include the interplay between algebraic structures and their applications, such as in automata theory and Markov chains. His work bridges pure mathematics with combinatorial and geometric approaches, often involving categorical and topological methods. Recent articles explore topics like Nekrashevych algebras, twisted Steinberg algebras, and Lyndon's identity theorem for monoids. He has contributed to the study of profinite groups and their connections to symbolic dynamics. Steinberg’s editorial roles and conference organization reflect his leadership in the mathematical community. Despite his defunct blog, his academic contributions remain prolific, with ongoing editorial work and research in algebraic combinatorics and representation theory.

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Yaoqing YangView profile

Assistant Professor

University of California, BerkeleyUnited States

Yaoqing Yang is an Assistant Professor at the Department of Computer Science, Dartmouth College. He earned his PhD in Electrical and Computer Engineering (ECE) from Carnegie Mellon University (CMU) and completed postdoctoral research at UC Berkeley's RISE Lab. His work focuses on robustness in machine learning systems, spectral analysis of neural networks, and algorithm design for structured data like graphs and point clouds. PhD in ECE, Carnegie Mellon University Postdoc, RISE Lab, UC Berkeley BS in Electrical Engineering, Tsinghua University Research interests include diagnosing and mitigating model failures through heavy-tailed spectral analysis, decision boundary studies, and loss landscape visualization. He develops methods such as AlphaPruning and SharpBalance to enhance large language models and ensemble learning. Recent work spans 2025 publications on spectral evolution of neural networks, agentic AI for science, and LLM safety. Key collaborations include Michael W. Mahoney and other researchers. Burke Research Initiation Award, Dartmouth (2024) DOE grant for scientific foundation models (2024) DARPA grant for AI robustness (2024) He serves as Area Chair at NeurIPS 2025 and ICLR 2026, and has presented at Google Research, Lawrence Berkeley National Laboratory, and leading universities worldwide. His lab at Dartmouth engages in theoretical and applied research, with connections to UC Berkeley's RISE Lab and collaborations across institutions like CMU and Tsinghua University.

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Svyatoslav KondratView profile

Research Fellow

University of StuttgartGermany

Dr. Svyatoslav Kondrat is a Research Fellow at the Institute for Computational Physics, University of Stuttgart, working in the group of Prof. Holm. His research focuses on computational studies of ionic systems, nanopores, and energy storage materials, utilizing large-scale molecular dynamics simulations to investigate electrolyte behavior under confinement and in bulk solutions. His primary research interests include: Electrolyte physics and ionic transport mechanisms Nanopore design for supercapacitors and energy applications Solvent effects in confined electrochemical systems High-performance computing for molecular simulations Kondrat's publications (2017-2024) demonstrate a consistent focus on optimizing ion transport and screening in nanoporous materials, with applications in energy storage. His work bridges theoretical electrochemistry, computational physics, and nanomaterials engineering, frequently exploring charge dynamics using advanced simulation techniques. He collaborates extensively within Prof. Holm's research group on projects involving confined electrolytes and decay length phenomena in concentrated ionic systems.

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James W. NavaltaView profile

Associate Professor

University of Nevada, Las VegasUnited States

Dr. James W. Navalta is an Associate Professor in the Department of Kinesiology and Nutrition Sciences at the University of Nevada, Las Vegas. His research focuses on physiological responses to outdoor exercise (hiking, trail running) and the validity/reliability of wearable technology. He earned his B.S. in Physical Education and Biology from Brigham Young University–Hawaii, M.S. in Kinesiology from UNLV, and Ph.D. in Exercise Physiology from Purdue University. Education: B.S. - Physical Education & Biology, Brigham Young University–Hawaii M.S. - Kinesiology, University of Nevada, Las Vegas Ph.D. - Exercise Physiology, Purdue University His research portfolio includes: Wearable technology validation for physiological measurements Comparative studies of indoor vs outdoor exercise environments Impact of gender-inclusive approaches on sports science Metabolic and cardiovascular responses to unconventional workouts Psychological benefits of nature immersion Recent publications demonstrate expertise in: Wearable device accuracy testing VO2max and lactate threshold validation Environmental influence on exercise physiology Methodological improvements in data collection Gender-inclusive research design Outdoor activity impact assessment As co-founder and executive editor of the International Journal of Exercise Science, he contributes significantly to academic discourse. He also serves on editorial boards for journals related to digital health and exercise technology.

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Anthony A GattiView profile

Research Fellow

Stanford UniversityUnited States

Anthony A Gatti is a Postdoctoral Scholar at Stanford University's Wu Tsai Human Performance Alliance and School of Medicine. His research integrates biomechanics , medical imaging , and machine learning to advance musculoskeletal health diagnostics, particularly focusing on knee osteoarthritis and exercise physiology. Education : Ph.D. in Rehabilitation Science (McMaster University, 2021), M.Sc. in Rehabilitation Science (McMaster University, 2015), B.Sc. in Kinesiology (McMaster University, 2013) His research develops automated tools for quantifying knee anatomy and integrating anatomical data with biomechanical models . These methods analyze acute responses to exercise and long-term joint degeneration, leveraging MRI , deep learning , and statistical shape modeling . Recent publications emphasize AI-driven segmentation , exercise-induced cartilage changes , and biomechanical simulations , spanning journals like Magnetic Resonance in Medicine and Arthritis & Rheumatology . Trends include machine learning validation for clinical predictions and open-source tool development for musculoskeletal analysis. Scientific Awards : CIHR Postdoctoral Fellowship (top 1%), Mitacs Accelerate Entrepreneur, Forge Student Start-Up Competition Winner, multiple scholarships from McMaster University He founded NeuralSeg , a company commercializing deep learning-based MRI segmentation technology. Collaborations include Stanford's Digital Athlete Moonshot Project with advisors like Scott Delp and Garry Gold.

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Regina RaganView profile

Professor

University of California, IrvineUnited States

Regina Ragan is a Professor in the Department of Materials Science and Engineering at the Samueli School of Engineering, University of California, Irvine. Her research focuses on nanomaterials, self-assembly, and surface-enhanced Raman scattering (SERS) for applications in optical communication, energy systems, and biomedical diagnostics. Education: Ph.D. in Applied Physics, California Institute of Technology, 2002 M.S. in Applied Physics, California Institute of Technology, 1998 B.S. in Materials Science and Engineering, University of California, Los Angeles, 1996 Her work integrates scanning probe microscopy and first-principles calculations to study thermodynamic driving forces in self-assembly and structure-function relationships. Recent publications highlight applications in antimicrobial susceptibility testing, environmental monitoring, and plasmonic device fabrication. The Ragan group develops low-cost diagnostic tools using SERS for telemedicine applications. Current lab members include graduate students and postdoctoral researchers working on nanoscale systems from atomic to mesoscale. Scientific Awards: NSF CAREER Award for fundamental studies of biological/inorganic interfaces Research Trends: Recent articles show a focus on SERS-based diagnostics, plasmonic nanoantennas, machine learning-assisted spectral analysis, and scalable synthesis of 3D graphene architectures. Subfields span quantum plasmonics, stress-activated materials, and biofilm monitoring.

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Victoria LemieuxView profile

Professor

University of British ColumbiaCanada

Victoria Lemieux is a Professor at the University of British Columbia (UBC) Faculty of Arts, School of Information, and Cluster Lead for Blockchain@UBC, Canada’s largest research cluster focused on blockchain technology. Her research centers on risks to trustworthy records in blockchain systems and their impact on transparency, financial stability, and human rights. She has pioneered Canada’s first research-oriented graduate blockchain training program and organized multiple interdisciplinary summer institutes. Education: Ph.D. in Archival Studies from University College London (2002), Certified Information Systems Security Professional (CISSP, 2005). Affiliated with UBC’s Peter Wall Institute for Advanced Studies, Sauder School of Business, and Institute for Computers, Information and Cognitive Systems (ICICS). Research interests span blockchain technology , trustworthy records , risk management , information governance , and visual analytics , with recent work addressing healthcare data frameworks, Web3 AI integration, and socio-cultural dynamics of decentralized systems. She has published extensively on blockchain applications in archives, land transactions, and privacy-preserving technologies. Scientific Awards : 2015 Emmett Leahy Award 2015 World Bank Big Data Innovation Award 2016 Emerald Literati Award 2016 Emerald Literati Outstanding Paper Award Supervision: Currently accepts doctoral students in Computational Archival Science and blockchain-related archival research. Affiliated with the Blockchain@UBC cluster and multidisciplinary research teams exploring decentralized systems for social good.

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Vinayak DixitView profile

Professor

University of New South WalesAustralia

Professor Vinayak Dixit serves as the IAG Chair of Risk in Smart Cities and Director of the Research Centre for Integrated Transport Innovation (rCITI) at the University of New South Wales (UNSW), within the School of Civil and Environmental Engineering. With a distinguished career spanning academia and research leadership, Professor Dixit has established himself as a leading expert in transportation risk analysis and smart city infrastructure. Professor Dixit's research focuses on studying risk in transportation infrastructure systems, with particular emphasis on highway safety, travel time uncertainty, and resilience against natural and man-made disasters. His work integrates cutting-edge approaches including quantum computing applications for transportation network optimization, analysis of connected and automated vehicles, and development of models for transportation resilience. His research interests span multiple disciplines, bridging civil engineering, transportation science, risk analysis, and computational methods. Professor Dixit has secured significant research funding from prestigious organizations including the United States National Science Foundation, Federal Highway Administration, and the Strategic Highway Research Program of the Transportation Research Board. His leadership extends to directing the Research Centre for Integrated Transport Innovation (rCITI), where he oversees a comprehensive research program addressing critical transportation challenges in smart cities through multiple focus areas including Connected Mobility Services, Deep Data and Digitization, Engineering Smart Cities & Logistics, Human-Centred And Automated Systems Design, and Integrated Infrastructure Strategic Planning. Professor Dixit previously served as the Associate Director of Research for the Gulf Coast Centre for Evacuation and Transportation Resiliency at Louisiana State University, where he founded the Driving Simulator Laboratory in collaboration with other faculty members. This demonstrates his longstanding commitment to innovative research infrastructure development and interdisciplinary collaboration across engineering, economics, computer science, and urban planning to address complex transportation challenges in the 21st century.

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Xenophon PapademetrisView profile

Professor

Yale UniversityUnited States

Xenophon Papademetris is a Professor of Biomedical Informatics & Data Science and Radiology & Biomedical Imaging at Yale School of Medicine. He serves as Associate Director of Biomedical Imaging Data Sciences at Yale Biomedical Imaging Institute and directs the Medical Software and Medical Artificial Intelligence Certificate Program. PhD in Electrical and Information Sciences from Yale University (2000) BA from Cambridge University (1994) Postdoctoral Fellowship at Yale University (2002) His research focuses on medical image analysis, machine learning, and biomedical software development. He has developed tools like BioImage Suite Web and contributed to standards committees at the Association for the Advancement of Medical Instrumentation (AAMI). His work spans modalities including MRI, CT, PET, and optical imaging. Recent publications emphasize neuroimaging analysis, explainable AI in healthcare, and multimodal data integration across species. He leads NIH-funded research under the BRAIN Initiative (R24 MH114805) and has authored a textbook on Medical Software published by Cambridge University Press. IEEE Senior Member Yale Brown-Coxe Postdoctoral Fellowship Harding Bliss Prize for Excellence in Engineering He directs the BioImage Suite Project, creating web-based image analysis tools using JavaScript and WebAssembly. His teaching includes both academic courses and a Coursera program on Medical Software with over 14,000 enrollments.

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Mark AndrejevicView profile

Professor

Monash UniversityAustralia

Mark Andrejevic is a Professor in the School of Media, Film, and Journalism at Monash University. He specializes in the socio-cultural implications of digital surveillance, data mining, and automated decision-making. His research spans Big Data ethics, privacy regulation, and the impact of technology on democracy. He leads major projects like the ARC Centre of Excellence for Automated Decision-Making and Society, focusing on AI’s societal implications. Andrejevic has been awarded Fellow of the Australian Academy of Humanities and the Nancy Baym Book Award. His work intersects with UN Sustainable Development Goals addressing justice and innovation. Research interests include automated surveillance systems, digital privacy policies, and the ethical governance of emerging technologies. He collaborates on projects such as facial recognition technology ethics and pandemic-era surveillance practices. His recent publications analyze platform accountability, algorithmic bias, and the societal effects of workplace monitoring technologies. He actively contributes to public discourse via media engagements and policy-focused talks on topics like facial recognition in education and pandemic-era data collection. Projects: ARC QEII Fellowship ($390k, 2010–2014), Australian Public Attitudes to AI, and facial recognition ethics studies. Grants: Over $390k from ARC for privacy research, plus ongoing funding through collaborative initiatives. Awards: Australian Academy of Humanities Fellowship (2020), Nancy Baym Book Award (2014). He supervises graduate students in Digital Media, Surveillance Studies, and Critical Theory. His interdisciplinary work bridges academia, policy, and industry to address technology’s societal challenges while advocating for democratic values.

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Cheuk Wai TaiView profile

Researcher

Stockholm UniversitySweden

Cheuk Wai Tai is a Senior Staff Researcher at Stockholm University's Department of Environmental and Materials Chemistry since 2009. He manages the transmission electron microscopes and sample preparation equipment at the Electron Microscopy Center and serves as Section Editor for the Journal of Electronic Materials. His work focuses on quantitative structure characterization in functional materials research, particularly within nanoscience and nanotechnology contexts. Education: Ph.D. in Applied Physics, The Hong Kong Polytechnic University, 2004 M.Phil. in Applied Physics, The Hong Kong Polytechnic University, 2001 M.Sc. in Physics, The Chinese University of Hong Kong, 1998 B.Sc. (Hons) in Engineering Physics, The Hong Kong Polytechnic University, 1997 Dip. in Mechanical Engineering (Computer Aided Engineering), Institute of Vocational Education (formerly Haking Wong Technical Institute), Hong Kong, 1992 His research centers on structure-property relationships in functional materials through advanced electron microscopy techniques. Current specializations include Pair Distribution Function (ePDF) & Diffuse Scattering, Energy Materials characterization, and EM sample preparation methodology development. The group maintains strong focus on translating structural data into functional performance metrics for nanomaterials. Recent publications (2013-2019) demonstrate consistent emphasis on electron microscopy applications for energy storage materials (batteries, photocatalysts) and functional ceramics. Key trends include structural disorder analysis in piezoelectrics, development of quantitative TEM methods like SUePDF, and nanoscale characterization of electrocatalyst surface phases. His work bridges materials chemistry with advanced imaging techniques. Scientific recognition includes: Fellow of The Royal Microscopical Society (U.K.) Senior Member of IEEE Marie Curie Fellowship (2007-2009) from European Commission Sir Edward Youde Memorial Fellowship (2003/2004) from Hong Kong S.A.R. Government He teaches Solid State Chemistry (KZ7003) and leads Introduction to Analytical Electron Microscopy (KZ8009), having previously taught Advanced Transmission Electron Microscopy (KZ8010) before 2011. Major grants supporting his work include: "Quantitative structural characterisation using 3D electron-based pair distribution function" (Swedish Research Council) "A Multidimensional Toolkit for Modern Electron Microscopy" (Swedish Foundation for Strategic Research) "Mitigating Ni-rich Li-ion cathode side-reactions" (Swedish Energy Agency, Co-applicant) He leads the Cheuk-Wai Tai group within Stockholm University's chemistry department and oversees operations at the Electron Microscopy Center, where his team develops and applies advanced characterization techniques for functional materials research.

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Özüm AsirimView profile

Researcher

Technical University of MunichGermany

Özüm Asirim is a Researcher at the Technical University of Munich (TUM) under the Associate Professorship of Computational Photonics led by Prof. Christian Jirauschek. Her work focuses on computational photonics , quantum optics , and nonlinear optical phenomena , particularly in micro-resonators and semiconductor devices. Education: Ph.D. in Electrical Engineering from Middle East Technical University (Ankara, Turkey). Research spans optical parametric amplification , Fourier domain mode-locked lasers , self-phase modulation , and machine learning applications in photonics . Her studies include optimizing gain factors, enhancing harmonic generation, and modeling supercontinuum sources via carrier injection. Recent publications (2019–2023) highlight interdisciplinary approaches, merging photonics with computational finance and nonlinear dynamics . She contributes to EU Project QOMBS and teaches courses like Python for Engineering Data Analysis and Quantum Engineering and Machine Learning seminars. Collaborations include Prof. Christian Jirauschek (TUM), Prof. Mustafa Kuzuoğlu (Middle East Technical University), and teams in computational photonics and quantum optics. Her work impacts semiconductor physics , laser technology , and adaptive optical systems .

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Reinhard HeckelView profile

Professor

Swiss Federal Institute of Technology in LausanneSwitzerland

Reinhard Heckel is a Tenured Associate Professor (equivalent to Professor) of Machine Learning at the Department of Computer Engineering, Technical University of Munich (TUM), and Adjunct Faculty in Electrical and Computer Engineering at Rice University. He was previously an Assistant Professor at Rice (2017–2019), a postdoc in the Berkeley Artificial Intelligence Research (BAIR) Lab at UC Berkeley, and a researcher at IBM Research Zurich. Education: PhD, 2014 – ETH Zurich Visiting PhD student – Department of Statistics, Stanford University Research Interests: His work centers on machine learning and information processing with three major thrusts: (1) developing algorithms and theoretical foundations for deep learning, especially for accelerated magnetic resonance imaging ; (2) establishing rigorous mathematical and empirical underpinnings for modern machine-learning systems; and (3) leveraging DNA as a digital information-storage medium , including error-correction coding and system design for DNA-based storage. Across more than 100 peer-reviewed papers since 2017, Heckel’s research exhibits a strong interdisciplinary blend of computational imaging , machine-learning theory , and molecular data storage . Recent 2024–2025 publications show intensive focus on robust MRI reconstruction using diffusion priors, evaluation of bias in large web-text corpora, and state-of-the-art error-correcting codes for DNA storage channels. A forthcoming book, Deep Learning for Computational Imaging (Oxford University Press), consolidates his contributions to the field. Outreach & Media: Keynote and panel talks at DLD, TUM, and major ML conferences Op-eds in Frankfurter Allgemeine on ChatGPT and DNA storage Science features on Netflix, BBC, and German television (Galileo, “Gut zu Wissen”) Research Environment: At TUM he leads a group investigating theoretical and applied aspects of deep learning, compressed sensing, and coding for DNA storage. Open-source repositories on GitHub (e.g., dna_data_storage , supplement_deep_decoder ) provide code and data supplements accompanying his publications.

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